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Empirical analysis of bitcoin price

• Published: 21 May 2021
• Volume 45 , pages 692–715, ( 2021 )

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• Yuanyuan (Catherine) Chen   ORCID: orcid.org/0000-0001-9462-4719 1  

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This paper, merging the stories of monetary theory, management analysis, computer science, and finance, comprehensively studies different forces that affect the bitcoin price. After conducting various stationarity tests and cointegration test, I choose the VEC model as the baseline to estimate the bitcoin price empirically. I also include competing methodologies, which have been used in previous studies, on my data set. These methodologies are VAR and ADRL models. With the daily data of 2009–2019, my baseline model shows that in the short run the bitcoin price is mainly affected by the medium of exchange and financial expectation forces, while blockchain technology factors only show a small impact on the bitcoin price. Moreover, using different econometric models yields different results in the short run. To investigate whether some conflicts among previous research can be explained by different specifications of the data, I also conduct two kinds of robustness checks. One is to estimate the same variables between bear and bull states. The states are found using Markov switching model. The other check is to focus on supply and demand forces, particularly during high volatility periods. Both checks find that in different states the effects can be different both in the size or in the significance. Lastly, I also find that effects are different between the short run and the long run.

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Chen, Y.(. Empirical analysis of bitcoin price. J Econ Finan 45 , 692–715 (2021). https://doi.org/10.1007/s12197-021-09549-5

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Accepted : 02 April 2021

Published : 21 May 2021

Issue Date : October 2021

DOI : https://doi.org/10.1007/s12197-021-09549-5

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Brian Lucey is professor of international finance and commodities at Trinity Business School, Trinity College, Dublin, Ireland; Institute of Business Research, University of Economics, Ho Chi Minh City, Vietnam; and Institute for Industrial Economics, Jiangxi University of Economics and Finance, Nanchang, China.

Money is at a crossroads. A race is on to decide who creates it, who can access it and how, who controls it, and to what degree and how it is regulated. The outcome could decide whether governments have access to all our financial data, whether criminals can easily launder vast sums unseen, and whether the benefits of finance can be extended to the billions of people globally who lack access to banks.

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Blockchain Analysis of the Bitcoin Market

In this paper, we provide detailed analyses of the Bitcoin network and its main participants. We build a novel database using a large number of public and proprietary sources to link Bitcoin addresses to real entities and develop an extensive suite of algorithms to extract information about the behavior of the main market participants. We conduct three major pieces of analysis of the Bitcoin eco-system. First, we analyze the transaction volume and network structure of the main participants on the blockchain. Second, we document the concentration and regional composition of the miners which are the backbone of the verification protocol and ensure the integrity of the blockchain ledger. Finally, we analyze the ownership concentration of the largest holders of Bitcoin.

We thank Jiageng Liu for excellent research assistance. We also thank Kyrylo Chykhradze for very helpful comments and help with the Crystal Blockchain data. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

MARC RIS BibTeΧ

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Our new research: Enhancing blockchain analytics through AI

Elliptic Research

• Elliptic researchers have made advances in the use of AI to detect money laundering in Bitcoin. A new paper describing this work is co-authored with researchers from the MIT-IBM Watson AI Lab.
• A deep learning model is used to successfully identify proceeds of crime deposited at a crypto exchange, new money laundering transaction patterns and previously-unknown illicit wallets. These outputs are already being used to enhance Elliptic’s products.
• Elliptic has also made the underlying data publicly available . Containing over 200 million transactions, it will enable the wider community to develop new AI techniques for the detection of illicit cryptocurrency activity.

At Elliptic we have always pushed the boundaries of blockchain analytics, to enable our customers to more accurately and efficiently assess risk in cryptoassets. Part of this innovation has been exploring how artificial intelligence can be leveraged to improve the detection of money laundering and other financial crime on blockchains. 

Blockchains provide fertile ground for machine learning techniques, thanks to the availability of both transaction data and information on the types of entities that are transacting, collected by us and others. This is in contrast to traditional finance where transaction data is typically siloed, making it challenging to apply these techniques.

Machine learning on the blockchain

We first published research on this topic in 2019, co-authored with researchers from the MIT-IBM Watson AI Lab. A machine learning model was trained to identify Bitcoin transactions made by illicit actors, such as ransomware groups or darknet marketplaces. The training data, compiled by Elliptic and containing over 200,000 bitcoin transactions, was made publicly available to encourage further experimentation and collaboration within this emerging field. That paper has now been cited nearly 400 times by researchers around the world, which demonstrates the impact it has had and continues to have in the fields of machine learning and anti-money laundering.

We have now released further research , applying new techniques to a much larger dataset, containing nearly 200 million transactions. This work is again co-authored by researchers from the MIT-IBM Watson AI Lab. Rather than identifying transactions made by illicit actors, a machine learning model is trained to identify “subgraphs”, chains of transactions that represent bitcoin being laundered. By identifying these subgraphs rather than illicit wallets, this approach allows us to focus on the “multi-hop” laundering process more generally rather than the on-chain behavior of specific illicit actors.

Testing our results

We worked with a cryptocurrency exchange to test whether this technique could be used to identify money laundering attempts through that business. Of 52 “money laundering” subgraphs predicted by the model and which ended with deposits to this exchange, the exchange confirmed that 14 had been received by users who had already been flagged as being linked to money laundering. On average less than one in 10,000 of these accounts are flagged as such, suggesting that the model performs very well * . Importantly, the exchange’s insights were based on off-chain information, suggesting that the model can identify money laundering that would not be identifiable using traditional blockchain analytical techniques alone.

We also investigated the types of money laundering patterns that the trained model was identifying. This revealed known money laundering patterns such as “peeling chains”, which can already be automatically detected in Elliptic’s transaction and wallet screening tools. However it also identified novel patterns such as the use of intermediary “nested services” in specific ways. Knowledge of these money laundering behaviors is of value to AML practitioners, and can be added to the suite of behaviours that can be detected with Elliptic’s tools.

The machine learning model can also be used to help identify previously-unknown illicit wallets. When the model predicts that a given subgraph is an instance of money laundering, it implies that the funds have potentially originated from some type of illicit activity. Directed research can then be performed on these wallets to try to identify them. This approach has already enabled us to identify a number of previously unknown wallets used by illicit actors including ponzi schemes and darknet markets. 

Sharing our data with the community

As well as releasing our research, we have also made the underlying data publicly available . The largest public dataset of its kind, “Elliptic2” will enable the development of new techniques for the detection of illicit cryptocurrency transactions by the wider community. It will also aid the development of the underlying graph neural network methods, used in applications including drug discovery, physics and computer vision.

This novel work demonstrates that AI methods can be applied to blockchain data to identify illicit wallets and money laundering patterns, which were previously hidden from view. This is made possible by the inherent transparency of blockchains and demonstrates that cryptoassets, far from being a haven for criminals, are far more amenable to AI-based financial crime detection than traditional financial assets. We have barely scratched the surface of what is possible in this domain, but this work has already led to benefits for Elliptic’s users. Further collaboration and data-sharing will be key to advancing these techniques further and combating financial crime in cryptoassets.

You can read the research paper in full here , and the Elliptic2 dataset is now available to access. To discuss the research, and find out more about how we are applying these new techniques to enhance our products, get in touch . 

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Why Bitcoin Might Drop Another 20%: 10x Research

C rypto research firm 10x Research on Wednesday underscored its prediction that the ongoing Bitcoin (CRYPTO: BTC) correction may take prices into the low $50,000s.

What Happened : In their latest email newsletter , the team around lead analyst Markus Thielen highlight the differing risk management approaches between institutional investors and retail traders.

Their research suggests the average entry price for U.S. Bitcoin ETF holders sits around $57,300, potentially even higher, a level Bitcoin prices are rapidly approaching.

Despite the predicted correction, 10x Research maintains a positive long-term outlook.

Bitcoin’s recent one-year high validates their early 2023 prediction of a new bull market, further reinforced by its proximity to their forecasted halving price of $63,160.

Trending: Trump Says If Supreme Court Decides President Doesn't Get Immunity Then Biden 'Will Be Prosecuted For All

Must Read: Home Prices Outpace 1990s, 2010s In Just 4 Years: Is It Sustainable?

Additionally, their 2023 year-end target of $45,000 was nearly achieved, and their post-ETF approval target of $57,000, later revised to $70,000, underscores their continuous analysis and adaptation.

Their ability to accurately predict market movements stems from a deep understanding of historical trends and the driving forces within the crypto industry.

As 10x Research points out in their previous reports, analyzing these factors has been instrumental in their successful predictions throughout the current bull market.

Also Read: Don’t Get Left Behind: Web3 Wallets Offer More Than You Think In Today’s Market

Why It Matters : Looking back, the August 2023 correction was attributed to rising 10-year Treasury bond yields and a hawkish Fed stance, mirroring the situation during the August 2015 RMB devaluation.

While the initial impact was a sharp decline, Bitcoin ultimately recovered significantly by year-end.

Similar foresight was applied in January 2024, where 10x Research anticipated a correction to $36,000/$38,000, citing the historical tendency for corrections following major Bitcoin product approvals.

They recommended hedging long exposure but turned bullish again as Bitcoin rebounded, reiterating their $70,000 target.

March 2024 saw another crucial turning point.

Recognizing the potential for a +/-10% move, 10x Research established a line in the sand at $68,300, indicating a potential significant top if breached.

This aligns with their current prediction of a correction towards $52,000/$55,000 within the ongoing bull market.

Several factors contribute to this anticipated correction.

The research highlights a shift in ETF buying behavior, with six consecutive days of outflows observed.

What’s Next : As the average entry price of $57,300 is approached, further ETF unwinding is expected, potentially pushing prices down.

Additionally, the estimated all-in mining cost of $53,000/$55,000 for Bitcoin miners could trigger selling to protect operations.

While a 25% to 29% correction is anticipated, 10x Research acknowledges the possibility of a consolidation period instead of a sharp snapback rally.

As industry leaders gather at events like Benzinga’s Future of Digital Assets on Nov. 19th, insights from 10x Research and other experts will be invaluable in navigating the complexities of the current bull market and preparing for potential corrections within the broader landscape.

Read Next: Ex-Binance CEO Changpeng Zhao Sees Crypto Industry Entering ‘New Phase’

This content was partially produced with the help of AI tools and was reviewed and published by Benzinga editors.

Image: Shutterstock

© 2024 Benzinga.com. Benzinga does not provide investment advice. All rights reserved.

This article Why Bitcoin Might Drop Another 20%: 10x Research originally appeared on Benzinga.com .

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Twitter founder and Block chief executive Jack Dorsey has said he expects bitcoin to become the ... [+] native cryptocurrency of the internet—and issued a huge bitcoin price prediction.

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The bitcoin price has bounced back this year, erasing the losses from its 2022 crash.

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Block has recently launched a bitcoin hardware wallet and earlier this year announced it is building a "full bitcoin mining system."

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