- Open access
- Published: 03 September 2022
A literature review of risk, regulation, and profitability of banks using a scientometric study
- Shailesh Rastogi 1 ,
- Arpita Sharma 1 ,
- Geetanjali Pinto 2 &
- Venkata Mrudula Bhimavarapu ORCID: orcid.org/0000-0002-9757-1904 1 , 3
Future Business Journal volume 8 , Article number: 28 ( 2022 ) Cite this article
This study presents a systematic literature review of regulation, profitability, and risk in the banking industry and explores the relationship between them. It proposes a policy initiative using a model that offers guidelines to establish the right mix among these variables. This is a systematic literature review study. Firstly, the necessary data are extracted using the relevant keywords from the Scopus database. The initial search results are then narrowed down, and the refined results are stored in a file. This file is finally used for data analysis. Data analysis is done using scientometrics tools, such as Table2net and Sciences cape software, and Gephi to conduct network, citation analysis, and page rank analysis. Additionally, content analysis of the relevant literature is done to construct a theoretical framework. The study identifies the prominent authors, keywords, and journals that researchers can use to understand the publication pattern in banking and the link between bank regulation, performance, and risk. It also finds that concentration banking, market power, large banks, and less competition significantly affect banks’ financial stability, profitability, and risk. Ownership structure and its impact on the performance of banks need to be investigated but have been inadequately explored in this study. This is an organized literature review exploring the relationship between regulation and bank performance. The limitations of the regulations and the importance of concentration banking are part of the findings.
Globally, banks are under extreme pressure to enhance their performance and risk management. The financial industry still recalls the ignoble 2008 World Financial Crisis (WFC) as the worst economic disaster after the Great Depression of 1929. The regulatory mechanism before 2008 (mainly Basel II) was strongly criticized for its failure to address banks’ risks [ 47 , 87 ]. Thus, it is essential to investigate the regulation of banks [ 75 ]. This study systematically reviews the relevant literature on banks’ performance and risk management and proposes a probable solution.
Issues of performance and risk management of banks
Banks have always been hailed as engines of economic growth and have been the axis of the development of financial systems [ 70 , 85 ]. A vital parameter of a bank’s financial health is the volume of its non-performing assets (NPAs) on its balance sheet. NPAs are advances that delay in payment of interest or principal beyond a few quarters [ 108 , 118 ]. According to Ghosh [ 51 ], NPAs negatively affect the liquidity and profitability of banks, thus affecting credit growth and leading to financial instability in the economy. Hence, healthy banks translate into a healthy economy.
Despite regulations, such as high capital buffers and liquidity ratio requirements, during the second decade of the twenty-first century, the Indian banking sector still witnessed a substantial increase in NPAs. A recent report by the Indian central bank indicates that the gross NPA ratio reached an all-time peak of 11% in March 2018 and 12.2% in March 2019 [ 49 ]. Basel II has been criticized for several reasons [ 98 ]. Schwerter [ 116 ] and Pakravan [ 98 ] highlighted the systemic risk and gaps in Basel II, which could not address the systemic risk of WFC 2008. Basel III was designed to close the gaps in Basel II. However, Schwerter [ 116 ] criticized Basel III and suggested that more focus should have been on active risk management practices to avoid any impending financial crisis. Basel III was proposed to solve these issues, but it could not [ 3 , 116 ]. Samitas and Polyzos [ 113 ] found that Basel III had made banking challenging since it had reduced liquidity and failed to shield the contagion effect. Therefore, exploring some solutions to establish the right balance between regulation, performance, and risk management of banks is vital.
Keeley [ 67 ] introduced the idea of a balance among banks’ profitability, regulation, and NPA (risk-taking). This study presents the balancing act of profitability, regulation, and NPA (risk-taking) of banks as a probable solution to the issues of bank performance and risk management and calls it a triad . Figure 1 illustrates the concept of a triad. Several authors have discussed the triad in parts [ 32 , 96 , 110 , 112 ]. Triad was empirically tested in different countries by Agoraki et al. [ 1 ]. Though the idea of a triad is quite old, it is relevant in the current scenario. The spirit of the triad strongly and collectively admonishes the Basel Accord and exhibits new and exhaustive measures to take up and solve the issue of performance and risk management in banks [ 16 , 98 ]. The 2008 WFC may have caused an imbalance among profitability, regulation, and risk-taking of banks [ 57 ]. Less regulation , more competition (less profitability ), and incentive to take the risk were the cornerstones of the 2008 WFC [ 56 ]. Achieving a balance among the three elements of a triad is a real challenge for banks’ performance and risk management, which this study addresses.
Triad of Profitability, regulation, and NPA (risk-taking). Note The triad [ 131 ] of profitability, regulation, and NPA (risk-taking) is shown in Fig. 1
Triki et al. [ 130 ] revealed that a bank’s performance is a trade-off between the elements of the triad. Reduction in competition increases the profitability of banks. However, in the long run, reduction in competition leads to either the success or failure of banks. Flexible but well-expressed regulation and less competition add value to a bank’s performance. The current review paper is an attempt to explore the literature on this triad of bank performance, regulation, and risk management. This paper has the following objectives:
To systematically explore the existing literature on the triad: performance, regulation, and risk management of banks; and
To propose a model for effective bank performance and risk management of banks.
Literature is replete with discussion across the world on the triad. However, there is a lack of acceptance of the triad as a solution to the woes of bank performance and risk management. Therefore, the findings of the current papers significantly contribute to this regard. This paper collates all the previous studies on the triad systematically and presents a curated view to facilitate the policy makers and stakeholders to make more informed decisions on the issue of bank performance and risk management. This paper also contributes significantly by proposing a DBS (differential banking system) model to solve the problem of banks (Fig. 7 ). This paper examines studies worldwide and therefore ensures the wider applicability of its findings. Applicability of the DBS model is not only limited to one nation but can also be implemented worldwide. To the best of the authors’ knowledge, this is the first study to systematically evaluate the publication pattern in banking using a blend of scientometrics analysis tools, network analysis tools, and content analysis to understand the link between bank regulation, performance, and risk.
This paper is divided into five sections. “ Data and research methods ” section discusses the research methodology used for the study. The data analysis for this study is presented in two parts. “ Bibliometric and network analysis ” section presents the results obtained using bibliometric and network analysis tools, followed by “ Content Analysis ” section, which presents the content analysis of the selected literature. “ Discussion of the findings ” section discusses the results and explains the study’s conclusion, followed by limitations and scope for further research.
Data and research methods
A literature review is a systematic, reproducible, and explicit way of identifying, evaluating, and synthesizing relevant research produced and published by researchers [ 50 , 100 ]. Analyzing existing literature helps researchers generate new themes and ideas to justify the contribution made to literature. The knowledge obtained through evidence-based research also improves decision-making leading to better practical implementation in the real corporate world [ 100 , 129 ].
As Kumar et al. [ 77 , 78 ] and Rowley and Slack [ 111 ] recommended conducting an SLR, this study also employs a three-step approach to understand the publication pattern in the banking area and establish a link between bank performance, regulation, and risk.
Determining the appropriate keywords for exploring the data
Many databases such as Google Scholar, Web of Science, and Scopus are available to extract the relevant data. The quality of a publication is associated with listing a journal in a database. Scopus is a quality database as it has a wider coverage of data [ 100 , 137 ]. Hence, this study uses the Scopus database to extract the relevant data.
For conducting an SLR, there is a need to determine the most appropriate keywords to be used in the database search engine [ 26 ]. Since this study seeks to explore a link between regulation, performance, and risk management of banks, the keywords used were “risk,” “regulation,” “profitability,” “bank,” and “banking.”
Initial search results and limiting criteria
Using the keywords identified in step 1, the search for relevant literature was conducted in December 2020 in the Scopus database. This resulted in the search of 4525 documents from inception till December 2020. Further, we limited our search to include “article” publications only and included subject areas: “Economics, Econometrics and Finance,” “Business, Management and Accounting,” and “Social sciences” only. This resulted in a final search result of 3457 articles. These results were stored in a.csv file which is then used as an input to conduct the SLR.
Data analysis tools and techniques
This study uses bibliometric and network analysis tools to understand the publication pattern in the area of research [ 13 , 48 , 100 , 122 , 129 , 134 ]. Some sub-analyses of network analysis are keyword word, author, citation, and page rank analysis. Author analysis explains the author’s contribution to literature or research collaboration, national and international [ 59 , 99 ]. Citation analysis focuses on many researchers’ most cited research articles [ 100 , 102 , 131 ].
The.csv file consists of all bibliometric data for 3457 articles. Gephi and other scientometrics tools, such as Table2net and ScienceScape software, were used for the network analysis. This.csv file is directly used as an input for this software to obtain network diagrams for better data visualization [ 77 ]. To ensure the study’s quality, the articles with 50 or more citations (216 in number) are selected for content analysis [ 53 , 102 ]. The contents of these 216 articles are analyzed to develop a conceptual model of banks’ triad of risk, regulation, and profitability. Figure 2 explains the data retrieval process for SLR.
Data retrieval process for SLR. Note Stepwise SLR process and corresponding results obtained
Bibliometric and network analysis
Figure 3 [ 58 ] depicts the total number of studies that have been published on “risk,” “regulation,” “profitability,” “bank,” and “banking.” Figure 3 also depicts the pattern of the quality of the publications from the beginning till 2020. It undoubtedly shows an increasing trend in the number of articles published in the area of the triad: “risk” regulation” and “profitability.” Moreover, out of the 3457 articles published in the said area, 2098 were published recently in the last five years and contribute to 61% of total publications in this area.
Articles published from 1976 till 2020 . Note The graph shows the number of documents published from 1976 till 2020 obtained from the Scopus database
Source of publications
A total of 160 journals have contributed to the publication of 3457 articles extracted from Scopus on the triad of risk, regulation, and profitability. Table 1 shows the top 10 sources of the publications based on the citation measure. Table 1 considers two sets of data. One data set is the universe of 3457 articles, and another is the set of 216 articles used for content analysis along with their corresponding citations. The global citations are considered for the study from the Scopus dataset, and the local citations are considered for the articles in the nodes [ 53 , 135 ]. The top 10 journals with 50 or more citations resulted in 96 articles. This is almost 45% of the literature used for content analysis ( n = 216). Table 1 also shows that the Journal of Banking and Finance is the most prominent in terms of the number of publications and citations. It has 46 articles published, which is about 21% of the literature used for content analysis. Table 1 also shows these core journals’ SCImago Journal Rank indicator and H index. SCImago Journal Rank indicator reflects the impact and prestige of the Journal. This indicator is calculated as the previous three years’ weighted average of the number of citations in the Journal since the year that the article was published. The h index is the number of articles (h) published in a journal and received at least h. The number explains the scientific impact and the scientific productivity of the Journal. Table 1 also explains the time span of the journals covering articles in the area of the triad of risk, regulation, and profitability [ 7 ].
Figure 4 depicts the network analysis, where the connections between the authors and source title (journals) are made. The network has 674 nodes and 911 edges. The network between the author and Journal is classified into 36 modularities. Sections of the graph with dense connections indicate high modularity. A modularity algorithm is a design that measures how strong the divided networks are grouped into modules; this means how well the nodes are connected through a denser route relative to other networks.
Network analysis between authors and journals. Note A node size explains the more linked authors to a journal
The size of the nodes is based on the rank of the degree. The degree explains the number of connections or edges linked to a node. In the current graph, a node represents the name of the Journal and authors; they are connected through the edges. Therefore, the more the authors are associated with the Journal, the higher the degree. The algorithm used for the layout is Yifan Hu’s.
Many authors are associated with the Journal of Banking and Finance, Journal of Accounting and Economics, Journal of Financial Economics, Journal of Financial Services Research, and Journal of Business Ethics. Therefore, they are the most relevant journals on banks’ risk, regulation, and profitability.
Location and affiliation analysis
Affiliation analysis helps to identify the top contributing countries and universities. Figure 5 shows the countries across the globe where articles have been published in the triad. The size of the circle in the map indicates the number of articles published in that country. Table 2 provides the details of the top contributing organizations.
Location of articles published on Triad of profitability, regulation, and risk
Figure 5 shows that the most significant number of articles is published in the USA, followed by the UK. Malaysia and China have also contributed many articles in this area. Table 2 shows that the top contributing universities are also from Malaysia, the UK, and the USA.
Key author analysis
Table 3 shows the number of articles written by the authors out of the 3457 articles. The table also shows the top 10 authors of bank risk, regulation, and profitability.
Fadzlan Sufian, affiliated with the Universiti Islam Malaysia, has the maximum number, with 33 articles. Philip Molyneux and M. Kabir Hassan are from the University of Sharjah and the University of New Orleans, respectively; they contributed significantly, with 20 and 18 articles, respectively.
However, when the quality of the article is selected based on 50 or more citations, Fadzlan Sufian has only 3 articles with more than 50 citations. At the same time, Philip Molyneux and Allen Berger contributed more quality articles, with 8 and 11 articles, respectively.
Table 4 shows the keyword analysis (times they appeared in the articles). The top 10 keywords are listed in Table 4 . Banking and banks appeared 324 and 194 times, respectively, which forms the scope of this study, covering articles from the beginning till 2020. The keyword analysis helps to determine the factors affecting banks, such as profitability (244), efficiency (129), performance (107, corporate governance (153), risk (90), and regulation (89).
The keywords also show that efficiency through data envelopment analysis is a determinant of the performance of banks. The other significant determinants that appeared as keywords are credit risk (73), competition (70), financial stability (69), ownership structure (57), capital (56), corporate social responsibility (56), liquidity (46), diversification (45), sustainability (44), credit provision (41), economic growth (41), capital structure (39), microfinance (39), Basel III (37), non-performing assets (37), cost efficiency (30), lending behavior (30), interest rate (29), mergers and acquisition (28), capital adequacy (26), developing countries (23), net interest margin (23), board of directors (21), disclosure (21), leverage (21), productivity (20), innovation (18), firm size (16), and firm value (16).
Keyword analysis also shows the theories of banking and their determinants. Some of the theories are agency theory (23), information asymmetry (21), moral hazard (17), and market efficiency (16), which can be used by researchers when building a theory. The analysis also helps to determine the methodology that was used in the published articles; some of them are data envelopment analysis (89), which measures technical efficiency, panel data analysis (61), DEA (32), Z scores (27), regression analysis (23), stochastic frontier analysis (20), event study (15), and literature review (15). The count for literature review is only 15, which confirms that very few studies have conducted an SLR on bank risk, regulation, and profitability.
One of the parameters used in judging the quality of the article is its “citation.” Table 5 shows the top 10 published articles with the highest number of citations. Ding and Cronin [ 44 ] indicated that the popularity of an article depends on the number of times it has been cited.
Tahamtan et al. [ 126 ] explained that the journal’s quality also affects its published articles’ citations. A quality journal will have a high impact factor and, therefore, more citations. The citation analysis helps researchers to identify seminal articles. The title of an article with 5900 citations is “A survey of corporate governance.”
Page Rank analysis
Goyal and Kumar [ 53 ] explain that the citation analysis indicates the ‘popularity’ and ‘prestige’ of the published research article. Apart from the citation analysis, one more analysis is essential: Page rank analysis. PageRank is given by Page et al. [ 97 ]. The impact of an article can be measured with one indicator called PageRank [ 135 ]. Page rank analysis indicates how many times an article is cited by other highly cited articles. The method helps analyze the web pages, which get the priority during any search done on google. The analysis helps in understanding the citation networks. Equation 1 explains the page rank (PR) of a published paper, N refers to the number of articles.
T 1,… T n indicates the paper, which refers paper P . C ( Ti ) indicates the number of citations. The damping factor is denoted by a “ d ” which varies in the range of 0 and 1. The page rank of all the papers is equal to 1. Table 6 shows the top papers based on page rank. Tables 5 and 6 together show a contrast in the top ranked articles based on citations and page rank, respectively. Only one article “A survey of corporate governance” falls under the prestigious articles based on the page rank.
Content Analysis is a research technique for conducting qualitative and quantitative analyses [ 124 ]. The content analysis is a helpful technique that provides the required information in classifying the articles depending on their nature (empirical or conceptual) [ 76 ]. By adopting the content analysis method [ 53 , 102 ], the selected articles are examined to determine their content. The classification of available content from the selected set of sample articles that are categorized under different subheads. The themes identified in the relationship between banking regulation, risk, and profitability are as follows.
Regulation and profitability of banks
The performance indicators of the banking industry have always been a topic of interest to researchers and practitioners. This area of research has assumed a special interest after the 2008 WFC [ 25 , 51 , 86 , 114 , 127 , 132 ]. According to research, the causes of poor performance and risk management are lousy banking practices, ineffective monitoring, inadequate supervision, and weak regulatory mechanisms [ 94 ]. Increased competition, deregulation, and complex financial instruments have made banks, including Indian banks, more vulnerable to risks [ 18 , 93 , 119 , 123 ]. Hence, it is essential to investigate the present regulatory machinery for the performance of banks.
There are two schools of thought on regulation and its possible impact on profitability. The first asserts that regulation does not affect profitability. The second asserts that regulation adds significant value to banks’ profitability and other performance indicators. This supports the concept that Delis et al. [ 41 ] advocated that the capital adequacy requirement and supervisory power do not affect productivity or profitability unless there is a financial crisis. Laeven and Majnoni [ 81 ] insisted that provision for loan loss should be part of capital requirements. This will significantly improve active risk management practices and ensure banks’ profitability.
Lee and Hsieh [ 83 ] proposed ambiguous findings that do not support either school of thought. According to Nguyen and Nghiem [ 95 ], while regulation is beneficial, it has a negative impact on bank profitability. As a result, when proposing regulations, it is critical to consider bank performance and risk management. According to Erfani and Vasigh [ 46 ], Islamic banks maintained their efficiency between 2006 and 2013, while most commercial banks lost, furthermore claimed that the financial crisis had no significant impact on Islamic bank profitability.
Regulation and NPA (risk-taking of banks)
The regulatory mechanism of banks in any country must address the following issues: capital adequacy ratio, prudent provisioning, concentration banking, the ownership structure of banks, market discipline, regulatory devices, presence of foreign capital, bank competition, official supervisory power, independence of supervisory bodies, private monitoring, and NPAs [ 25 ].
Kanoujiya et al. [ 64 ] revealed through empirical evidence that Indian bank regulations lack a proper understanding of what banks require and propose reforming and transforming regulation in Indian banks so that responsive governance and regulation can occur to make banks safer, supported by Rastogi et al. [ 105 ]. The positive impact of regulation on NPAs is widely discussed in the literature. [ 94 ] argue that regulation has multiple effects on banks, including reducing NPAs. The influence is more powerful if the country’s banking system is fragile. Regulation, particularly capital regulation, is extremely effective in reducing risk-taking in banks [ 103 ].
Rastogi and Kanoujiya [ 106 ] discovered evidence that disclosure regulations do not affect the profitability of Indian banks, supported by Karyani et al. [ 65 ] for the banks located in Asia. Furthermore, Rastogi and Kanoujiya [ 106 ] explain that disclosure is a difficult task as a regulatory requirement. It is less sustainable due to the nature of the imposed regulations in banks and may thus be perceived as a burden and may be overcome by realizing the benefits associated with disclosure regulation [ 31 , 54 , 101 ]. Zheng et al. [ 138 ] empirically discovered that regulation has no impact on the banks’ profitability in Bangladesh.
Governments enforce banking regulations to achieve a stable and efficient financial system [ 20 , 94 ]. The existing literature is inconclusive on the effects of regulatory compliance on banks’ risks or the reduction of NPAs [ 10 , 11 ]. Boudriga et al. [ 25 ] concluded that the regulatory mechanism plays an insignificant role in reducing NPAs. This is especially true in weak institutions, which are susceptible to corruption. Gonzalez [ 52 ] reported that firm regulations have a positive relationship with banks’ risk-taking, increasing the probability of NPAs. However, Boudriga et al. [ 25 ], Samitas and Polyzos [ 113 ], and Allen et al. [ 3 ] strongly oppose the use of regulation as a tool to reduce banks’ risk-taking.
Kwan and Laderman [ 79 ] proposed three levels in regulating banks, which are lax, liberal, and strict. The liberal regulatory framework leads to more diversification in banks. By contrast, the strict regulatory framework forces the banks to take inappropriate risks to compensate for the loss of business; this is a global problem [ 73 ].
Capital regulation reduces banks’ risk-taking [ 103 , 110 ]. Capital regulation leads to cost escalation, but the benefits outweigh the cost [ 103 ]. The trade-off is worth striking. Altman Z score is used to predict banks’ bankruptcy, and it found that the regulation increased the Altman’s Z-score [ 4 , 46 , 63 , 68 , 72 , 120 ]. Jin et al. [ 62 ] report a negative relationship between regulation and banks’ risk-taking. Capital requirements empowered regulators, and competition significantly reduced banks’ risk-taking [ 1 , 122 ]. Capital regulation has a limited impact on banks’ risk-taking [ 90 , 103 ].
Maji and De [ 90 ] suggested that human capital is more effective in managing banks’ credit risks. Besanko and Kanatas [ 21 ] highlighted that regulation on capital requirements might not mitigate risks in all scenarios, especially when recapitalization has been enforced. Klomp and De Haan [ 72 ] proposed that capital requirements and supervision substantially reduce banks’ risks.
A third-party audit may impart more legitimacy to the banking system [ 23 ]. The absence of third-party intervention is conspicuous, and this may raise a doubt about the reliability and effectiveness of the impact of regulation on bank’s risk-taking.
NPA (risk-taking) in banks and profitability
Profitability affects NPAs, and NPAs, in turn, affect profitability. According to the bad management hypothesis [ 17 ], higher profits would negatively affect NPAs. By contrast, higher profits may lead management to resort to a liberal credit policy (high earnings), which may eventually lead to higher NPAs [ 104 ].
Balasubramaniam [ 8 ] demonstrated that NPA has double negative effects on banks. NPAs increase stressed assets, reducing banks’ productive assets [ 92 , 117 , 136 ]. This phenomenon is relatively underexplored and therefore renders itself for future research.
Triad and the performance of banks
Regulation and triad.
Regulations and their impact on banks have been a matter of debate for a long time. Barth et al. [ 12 ] demonstrated that countries with a central bank as the sole regulatory body are prone to high NPAs. Although countries with multiple regulatory bodies have high liquidity risks, they have low capital requirements [ 40 ]. Barth et al. [ 12 ] supported the following steps to rationalize the existing regulatory mechanism on banks: (1) mandatory information [ 22 ], (2) empowered management of banks, and (3) increased incentive for private agents to exert corporate control. They show that profitability has an inverse relationship with banks’ risk-taking [ 114 ]. Therefore, standard regulatory practices, such as capital requirements, are not beneficial. However, small domestic banks benefit from capital restrictions.
DeYoung and Jang [ 43 ] showed that Basel III-based policies of liquidity convergence ratio (LCR) and net stable funding ratio (NSFR) are not fully executed across the globe, including the US. Dahir et al. [ 39 ] found that a decrease in liquidity and funding increases banks’ risk-taking, making banks vulnerable and reducing stability. Therefore, any regulation on liquidity risk is more likely to create problems for banks.
Concentration banking and triad
Kiran and Jones [ 71 ] asserted that large banks are marginally affected by NPAs, whereas small banks are significantly affected by high NPAs. They added a new dimension to NPAs and their impact on profitability: concentration banking or banks’ market power. Market power leads to less cost and more profitability, which can easily counter the adverse impact of NPAs on profitability [ 6 , 15 ].
The connection between the huge volume of research on the performance of banks and competition is the underlying concept of market power. Competition reduces market power, whereas concentration banking increases market power [ 25 ]. Concentration banking reduces competition, increases market power, rationalizes the banks’ risk-taking, and ensures profitability.
Tabak et al. [ 125 ] advocated that market power incentivizes banks to become risk-averse, leading to lower costs and high profits. They explained that an increase in market power reduces the risk-taking requirement of banks. Reducing banks’ risks due to market power significantly increases when capital regulation is executed objectively. Ariss [ 6 ] suggested that increased market power decreases competition, and thus, NPAs reduce, leading to increased banks’ stability.
Competition, the performance of banks, and triad
Boyd and De Nicolo [ 27 ] supported that competition and concentration banking are inversely related, whereas competition increases risk, and concentration banking decreases risk. A mere shift toward concentration banking can lead to risk rationalization. This finding has significant policy implications. Risk reduction can also be achieved through stringent regulations. Bolt and Tieman [ 24 ] explained that stringent regulation coupled with intense competition does more harm than good, especially concerning banks’ risk-taking.
Market deregulation, as well as intensifying competition, would reduce the market power of large banks. Thus, the entire banking system might take inappropriate and irrational risks [ 112 ]. Maji and Hazarika [ 91 ] added more confusion to the existing policy by proposing that, often, there is no relationship between capital regulation and banks’ risk-taking. However, some cases have reported a positive relationship. This implies that banks’ risk-taking is neutral to regulation or leads to increased risk. Furthermore, Maji and Hazarika [ 91 ] revealed that competition reduces banks’ risk-taking, contrary to popular belief.
Claessens and Laeven [ 36 ] posited that concentration banking influences competition. However, this competition exists only within the restricted circle of banks, which are part of concentration banking. Kasman and Kasman [ 66 ] found that low concentration banking increases banks’ stability. However, they were silent on the impact of low concentration banking on banks’ risk-taking. Baselga-Pascual et al. [ 14 ] endorsed the earlier findings that concentration banking reduces banks’ risk-taking.
Concentration banking and competition are inversely related because of the inherent design of concentration banking. Market power increases when only a few large banks are operating; thus, reduced competition is an obvious outcome. Barra and Zotti [ 9 ] supported the idea that market power, coupled with competition between the given players, injects financial stability into banks. Market power and concentration banking affect each other. Therefore, concentration banking with a moderate level of regulation, instead of indiscriminate regulation, would serve the purpose better. Baselga-Pascual et al. [ 14 ] also showed that concentration banking addresses banks’ risk-taking.
Schaeck et al. [ 115 ], in a landmark study, presented that concentration banking and competition reduce banks’ risk-taking. However, they did not address the relationship between concentration banking and competition, which are usually inversely related. This could be a subject for future research. Research on the relationship between concentration banking and competition is scant, identified as a research gap (“ Research Implications of the study ” section).
Transparency, corporate governance, and triad
One of the big problems with NPAs is the lack of transparency in both the regulatory bodies and banks [ 25 ]. Boudriga et al. [ 25 ] preferred to view NPAs as a governance issue and thus, recommended viewing it from a governance perspective. Ahmad and Ariff [ 2 ] concluded that regulatory capital and top-management quality determine banks’ credit risk. Furthermore, they asserted that credit risk in emerging economies is higher than that of developed economies.
Bad management practices and moral vulnerabilities are the key determinants of insolvency risks of Indian banks [ 95 ]. Banks are an integral part of the economy and engines of social growth. Therefore, banks enjoy liberal insolvency protection in India, especially public sector banks, which is a critical issue. Such a benevolent insolvency cover encourages a bank to be indifferent to its capital requirements. This indifference takes its toll on insolvency risk and profit efficiency. Insolvency protection makes the bank operationally inefficient and complacent.
Foreign equity and corporate governance practices help manage the adverse impact of banks’ risk-taking to ensure the profitability and stability of banks [ 33 , 34 ]. Eastburn and Sharland [ 45 ] advocated that sound management and a risk management system that can anticipate any impending risk are essential. A pragmatic risk mechanism should replace the existing conceptual risk management system.
Lo [ 87 ] found and advocated that the existing legislation and regulations are outdated. He insisted on a new perspective and asserted that giving equal importance to behavioral aspects and the rational expectations of customers of banks is vital. Buston [ 29 ] critiqued the balance sheet risk management practices prevailing globally. He proposed active risk management practices that provided risk protection measures to contain banks’ liquidity and solvency risks.
Klomp and De Haan [ 72 ] championed the cause of giving more autonomy to central banks of countries to provide stability in the banking system. Louzis et al. [ 88 ] showed that macroeconomic variables and the quality of bank management determine banks’ level of NPAs. Regulatory authorities are striving hard to make regulatory frameworks more structured and stringent. However, the recent increase in loan defaults (NPAs), scams, frauds, and cyber-attacks raise concerns about the effectiveness [ 19 ] of the existing banking regulations in India as well as globally.
Discussion of the findings
The findings of this study are based on the bibliometric and content analysis of the sample published articles.
The bibliometric study concludes that there is a growing demand for researchers and good quality research
The keyword analysis suggests that risk regulation, competition, profitability, and performance are key elements in understanding the banking system. The main authors, keywords, and journals are grouped in a Sankey diagram in Fig. 6 . Researchers can use the following information to understand the publication pattern on banking and its determinants.
Sankey Diagram of main authors, keywords, and journals. Note Authors contribution using scientometrics tools
Research Implications of the study
The study also concludes that a balance among the three components of triad is the solution to the challenges of banks worldwide, including India. We propose the following recommendations and implications for banks:
This study found that “the lesser the better,” that is, less regulation enhances the performance and risk management of banks. However, less regulation does not imply the absence of regulation. Less regulation means the following:
Flexible but full enforcement of the regulations
Customization, instead of a one-size-fits-all regulatory system rooted in a nation’s indigenous requirements, is needed. Basel or generic regulation can never achieve what a customized compliance system can.
A third-party audit, which is above the country's central bank, should be mandatory, and this would ensure that all three aspects of audit (policy formulation, execution, and audit) are handled by different entities.
This study asserts that the existing literature is replete with poor performance and risk management due to excessive competition. Banking is an industry of a different genre, and it would be unfair to compare it with the fast-moving consumer goods (FMCG) or telecommunication industry, where competition injects efficiency into the system, leading to customer empowerment and satisfaction. By contrast, competition is a deterrent to the basic tenets of safe banking. Concentration banking is more effective in handling the multi-pronged balance between the elements of the triad. Concentration banking reduces competition to lower and manageable levels, reduces banks’ risk-taking, and enhances profitability.
No incentive to take risks
It is found that unless banks’ risk-taking is discouraged, the problem of high NPA (risk-taking) cannot be addressed. Concentration banking is a disincentive to risk-taking and can be a game-changer in handling banks’ performance and risk management.
Research on the risk and performance of banks reveals that the existing regulatory and policy arrangement is not a sustainable proposition, especially for a country where half of the people are unbanked [ 37 ]. Further, the triad presented by Keeley [ 67 ] is a formidable real challenge to bankers. The balance among profitability, risk-taking, and regulation is very subtle and becomes harder to strike, just as the banks globally have tried hard to achieve it. A pragmatic intervention is needed; hence, this study proposes a change in the banking structure by having two types of banks functioning simultaneously to solve the problems of risk and performance of banks. The proposed two-tier banking system explained in Fig. 7 can be a great solution. This arrangement will help achieve the much-needed balance among the elements of triad as presented by Keeley [ 67 ].
Conceptual Framework. Note Fig. 7 describes the conceptual framework of the study
The first set of banks could be conventional in terms of their structure and should primarily be large-sized. The number of such banks should be moderate. There is a logic in having only a few such banks to restrict competition; thus, reasonable market power could be assigned to them [ 55 ]. However, a reduction in competition cannot be over-assumed, and banks cannot become complacent. As customary, lending would be the main source of revenue and income for these banks (fund based activities) [ 82 ]. The proposed two-tier system can be successful only when regulation especially for risk is objectively executed [ 29 ]. The second set of banks could be smaller in size and more in number. Since they are more in number, they would encounter intense competition for survival and for generating more business. Small is beautiful, and thus, this set of banks would be more agile and adaptable and consequently more efficient and profitable. The main source of revenue for this set of banks would not be loans and advances. However, non-funding and non-interest-bearing activities would be the major revenue source. Unlike their traditional and large-sized counterparts, since these banks are smaller in size, they are less likely to face risk-taking and NPAs [ 74 ].
Sarmiento and Galán [ 114 ] presented the concerns of large and small banks and their relative ability and appetite for risk-taking. High risk could threaten the existence of small-sized banks; thus, they need robust risk shielding. Small size makes them prone to failure, and they cannot convert their risk into profitability. However, large banks benefit from their size and are thus less vulnerable and can convert risk into profitable opportunities.
India has experimented with this Differential Banking System (DBS) (two-tier system) only at the policy planning level. The execution is impending, and it highly depends on the political will, which does not appear to be strong now. The current agenda behind the DBS model is not to ensure the long-term sustainability of banks. However, it is currently being directed to support the agenda of financial inclusion by extending the formal credit system to the unbanked masses [ 107 ]. A shift in goal is needed to employ the DBS as a strategic decision, but not merely a tool for financial inclusion. Thus, the proposed two-tier banking system (DBS) can solve the issue of profitability through proper regulation and less risk-taking.
The findings of Triki et al. [ 130 ] support the proposed DBS model, in this study. Triki et al. [ 130 ] advocated that different component of regulations affect banks based on their size, risk-taking, and concentration banking (or market power). Large size, more concentration banking with high market power, and high risk-taking coupled with stringent regulation make the most efficient banks in African countries. Sharifi et al. [ 119 ] confirmed that size advantage offers better risk management to large banks than small banks. The banks should modify and work according to the economic environment in the country [ 69 ], and therefore, the proposed model could help in solving the current economic problems.
This is a fact that DBS is running across the world, including in India [ 60 ] and other countries [ 133 ]. India experimented with DBS in the form of not only regional rural banks (RRBs) but payments banks [ 109 ] and small finance banks as well [ 61 ]. However, the purpose of all the existing DBS models, whether RRBs [ 60 ], payment banks, or small finance banks, is financial inclusion, not bank performance and risk management. Hence, they are unable to sustain and are failing because their model is only social instead of a much-needed dual business-cum-social model. The two-tier model of DBS proposed in the current paper can help serve the dual purpose. It may not only be able to ensure bank performance and risk management but also serve the purpose of inclusive growth of the economy.
Conclusion of the study
The study’s conclusions have some significant ramifications. This study can assist researchers in determining their study plan on the current topic by using a scientific approach. Citation analysis has aided in the objective identification of essential papers and scholars. More collaboration between authors from various countries/universities may help countries/universities better understand risk regulation, competition, profitability, and performance, which are critical elements in understanding the banking system. The regulatory mechanism in place prior to 2008 failed to address the risk associated with banks [ 47 , 87 ]. There arises a necessity and motivates authors to investigate the current topic. The present study systematically explores the existing literature on banks’ triad: performance, regulation, and risk management and proposes a probable solution.
To conclude the bibliometric results obtained from the current study, from the number of articles published from 1976 to 2020, it is evident that most of the articles were published from the year 2010, and the highest number of articles were published in the last five years, i.e., is from 2015. The authors discovered that researchers evaluate articles based on the scope of critical journals within the subject area based on the detailed review. Most risk, regulation, and profitability articles are published in peer-reviewed journals like; “Journal of Banking and Finance,” “Journal of Accounting and Economics,” and “Journal of Financial Economics.” The rest of the journals are presented in Table 1 . From the affiliation statistics, it is clear that most of the research conducted was affiliated with developed countries such as Malaysia, the USA, and the UK. The researchers perform content analysis and Citation analysis to access the type of content where the research on the current field of knowledge is focused, and citation analysis helps the academicians understand the highest cited articles that have more impact in the current research area.
Practical implications of the study
The current study is unique in that it is the first to systematically evaluate the publication pattern in banking using a combination of scientometrics analysis tools, network analysis tools, and content analysis to understand the relationship between bank regulation, performance, and risk. The study’s practical implications are that analyzing existing literature helps researchers generate new themes and ideas to justify their contribution to literature. Evidence-based research knowledge also improves decision-making, resulting in better practical implementation in the real corporate world [ 100 , 129 ].
Limitations and scope for future research
The current study only considers a single database Scopus to conduct the study, and this is one of the limitations of the study spanning around the multiple databases can provide diverse results. The proposed DBS model is a conceptual framework that requires empirical testing, which is a limitation of this study. As a result, empirical testing of the proposed DBS model could be a future research topic.
Availability of data and materials
Systematic literature review
World Financial Crisis
Differential banking system
SCImago Journal Rank Indicator
Liquidity convergence ratio
Net stable funding ratio
Fast moving consumer goods
Regional rural banks
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‘SR’ performed Abstract, Introduction, and Data methodology sections and was the major contributor; ‘AS’ performed Bibliometric and Network analysis and conceptual framework; ‘GP’ performed citation analysis and discussion section; ‘VMB’ collated data from the database and concluded the article. All authors read and approved the final manuscript.
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Rastogi, S., Sharma, A., Pinto, G. et al. A literature review of risk, regulation, and profitability of banks using a scientometric study. Futur Bus J 8 , 28 (2022). https://doi.org/10.1186/s43093-022-00146-4
Received : 11 March 2022
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Published : 03 September 2022
DOI : https://doi.org/10.1186/s43093-022-00146-4
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- Bank performance
- Bibliometric analysis
- Scientometric analysis
- Theoretical Article
- Published: 03 February 2018
A review of bank efficiency and productivity
- Vaneet Bhatia 1 ,
- Sankarshan Basu 2 ,
- Subrata Kumar Mitra 1 &
- Pradyumna Dash 1
OPSEARCH volume 55 , pages 557–600 ( 2018 ) Cite this article
The objective of this study is to present a systematic literature review in the context of bank efficiency and productivity. It focuses on the recent developments related to empirical methodological advances and new dimensions added to the ever-growing field of bank performance analysis. Selected research papers were coded in terms of their key objectives and were segregated into 11 themes—Branch, Comparison, Consolidation and Expansion, Deregulation and Regulation, Environment, Input–output, Methodological advances, Non-traditional activities, Risk, Stock performance and Others. The 103 selected studies were further analysed based on efficiency measures, input–output approaches and methodology. While summarising the extant literature on bank efficiency and productivity, the ongoing debate regarding the optimal input output approaches and ideal frontier techniques for bank performance analysis has also been dealt with. The current study also highlights the possible future research avenues in this area.
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Productivity in simple terms can be defined as the ratio of output to input. Efficiency in simple terms can be defined as the comparison between actual and optimal or achievable values of outputs and inputs. For standard definitions, please refer to Coelli et al. [ 7 ].
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We are thankful to N. M. Ganguli, Managing Editor, OPSEARCH and the reviewers for the constructive comments and an opportunity to improve our work.
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Vaneet Bhatia, Subrata Kumar Mitra & Pradyumna Dash
Indian Institute of Management Bangalore, Bengaluru, Karnataka, India
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Bhatia, V., Basu, S., Mitra, S.K. et al. A review of bank efficiency and productivity. OPSEARCH 55 , 557–600 (2018). https://doi.org/10.1007/s12597-018-0332-2
Accepted : 22 January 2018
Published : 03 February 2018
Issue Date : November 2018
DOI : https://doi.org/10.1007/s12597-018-0332-2
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M Bilal , Kashif. Mughal. , Ahmad Waleed
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- About Journal
Asian Journal of Management
2321-5763 (Online) 0976-495X (Print)
Comparative analysis of Financial performance of HDFC and SBI bank on the basis of Ratio analysis
Author(s): Reetika Verma
Email(s): [email protected]
Address: Reetika Verma Student of M. Com, Final Year, Pt. L.M.S. PG Autonomous College, Rishikesh, Uttarakhand. *Corresponding Author
Published In: Volume - 12 , Issue - 2 , Year - 2021
ABSTRACT: The banking sector in any economy plays a significant role in its growth and development. This paper is based on financial performance analysis of two leading banks of India. This paper aims to evaluate financial performance of HDFC and SBI bank on the basis of accounting ratios and also to study the functioning of the Indian banking system . In this paper different ratios of both the banks are compared. Capital adequacy ratio, debt equity ratio, leverage ratios, profit and loss account ratios, net interest margin ratio, return on equity and other ratios are used to compare the performance of both the banks. This research is based on the data collected from financial statements of the banks. The performance of both the banks are compared from the year 2015 to 2020. It is observed that performance of HDFC is better than SBI not only in terms of ratio analysis but also in terms of customer satisfaction.
- Financial Performance
- Ratio Analysis
- Liquidity Comparison
- Enterprise value
- Customer Satisfaction.
Cite this article: Reetika Verma. Comparative analysis of Financial performance of HDFC and SBI bank on the basis of Ratio analysis. Asian Journal of Management. 2021; 12(2):111-4. doi: 10.52711/2321-5763.2021.00016 Cite(Electronic): Reetika Verma. Comparative analysis of Financial performance of HDFC and SBI bank on the basis of Ratio analysis. Asian Journal of Management. 2021; 12(2):111-4. doi: 10.52711/2321-5763.2021.00016 Available on: https://ajmjournal.com/AbstractView.aspx?PID=2021-12-2-3
Asian Journal of Management (AJM) is an international, peer-reviewed journal, devoted to managerial sciences. The aim of AJM is to publish the relevant to applied management theory and practice...... Read more >>>
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Implications of Bank Equity Price Declines for Inflation
- Ina Hajdini
This Economic Commentary examines the relationship between bank equity price index returns and inflation in advanced economies. While large declines in bank equity price indices are generally followed by declines in the ratio of bank credit to GDP, a measure of credit supply, and economic activity as measured by GDP, they have essentially no effect on inflation. These findings suggest that the collapse of several regional banks in early 2023 would not, on its own, put downward pressure on inflation.
The aftermath of the collapse of several regional banks, including Silicon Valley Bank and Signature Bank, in early 2023 raised important questions regarding the effects that banking distress would have on economic activity and inflation. Understanding the latter is of particular importance in the current economic environment when inflation is running well above the Federal Open Market Committee’s long-term target of 2 percent. The effects of banking distress on bank credit and output have been well-studied: empirical research finds that distress in the banking sector typically shrinks credit supply and depresses output growth. The relationship between banking distress and inflation has not been examined to the same extent. Phillips curve logic would suggest that since a banking crisis puts downward pressure on output, inflation should come down, as well, but this line of thinking may not be consistent with the evidence.
The goal of this Economic Commentary is to shed more light on the effects of widespread declines in the stock prices of banks—as measured by bank equity price indices, which are constructed by Baron et al. (2021) and capture distress in the banking sector—on inflation, as measured by the consumer price index (CPI). 1 Specifically, I extend the analysis of Baron et al. (2021) to explore the effects that changes in the bank equity price index from one year to the next have on annual CPI inflation for advanced economies, including those of the United States, Canada, Australia, Japan, and several Western European countries, over a multidecade timespan. 2
I highlight three main results. First, through the lens of a simple event study, I find that the behavior of CPI inflation in the years before and after a decline of 30 percent or more in the bank equity price index (an event defined as a “bank equity crash” in Baron et al. (2021)) is very similar to its behavior around years when there is no crash in bank stock values. Second, in an exercise using data from advanced economies during the period 1972–2016, I find that large declines in bank equity price index returns have tended to lead to decreases in the credit-to-GDP ratio and in output growth, relative to those experienced during normal times, defined as periods when bank equity price index returns rise by a modest amount. This result is similar to the findings in Baron et al. (2021). Third, when I conduct a similar exercise for CPI inflation, I find that large declines in bank equity price index returns generally have not had any significant effect on CPI inflation in advanced economies in recent decades. Hence, this Economic Commentary finds that large declines in bank stock values appear to have little to no effect on inflation, even if they tend to weigh on credit supply and output growth.
The data used in this Economic Commentary come from the publicly available dataset constructed by Baron et al. (2021). 3 The dataset comprises information on annual bank and nonfinancial equity indices and on macroeconomic variables for a large number of countries from 1870 to 2016. 4 I focus on advanced economies, that is, the United States, Canada, Australia, Japan, and a number of Western European countries. 5 To draw inferences from this analysis applicable to the economic events that occurred in early 2023, when multiple banks failed and overall bank stock values experienced a sharp decline, my primary emphasis will be on real bank equity index returns in the following ranges: i) between -15 percent and 0 percent; ii) between -30 percent and -15 percent; and iii) below -30 percent.
Figure 1 plots the annual share of advanced economies that have suffered from bank stock value declines of various ranges (blue bars) along with an indicator for the years during which the United States experienced similarly sized declines (red dashed lines). This graphic highlights three interesting features. First, the most negative bank equity index returns for both the advanced economies cohort and for the United States are less common, as illustrated by fewer blue bars in the bottom panel than in the top panel. Second, as shown in panels b and c, bank equity index returns between -15 percent and -30 percent and bank equity crashes have become more frequent after the 1970s. Third, bank equity crashes seem to have become more interconnected across countries over time. During the Great Recession, for example, almost all of the advanced economies experienced bank equity crashes.
I start the analysis with an event study over the full sample, from 1870 to 2016, that compares the average dynamics of the bank credit to GDP ratio, real GDP, and inflation as measured by the CPI for advanced economies around years when bank equity crashes occurred; I compare those dynamics to the average behavior during periods in which there was not a crash. Figure 2 visualizes the results. The lines in red reveal the cumulative growth of the variable of interest beginning five years before the occurrence of a bank equity crash and extending five years after. The curves in dashed blue show the evolution of the growth of that same variable on average over the span of 10 years during which no bank equity crash occurred. Panel a reveals that the credit-to-GDP ratio in the years leading up to the crash is systematically higher than what it would typically be during years without a crash. It then declines significantly below the level during years without a crash, typically for some time after the crash occurs. On the other hand, panel b shows that in the years leading to a crash, real GDP growth evolves quite similarly to during normal times, but once the crash occurs, real GDP is suppressed even five years after the crash. Finally, panel c shows that there are virtually no differences in the path of CPI inflation in the years leading up to and after a bank equity crash when compared with its path during years with no crashes.
Next, to quantify the relationship between a bank equity crash occurring in year t and cumulative CPI inflation h years later, I rely on a similar methodology to that of Baron et al. (2021) and estimate the following regression for various values of h : 6
π i , t + h = α i h + ϕ t h + ∑ j β j h 𝟏 [ r i t B ∈ B j ] + Γ h X i t + ϵ i t h
where π i , t + h denotes CPI inflation in country i between year t and year t + h ; α i h is a country fixed effect that absorbs variation in inflation as a result of country-specific characteristics, events, and so on; ϕ t h is a year fixed effect that absorbs common events across the advanced economies; 𝟏 [ r i t B ∈ B j ] is an indicator variable taking a value of 1 if the bank equity index return is within bin B j and 0 otherwise, where the possible ranges are less than -30 percent, between -15 and -30 percent, between -15 and 0 percent, between 15 percent and 30 percent, and higher than 30 percent; X it is a vector embedding various controls; and ϵ i t h is an error term. 7 The parameters of interest are the coefficients β j h that quantify the relationship between an annual bank equity index return within range B j and CPI inflation between year t and year t + h , relative to “normal times,” defined as periods when annual bank equity returns are between 0 and 15 percent. 8 I run this same regression also using either the net change in the credit-to-GDP ratio from t to t + h or the cumulative change in real GDP as the left-side variables.
Table 1 summarizes the estimates of β j h when the dependent variable is the cumulative change in the credit-to-GDP ratio (panel a), cumulative real GDP growth (panel b), and cumulative CPI inflation (panel c) for the sample period from 1972 to 2016. I focus primarily on this subperiod of the dataset to mitigate concerns around the effects of substantial changes in monetary policy frameworks on the behavior of inflation (and other macro variables). 9
The analysis reveals that, relative to normal times, bank equity index declines that are smaller than 15 percent have historically been followed by a very slight decline in CPI inflation one year and three years after, whereas larger bank equity index declines that were greater than 15 percent have historically been followed by a very slight increase in CPI inflation one year and three years after. However, it is crucial to note that none of these estimates is statistically significant. 10 On the other hand, similar to results in Baron et al. (2021), I find that, compared to normal times, bank equity index declines of more than 15 percent have been historically associated with declines in real GDP of 0.5 percent to 1.1 percent after one year and declines of 1.4 percent to 1.7 percent after three years. Similarly, relative to normal times, bank equity index declines between 15 percent and 30 percent have historically been associated with reductions in the credit-to-GDP ratio of 4.4 percentage points, while bank equity crashes have been associated with reductions of as much as 9 percentage points over a three-year horizon. Unlike the results for CPI inflation, the estimated relationships between bank equity index returns and credit supply or output growth are larger and statistically significant.
To get a full picture of the effects of bank equity index declines on the three variables over time, Figure 3 plots the point estimates of β j h together with the 95 percent confidence intervals over a six-year horizon. Panel c shows that the effect of banking distress on CPI inflation is never statistically significant and that while the point estimate is negative for bank equity index declines that are smaller than 15 percent (in blue), the point estimate turns positive when the bank equity index falls by more than 15 percent (in red and black). The estimates for CPI inflation are insignificant despite the negative effects that banking distress can have on credit-to-GDP ratio changes and real GDP growth, as shown in panels a and b, respectively. For instance, panel b shows that negative bank equity index returns are associated with significant declines in real output during the first three years and that this effect is more pronounced the more negative the equity return. After the first three years, the effect on real GDP diminishes and becomes statistically insignificant. By contrast, the effects of banking stress on the credit-to-GDP ratio can be longer lasting. Specifically, over the span of six years, credit supply can shrink by about 8 percentage points if bank equity index returns are in the range between -15 percent and -30 percent (black square) and can shrink by more than 10 percentage points in the case of a bank equity crash (red circle). Finally, the real annual S&P bank equity index return in the United States has varied from -20 percent in May 2023 to a low of -25 percent in March 2023. 11 Therefore, the findings that are relevant for the implications of the bank failures that occurred in early 2023 for the United States economy are the ones associated with bank equity returns belonging to the range between -15 percent and -30 percent.
Is there an economic mechanism that can explain the lack of significant impact of banking distress on inflation? Several papers in the literature have documented that incentives guiding firms’ pricing decisions might change when there is stress in the banking sector. For instance, Gilchrist et al. (2017) show that, during the Great Recession in the United States, liquidity-constrained firms were more likely to raise—instead of lower—prices as part of an effort to preserve internal liquidity, whereas firms that did not face liquidity constraints lowered prices. In an earlier paper, Chevalier and Scharfstein (1996) document similar actions by firms in the United States during the recession in oil-producing states in 1986 and during the macroeconomic recession in 1990–1991 and its aftermath. This pricing behavior by a share of firms facing liquidity constraints in the wake of banking stress can put upward pressure on prices, thus mitigating the downward pressures on inflation resulting from shrinking real GDP and bank credit supply.
Given the interest in the economic effects that distress in the banking sector has on the economy in the aftermath of the bank failures that occurred early in 2023, this Economic Commentary seeks to further illuminate the relationship between declines in bank equity price index returns and inflation. Focusing on advanced economies, I find that, historically, large declines in bank equity indices have not had a statistically significant impact on CPI inflation even though banking distress tends to significantly depress bank credit and real GDP. This result suggests that the failure of several regional banks in early 2023 has likely not put downward pressure on inflation. This finding could reflect that firms facing liquidity constraints from banking distress could choose to increase prices to preserve internal liquidity, whereas other firms could choose to decrease prices, thereby offsetting downward pressures on inflation from slower economic growth.
The views authors express in Economic Commentary are theirs and not necessarily those of the Federal Reserve Bank of Cleveland or the Board of Governors of the Federal Reserve System. The series editor is Tasia Hane. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. This paper and its data are subject to revision; please visit clevelandfed.org for updates.
- Baron, Matthew, Emil Verner, and Wei Xiong. 2020. “Replication Data for: ‘Banking Crises without Panics.’” V1. Harvard Dataverse. https://doi.org/10.7910/DVN/ECC9GE .
- Baron, Matthew, Emil Verner, and Wei Xiong. 2021. “Banking Crises Without Panics.” The Quarterly Journal of Economics 136 (1): 51–113. https://doi.org/10.1093/qje/qjaa034 .
- Chevalier, Judith A., and David S. Scharfstein. 1996. “Capital-Market Imperfections and Countercyclical Markups: Theory and Evidence.” American Economic Review 86 (4): 703–25. https://www.jstor.org/stable/2118301 .
- Gilchrist, Simon, Raphael Schoenle, Jae Sim, and Egon Zakrajšek. 2017. “Inflation Dynamics during the Financial Crisis.” American Economic Review 107 (3): 785–823. https://doi.org/10.1257/aer.20150248 .
- Jordà, Òscar. 2005. “Estimation and Inference of Impulse Responses by Local Projections.” American Economic Review 95 (1): 161–82. https://doi.org/10.1257/0002828053828518 .
- The bank equity price index primarily covers large commercial banks. See Baron et al. (2021) for more details on the construction of the index. Return to 1
- The list of countries considered in this Economic Commentary is a subset of the countries considered in Baron et al. (2021). Return to 2
- The dataset can be found at https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/ECC9GE . Return to 3
- I refer the reader to Baron et al. (2021) for a detailed description of the dataset. Return to 4
- The list of countries comprises Australia, Belgium, Canada, Denmark, Finland, France, Germany, Ireland, Italy, Japan, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, the United Kingdom, and the United States. Return to 5
- The regression given in equation (1) is a Jordà (2005) local projection. Return to 6
- The controls embedded in X it include current and three lags of real annual GDP growth, bank credit-to-GDP change, and inflation; lags of bank and nonfinancial equity return bins; along with an indicator variable taking the value of 1 if nonfinancial returns (such as stock indices) are within range B j and 0 otherwise. The regression I run is exactly the same one as in Baron et al. (2021), but here I abstract from discussing the effects that nonfinancial returns have for the three variables of interest. Return to 7
- Identification in the regression comes from cross-country or cross-time variation in the size of bank equity price declines. The results of the current analysis apply to cases when bank equity return changes are not perfectly coordinated across advanced economies; otherwise, the time fixed effect would absorb the variation in CPI growth that is due to a common shock to bank returns. For instance, estimates of β j h would not accurately capture the effects that a bank equity crash would have on inflation, output growth, or credit supply during the Great Recession since almost all countries in the sample suffered from bank equity price index declines that were larger than 30 percent (see panel c in Figure 1). Return to 8
- The early 1970s marked the end of the Bretton Woods system, which was the prevailing international monetary system of the developed economies. Return to 9
- The results remain largely unchanged when year fixed effects are not controlled for and when focusing on the full sample from 1870 to 2016. Return to 10
- The real annual bank equity price index return is computed as the average of the annual growth rate of the bank equity price index in a given month in deviation from CPI inflation in that month. The source of bank equity price index data is Dow Jones US Banks Index, and the source for CPI inflation is the United States Bureau of Labor Statistics. Return to 11
Hajdini, Ina. 2023. “Implications of Bank Equity Price Declines for Inflation.” Federal Reserve Bank of Cleveland, Economic Commentary 2023-18. https://doi.org/10.26509/frbc-ec-202318
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- Published: 13 November 2023
Automated synthesis of oxygen-producing catalysts from Martian meteorites by a robotic AI chemist
- Qing Zhu ORCID: orcid.org/0000-0003-4278-4205 1 na1 ,
- Yan Huang ORCID: orcid.org/0000-0003-2512-2509 1 na1 ,
- Donglai Zhou 1 na1 ,
- Luyuan Zhao 1 na1 ,
- Lulu Guo 1 ,
- Ruyu Yang 1 ,
- Zixu Sun ORCID: orcid.org/0009-0009-3581-7532 1 ,
- Man Luo 1 ,
- Fei Zhang 2 ,
- Hengyu Xiao 1 ,
- Xinsheng Tang 2 ,
- Xuchun Zhang 2 ,
- Tao Song 2 ,
- Xiang Li 2 ,
- Baochen Chong 2 ,
- Junyi Zhou 2 ,
- Yihan Zhang 2 ,
- Baicheng Zhang ORCID: orcid.org/0000-0002-1899-028X 1 ,
- Jiaqi Cao 1 ,
- Guozhen Zhang ORCID: orcid.org/0000-0003-0125-9666 1 ,
- Song Wang 1 ,
- Guilin Ye 3 ,
- Wanjun Zhang 3 ,
- Haitao Zhao ORCID: orcid.org/0000-0002-2448-8448 4 ,
- Shuang Cong ORCID: orcid.org/0000-0001-8101-0128 2 ,
- Huirong Li 1 ,
- Li-Li Ling 5 ,
- Zhe Zhang 5 , 6 ,
- Weiwei Shang ORCID: orcid.org/0000-0001-7541-2198 2 ,
- Jun Jiang ORCID: orcid.org/0000-0002-6116-5605 1 , 7 &
- Yi Luo ORCID: orcid.org/0000-0003-0007-0394 1 , 7
Nature Synthesis ( 2023 ) Cite this article
- Computational methods
- Materials chemistry
Living on Mars requires the ability to synthesize chemicals that are essential for survival, such as oxygen, from local Martian resources. However, this is a challenging task. Here we demonstrate a robotic artificial-intelligence chemist for automated synthesis and intelligent optimization of catalysts for the oxygen evolution reaction from Martian meteorites. The entire process, including Martian ore pretreatment, catalyst synthesis, characterization, testing and, most importantly, the search for the optimal catalyst formula, is performed without human intervention. Using a machine-learning model derived from both first-principles data and experimental measurements, this method automatically and rapidly identifies the optimal catalyst formula from more than three million possible compositions. The synthesized catalyst operates at a current density of 10 mA cm −2 for over 550,000 s of operation with an overpotential of 445.1 mV, demonstrating the feasibility of the artificial-intelligence chemist in the automated synthesis of chemicals and materials for Mars exploration.
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Mars has for decades attracted intensive scientific exploration and research in countries worldwide. Finding signs of past life and building potentially habitable regions on Mars have long been a dream of humanity. In situ resource utilization on Mars will be applied to substantially reduce the cost and complexity of human missions, enabling sustainable exploration by utilizing local resources to produce necessary supplies. Oxygen supply must be the top priority for any human activity on Mars, because rocket propellants and life support systems consume substantial amounts of oxygen, which cannot be replenished from the Martian atmosphere 1 , 2 . Fortunately, recent evidence of water activity 3 , 4 has raised the prospect of large-scale oxygen production on the planet through solar-power-driven electrochemical water oxidation processes using an oxygen evolution reaction (OER) catalyst. Using extraterrestrial catalysts developed from local materials to drive oxygen production allows for the on-site production of fuel and oxygen on Mars, which represents a low-hanging fruit in the exploration of this planet. However, two major technical challenges must be overcome to synthesize usable OER catalysts by using local Martian raw materials 5 , 6 . First, the synthetic system must be unmanned and self-directing, as the vast astronomical distance hinders real-time remote guidance from humans. Second, it should be equipped with the scientific intelligence needed to efficiently identify the best formula of catalyst ingredients through artificial-intelligence (AI) algorithms, given knowledge of elemental abundances in the Martian local ores. Designing a catalyst from a given list of elements requires the exploration of a vast chemical space, which poses a daunting task using the conventional ‘trial-and-error’ paradigm. Given five different local Martian ores as feedstocks, there are 3,764,376 possible formulas, estimated by the combination of integer percentages in 1% intervals; finding the optimal formula would require 2,000 years of human labour to finish such a screening, where each complete experiment takes 5 hours, at least.
Robotic synthetic systems with AI appear to be the only viable technology for addressing these two challenges, as suggested by recent advances in automated chemical synthesis systems. The mobile chemist by Cooper and colleagues shows excellent ability to perform high-throughput performance testing for human-made photo-catalysts, providing local optimization with measured data to achieve better formulations 7 . The ChemPU system by Cronin and colleagues demonstrates its extraordinary power in automatic synthesis of organic molecules starting from machine-reading synthetic chemistry literature 8 . These robotic systems need an intelligent subsystem to acquire chemical knowledge and form predictive physical models to direct local optimization in chemical synthesis. Inspired by these researchers' pioneering work on robotic chemical synthesis systems, we have developed an all-in-one robotic artificial-intelligence chemist (AI chemist) to enable automated, self-directed synthesis. Not only can it conduct the entire process of chemical synthesis, structural characterization and performance testing using a mobile robot and 14 task-specific chemistry workstations but it can design the best formula for a chemical synthesis task through a powerful computational module that combines machine learning (ML) algorithms and theoretical models to analyse both robot-acquired experimental data and massive first-principles simulation data 9 . Our AI chemist has accelerated the discovery of the optimal synthetic formulas for high-entropy electrocatalysts by five orders of magnitude compared to conventional trial-and-error experiment paradigm. Without prior knowledge about the exact composition of available Martian ores for making OER catalysts, the proposed automated approach must not only be capable of screening numerous candidates for the best formula, but also be intelligent to dissect usable yet unidentified raw materials and determine the predictive model on-the-fly. We developed a specific protocol for our AI-chemist system to tackle this challenge, advancing the in situ resource utilization strategy for Mars and interstellar exploration in the future.
In this proof-of-concept work, we demonstrate the superiority of the data-driven protocol using an AI chemist over the conventional trial-and-error protocol by the design of a six-metallic element OER catalyst from a pool of 3,764,376 compositions. Within six weeks, the AI chemist built a predictive model by learning from nearly 30,000 theoretical datasets and 243 experimental datasets using ML and Bayesian optimization algorithms, which delivers a promising OER catalyst formula coupled with the most suitable synthetic conditions. The resulting polymetallic material (comprising Mn, Fe, Ni, Mg, Al and Ca) catalysed the OER with an overpotential of 445.1 mV at a current density of 10 mA cm −2 , maintained for 550,000 s. Further, the stress test at −37 °C, which mimics the temperature condition on Mars, confirmed that it can steadily produce oxygen without apparent deterioration, suggesting that it can work in the harsh conditions on Mars. A ground-based verification system is currently being developed to provide more realistic space conditions for the AI chemist, which will be essential for the construction of the International Lunar Research Station and Mars Research Station; both were designed for long-term robotic operation and short-term human participation. The AI chemist thus represents a promising technique for on-site synthesis of OER electrocatalysts on Mars and constitutes a versatile and efficient platform for the supply of complex functional materials for planetary and space exploration.
Protocol for the AI chemist making OER electrocatalysts on Mars
To facilitate the work of the AI chemist on Mars, we proposed a double-layer workflow for the on-site synthesis of OER electrocatalysts (Fig. 1 ). The outer layer, which comprises a 12-step automated experiment and data management, is done by the robot and various ‘smart’ chemical workstations; the inner layer, which includes nine consecutive digital operations, is executed by the intelligent computational ‘brain’ (Supplementary Video 1 and Supplementary Figs. 1 – 3 ).
The dual-cycle automated process integrates material preparation, catalyst production, performance characterization and formula optimization in the following steps, as labelled. Step 1: Analyse the precise composition of Martian ores by LIBS. Step 2: Generate polymetallic catalyst structures by classical MD simulations. Step 3: Calculate the OER activities of the structures using DFT. Step 4: Build an NN model using simulation data. Step 5: Re-train the NN model using robotic experimental data. Step 6: Fine-tune the parameters in the NN model to predict experimental overpotential with confidence level over 0.95. Step 7: Screen for the optimum formula using Bayesian optimization algorithms. Step 8: Predict the optimal synthetic formula with the lowest OER overpotential using available Martian ores. Step 9: Validate the OER performance of the catalyst prepared with predicted formula (arrow pointing back to ‘Martian Ore’ box for feedstocks configuration).
In the experimental cycle, samples of local ore (Supplementary Figs. 4 – 9 ) obtained by an exploratory robot are sent to the laser-induced breakdown spectroscopy (LIBS) facility for elemental analysis (Supplementary Fig. 10 ). The robot carries out a set of physical and chemical pretreatment of ores needed for catalyst synthesis, including weighing (with a precision of 0.1 mg) in the solid-dispensing workstation, preparation of feedstock solutions in the liquid-dispensing and mixing workstations (Supplementary Table 1 ), separation from liquid in the centrifugation workstation and solidification in the dryer workstation. Then, the catalyst ink prepared by adding Nafion adhesive into the resulting metal hydroxides is used for making the working electrode for electrochemical OER testing at the electrochemical workstation. Experimental data are sent to a cloud server for ML processing by the computational ‘brain’.
In the computational cycle, the ‘brain’ employs molecular dynamics (MD) simulations for tens of thousands of high-entropy hydroxides with different elemental ratios and applies density functional theory (DFT) calculations to estimate OER activities. Simulation data are used to train a theory-based neural network (NN) model for OER with varying elemental composition, which is soon re-trained and optimized with robot-driven experimental data. By embedding the optimized NN model in a Bayesian algorithm, the ‘brain’ predicts the best combination of available Martian ores for synthesizing an optimal OER catalyst, which is then verified experimentally by the AI chemist.
Building pretrained ML models using the computational ‘brain’
The OER is a thermodynamically uphill reaction involving four consecutive oxidation steps and O–O bond formation, which requires an applied voltage of no less than 1.23 V to operate. The OER overpotential, which is defined as the extra voltage above 1.23 V required for catalysis to occur, characterizes the voltage efficiency of the electrochemical device. Therefore, we chose the measured overpotential as the primary target of our ML model in searching for the optimal OER catalyst 10 , 11 , 12 . We first created 29,902 unique compositions and simulated atomic structures of resulting high-entropy hydroxides (Fig. 2a ) from classical MD simulations (Supplementary Figs. 11 and 12 ). The obtained structural features, such as averaged metal–metal and metal–oxygen distances (Supplementary Figs. 13 and 14 and Supplementary Table 2 ), are passed to previously established bimetallic hydroxide models 13 , 14 (Fig. 2b ) to determine the OER activity of each multimetallic hydroxide by DFT calculation. Three DFT-predicted OER activity descriptors—including the Gibbs free energy change of hydroxyl adsorption Δ G OH* (ref. 15 ) and differences between the Gibbs free energy change for oxygen adsorption and hydroxyl adsorption Δ G O*−OH* (ref. 16 ), the amount of charge transferred for hydroxyl adsorption on the activate site Δ q (ref. 17 )—and the paired composition information are used for NN training. As Fig. 2c shows, the NN model can accurately reproduce these DFT results. With the NN model, we can now rapidly predict the OER activity of high-entropy hydroxides obtained from any given composition of selected Martian ores (Supplementary Fig. 15 ), and these theoretical values are then connected with experimentally measured overpotentials. The ML model achieves remarkable accuracy in predicting true overpotentials (Fig. 2d ).
a , Representative structure of multimetallic hydroxides generated by classical MD simulation. b , Reaction mechanism of the OER. c , Statistics of three OER descriptors, ∆ G OH* , ∆ G O*−OH* and ∆ q , predicted by the NN. d , The prediction results of measured OER overpotentials by predictive model that was calibrated by experiments. In c and d , r is the Pearson correlation coefficient.
High-throughput automated synthesis-characterization-performance optimization executed by the ai chemist.
Using the LIBS-determined elemental composition of each Martian ore in Fig. 3a (here we use Martian meteorites to represent in situ Martian ores), the AI chemist prepared 243 different formulas with randomly selected compositions of six metal elements, performed electrocatalytic OER testing using each of them as catalyst and measured overpotentials by analysing linear sweep voltammetry (LSV) polarization curves at a current density of 10 mA cm −2 ( η 10 ) per geometric area. The reason for choosing this specific current density is that it is approximately the current density expected at the anode of a 23% efficient solar-to-fuels conversion device under 1-sun illumination received on Mars 18 , 19 . This preliminary screening generated an array of η 10 values ranging from 482.2 to 1,056.2 mV (Fig. 3b ). Then we trained the second NN model by using three computed OER activity descriptors and 243 sets of compositions as inputs and their corresponding experimental overpotentials as output (Fig. 2d ). By concatenating these two NN models, the OER overpotentials for all 29,902 compositions can be easily predicted, creating a much larger dataset for Bayesian optimization 10 to generate the optimal formula for a desired OER catalyst (Fig. 3c ).
a , Representative LIBS spectral curves of five meteorite specimens in 200–380 nm with the ownership of main elemental emission lines. b , The η 10 values of 243 ‘trial-and-error’ experiments performed by the mobile robot and workstations. The blue line indicates the best sample, ‘Experiment No. 197’ (in c and d , referred to as Exp-197), with the lowest overpotential among 243 pilot experiments; green line represents the catalyst obtained through experimental-data-guided local optimal search (in c and d , referred to as Exp-guided OPT); pink line demonstrates the result from Bayesian optimization using both simulated and experimental datasets (in c and d , referred to as Model-guided OPT). The pink star highlights the lowest OER overpotential, suggesting that the ML model using both theoretical and experimental data achieves a global best synthetic formula, outperforming any other approach. c , LSV curves collected at a sweep rate of 5 mV s −1 in 1 M KOH electrolyte. Insets are photographs of catalyst materials synthesized with components based on Martian meteorite composition, from left to right, Exp-197, Exp-guided OPT and Model-guided OPT. d , Kiviat diagram of elemental ratios. e , LSV curves of Model-guided OPT collected in CO 2 -saturated 2.8 M Mg(ClO 4 ) 2 electrolyte at 23 °C and −37 °C, respectively. f , Corresponding Tafel plots for the anodic water oxidation derived from the LSV data in e to evaluate the reaction kinetics.
As the Kiviat plot indicates (Fig. 3d ), the optimal compositions identified by the Bayesian model differ greatly from those of the best sample, namely Experiment No. 197 (Exp-197) from the pilot experiments, indicating that Bayesian optimization based on both simulated and experimental datasets can surpass experimental-data-guided local search. Meanwhile, the product with optimal composition predicted by the model that uses theoretical data also gives worse performance than the one from Bayesian optimization that relies on both simulated and experimental data (Supplementary Fig. 16 ). The catalyst with optimal composition (Model-guided OPT) was synthesized and verified by the AI chemist to have η 10 = 445.1 mV, showing a substantial improvement (37.1 mV lower) with respect to the best result from a purely experimental search. Bayesian optimization suggested a metal composition that was almost identical to that suggested by the grid point scanning in the simulation with the re-trained NN model, but with much less time consumption, suggesting that Bayesian optimization is more effective in finding a solution (Supplementary Table 3 ). For comparison, we also made a test using the experimental data only as input for Bayesian optimization. The resultant optimal composition of meteorites (Exp-guided OPT) gives η 10 = 467.4 mV, which is very close to the best result among the 243 pilot experiments (Fig. 3d ). Hence, the intrinsic limitation of local optimization with limited experimental data would likely be overcome by concatenating NN models trained from both theoretical and experimental data. After all, attempting to achieve the global best synthetic formula by exhaustive trial-and-error approach requires 3,764,376 possible experimental traversal searches (Supplementary Note 1 ), which is a nearly impossible task. The synthetic formulas of the several studied catalysts are listed in Supplementary Table 4 , clearly and quantitatively comparing the differences in metal ratios among them. We also synthesized catalysts using only one meteorite as feedstock and found that all performances were inferior to the local optimum solution found (Exp-197) (Supplementary Fig. 17 ).
After determining the optimal metal ratio with minimum overpotential, we performed detailed comparisons of other electrochemical parameters (Supplementary Figs. 18 – 20 ). We derived the trend of the reaction activation energy from the Tafel slope. The Model-guided OPT in 1.0 M KOH required a low value of only 61.35 mV dec −1 to reach η 10 , outperforming Exp-197 (83.59 mV dec −1 ) and Exp-guided OPT (65.02 mV dec −1 ) catalysts, indicating that it possesses a favourable kinetic process for an OER. The electrochemical surface area reflects the chemisorption capacity of the reacting substrate and the exposure of surface active sites. This parameter can be estimated by measuring the Helmholtz double-layer capacitance, allowing comparison of the intrinsic electrochemical activity of different catalysts 20 . We found that the Model-guided OPT catalyst possesses the highest double-layer capacitance, which is about twice that of either the Exp-197 or Exp-guided OPT catalyst. This result implies that H 2 O molecules can be in close contact with the surface of this catalyst and that both the input of electrolyte and diffusion of the gaseous O 2 product are effectively accelerated. Electrochemical impedance spectroscopy is a technique that probes the internal processes of an electrochemical system and allows measurement of the operating state of the electrodes for kinetics study. By measuring the location and size of the semicircular region in the Nyquist plots, the voltages of solution and working electrode resistance losses can be obtained, which helps to analyse impedance changes as an aid in the assessment of electrocatalytic efficiency. We found that the semicircular diameters of the Exp-197, Exp-guided OPT and Model-guided OPT catalysts decrease sequentially, indicating that their charge transfer (or Faradaic) resistances follow the same trend.
Feasibility validation of oxygen production under simulated Martian environment
To verify the usability of catalysts under the low-temperature condition on the Martian surface, we performed an experiment based on the previous work of Gayen et al. 1 and the fact that large-scale water resources have been found in the Martian regolith 21 , 22 . The operating conditions on the Martian surface were constructed with a 2.8 M Mg(ClO 4 ) 2 brine solution as the electrolyte, platinum mesh as counter electrode and robust Ag wire as the reference at 23 °C and −37 °C (Fig. 3e ). The LSV polarization curves suggest low voltage values of 1.5685 V and 1.7289 V to reach the current density of 10 mA cm −2 with Tafel slopes of 174.1 mV dec −1 and 200.3 mV dec −1 , respectively (Fig. 3f and Supplementary Fig. 21 ).
Long-term stability is crucially important for the practical application of OER catalysts. We performed cycling stability tests on the catalyst under various conditions by applying a certain voltage to the catalyst working electrode and assembling the electrolyser to maintain its initial oxygen production current at 10 mA cm −2 in a classical three-electrode system (Fig. 4a–f ). Prolonged testing showed that the as-prepared Model-guided OPT catalyst works steadily at the current density of 10 mA cm −2 for more than 550,000 s (~153 h) in 1 M KOH at 23 °C and 350,000 s (~97 h) in 2.8 M Mg(ClO 4 ) 2 brine solution at −37 °C (Fig. 4g ), indicating that this AI-chemist-designed catalyst is as stable as other state-of-the-art OER catalysts. It can also be estimated that in 1 M KOH, the catalyst made by AI chemist can achieve an average O 2 production rate of 59.08 g h −1 m −2 . For a Martian station room with 300 m 3 volume (100 m 2 area and 3 m height) coated with the produced OER catalyst film on its roof, it will take about 15.2 h to achieve oxygen self-sufficiency. This process could be accelerated with the catalyst directly grown on the conductive nickel foam substrate as it is synthesized, which maintains considerably efficient and stable O 2 production capability at an even higher current density condition (Supplementary Fig. 22 ), albeit requiring larger area solar panels to generate more electricity to boost the OER reaction. It is likely that given more types of metal elements in Martian ores and advanced mineral refining facilities, the performance of Mars-mineral-derived chemicals and materials can be further improved in the future.
a , Positioning of the electrochemical workstation. b , Clamping of the carbon paper. c , Assembly of electrodes. d , Dripping of the catalyst ink. e , Placement of the working electrodes into the electrolyser. f , Sunlight-driven OER process. g , The time-dependent current density curve of a Mode-guided OPT sample on carbon-paper substrate acquired at working current density of 10 mA cm −2 under 1 M KOH alkaline conditions at 23 °C and CO 2 -saturated 2.8 M Mg(ClO 4 ) 2 electrolyte at −37 °C, respectively.
Our study provides a demonstration that an advanced AI chemist can, without human intervention, synthesize OER catalysts on Mars from local ores. This system has demonstrated its ability to perform all required experimental steps, including raw material analysis, pretreatment, synthesis, characterization and performance testing with high precision and also shown its intelligent analysis power in identifying the best formula for a Martian OER catalyst from millions of possible combinations. Particularly powerful is the in situ optimization, which seamlessly combines the experimental data and computational data during the synthesis process, greatly accelerating the generation of a reliable model and finding of an optimal formula. The established protocol and system, which are generic and adaptive, are expected to advance automated material discovery and synthesis of chemicals for the occupation and exploration of extraterrestrial planets.
Chemicals and materials
NaOH (99.9%), KOH (99.9%), HCl (37% trace metals), K 3 [Fe(CN) 6 ] (99%), K 4 [Fe(CN) 6 ]•3H 2 O (99.5%), anhydrous ethanol and 5 wt% Nafion 117 solution were purchased from Sigma-Aldrich. The counter electrode of graphite rod and reference electrodes of Ag/AgCl in saturated KCl were purchased from CH Instruments. The deionized water (18.2 MΩ cm −1 ) used for the feedstocks solution and aqueous electrolytes preparation were made with a Milli-Q EQ 7000 Ultrapure water purification system.
Five different categories of meteorites that come from or have been confirmed to exist on Mars were selected 23 , 24 ; complete information describing these approved meteorites can be found on the website of the Meteoritical Bulletin Database available at http://www.lpi.usra.edu/meteor/ . We digested various masses of these individual meteorites in 1 mol l −1 hydrochloric acid solution based on the results of elemental analysis by a LIBS spectrometer, which was used to configure the feedstocks solution to control the total mass concentration of the six key catalytic metals (that is, Fe, Mn, Ni, Ca, Mg and Al) to 200 mg l −1 . For the catalyst preparation, the AI chemist set the addition amount of total feedstocks solution to reaction vial to 10 g at the liquid-dispensing workstation, but randomly varied the proportion of each feedstock entered. In this manner, the atomic ratio of metals in the final product can be finely adjusted. Afterwards, 3 g of aqueous NaOH solution with a concentration of 4 mol l −1 was added to the reaction vials and stirred for 5 minutes, followed by centrifugation at 7,500 g for 5 minutes, aspiration of the upper waste solution, washing with anhydrous ethanol and drying at 60 °C. The described synthetic procedure was done to perform the initial search for the optimal metal ratio in a catalyst and was performed for 243 groups of experiments. During the whole process, the intelligent ‘brain’ of the AI-chemist system automatically generates .xml execution files and sends them to the experimental robot, which—with various synthesis and testing workstations—sequentially automates the preparation of the catalyst material. Similarly, the synthesis of samples Exp-guided OPT and Model-guided OPT is based on the same method as above, except that the respective metal ratios are determined and given by the intelligence ‘brain’ and converted by a transformation matrix.
Analysis of metal content in meteorites by LIBS
LIBS is a rapid chemical analysis technology that offers many compelling advantages compared to other elemental analysis techniques in geoscience. The main physical process that constitutes the essence of LIBS technology is the formation of a high-temperature plasma created by ultrafast laser pulses (UFLPs). When the UFLP beam is focused on the surface, a small portion of sample mass is ablated, a process called laser ablation. This ablated mass further interacts with the trailing portion of the UFLP to form a short-lived high-temperature plasma containing free electrons, excited atoms and ions. When the laser pulse is terminated, the plasma cools and the electrons in atoms or ions at the excited state decay to their natural ground state. Correspondingly, the wavelengths of the emitted photons are inversely proportional to the energy difference between the excited and ground states, so that each element has its own set of characteristic emission wavelengths, a fingerprint signature, which is then collected and coupled to the spectrometer detector module for LIBS spectroscopy. Each element in the periodic table is associated with a unique LIBS spectral peak. The high energy density of the focused UFLP allows the excitation of material in any physical state (in our case, solid) to form a plasma, allowing the LIBS technique to analyse samples and assess the relative abundance of each constituent element.
Our LIBS workstation consists of a researcher-developed sample feed system, a nanosecond laser generator (Quantel Viron), a fibre-optic spectrometer (AvaSpec-ULS2048CL-2-EVO), an optical system and a high-performance computer. The feed system consists of a motorized delivery track, a slowly rotating sample stage and a motor control unit. The control unit is designed independently, with the main control chip being the Atmel-produced MEGA 2560V chip. The robotic arm of the AI chemist places the sample on the stage, which is then delivered by the feed system for laser irradiation with slow rotation so that different points on the surface are excited by the laser to obtain an unattenuated signal. The pulsed laser emitted from the laser generator is focused on the sample surface by the optical system to produce a transient high-temperature plasma. The signal is captured by the optical fibre of the spectrometer. A researcher-written programme in the computer controls the measurement process automatically and acquires spectral data from the spectrometer for subsequent analysis and processing.
The LIBS spectra were collected under optimized conditions: laser pulse energy, 105 mJ; spot diameter, 2 mm; spectrometer slit width, 15 mm; gate width, 1 ms and acquisition delay, 180 ms. In total, 388 sets of data points were obtained, these spectra were accumulated, and then the peak line region of the target element was marked according to the elemental peak lines obtained from the NIST database ( https://physics.nist.gov/PhysRefData/ASD/lines_form.html ) and the baseline correction was performed for peak line region. After wavelet filtering, the best Lorentz peak shape and the offset of the actual peak line relative to its standard spectrum were obtained by fitting with the Levenberg–Marquadt method. Here, the half-height width of the peak, the wavelength of crest and the signal intensity were used as intrinsic characteristics. The preliminary element content was calculated by linear regression. Subsequently, the top 50 data points in the original spectrum with the highest correlation with the elemental content (found by the LASSO algorithm after normalization from the pretraining set data) are transformed in the same way as the pretraining set data and then entered into a pretrained backpropagation NN together with the preliminary content calculated in the previous step to obtain a more accurate elemental content. Similarly, the above analysis is repeated for each targeted element to obtain the exact content with a relative error within ±5%.
Transfer metals molar ratio to Martian ores mass ratio
The feedstock solutions are prepared by the following procedure: Take out 271.05 mg of Aletai, 567 mg of NWA 8171, 563.2 mg of NWA 13669, 935 mg of NWA 12564, 688.5 mg of Hassi Messaoud 001, each dissolved in 1 l of acidic solution to prepare the feedstock solution for the experiments. In this way, the total mass concentrations of metal ions in all feedstock solutions are controlled at about 200 mg l −1 ; the concentrations of metals are listed in Supplementary Table 1 .
Because the ML-model-predicted results are the metal molar ratios, we prepared a researcher-developed software programme to transfer metals molar ratio to Martian ores mass ratio for convenient robotic weighting operation. The software is developed using Python, and it is also converted to a windows-based executable programme (Supplementary Fig. 15 ). The source code is as follows:
import tkinter as tk
import numpy as np
from scipy.linalg import solve
window = tk.Tk()
s1 = tk.Label(window,text = ‘Fe’)
a1 = tk.Entry(window,show=None)
s2 = tk.Label(window,text = ‘Mn’)
a2 = tk.Entry(window,show=None)
s3 = tk.Label(window,text = ‘Ni’)
a3 = tk.Entry(window,show=None)
s4 = tk.Label(window,text = ‘Ca’)
a4 = tk.Entry(window,show=None)
s5 = tk.Label(window,text = ‘Mg’)
a5 = tk.Entry(window,show=None)
s6 = tk.Label(window,text = ‘Al’)
a6 = tk.Entry(window,show=None)
b1 = a1.get()
b2 = a2.get()
b3 = a3.get()
b4 = a4.get()
b5 = a5.get()
b6 = a6.get()
metal_ratio = np.array([float(b1),float(b2),float(b3),float(b4),float(b5),float(b6)])
abundance = np.array([[3.303,0,0.328,0.0055,0.00155,0.00149],
transfer_matrix = abundance.T
meteorite_ratio = solve(transfer_matrix[0:5], metal_ratio[0:5])
meteorite_ratio = meteorite_ratio/sum(meteorite_ratio)
abcdelist = [0.27105,0.567,0.5632,0.935,0.6885]
meteorite_ratio = meteorite_ratio*10*abcdelist
meteorite_ratio = meteorite_ratio.round(4)
result = ‘,’.join(str(i) for i in meteorite_ratio)
t.insert(‘insert’,‘meteorite ratio (A,B,C,D,E) is: ‘+result + ‘\n’)
button = tk.Button(window,
text = ‘Transfer’,
t = tk.Text(window)
OER measurement under 1 M KOH alkaline condition
All the electrochemical measurements were conducted at the electrochemical workstation (CHI660E, CH Instruments) in a standard three-electrode setup with the catalyst derived from Martian meteorites as the working electrode, a graphite rod as the counter electrode and Ag/AgCl in saturated KCl as reference electrode. All the electrocatalytic OER performance was studied under alkaline conditions (1 mol l −1 KOH). The applied potential was calibrated to reversible hydrogen electrode (RHE) following the equation E RHE = E Ag/AgCl + 0.0591 × pH + 0.197 V. Unless otherwise specified, neither iR-compensation nor background current correction was applied. For the working electrode preparation, the as-prepared catalyst dispersing in 5 ml of a mixed solution of ethanol (4.8 ml) and 5 wt% Nafion (0.2 ml) under magnetic stirring to form a uniform catalyst ink. Then, 200 μl of the resulting catalyst ink was drop-casted onto a carbon paper with the loading area of 2.5 × 2 cm 2 , and the corresponding final metal loading was calculated to be 0.08 mg cm −2 . Cyclic voltammetry activation curve was performed 40 times from 1.0 V to 1.5 V with respect to the RHE reference at a sweep rate of 50 mV s −1 . LSV measurements were performed from 1.0 V to 2.0 V with respect to the RHE reference at a scan rate of 5 mV s −1 . Tafel slope ( b ) is obtained by fitting the linear portion according to the Tafel equation ( η = a + blog j ) using the overpotential ( η ) as a function of the logarithmic scale of current density (log j ). Electrochemical impedance spectroscopy measurements performed at an overpotential of 0.4 V for working electrodes. Electrochemical active surface areas are evaluated based on the double-layer capacitance via the analysis of a series of cyclic voltammetry measurements performed within the non-Faradaic potential region (1.05 to 1.15 V with respect to the RHE reference) at various scan rates (10, 20, 40, 60, 80, 100, 120, 140, 160, 180 and 200 mV s −1 ). The chronoamperometry ( i – t ) test was collected at a constant potential at 1.7 V with respect to the RHE reference for 550,000 seconds. All the electrochemical characterizations can be performed automatically by one-click measurements and generate the experimental reports using a researcher-written Python code. To grow the catalyst on nickel foam substrate for oxygen production at industrial current density, we added feedstocks solution prepared from five Martian meteorites to the autoclave reactor, followed by NaOH addition to adjust the pH to 5–6, then 0.2 g of urea was added to dissolve, and finally cleaned nickel foam with thickness of 2 mm was placed vertically, encapsulated and held at 130 °C for 10 hours. When the reaction is completed, the nickel foam is taken out, washed and dried for OER performance testing in compliance with the described three-electrode system.
In situ electrocatalytic oxygen generation experiments under simulated Martian surface environmental conditions
As the Martian surface is well below 0 °C for most of the Mars year and its atmosphere is rich in CO 2 , we used aqueous Mg(ClO 4 ) 2 solution (pH ≈ 7) with a concentration of 2.8 M as a mimic of the brine solution already explored on Mars and then used a dry ice solution of ethanol-ethylene glycol mixture at a constant temperature of −37 °C for OER testing 25 . Considering such a low temperature, the conventional Ag/AgCl electrode is no longer suitable as a reference; therefore, we used Ag wire (99.99%, φ = 1.5 mm) as a reference electrode at low-temperature conditions and potassium ferrocyanide-potassium ferricyanide oxidation-reduction potential buffer as internal standard to determine the potential of Ag wire at approximately 0.427 V with respect to an RHE reference. All electrochemical test steps and data processing procedures are similar to those performed in 1 M KOH, except that the corresponding voltage window is changed and the Mg(ClO 4 ) 2 solution is saturated with CO 2 (99.999%) prior to testing.
To extract structural features of high-entropy hydroxides, we sampled one million equilibrated structures for each of 29,902 unique formulas of six-metallic elements (Fe, Ni, Mn, Ca, Mg and Al) using classical MD simulation. The initial configuration of each composition was generated by randomly placing 60 different metal cations and sthe corresponding number of hydroxyl anions for maintaining neutrality into a cubic box of 3 × 3 × 3 nm 3 using GROMACS 26 . The universal force field 27 was adopted and all parameters for high-entropy hydroxides were generated by the LAMMPS Interface programme 28 . The cutoff distances for both Lennard–Jones and Coulombic potential were set to be 12.5 Å. Then, each initial structure was pre-equilibrated by energy minimization. In a production MD run, a trajectory of 1 ns with a time step of 1 fs was collected in an NPT ensemble with P = 1 atm and T = 2,000 K using the Nosé–Hoover barostat and thermostat 29 , 30 . For each trajectory, we retrieved 100 configurations in an evenly divided 10 ps interval and computed averaged metal–metal and metal–oxygen distances as structure features of these high-entropy hydroxides. All force field base simulations were carried out with the LAMMPS package 31 .
To describe the OER activity of high-entropy hydroxides, DFT calculations on the simplified bimetallic hydroxide model with the information of statistical structure features of each unique composition embedded were performed using the Perdew–Burke–Ernzerhof functional 32 and the projector augmented wave method 33 as implemented in the Vienna ab initio simulation package 34 . The kinetic energy cutoff of the plane-waved basis set was 400 eV. The Brillouin zone was sampled with 3 × 2 × 1 Monkhorst–Pack k-mesh with the vacuum size chosen to be 15 Å to avoid interaction between two layers for all structures. The long-range van der Waals interaction corrections were described using Grimme’s D3 correction 35 . All geometry but the metal–( η 2 -oxygen) 2 –metal moiety is allowed to relax. The convergences of total energy for wave function self-consistency and force between atoms for optimization were set to be 10 −5 eV and 0.01 eV Å −1 , respectively.
Calculation for the free energies
The Gibbs free energies ∆ G OH* and ∆ G O*−OH* = ∆ G O* −∆ G OH* and ∆ q (the amount of charge transferred for hydroxyl adsorption on the activate site) were used as computational descriptors of OER activity. For all possible combinations of dual metal atoms in every high-entropy hydroxide, their OER descriptors were calculated using the following procedure.
The elementary steps of hydroxyl adsorption and oxygen adsorption can be given as:
Under zero potential, the Gibbs free energy of each elementary step was given by the expression:
where ∆ E is the change in reaction energy. The ∆ZPE is the zero-point energy change, and ∆ S is the entropy change for each elementary step with the temperature at 298.15 K.
The first NN model—which uses information of metal composition as input and the DFT calculated three catalytic properties (∆ G OH* , ∆ G O*−OH* , ∆ q ) as output—comprises one input layer, two hidden layers and one output layer. The number of neurons in both hidden layers is 512. To link computed catalytic properties and experimentally measured overpotential, the second NN model was built with one input layer, three hidden layers involving 128 neurons each and one output layer. For the training of each NN, the dataset was divided into two subsets, one for training (80%) and the other for testing (20%). The NN model was trained with a backpropagation algorithm and the Rectified Linear Unit activation function 36 as implemented in TensorFlow 37 .
Two NNs were combined to create a predicting model that used the metal composition as a descriptor to estimate the real overpotential. A Bayesian approach, taking above predicting model as objective function, was then applied to identify the optimal metal composition with the highest catalytic activity. The Bayesian optimization loop consisted of 280 iterations, and the surrogate model was a basic Gaussian process, which could capture the uncertainty and noise in the data and handle different types of objective functions. We also used the upper confidence bound as our acquisition function, which balanced exploration and exploitation by adding a positive term depending on the standard deviation to the mean estimate of the objective function.
The data that support the findings of this study are available in the paper, its Supplementary Information and Supplementary Video 1 .
The code used for training an NN model for OER prediction with theoretical data and robot-driven experimental data is available on GitHub at https://github.com/Lulu971231/code-for-Oxygen-Producing-Catalysts-from-Martian-Meteorites .
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Y.L. acknowledges funding support for this research from the Innovation Program for Quantum Science and Technology (Grant 2021ZD0303303). J.J. gratefully acknowledges financial support by the National Natural Science Foundation of China (Grants 22025304, 22033007) and the CAS Project for Young Scientists in Basic Research (Grant YSBR-005). Q.Z. gratefully acknowledges the financial support of the National Natural Science Foundation of China (Grant 22103076) and Anhui Provincial Natural Science Foundation (Grant 2108085QB63). We also gratefully acknowledge the USTC Center for Micro- and Nanoscale Research and Fabrication for providing experimental resources and the USTC supercomputing centre for providing computational resources.
These authors contributed equally: Qing Zhu, Yan Huang, Donglai Zhou, Luyuan Zhao.
Authors and Affiliations
Key Laboratory of Precision and Intelligent Chemistry, Hefei National Research Center for Physical Sciences at the Microscale, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, China
Qing Zhu, Yan Huang, Donglai Zhou, Luyuan Zhao, Lulu Guo, Ruyu Yang, Zixu Sun, Man Luo, Hengyu Xiao, Baicheng Zhang, Jiaqi Cao, Guozhen Zhang, Song Wang, Huirong Li, Jun Jiang & Yi Luo
School of Information Science and Technology, University of Science and Technology of China, Hefei, China
Fei Zhang, Xinsheng Tang, Xuchun Zhang, Tao Song, Xiang Li, Baochen Chong, Junyi Zhou, Yihan Zhang, Shuang Cong & Weiwei Shang
Hefei JiShu Quantum Technology Co. Ltd., Hefei, China
Guilin Ye & Wanjun Zhang
Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
Deep Space Exploration Laboratory, Hefei, China
Li-Li Ling & Zhe Zhang
School of Aerospace Engineering, Beijing Institute of Technology, Beijing, China
Hefei National Laboratory, University of Science and Technology of China, Hefei, China
Jun Jiang & Yi Luo
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These authors contributed equally: Q.Z., Y.H., D.Z., L.Z. Q.Z. planned and conducted the robotic experiments and collected and analysed the experiment data. Y.H., D.Z., L.Z. and H.L. performed theoretical simulations and ML training. L.G., R.Y., Z.S. and M.L. assisted with the spectroscopic characterization and data analysis. H.X., B.Z. and J.C. were responsible for writing test scripts. X.T. and Y.Z. contributed to the development of robotic operation module, robotic arm motion planning and force control. J.Z. and B.C. helped with the robot platform communication, SLAM, platform motion planning and navigation. T.S. planned robot movement and operation task management system. X.L. and S.C. managed the scheduling optimization of robot experimental tasks at various workstations. X.Z. developed the robotic visual localization algorithm. F.Z. and W.S. designed the entire robot system. G.Y. and W.Z. worked on non-standardized equipment development. S.W., G.Z. and H.Z. contributed to the original draft preparation. L.-L.L. and Z.Z. assisted in the design and execution of experiments under simulated Martian environments. J.J. and Y.L. conceptualized the study, developed the methodology and conducted the investigation and wrote, reviewed and edited the paper. All authors participated in discussions and revisions and provided comments on the paper.
Correspondence to Weiwei Shang , Jun Jiang or Yi Luo .
The authors declare no competing interests.
Peer review information.
Nature Synthesis thanks Leroy Cronin, Zhigang Zou and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Peter Seavill, in collaboration with the Nature Synthesis team.
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Experimental details, Supplementary Figs. 1–22, Note 1 and Tables 1–4.
Supplementary Video 1
This video showcases the capabilities of the AI chemist in synthesizing and optimizing oxygen-producing catalysts from Martian meteorites. The process involves automated analysis of Martian ore, catalyst synthesis, characterization, intelligent computing and OER performance testing, which highlights the integration of robotics and AI for complex materials design and manufacture under challenging circumstances.
Source Data Fig. 2
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Zhu, Q., Huang, Y., Zhou, D. et al. Automated synthesis of oxygen-producing catalysts from Martian meteorites by a robotic AI chemist. Nat. Synth (2023). https://doi.org/10.1038/s44160-023-00424-1
Received : 11 March 2023
Accepted : 27 September 2023
Published : 13 November 2023
DOI : https://doi.org/10.1038/s44160-023-00424-1
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How to Analyze a Bank's Financial Ratios
by Eliah Sekirin
Published on 25 Jan 2019
Financial ratios are widely used to analyze a bank's performance, specifically to gauge and benchmark the bank's level of solvency and liquidity. A financial ratio is a relative magnitude of two financial variables taken from a business's financial statements, such as sales, assets, investments and share price. Bank financial ratios can be used by the bank's clients, partners, investors, regulators or other interested parties.
Place the financial data you'd like to analyze in a spreadsheet application such as Microsoft Excel. Calculating ratios on a spreadsheet is much easier than on a piece of paper, even with the help of a financial calculator.
If you are not sure which data to input into the cells, limit yourself to the most important variables such as the number of shares outstanding, their current market price, total assets and liabilities, current assets and liabilities, number of bad debts and annual income (net income and earnings before interest payments, taxes, depreciation and amortization-EBITDA). You can add other financial data later.
Calculate solvency ratios. Solvency ratios are ratios that tell us whether the bank is a healthy long-term business or not. A good ratio here is the Loans to Assets ratio. It is calculated by dividing the amount of loans by the amount of assets (deposits) at a bank.
The higher the loan/assets ratio, the more risky the bank. The Loans to Assets ratio should be as close to 1 as possible, but anything bigger than 1.1 can mean that the bank gives more loans than it has in deposits, borrowing from other banks to cover the shortfall. That is considered risky behavior.
Another ratio to be considered here is the Non-Performing Loans to All Loans Ratio, or, more simply put, the Bad Loans ratio. The Bad Loans Ratio indicates the percentage of nonperforming loans a bank has on its books.
This ratio should be about 1 to 3 percent, but a figure of more than 10 percent indicates the bank has serious problems collecting its debts. A nonperforming loan is a loan the bank says will not recover. Banks use a pretty sophisticated methodology to calculate the number of those loans.
Calculate and analyze liquidity ratios. Liquidity ratios are ratios that reveal whether a bank is able to honor its short-term obligations and is viable in the short-term future.
The primary ratio here is the Current Ratio. The Current Ratio indicates whether the bank has enough cash and cash-equivalents to cover its short-term liabilities.
Current Ratio = Total Current Assets / Total Current Liabilities
The current ratio of a good bank should always be greater than 1. A ratio of less than 1 poses a concern about the bank's ability to cover its short-term liabilities.
Calculate and analyze the Return to Shareholders Ratio and the Price to Earning Ratio.
To calculate the Return to Shareholders Ratio, divide the dividends and capital gains of a stock by the price of the stock at the start of the period being analyzed, usually a calendar year.
For example, if the stock on Jan. 1, 2010, cost $10, dividends per share were $1, and on Jan. 1, 2011, the stock cost $11, then the Return to Shareholders Ratio will be as follows: [($11-$10)+$1] / $10 = 0.2 or 20 percent.
The return to shareholders should be at least the interest rate paid on a bank term deposit. Otherwise shareholders would be better off having their money in a safe bank deposit, guaranteed by the government.
The Price to Earning Ratio is calculated by dividing the bank's share price by the earning per share: P/E = price of one share / earnings per share. The P/E ratio typically varies in the 10 to 20 range.
Cash Flow Statement
Spreadsheet application (e.g. Microsoft Excel)