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INDIAN STOCK MARKET -REVIEW OF LITERATURE
The capital market is an important constituent of the financial system. It is a market for long term funds both equity and debt. It mainly deals with financial assets, excluding coin and currency; the financial assets comprise shares, debentures, bonds, mutual funds, fixed deposits, pension funds, provident fund, insurance policies, and derivatives. The Indian Stock Exchanges, as they are well-regulated, function smoothly, and it is an indication of healthy capital market. Stock exchange provides a good leverage to the capital market and it helps women investors to maximize their returns. This article clearly deprives the review of Indian Capital Market
Shanlax International Journal of Economics
The history of Indian capital market goes back to the 18th century when the securities of East Indian company was traded. The contribution of Indian capital market for the sustainability of Indian economy is considerably since the year 1890’s. The capital market plays a role in terms of wealth distribution and economic development of a country like India. Capital market acts as a transformer of savings into capital investment. The capital market has witnessed a major reforms since the implementation of New Economic Policy 1991 and thereafter. The Indian government and SEBI have adopted the various reforms in order to enhance the performance of Indian stock exchanges. The present study tries to analyze the recent reforms in Indian capital market from the year 2010 onwards. The present research is largely based on the secondary data. The statistical facts and figures regarding the growth and development of the capital market was available from various journals, publications and we...
In this paper we present a review of research done in the field of Indian capital markets during the fifteen years from 1977 to 1992. The research works included in the survey were identified by two search procedures. Firstly, we wrote to 118 Indian university departments and research institutions requesting information on the works done in this field in their department/institution. After three reminders, we obtained responses from 53 institutions. Simultaneously, we searched through various Indian journals in our library, located books listed in the library catalogue and traced through the list of references provided in various research works. Considering the size, vintage and development of the Indian capital market, the total volume of research on it appears to be woefully modest - about 0.1 unit of work per institution per year! Moreover, a large number of works are merely descriptive or prescriptive without rigorous analysis. Certain areas such as arbitrage pricing theory, opt...
Bonfring International Journal
In India, the history of capital markets dates back to the 18th century when East India Company securities traded the country. The present study is largely based on the available secondary data. The statistical data regarding growth of the capital markets was available from various websites. Capital markets help to channelize surplus funds into productive use. Generally, this market trades mostly in long-term securities. The important divisions of the capital market are stock market, bond market and primary, secondary markets. Primary markets deal with the trade of new issues of stocks and other securities, whereas secondary market deals with the exchange of existing or previously-issued securities. Our finding is that during the first and second five year plans, the Government emphasized on the development of agriculture and public undertakings. The Public sector undertaking was healthier than Private undertakings, but shares were not listed in the stock exchange. More over controller of Capital Issue (CCI) closely supervised everything. A number of investors were interested to invest their savings in debentures instead of company deposits. We conclude that Capital markets were not well organized and developed during the British rule. But in the present scenario, we find that Capital markets are well developed after the introduction of SEBI. Through provision of long term loans, the capital market brings about effective functioning of various sectors of the economy. A sound and efficient capital market is one of the most instrumental factors in the economic development of a nation.
MIRZA S H A H A B BAIG
The paper explores the investment and trading strategies for the Indian stock market using daily data for the CNX 100 companies over the period 01 April 2009 to 31 March 2014. The paper sets up the argument for beta and debt-equity ratio as the important variables for explaining the investment and trading strategies for Indian stock market. Following mean reversion principle without advocating for it, we also develop the strategy for trading in the stock market. The study uses data analysis techniques and arrives at the findings that long term investment in the stocks with low beta and low debt-equity ratio provide higher return, though short term trading the stocks with high beta and high debt-equity ratio provide high return in the short time period. The financial service sector shows that service sector provides very high return in the short time period and low return in the long run as compared to returns from the average stocks.
S Chalapati Rao K
Journal of emerging technologies and innovative research
Market signalling has measured based on announcements made by corporates and efficiency has measured with the impact of announcements which may react in the stock prices. Stock markets are indicators to predict the behaviour of the economy. Stock price movements have measured in terms of the market trends, current economic conditions, announcements of the company and other factors. This study is the nature of Empirical research, where there should be existing theoretical design is considered as empirical evidence. This study adopted the Efficient Market Hypothesis (EMH) to test the efficiency of the market based on signalling in terms of the announcements given by the companies. Data for this study has collected from financial services, energy and IT indices listed in NSE India Limited to measure the overall market conditions. These three sectors have selected based on market capitalization. These are, the financial services index has 37.78%, contribution to the overall market capitalization, energy index has 15.29% and IT index has 14.77% contribution to the overall market capitalization. The significant findings of this study focusing on events like profit booking, dividend distribution has a significant impact on stock prices of HDFC Bank Ltd, Reliance Industries Limited and Infosys Limited. Investors can invest in the market based on the events like profit booking announcements, and dividend declaration has a significant and positive impact in the market. Hence, the researcher suggested that investors can invest based on the intensity of information.
Market Overreaction is a very familiar and age-old craze amongst traders. Pigou (1929) defined it as a ‘conducting rod along which an error of optimism or pessimism, once generated, propagates itself about the business world. ’ The question of whether or not Indian stock prices market is overreacted during any stock-specific news is best answered by a comprehensive and concurrent analysis of the various tests and data available while using the event study. This study wants to address the impact of size, volatility and asymmetry in the terms of investors ’ overreaction to the firm-specific news not only individually but also jointly. The outcome of this study helps to solve the problem concerning the extent to which quarterly announcements have informational content, and whether the investors are affected by the signals. The present study substantiates the policy recommendation for the market players as well as for the analysts in estimating earning announcement events under differen...
International Journal for Research in Applied Science & Engineering Technology (IJRASET)
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Prof (Dr) Gurinder Singh
Prof. Milan Padariya
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South Asian Journal of Marketing and Management Research
INTERNATIONAL JOURNAL OF BUSINESS, MANAGEMENT AND ALLIED SCIENCES (IJBMAS)
Dr. Mitesh Patel
Krishna Reddy Chittedi
Pacific-Basin Finance Journal
Huson Ahmed , Susan Sharma
International Journal of Engineering and Advanced Technology
Dr. Chetan Vyas
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International Journal of Financial Management
INTERNATIONAL JOURNAL OF MANAGEMENT AND DEVELOPMENT STUDIES
Theoretical Economics Letters
AMAN SRIVASTAVA 19BEE0146
International Journal of Management (IJM)
THE JOURNAL OF ORIENTAL RESEARCH MADRAS
PROF. MAHUL BRAHMA
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- Original Research
- Published: 08 January 2022
Month-of-the-Year Effect: Empirical Evidence from Indian Stock Market
- Rajesh Elangovan 1 ,
- Francis Gnanasekar Irudayasamy 2 &
- Satyanarayana Parayitam ORCID: orcid.org/0000-0001-5565-4413 3
Asia-Pacific Financial Markets volume 29 , pages 449–476 ( 2022 ) Cite this article
The present study aims to examine the existence of month-of-the-year effects in the Indian stock market. For analysis, we selected the BSE Ltd and NSE broad market cap indices, namely S&P BSE 500 and NIFTY 500, which are a comprehensive representation of the Indian stock market. The time selected for this study is from April 1, 2011, to March 31, 2021 (i.e., ten years). The study used secondary data collected from the 'monthly open, high, low and closing prices of broad market indices of the Indian stock market through the official websites ( www.bseindia.com ; and www.nseindia.com ). The study's findings indicate that the ADF and PP test confirms the presence of unit root of the return series of S&P BSE 500 and NIFTY 500 Indices. The results from the KPSS test confirm the stationarity of the return series of both Indices. The regression coefficients for March were negative and significant for both indices. These results suggest that the month-the-of-the-year effect is the 'March effect.'
Working on a manuscript?
The humorous comment by Mark Twain implies that speculation has no place in stock markets, and there is no good time to speculate. One of the heavily debated topics in finance and economics is the efficient market hypothesis (EMH) which states that stock prices reflect all the available information (Fama, 1970 ) Therefore, investors can't enjoy abnormal returns than the market rate of return. If stock markets are efficient, the market capitalization represents the firm's fair value, i.e., future cash flows discounted by the cost of capital. The EMH is built on two assumptions: (i) all available information is fully incorporated into the stock prices, and (ii) investors would not be in a position to earn a risk-weighted excess return than the market return. In the weak form, market efficiency does not yield any excess return. In semi-strongly efficient markets, the current stock prices reflect historical information. Still, they include the information which is currently available (for example, announcements of mergers, dividend pay-outs, changes in the top management team members, etc.). In the strong form of efficient markets, current stock prices reflect all available, including insider information, in addition to the publicly available information. Therefore, in a strong form of market efficiency, researchers contend that it is impossible to earn excess profits relying on insider information (Malkiel, 2003 ).
Fluctuations in stock prices challenge EMH due to calendar anomalies, and researchers in finance and economics documented the effect of these anomalies. Changes in calendar effects have been on the research agenda for the last four decades. Researchers identified three types of anomalies seasonal, technical, and fundamental anomalies that significantly affect stock returns (Srinivasan & Kalaivani, 2013 ). A plethora of research is available on the day-of-the-week effect, weekly effect, weekend effect, month-of-the year effect, semi-month effect, turn-of-the-month effect, holiday effect, Ramadan effect, Diwali effect, Independence Day effect (July 4th in the USA), Halloween effect, etc., (Agrawal & Tandon, 1994 ; Barone, 1990 ; Compton et al., 2013 ; Haroon & Shah, 2013 ; Harshita et al., 2018 ; Patel & Sewell, 2015 ). Digging up literature, one can find that some studies provided strong support for the seasonal fluctuations (e.g., Easterday et al., 2009 ). On the other hand, some did not support evidence for seasonality in stock prices (Cheung & Coutts, 1999 ; Zinbarg & Harrington, 1964 ). Some critics argue that the data might have been tortured until it confessed (Merton, 1987 ).
The objective of the present study is to test the presence or absence of the month-of-the-year effect in the Indian stock market. Several studies in the past have documented that returns on financial assets exhibited systematic patterns at certain calendar months so that investors can benefit from these seasonal variations (Brooks, 2019 ; Jacobs & Levy, 1988 ). The financial year in India runs from April to March, and extant research supported effects in January and November (see Table 1 for pieces of evidence from previous studies conducted in India). The study period covered transition of political power in India in 2014, thus necessitates study of stock market anomalies. We organize the paper as follows. First, we present the literature review in Sect. 2 , followed by data and methodology in Sect. 3. We offer the analysis and findings in Sect. 4 and discussion and managerial implications in the final section.
2 Literature Review
While the historical evidence on the seasonality of stock market returns provided by Fields ( 1931 ) and Wachtel ( 1942 ), systematic analysis of stock market anomalies started with Cross ( 1973 ), followed by French ( 1980 ). History is replete with studies that focused on day-of-the-week effect, weekly effect, weekend effect, month-of-the-year effect, semi-month effect, turn-of-the-month effect, and holiday effect. Unfortunately, the results are inconclusive and inconsistent. Since our objective in the present research is to examine the month-of-the-year impact empirically, we found that most of the studies documented the January effect (Aggarwal & Rivoli, 1989 ; Agrawal & Tandon, 1994 ; Asteriou & Kavetsos, 2006 ; Beyer et al., 2013 ; Floros, 2011 ; Pettengill, 1986 ; Wilson & Jones, 1993 ).
One of the classic studies that analyzed data from 1904 to 1974 in the US stock market, Rozeff and Kinney ( 1976 ), showed that the returns in January were significantly higher than in the other eleven months. High returns in January were labelled as ‘month-of-the-year effect,’ and subsequent research conducted on seventeen countries, the January effect was significant in thirteen countries (Gultekin & Gultekin, 1983 ). The January effect was explained by Brown et al. ( 1983 ) as the tax-loss selling hypothesis according to which investors are motivated to sell stock at the end of the year to get tax benefits. Therefore the prices get depressed and then bounce back in January. This was supported because, in 1917, there was no January effect. After all, there was no tax incentive (Schultz, 1985 ), which made the January hypothesis strong. The subsequent researchers also supported the tax-loss selling hypothesis for the January effect (Chen & Singal, 2003 ). However, some researchers argue that the tax-loss selling hypothesis was true before the Federal income taxes (Berges et al., 1984 ; Jones et al., 1991 ), and the January effect was insignificant.
A plethora of researches has been done concerning the month-year-of-the-year effect in both international markets and the Indian stock market. There is a diversity of results, and there is no unanimity in finding out the month-of-the-year effect. In the US, Denmark, France, Germany, Norway, Sin/Mal, Spain, Switzerland, and Malaysia, the January effect found support from several researchers (Boudreaux, 1995 ; Keim, 1983 ; Wong et al., 2007 ). The January effect was not found in the New Zealand stock market (Li & Liu, 2010 ), In the Chinese market, while Gao and Kling ( 2005 ) found support for March and April effects, whereas Luo et al. ( 2009 ) did not find any month-of-the-year effects. In a study by Silva ( 2010 ), the January effect was not found in the Portuguese stock market. The July effect was found in Ghana (Albert et al., 2013 ), December was the best month in the Indonesian stock market (Rahario et al., 2013 ).
Regarding literature review concerning the stock market in India, March to May effect was found by Patel ( 2008 ), April, November, and December effects were found by Rengasamy & Pandey ( 2008 ), Diwali effect was found in Harshita et al. ( 2018 ). Moreover, some researchers found significant results during September and December (Lodha, 2015 ). On the contrary, some studies did not find any month-of-the-year effect (Verma & Kumar, 2012 ; Sairam & Devi, 2013 ; Patel, 2014 ). The summary of previous studies conducted in the Indian and international stock markets is presented in Tables 1 and 2 .
2.1 Rationale for the Present Study
After going over the literature review, one may question why another study? The rationale for the present study stems from three reasons: First, the literature review reveals that different scholars have focused on different years to study the month-of-the-year effect. For example, Patel ( 2008 ) studied the Bombay Stock Exchange (BSE) 500 index during 1997–2007; Verma and Kumar ( 2012 ) studied BSE SENSEX during 1998–2008, and Kushwah and Munshi ( 2018 ) have studied National Stock Exchange (NSE) NIFTY 500 from 2007–2017. Likewise, several researchers in the past studied BSE, NSE during different periods. The present study explores BSE 500 and NIFTY 500 indices during the last decade (2011–2021). Our study extends growing literature on stock market returns concerning the month-of-the-year effect. The other studies conducted in the Indian context were presented in Table 1 , and the reflections in the non-Indian context are shown in Table 2 .
Second, the rationale for the present study is because of the diversity of results from previous studies, as documented in Tables 1 and 2 . For example, while some studies found the March to May effect, others found the September and December effects. Now, during this current period, we wanted to explore what was regarded as the month-of-the-year effect.
Third, researchers have been trying to find if there is any possibility for the investors to secure an above-average rate of returns (example, January effect, April effect, November effect, etc.). In simple words, the month-of-the-year effect refers to the identification of month (or months) in a calendar year that gives statistically significant abnormal returns. The term month of the year effect implies that among the month of the year, i.e., (January to December), any one of the months gives statistically significant abnormal returns. Therefore, to earn an abnormal return, the investors have to frame the trading strategies called seasonal anomalies viz., day of the week effect, the month of the year effect, monthly effect, holiday effect, and quarter of the year effect. The present study focuses on identifying the month-of-the-year effect in the Indian stock market.
2.2 Objectives of the Study
The study's primary objective is to examine the existence of the month-of-the-year effect in Indian Stock Markets. The primary objective is split into the following sub-objectives:
To empirically examine the month-of-the-year effect from 1st April 2011 to 31st March 2021 in India.
To explain the reasons for the month-of-the-year effect and suggest to investors the consequences of the month-of-the-year impact in India.
To provide directions for future research concerning the month-of-the-year effect in the Indian context.
To study the month-of-the-year effect, we need to examine whether monthly return series of the indices are stationary or not. We also need to examine the descriptive statistics for S&P BSE 500 and NIFTY 500 indices for the month of the year. Thirdly, it is essential to examine whether the stock market indices follow a normal distribution. To achieve these, the following hypotheses are formulated.
Since the objective of the present study is to empirically examine the month-of-the-year effect in the Indian stock market, the following hypothesis is formulated:
H0 (Null Hypothesis): In Indian stock market, there is no month-of-the-year effect.
H1 (Alternate hypothesis): In Indian stock market, there is month-of-the-year effect.
The sub-hypotheses to test the above main hypothesis are:
H 01 : The series has a unit root
H 02 : The series is stationary
H 03 : The monthly mean returns are statistically equal across the trading month of the year
In this research, we started with the identification of the problem, outlined the study’s objectives and hypotheses, selected the sample and collected the data, selected the right econometric tools to analyze the data, and presented the results. The methodology is mentioned in the flowchart (Fig. 1 ).
In India, the two leading recognized stock exchanges, viz., the BSE Ltd and NSE, play a vital role in the growth of the Indian economy. Hence, most researchers consider these two-stock exchange's market cap indices, namely S&P BSE 500 and NIFTY 500. The sample included for the study is from 1st April 2011 to 31st March 2021, which consists of 10 years.
4.2 Data Collection
The present empirical study was mainly based on the secondary data collected from the official websites of the Bombay Stock Exchange (i.e., www.bseindia.com ) and the National Stock Exchange (i.e., www.nseindia.com ). The data collected for this study included: 'monthly open,' 'high,' 'low,' and 'closing prices' of market cap indices of the Indian Stock Market. It should be noted that NSE does not provide monthly open, high, low, and closing prices. Instead, NSE provides daily open, high, low, and closing prices, and hence we took the prices on the first day of every month. The monthly returns are calculated on the basis of average price. While prior researchers have used only closing prices as if the trading is done at the closing price, Lodha and Soral ( 2015 ) recommended using the simple arithmetic mean of the four prices. The underlying logic is that by considering the average price the volatility of prices can be controlled to some extent. We used the following formulae for the returns:
where r t is the return at the time t , ln represents natural log, p t and p t-1 are closing prices at time t and t-1 respectively. If the returns series is detected to be stationary, the analysis is supposed to be performed on the stationary series. As we recall the fundamentals, a series is said to be stationary only if has (a) a constant mean (μ), (ii) a constant variance (ϭ 2 ), and (iii) a constant covariance structure (γ s ). Though there are two types of stationarity (viz., deterministic, stochastic). When the series has a linear trend making it non-stationary, it is called a deterministic non-stationarity series. It should, however, be noted that if the series is stationary around that trend, it is called trend-stationary series (Brooks, 2019 ). In financial economics, time series in general exhibit stochastic non-stationarity, if not stationarity.
4.3 Econometric Tools used in the Study
For analysing the data, we used Eviews-7. The econometric tools used in this study are:
Augmented Dickey-Fuller (ADF) test and Phillips-Perron (PP) tests to detect the unit root of the monthly return series; PP test is used to adjust possible autocorrelation in the residuals
Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test and correlogram to verify the stationarity of monthly return series; KPSS test serves as a confirmatory data analysis. One should note that while ADF and PP tests have null hypotheses that unit root is present in the series, the null hypothesis in KPSS is that the series is stationary
Regression for examining the month of the year effect by considering all twelve months in a year. The following regression equation used to find the month of the year effect
where, the constant term ( α 1 ) is the average mean return for the January trading month, and coefficients α 2 to α 12 denotes the average differences between the return from February trading month to December trading month, except for January. If the value of the coefficients of α 2 to α 12 is zero, then the return for each month of the year is identical and no evidence of month-of-the-year effect exists. and ε 1 represents the white noise error term.
ARIMA model to remove the persistence of serial correlation
GARCH model to remove the heteroskedasticity effect.
The Unit root test and stationarity test results for monthly closing prices and monthly returns of S&P BSE 500 and NIFTY 500 Indices return series were presented in Table 3 .
As can be seen in the Intercept of ADF test statistics of the closing price of S&P BSE 500 (0.380) and NIFTY 500 (0.357) is greater than their critical values of 1 percent, 5 percent, and 10 percent levels of insignificant respectively (the values were given in parenthesis). Hence, H0 (the series has a unit root) is not rejected (following the ADF test). Likewise, the Intercept of PP test statistics of the closing price of S&P BSE 500 (0.498) and NIFTY 500 (0.457) is greater than their critical values of 1 percent, 5 percent, and 10 percent levels of insignificant respectively (the values were given in open parenthesis). Hence, H0 (the series has a unit root) is not rejected (following the PP test). Finally, the KPSS test statistic (1.261) of the closing price of S&P BSE 500 and NIFTY 500 is greater than the critical value at a 1% significance level (0.739), rejects the null hypothesis of a stationary series. Hence, H0 (the series is stationary) is rejected (following the KPSS test).
The Intercept of ADF test statistics of return series of S&P BSE 500 (− 7.588) and NIFTY 500 (− 7.641) is less than their critical values of 1 percent, 5 percent and 10 percent levels of significance respectively (the values were given in parenthesis). Hence, H 0 (the series has a unit root) is rejected (following the ADF test). The Intercept of PP test statistics of return series of S&P BSE 500 (− 6.484) and NIFTY 500 (− 6.454) is less than their critical values of 1 percent, 5 percent and 10 percent levels of significance respectively (the values were given in parenthesis). Hence, H 0 (the series has a unit root) is rejected (following the PP test). Therefore, it could be interpreted that the ADF and PP test confirms the unit root of the return series of S&P BSE 500 and NSE NIFTY 500 Indices. The p-value of the KPSS test of return series of S&P BSE 500 (0.808) and NIFTY 500 (0.758) is greater than the significance level of (0.05). Hence, H 0 (the series is stationary) is not rejected. KPSS test confirms the stationarity of the return series of both Indices.
The confirmatory data analysis revealed that (i) the monthly closing prices series is not stationary, and (ii) the returns series exhibit stationarity. These findings are consistent with the results from the previous studies in the literature on finance, adding to the growing body of literature. The graphical representation of these two series over time are presented in Figs. 2 and 3 .
Source: Compiled from Eviews7
Monthly closing price of S&P BSE 500.
Monthly closing price of NIFTY 500.
As can be seen in the Figs. 2 and 3 , the closing share price is non-stationarity because it is continuously changing in the line graph.
The graphs of the return series of S&P BSE 500 and NIFTY 500 indices were presented in Figs. 4 and 5 .
Monthly return series of S&P BSE 500.
Monthly return series of NIFTY 500.
The figures indicate that indices are fluctuating between high and low, and the proportions of deviation are different at different time periods of the study, but they return to their respective means. It is not surprising to find that during the year 2020, both indices of the graph line showed high volatility because of the impact of Covid-19 global pandemic. It is understood that both indices have constant mean returns over the period and the mean values lie on 0. It can be interpreted that both indices are stationarity in nature.
The descriptive statistics of monthly returns of S&P BSE 500 and NIFTY 500 indices were shown in Figs. 6 and 7 .
Histogram and descriptive statistics of monthly returns of S&P BSE 500.
Histogram and descriptive statistics of monthly returns of NIFTY 500.
As can be seen in Fig. 6 , descriptive statistics of monthly returns of S&P BSE 500 report a kurtosis of 7.142. Since the value is greater than 3, the returns series is leptokurtic (Brooks, 2019 ). The descriptive statistics presented in Fig. 7 for the monthly returns of NIFTY 500 show the kurtosis of 6.3707 reveal that the returns series is leptokurtic as the value is greater than 3 (Brooks, 2019 ).
Kurtosis represents the possibility of the stock prices to fluctuate significantly and hence is very important from investors’ point of view. The shape of distribution explains whether the stocks are pricing risky assets by looking at the distribution and volatility of the prices (Ivanovski et al., 2015 ). The results indicate that significant variations in daily prices are noticeable than those estimated by normal distribution. The leptokurtic distribution, representing the excessive positive kurtosis, suggests that risk-seeking investors experience fluctuations resulting in substantially high or low returns. Sometimes, the investors make maximum profits when returns are very high and sometimes they also suffer losses when the returns are low.
The Correlogram analysis was done and the results were shown in Tables 4 and 5 .
The autocorrelation function did not show large spikes at lag 1 and autocorrelation hurriedly fall from 0.420 to − 0.046 for S&P BSE 500 and 0.422 to − 0.050 for NIFTY 500, when the lag length increased. The ACF after the lag 1 to lag 16 coefficients values of both indices are nearly zero. Moreover, the PACF after the lag 1 to lag 16 coefficients values of both indices are adjacent zero. Hence, the Correlogram clearly corroborate the stationarity in the series.
Month-wise descriptive statistics of S&P BSE 500 and NIFTY 500 indices return series were presented in Table 6
As can be observed in Table 6 , the maximum average (or mean return) occurred in July, and the lowest returns occurred in March for both indices. These results suggest that among the trading month of the year, mean returns for all the months were having different returns distributions. Therefore, H 02 (the monthly mean returns are statistically equal across the trading month of the year) is rejected. The standard deviation of returns series was greater in March when compared to other months for both the indices. These results indicate that volatility in stock was maximum in March. From intraday trader standpoint, these results suggest that March is an appropriate month. The value of Skewness returns distribution is found to be negative for all the months of the year except February, June and December. The monthly trading returns are asymmetrical distributions. The values of Kurtosis are less than three for all the months of the year except March, October and December for both the indices. It represents that return distribution for the months of the year shows platykurtic curve. The coefficient value of the Jarque–Bera test statistic is insignificant for all the month of the year except March and October. It evident that the month-wise average returns distribution follows the normal distribution.
5.1 OLS Dummy Variable Regression Equation Model
The descriptive statistics from the study reveal that there is no strong evidence of the month-of-the-year effect of both indices. Therefore, we wanted to identify the month-of-the-year effect by using the Ordinary Least Square (OLS). The regression equation used to find the month of the year effect is as follows:
The dummy variables for February, March, April, May, June, July, August, September, October, November and December were included in the regression equation. For each dummy variable, the value ‘1’ represents the corresponding month and 0 represents the rest of the months. Since majority of previous studies reported January effect in the stock market, the monthly average mean returns of January taken as ‘Yardstick month’ for comparing returns of other trading months for both indices.
The results of the OLS dummy variable regression model of the month-of-the-year effect for S&P BSE 500 and NIFTY 500 indices were presented in Table 7 .
As can be seen in Table 7 , the regression coefficients of February, March, April, May, June, August, September and December are negative but insignificant for both indices. The regression coefficient for January, July, October and November are positive but not significant for both indices. The Adjusted R-square is negative for both indices. Moreover, the F.statistic was very low with the p-value. These results indicate that the OLS dummy variable regression model spurious. i.e., the OLS dummy variable regression model does not indicate the month-of-the-year effect. Besides, the Durbin-Watson statistics values for both indices are less than the acceptable value of two, indicating the existence of serial correlation in this model. To confirm the serial correlation, we applied the Breusch-Godfrey LM test to the OLS dummy variable regression results.
It should be noted that when serial correlation is present, it is necessary to test it further by conducting the Breusch-Godfrey Lagrange-Multiplier (LM). In general, the LM-test is conducted before and after ARIMA, which is consistent with the procedures by the previous researchers. The results of LM-test statistics before and after ARIMA modelling of the month-of-the-year effect in S&P BSE 500 and NIFTY 500 indices were presented in Table 8 .
As can be seen in Table 8 , the LM test value before ARIMA for both indices had a very high value ( p < 0.000), indicating strong evidence for the existence of serial correlation. Since serial correlation in the time series data was confirmed, removing the serial correlation by employing appropriate ARIMA is essential.
Following the Box-Jenkins methodology, we added the appropriate ARIMA terms to the equation for both indices to remove the persistence of serial correlation. After the inclusion of ARIMA terms in the OLS dummy variable regression equation, the Breusch-Godfrey LM test value for S&P BSE 500 decreased from 30.035 ( p < 0.000) to 0.643 ( p = 0.725); and Breusch-Godfrey LM test value for NIFTY 500 decreased from 30.688 ( p < 0.000) to 0.602 ( p = 0.740). That means after inclusion, the appropriate AR and MA terms in the equation serial correlation have been removed.
Following the previous researchers, we used GARCH model in this study. The Auto Regressive Conditionally Heteroskedastic (ARCH) is the most frequently used volatility models by the researchers in the field of finance. The basic model consists of equations bout (i) conditional mean, and (ii) conditional variance of the error term (of conditional mean).
The conditional variance equation is:
where σ t 2 = conditional variance of the error term.
μ 2 t-1 = lagged error square term.
The Generalized Auto Regressive Conditionally Heteroskedastic (GARCH) is used (called GARCH (1,1) is employed where the conditional variance is expressed in the following equation:
It can be observed that the lagged value of the error term and its own lagged value determines the conditional variance of the error term. A higher order GARCH (p,q) which includes ‘p’ terms of squared own lags, and ‘q’ terms of squared error terms, is rarely used in the finance literature (Brooks, 2019 ).
The results of GARCH Model in S&P BSE 500 and NSE NIFTY 500 indices for month-of-the-year effect were presented in Table 9 .
As can be seen in Table 9 , the summarized estimates viz., the coefficients of the different trading months of the year, z-Statistic (shown in parentheses), adjusted R-square, Akaike Info Criterion (AIC), and Schwarz Criterion (SBC). The regression coefficients for February, March, April, August, September, October, December were negative but insignificant for both indices. The regression coefficients of November were negative and insignificant for the NIFTY 500 Index alone. However, the regression coefficients for March are negative and significant for both indices. On the other hand, the regression coefficients for January, May, June, July and (November only for S&P BSE 500) are positive but not significant for the index. It is exciting to note that the month-of-the-year effect was negative and significant only for March for both indices. Therefore, the results document that the month-of-the-year effect is the 'March effect.'
Consistent with the previous researchers, the findings of the study reveal that the ADF and PP test confirms the unit root of the return series of S&P BSE 500 and NIFTY 500 Indices. Likewise, the KPSS test confirms the stationarity of the return series of both indices. Moreover, the graphical analysis and correlogram of the study also demonstrate the stationarity of the return series of both Indices.
The maximum average or mean return occurred in July for both indices. The lowest returns occurred in March for both indices. These results indicate that among the trading months of the year, mean returns for all the months were different returns distributions. The standard deviation of the returns series occurred maximum in March for both indices. The series found that utmost volatility occurred during March. It is suggested that for intraday traders, March month is an appropriate month. The coefficient value of the Jarque–Bera test statistic is insignificant for all the months of the year except March and October. It is evident that the month-wise average returns distribution follows the normal distribution. The regression coefficients for March were negatively significant for both indices. It is exciting to note that the month-of-the-year effect was negatively significant only during March for both indices. Therefore, the results document that the month-of-the-year effect is the 'March effect.'
6.1 Explanation to the March Effect
The objective of the present study is to empirically examine the evidence of monthly seasonality in the Indian stock market during 2011 and 2021. We tested the seasonality using standard methods–testing the stationarity, kurtosis, and volatility clustering- and found that the returns showed high volatility during March. In one of the recent studies conducted in India, Harshita et al. ( 2018 ) reported significant volatility in November because of the Diwali effect. Surprisingly, we did not find the November (Diwali effect) effect in our study.
In India, the Union Finance Minister announces the budget every year at the end of February. The budget effect can be found in the prices of goods and services, and the stock market is not an exception. Now, the fundamental question remains whether the presence of the March effect signifies market inefficiency. As Brooks ( 2019 ) contends, if investment brokers cannot employ seasonality in their investment strategy, it would not be considered market inefficiency. However, some scholars argue that intelligent investors can still profit from the seasonal fluctuations (Beyer et al., 2013 ; Chen & Singhal, 2003 ; Jaisinghani, 2016 ). As the results indicated that significant volatility occurred in March from 2011 to 2021, it is necessary to identify the reasons or causes for the March effect. Digging up literature, various reasons were identified for results in other months. These are tax-loss selling hypothesis (Johnston & Cox, 1996 ; Ligon, 1997 ; Sias & Starks, 1997 ), tax-gain selling hypothesis for December month (Chen & Singal, 2003 ), information hypothesis (Gultekin & Gultekin, 1983 ), portfolio rebalancing hypothesis (Beyer et al., 2013 ), liquidity hypothesis (Sharma & Narayan, 2014 ), optimistic expectations hypothesis (Barone, 1990 ).
Our results indicate the budget effect (March effect). The budget presented by Union Finance Minister plays a critical role in influencing the cost-of-living index, consumer price index, and prices in stock markets. The annual budget announced by Union Finance Minister gives information about the proposed government spending and income and sectoral allocation of funds in the coming year. The budget announcement about annual financial of estimated revenues and expenditures of the government for a fiscal year (which runs from 1st April to 31st March) provides not only the ‘quantity (in terms of money to be spent and taxes on various commodities) but ‘quality (which is reflected in terms of macroeconomic impact on the country) of information that is received and digested by population significantly impact the consumer behavior and investor behavior. To minimize risk and maximize returns, investors pay close attention to the budget announcements. Several studies were conducted to see the reaction of policy announcements such as budget, natural disasters such as floods, elections, and change of leadership at the Parliament on stock prices (Kaur, 2004 ). Some studies predominantly focused on the impact of union budget on stock prices and found that budget announcements resulted in volatility in stock prices (Gupta & Kundu, 2006 ; Singhvi, 2014 ). For example, in one study conducted by Varadharajan and Vikkraman ( 2011 ), the findings indicated that volatility in the stock market was higher in the post-budget month when compared to the pre-budget month. Other studies by Kutchu ( 2012 ) and Babu and Venkateswarlu ( 2013 ) also supported the findings of earlier researchers about the effect of the budget on the stock market. The results from our study corroborate the earlier findings of the budget effect on the stock market. Though we did not study specifically the effect of the budget on stock prices, the plausible explanation for the March effect could be linked to the Union budget announcement.
One very interesting finding from our study is the March effect, which is in a sharp contract to some of the earlier studies conducted during June1999- September 2015, that found November effect (Harshita et al., 2018 ). We were also surprised to see that there was no Diwali effect in our study period (2011 to 2021), though the Diwali effect which comes normally in the month of November. The logical explanation for the lack of Diwali effect (during November) is that the investors expect the stock prices to deviate from the ordinary course. As such, the information gets factored into the current prices. Therefore, it is not abnormal not to have a Diwali effect.
One plausible explanation for the negative deviation in March effect, which comes immediately after the budget announcement, could be in terms of pessimistic behavior of investors. Second, India being one of the largest democratic countries, the sentiments of the investors reflect the conditions in the stock market, and political factors could be factored into the equation. Though it is very difficult to explain how and why political influences would hold, investors expect growth in the economy and when they see that the growth is less than expected, the sentiments get reflected in the stock market. It is also equally possible to have positive deviation in the March effect, provided the investors are optimistic about the rate of growth in the economy. Thus, the macroeconomic factors combined with the political influences affect the stock market anomalies, especially in Indian context.
We need to explain that when the budget is declared on 1st February every year, why could we not find the February effect? Up to 2016, the finance minister presented the budget on the last working day of February. Since 2016, the budget was presented on the 1st February in the parliament around 11 am, and there is hardly any time for the investors to assimilate information, interpret and react (businessinsider.in, 2021 ). Therefore, from 2011 to 2016, the budget effect was reflected in March every year. However, from 2016 to 2021, though the budget was presented on 1st February, the influence was felt only in March. It would be logical to assume that it takes at least some days to gather complete information and infer the effects of the budget on the economy. This information may be reflected in March (when we see the month-of-the-year effect). Our results support this assumption that investors take time to evaluate the budget and react to it, and hence the results are not contrary to the expectation.
6.2 Contributions of the Present Study
Despite several studies conducted about the calendar anomalies (month-of-the-year effect) by various scholars, the present study was conducted to analyze the stock market anomalies during the decade 2011–2011. The results from the study have several contributions to both the literature on finance and practicing managers. First, the study highlights the importance of studying the month-of-the-year effect during the fascinating period that has changed political power (shifting of power from the Congress to Bharatiya Janata Party in 2014), which may profoundly influence the stock market. Though we did not study the effect of change in political power, we wanted to see whether there is any month-of-year effect during this crucial period. One significant contribution of the results from the study is that throughout the ten years, the ‘March effect ‘ was pronounced, implying that change in political power did not have any impact on the month-of-the-year effect.
Second, the results show that contrary to the previous studies that documented the November effect (Harshita et al., 2018 ), December effect (Choithala & Ajmal, 2016 ), January effect (Sudarvel & Velmurugan, 2015 ), and February, August, and September (Debasish, 2012 ), our results showed March effect during the present decade (2011–2021). The study periods were, however, different from the previous studies.
Third, our results provide statistical evidence for the abnormal returns during March of every year during the study period. The results also suggest the investors be mindful of the March effect in subsequent years (2021 onwards). It is also astonishing to notice that the March effect was also pronounced even during the global pandemic COVID-19. Though the world economy was hard-hit by the pandemic, the Indian economy is not an exception, as stock prices have become highly volatile and showed a somewhat downward return trend. Finally, as the countries are coming back to normal, the stock market is expected to return to normal in the years to come.
6.3 Suggestions for Future Research
The present study provides several avenues for future research. First, future researchers may study the pre-budget and post-budget effects on the stock market over the last ten years (from 2011–2021) and see whether the effect is significant one week or two weeks before implementing the budget (during April). However, insider information about budget announcements may also have some impact on stock prices. In a democratic country, it is not impossible to get inside information before the announcement of the budget by the Union Minister. Second, future researchers can make international comparisons (by comparing the stock markets in developing countries such as Pakistan and Bangladesh) to see whether the March effect is present in those comparable countries. Third, future research can focus on the differences in stock market volatility due to global disturbances (for example, COVID global pandemic) at the international level. In the present study, the last year (the year 2020) fell under the worldwide pandemic's grip and showed some deep dip; future researchers can see how the drop was for different countries. However, international comparisons were outside the scope of the present research.
The findings of the study disclose the regression coefficients for March are negatively significant for both indices. It is fascinating to note that the month-of-the-year effect was negatively significant only during March for both indices. Therefore, the results document that the month-of-the-year effect is the 'March effect.' The results suggest that investment brokers and organizations take into account the March effect while making strategic financial and investment decisions. Since the investment climate has undergone paradigmatic change during the COVID global pandemic, it would be interesting to examine the post-pandemic effect on the stock market. It also would be interesting to see whether the history gets repeated after the pandemic about a century ago in the world, where most of the countries had suffered. However, the stock market was not that robust. With the increase in technology and resilience strategies of global companies to turn around and bounce back, it would be interesting to see how investors react during the post-pandemic era. Despite the setbacks, we hope the stock market bounces back and assumes normality. The interest in studying stock market anomalies continues to remain on the agenda of research in the field of finance and economics.
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Department of Commerce, Bishop Heber College (Autonomous), Tiruchirappalli, India
Department of Commerce, St. Joseph’s College (Autonomous), Tiruchirappalli, India
Francis Gnanasekar Irudayasamy
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Elangovan, R., Irudayasamy, F. & Parayitam, S. Month-of-the-Year Effect: Empirical Evidence from Indian Stock Market. Asia-Pac Financ Markets 29 , 449–476 (2022). https://doi.org/10.1007/s10690-021-09356-2
Accepted : 09 December 2021
Published : 08 January 2022
Issue Date : September 2022
DOI : https://doi.org/10.1007/s10690-021-09356-2
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Peer Reviewed Research Manuscript
A Study on Comparative Analysis of Major Stock Indices of World
IARS' International Research Journal , vol. 10 , núm. 1 , 2020
International Association of Research Scholars
Abstract: – Experts talk lots on integration of major stock indices of the world. In this research paper researcher has tried to establish integration between major stock indices of the world by calculating correlation and applying anova on daily return of 16 major stock indices of the world. In research it is found that preceding and succeeding time of opening the stock market plays vital roles in terms of effect on each other. To achieve the objectives of research, last 5 years daily closing price of these 16 indices is collected and analyzed for quantifying the level of correlation between different stock indices. As sufficient time period is taken and daily closing prices are analyzed so it is found there is not significant difference in the daily return of these stock indices.
Keywords: STOCK INDEX, WORLD MAJOR STOCK INDICES, NIFTY 50, SENSEX.
A stock market or share market is the aggregation of buyers and sellers of stocks (also called shares), which shows ownership rights on businesses; these may include securities listed on a public stock exchange, as well as stock that is only traded privately, such as shares of private companies which are sold to investors through equity crowd funding platforms. Investment in the stock market is most often done via brokerage houses and electronic trading platforms. Investors make investments as per their investment strategies.
This research focused on the analysis of major stock indices of world. In this research, Researcher have taken previous five years data of sixteen stock indices of world. The analysis includes data of sixteen different stock indices of countries from continents like Asia, Europe, America, Africa, and Australia. Six stock indices are from Asia and five are from Europe. In data collection, Researcher have taken the opening and closing values of all sixteen stock indices of last five years (1st Jan 2015 to 31st Dec 2019). Researcher have calculated returns given by these stock indices on day to day basis and also for the period of five years as a whole.
In this research, Researcher have also stated the opening and closing times of all the selected stock indices as per the Indian standard time. It has played a very important role in getting meaningful findings from the project report. It helped me to find out that which stock indices open earlier than other indices. I can predict the movement of indices at some extent by studying the movement of other indices which opens earlier than them.
II. Literature Review
Jayshree, 2014 : The research gap of this study was found out by conducting a detailed literature review of studies in different countries during the recent years.
Found that the popular belief that the markets in general and Indian market in particular is more integrated with other global exchanges from 2002-03 onwards. This can very well be seen since the South Asian crisis of the mid- late nineties barely affected us particularly because we were insulated due to government policies and was just making the transition. However, in the later time periods, the influence of other stock markets increased on our BSE or NSE, but at a very low almost insignificant level.
Ahmad Raza Bilal, 2013, The purpose was to examine the long-run relationship between gold prices and Karachi Stock Exchange (KSE) and Bombay Stock Exchange (BSE). The statistical techniques used for this study includes Unit Root Augmented Dickey Fuller test, Phillips-Perron, Johnson Co- integration and Granger’s Causality tests to measure the long-run relationship between gold prices, KSE and BSE using monthly data from 1st July 2005 to 30th June 2011. Findings of the co-integration test indicated that no long-run relationship exist between monthly average gold prices and KSE stock index; whereas, a significant long-run relationship is proved between BSE stock index and average gold prices. Results of Granger causality test demonstrated that no causal relationship exists among average gold prices, KSE and BSE stock indices.
Swetadri Samadder, 2018, This study investigated the stock market integration amongst major global stock markets, namely, Australia, Canada, France, Germany, India, UK and USA to examine the short-run and long-run relationships with Indian stock market and selected developed stock markets based on time series data for the period between 2001 (January 2) and 2016 (December 31). This study also examines the possibility of portfolio diversification between the Indian stock market and the developed stock markets. Low correlation is observed between Indian stock market and France stock market that indicates the possible gains from international diversifications. Granger causality test results based on VECM show that both Indian stock market and USA stock market are associated in the long-run but it would take long time to return to equilibrium and Indian stock market is associated with France, Germany and USA stock markets in the short-run, which entails that investors can earn reasonable benefits from international portfolio diversification in the short- run but benefits from international portfolio diversification in the long-run are restricted.
Kaur, 2017, Indian Stock Market has a predominant place in world’s economy. Bombay Stock Exchange and National Stock Exchange are having the latest technologies. When compared to stock exchanges of other countries, india holds a very significant place in global stock markets. There are various factors that affect stock exchange including trade barriers or requirements both globally or individually. Bombay stock Exchange is the oldest stock exchange with major index as Sensex and National Stock Exchange has better technologies and has NIFTY 50 as major index.. There are various regulations that are differently applied on different stock exchanges over the world.
Sukhmander Singh, 2018, This paper has been prepared with the intention of capturing the global trends and patterns and the simultaneously flow of the global stock market, as the Indian stock market has been integrated with the world’s top stock markets. The stock markets and exchanges covered hereby are of India, USA, China, Taiwan, Japan, Hong Kong, and South Korea and UK during post global financial crisis period. For the purpose of studying the integration of the Indian Stock market with these international Stock exchanges, we include the National Stock Exchange and the Bombay Stock Exchange.
The Time Period taken in this study is from31st Jan 2014 to 31st January 2018 based on daily closing prices of stock market indices for all the selected countries.
Mukherjee, 2007, This study covers New York Stock Exchange (NYSE), Hong Kong Stock exchange (HSE), Tokyo Stock exchange (TSE), Russian Stock exchange (RSE), Korean Stock exchange (KSE) from various socio- politico-economic backgrounds. Both the Bombay Stock exchange (BSE) and the National Stock Exchange of Indian Limited (NSE) have been used in the study as a part of Indian Stock Market. The time period has been divided into various eras to test the correlation between the various exchanges to prove that the Indian markets have become more integrated with its global counterparts and its reaction are in tandem with that are seen globally.
Menon, 2018, The study at hand compares the Indian Stock Market through BSE and the stock markets in the USA (New York Stock Exchange) and Japan (Tokyo Stock Exchange) to measure and comparatively study where the BSE stands, in comparison with the world’s biggest stock markets.
Ishaq Ahmad Bhat, 2014, The present study focuses on analyzing and comparing the efficiency of the capital markets of India and Pakistan. For the purpose of realizing the objectives, Adjusted Daily closing prices of CNX Nifty (NSE India) and KSE 100 (KSE Pakistan) are taken into consideration for the period ranging between 01/04/2003 to 31/03/2013.The researchers have relied on Descriptive Statistics, ADF test, Auto-Correlation test and Jarque-Bera Statistic, Runs test to analyze the data and reach to the results. The results derived by using various parametric and non-parametric tests clearly reject the null hypothesis of the stock markets of India and Pakistan being efficient in weak form. The study provides vital indications to investors, hedgers, arbitragers and speculators as well as the relevance of fundamental and technical analysis as far as the trading/investing in the capital markets of India and Pakistan is concerned.
III. Research Objective
To find out the returns given by the selected stock exchanges.
Comparing the returns given by selected stock indices to each other.
Get an idea about the direction of movement of stock indices.
Find out the correlation between all selected stock indices to measure the strength of movement of stock indices towards each other.
To find out if the difference between the return of stock indices is significant or not by performing the single factor ANOVA.
To get an idea about the dependence of stock indices on the performance of other stock indices.
IV. Research Design
Here I am using Quantitative Research which is an organized way of gathering and analyzing data obtained from diverse sources. It includes the use of computational, statistical and mathematical tools to derive results.
Population: Major stock indices are the population for our research
Sample: Researcher have taken 5 years data of stock indices of 16 countries
Sampling Method: Convenience Sampling Method Sampling Period: 5 Years (i.e.2015 to 2019)
This research report is totally based on the secondary data. Researcher have collected required data from websites and journals which are included in literature reviews
Data Analysis Tools
ANNOVA: Single Factor
V. Data Analysis and Interpretation
A. Total returns given by stock indices for the period of 5 years: Period (1st Jan 2015 to 31st Dec 2019)
Interpretation : Stock exchange of Brazil has given the maximum return from the selected stock exchanges. It has given 131.26 % return in last 5 years.
The American stock exchange (New York stock exchange) has given second highest return from the selected stock exchanges.
National stock exchange of India is on the third place by giving 46.89% return.
Indian stock exchange has given more returns than most of the stock exchanges during the 5 years period. It has given 3rd highest return from the selected stock exchanges. Stock indices with zero or negative returns:
- 1. Spain (- 7.10%)
- 2. Shangai (- 5.71%)
- 3. Singapore (- 4.23%)
B. Correlation of stock indices
Interpretation: From the above correlation analysis of stock indices, we can say that all the stock indices are positively correlated with each other.
That means the stock indices are moving in the same direction. if one indices is going in positive direction then the others will also go towards positive direction. Asian markets have good correlation with each other which is between 20-60%. This means the Asian markets are moving in the same direction with good strength.
European markets have the highest correlation between them which is between 50 to 90%. This means European markets are moving in same direction with high strength. American markets also have a good correlation between them which is 40% to 67%. That means American markets are also moving in the same direction but with comparatively less strength than European markets.
C. ANOVA Test
H0 - There is no significance difference in the daily return of selected major stock indices of the world.
H1 - There is significance difference in the daily return of selected major stock indices of the world
Interpretation: In single factor ANOVA, if the F value is bigger than the F-crit value than the null hypothesis (there is no significant difference in the daily return of selected major stock indices of the world) is rejected. And if not, the null hypothesis is accepted.
F < F crit
In our analysis, the F value is lower than the F-crit value which means the null hypothesis (there is no significant difference in the daily return of selected major stock indices of the world) is accepted.
From analysis of variance we found out that there is no significant difference in the daily return of the selected major stock indices of the world.
So, the null hypothesis is accepted and the alternate hypothesis is rejected.
D. Analysis on yearly basis
Two-way ANOVA will be performed in which two factors will be studied.
- 1. Average return given by individual stock indices in five years.
- 2. Yearly return from all stock indices for five years on year to year basis.
E. Hypothesis for Stock Indices (Columns)
Below table shows the average return given by the selected major stock indices every year.
H0 - There is no significant difference in the yearly return of selected major stock indices of the world
H1 - There is significant difference in the yearly return of selected major stock indices of the world
F. Hypothesis for Year (Rows)
H0 - There is no significant difference in the return of selected major stock indices of the world on year to year basis.
H1 - There is significant difference in the return of selected major stock indices of the world on year to year basis
Columns (total return from all stock indices on year to year basis)
F value is smaller than F crit F < Fcrit
This means, the null hypothesis H0 (There is no significant difference in the yearly return of selected major stock indices of the world) is accepted.
Returns given by individual stock indices on year to year basis have no significant difference from year to year.
According to this two-way anova, there is significant difference in yearly returns from selected d stock indices and there is no significant difference in total return from all stock indices on year to year basis.
Rows (yearly return from individual stock indices)
Here the F value is bigger than F crit, F > Fcrit
So, the null hypothesis H0 (There is no significant difference in the return of selected major stock indices of the world on year to year basis) will be rejected.
This means that there is significant difference in the total yearly returns from all stock indices.
Brazil, New York and Indian stock exchanges have given highest returns respectively in period of five years.
Spain, Shanghai and Singapore have given lowest returns respectively in period of five years. All of them have given negative return.
Asian markets open earlier than other stock markets of world.
Indian stock index has given more return from other five stock indices of Asia in last five years.
All of the selected major stock indices for our study are positively correlated which means they are moving in the same direction.
European stock markets are highly correlated because the correlation between them is up to 90 percent.
Asian and American stock indices are moderately correlated up to 60 and 67 % respectively.
According to single factor analysis of variance there is no significant difference between the returns of selected major stock indices of the world.
Highest correlation in our study is between stock indices of Germany and France which is 92 percent on the basis of analysis of daily return.
Lowest correlation in our study is between stock indices of Brazil and shangai which is 11.09 percent.
There is no significance difference in the yearly return of selected major stock indices of the world.
There is significance difference in the return of selected major stock indices of the world on year to year basis.
A stock market participant at some extent can predict the movement of stock indices opening later than other early opening stock indices based on the performance of those early opening stock indices. There are high probabilities of having a high correlation between stock indices belonging to same continent. European stock indices are the most correlated indices from selected major stock indices of the world. All sixteen stock indices from our study are positively correlated which means they all are moving in the same direction. Lower correlation is seen between the stock indices belonging to different continents as compared to those belonging to same continent.
Positive correlation is seen between all stock indices which means all are moving in the same direction. Strength of movement depends upon the level of correlation. A good performance by the major stock indices of a country represents the good economic health and progress of that country. For example, stock markets of most of the countries are facing decrease in value because of corona virus pandemic.
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