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Open Access Peer-reviewed

Trading Volume and Cryptocurrency Returns

Frederick Adjei , Mavis Adjei
Journal of Finance and Accounting. 2022, 10(1), 23-27. DOI: 10.12691/jfa-10-1-4
Received July 06, 2022; Revised August 09, 2022; Accepted August 16, 2022

Abstract

In this study, we examine the relationship between changes in cryptocurrency trading volume and contemporaneous cryptocurrency returns. Additionally, we investigate the predictive power of changes in cryptocurrency trading volume for future cryptocurrency returns. We find a direct relationship between change in trading volume and the contemporaneous returns, consistent with Gervars, Kaniel and Mingelgrin for cryptocurrencies tradable on Coinbase exchange. Additionally, we uncover that the changes in trading volume have significant predictive power for future cryptocurrency returns for cryptocurrencies tradable on Coinbase exchange. We, however, do not find a connection between changes in trading volume and returns for cryptocurrencies not tradable on Coinbase exchange. Our findings suggest the presence of a weak-form inefficiency in the cryptocurrency market; cryptocurrency prices do not reflect available information.

Keywords: Cryptocurrency

1. Introduction

Cryptocurrencies have received a deserved skepticism from policymakers, speculative investors, and researchers alike owing to their extreme returns, the opacity about their origin and fundamental determinants of their performance, and so many other innumerable reasons. The peculiarly high volatility of cryptocurrency returns is intriguing and of significant interest to researchers 2, 3. Extant research on cryptocurrency performance finds that there are inefficiencies in the market 4 and that there are predictable performance trends 5.

A recent development in the cryptocurrency literature is the use of asset-pricing models to identify determinants of cryptocurrency excess returns. Studies such as Liu and Tsyvinski 6 and Borri and Shakhnov 7 find that determinants such as momentum, investor attention, and size explain excess returns.We add to this area of the cryptocurrency research by investigating the role of trading volume in explaining cryptocurrency returns. The literature on the volume-price relationship indicates that there is a direct link between daily volume and the absolute value of daily returns for individual stocks as well as market indices 8, 9. Building on the volume-price relationship from the stock market, we investigate the effect of daily trading volume on daily returns in the cryptocurrency market. Particularly, we examine the contemporaneous link as well as the predictive power of changes in trading volume for future returns.

2. Literature Review

There is extensive research on the volume-price relationship. Osborne 10 presents a theoretical framework for the volume-price link: uses Brownian motion to explain price movements and theorizes that trading offers a sufficient condition for price movements. Additionally, Gervars, Kaniel and Mingelgrin, 1 investigate the information content of trading activity, specifically, the predictive power of trading volume for future stock returns. Gervars et al. theorize that with the number of shares available for sale limited by short sellers or margin traders, a positive shock to the number of traders interested in a stock will lead to an increase in the stock’s demand. With supply of the stock remaining unchanged, the result will be an increase in trading volume and price of the stock.

Empirical studies such as Ying 8 and Harris and Gurel 11 find a relationship between volume change and absolute price change. Other studies also find contemporaneous and lead-lag relationship between trading volume and stock returns 12, 13. Grobys and Sapkota 14 finds that market maturation has led to improvements in efficiencies in the cryptocurrency market. This is partly due to the increased liquidity in the market; improved price discovery as trading activity has increased in this market. Also, financial analysts believe volume moves and establishes prices 15. In our study, we draw from the knowledge of volume-price relationship from the stock return literature to examine the predictive power of trading volume for cryptocurrency returns.

Conventional wisdom suggests that the pricing model of digital assets is different from that of traditional asset classes, in other words, cryptocurrencies are not exposed to similar sources of risks as traditional asset classes 16, 17. Based on the assumption of the difference in pricing kernels, some of the extant research investigates the effects of risk factors such as momentum, size, liquidity, and reversal on cryptocurrency returns. For example, Shen et al. 18 suggests that for cryptocurrencies, market, size, and reversal; a three-factor pricing model, explains returns. However, using more recent data, Boxiang, and 19 find more evidence of a momentum effect but not a reversal effect. These findings suggest that the cryptocurrency market and stock market may be exposed to similar risks.

Another line of research argues that cryptocurrency price is related to the cost of mining the coins 20, 21. Kristoufek 22 discovers that long-term Bitcoin price affects the cost of mining equipment. Mueller 23 examines Bitcoin and Ethereum miners’ entry and exit thresholds. Particularly, Mueller estimates the relationship between cryptocurrency price and hashrate (the collective computing power of miners), and finds that Bitcoin miners, who use the Application Specific Integrated Circuit equipment, only react to negative disequilibrium: when the hashrate is low relative to price. However, Ethereum miners who use the Graphics Processing Unit equipment, react symmetrically to disequilibria: when the hashrate is low (or high) relative to price. Additionally, Cong et al. 21 uncover that mining pools assist cryptocurrency miners in reducing idiosyncratic risk.

Other studies document the extreme volatility of cryptocurrency returns. Dwyer 24 and Smales 3 document the high return volatility. Cheah and Fry 25 show that cryptocurrencies are predisposed to speculative bubbles and Urquhart 4 finds that cryptocurrencies cluster at round numbers, particularly in Bitcoin. Additionally, Urquhart finds that volume and price have a significant direct link with price clustering at whole numbers.

Adding to the literature on the volume-price relationship, we investigate the effect of daily trading volume on daily returns in the cryptocurrency market. Particularly, we examine the predictive power of trading volume for cryptocurrency returns using daily return and trading volume data. Evidence of a predictive power of trading volume for future cryptocurrency returns will suggest inefficiency in the cryptocurrency market at the lowest level. Specifically, that the cryptocurrency market is not even weak-form efficient.

3. Hypotheses

Following the Osborne 10 theoretical framework for the volume-price relationship and the findings of Gervars, Kaniel and Mingelgrin, 1 we hypothesize that if the change in cryptocurrency trading volume contains information about cryptocurrency returns, there should be a statistically significant relationship between the change in volume and cryptocurrency returns. The null (H10) and alternate (H11) hypotheses for the contemporaneous relationship are stated as follows:

H10: There is no relationship between the change in cryptocurrency trading volume and the contemporaneous cryptocurrency returns.

H11: There is a relationship between the change in cryptocurrency trading volume and the contemporaneous cryptocurrency returns.

Additionally, if the change in cryptocurrency trading volume holds a predictive power for future cryptocurrency returns, there will be a statistically significant link between the change in volume and future cryptocurrency returns. The null (H20) and alternate (H21) hypotheses for the predictive power of cryptocurrency trading volume for future cryptocurrency returns are advanced as follows:

H20: There is no relationship between the change in cryptocurrency trading volume and future cryptocurrency returns.

H21: There is a relationship between the change in cryptocurrency trading volume and future cryptocurrency returns.

4. Data

Data on cryptocurrency prices, market capitalization, and trading volume is obtained from coingecko.com [www.coingecko.com]. The sample period, consisting of daily data, is from January 01, 2014, to July 31, 2021. The sample period beginning date is selected due to the lack of volume data on most of the cryptocurrencies prior to that date. We limit our sample to cryptocurrencies with a market capitalization of at least one million dollars and with at least one month of trading data on coingecko.com. The resulting sample is 1705 cryptocurrencies. Tradable cryptocurrencies meet the metrics set by Coinbase exchange for a healthy market. Presumably, tradable cryptocurrencies are more stable than non-tradable cryptocurrencies. Hence, we divide our sample into two subsamples: cryptocurrencies tradable on Coinbase exchange [175 cryptocurrencies] and cryptocurrencies that are not tradable on Coinbase exchange [1530 cryptocurrencies] by the end of the sample period.

5. Method

To examine the trading volume-return relationship of cryptocurrencies, we first investigate the contemporaneous relationship between change in trading volume and returns using an ordinary least squares regression (OLS) setup. To test our first hypothesis that there is no relationship between the change in cryptocurrency trading volume and the contemporaneous cryptocurrency returns, we run the following OLS regression;

(1)

where

(2)

Rit is the return of cryptocurrency i on day t, Vit is the trading volume (defined as the total dollar value of all transactions in cryptocurrency i across all exchange in the last 24 hours according to Coingecko.com) of cryptocurrency i on day t, Eit is the error term.

Next, consistent with our second hypothesis, we examine the predictive power of the change in cryptocurrency trading volume for future cryptocurrency returns using the forecasting model of Fama and French 26. We examine the predictive power of the change in cryptocurrency trading volume for future cryptocurrency returns by employing the forecasting model of Fama and French 26. In univariate regressions using the change in cryptocurrency trading volume as a predictor [as in Li, Ng, and Swaminathan 27]; we run the forecasting model of Fama and French 26, modifying for individual cryptocurrencies as follows;

(3)

where is the continuously compounded daily cryptocurrency return, N is the forecasting period in days, is the of slope coefficient, ΔVit = [Vit - Vit-1]/Vit-1 and is the regression residual. We estimate the univariate regressions for horizons: N = 2, 3, 4, 5, 6, and 7 days.

6. Empirical Results

Table 1 presents the summary statistics of the main variables used in the study. For the full sample, the mean and median cryptocurrency prices throughout the study period are $243.75 and $0.08 respectively. The mean and median daily returns are -0.0032 and -0.0022 respectively. The mean and median daily trading volumes are $1,469,918,677 and $8,872,340 respectively. Trading volume is heavily skewed towards the well-known cryptocurrencies such as Bitcoin, Ethereum, and Dogecoin.

Next, we divide the sample into subsamples of tradable and non-tradable cryptocurrencies and run our OLS regression (1) to test the hypothesis that there is no relationship between the change in cryptocurrency trading volume and the contemporaneous cryptocurrency returns. The results are presented in Table 2. Panel A of Table 2 presents the results for the subsample of tradable cryptocurrencies. The beta coefficient for the change in trading volume is statistically significant, supporting the alternate hypothesis that there is a relationship between the change in cryptocurrency trading volume and the contemporaneous cryptocurrency returns.

Panel B of Table 2 presents the results for the subsample of non-tradable cryptocurrencies. The results are not statistically significant, supporting the null hypothesis that there is no relationship between the change in cryptocurrency trading volume and the contemporaneous cryptocurrency returns. These results are not surprising, given that a disproportionately larger proportion of the aggregate trading volume and investor attention is on the tradable cryptocurrencies.

To test our second hypothesis that there is no relationship between the change in cryptocurrency trading volume and future cryptocurrency returns, we examine the forecasting power of daily cryptocurrency trading volume in a set of univariate regressions, consistent with Fama and French 26. The results are presented in Table 3. For the subsample of tradable cryptocurrencies, the beta coefficients for the change in trading volume are statistically significant for all time horizons, although, with declining power the longer the horizon. This finding supports the assertion that the change in trading volume has predictive power for future returns of tradable cryptocurrencies (up to seven days per our findings).

Results for the subsample of non-tradable cryptocurrencies are not significant, supporting the null hypothesis that the change in trading volume has no predictive power for future returns of non-tradable cryptocurrencies.

7. Robustness Check

To further confirm our findings about the effect of tradability, we run the following regression for the full sample;

(4)

where

Rit is the return of cryptocurrency i on day t, Ti is a tradability dummy variable: set to 1 if the cryptocurrency is tradable on the Coinbase exchange, and set to zero otherwise, Vit is the trading volume (defined as the total dollar value of all transactions in cryptocurrency i across all exchanges in the last 24 hours according to Coingecko.com) of cryptocurrency i on day t, Eit is the error term. Table 4 presents the results. Consistent with our earlier findings, the coefficient of the ΔVit * Ti interaction term is statistically significant confirming that the change in trading volume has higher predictive power for future returns in tradable cryptocurrencies than in non-tradable cryptocurrencies.

8. Conclusions

In this study, we investigate the effect of daily trading volume on daily returns in the cryptocurrency market. Mainly, we examine the contemporaneous relationship between changes in trading volume and cryptocurrency returns as well as the predictive power of changes in cryptocurrency trading volume on future cryptocurrency returns.

For tradable cryptocurrencies, we find a direct relationship between change in trading volume and the contemporaneous returns, consistent with Gervars, Kaniel and Mingelgrin 1. Additionally, for tradable cryptocurrencies, we uncover that the change in trading volume has significant predictive power for future cryptocurrency returns. However, we do not find a connection between changes in trading volume and returns for non-tradable cryptocurrencies.

As cryptocurrency trading volume has predictive power on future cryptocurrency returns, investors could develop trading strategies based on trading volume. Specifically, knowledge of historical trading volume could be used to obtain abnormal returns in the cryptocurrency market. Our findings suggest the presence of weak-form inefficiency in the cryptocurrency market. Simply, cryptocurrency prices do not reflect available information. This study adds to the growing knowledge that investors should be wary of cryptocurrencies and trade them with extreme caution.

References

[1]  Gervais, S., Kaniel, R. and Mingelgrin, D.H. (2001). The high-volume return premium. The Journal of Finance, 51, 877-919.
In article      View Article
 
[2]  Chaim, P., and M.P. Laurini (2019). Is Bitcoin a bubble? Physica A: Statistical Mechanics and its Applications 517, 222-232.
In article      View Article
 
[3]  Smales, L. A. (2019). Bitcoin as a safe haven: Is it even worth considering? Finance Research Letters 30, 385-393.
In article      View Article
 
[4]  Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters 148, 80-82.
In article      View Article
 
[5]  Phillip, Andrew, Jennifer S.K. Chan and Peiris, Shelton (2018). A new look at Cryptocurrencies. Economics Letters, Elsevier, 163(C), 6-9.
In article      View Article
 
[6]  Liu, Y. and Tsyvinski, A. (2020). Risks and Returns of Cryptocurrency. The Review of Financial Studies, 34(6), 2689-2727.
In article      View Article
 
[7]  Borri, N. and Shakhnov, K. (2020). Regulation spillovers across cryptocurrency markets. Finance Research Letters, 36:101333.
In article      View Article
 
[8]  Ying. C. C. (1966). Stock Market Prices and Volumes of Sales. Ecommetrica, 34, 676-686.
In article      View Article
 
[9]  Rutledge. D. J. S. (1984). Trading Volume and Price Variability: New Evidence on the Price Effects of Speculation. In Peck A. E. (ed.), Selected Writings on Futures Markets: Research Directions in Commodity Markets (237-251). Chicago: Chicago Board of Trade.
In article      
 
[10]  Osbome. M. F. M. (1959). Brownian Motion in the Stock Market. Operations Research, 1, 145-173.
In article      View Article
 
[11]  Harris Lawrence and Eitan Gurel (1986). Price and Volume Effects Associated with Changes in the S&P 500 List: New Evidence for the Existence of Price Pressures, The Journal of Finance, 41 (4), 815-829.
In article      View Article
 
[12]  Lee, S.B. and Rui, O. M. (2002). The dynamic relationship between stock return and trading volume: Domestic and cross-country evidence. Journal of Banking and Finance, 26, 51-78.
In article      View Article
 
[13]  Pisedtasalasai, A. and Gunasekarage, A. (2007). Causal and dynamic relationships among stock returns, return volatility and trading volume: Evidence from emerging markets in South-East Asia. Asia-Pacific Financial Markets, 14, 277-297.
In article      View Article
 
[14]  Grobys Klaus and Niranjan Sapkota (2019). Cryptocurrencies and momentum, Economics Letters, 180 (C), 6-10.
In article      View Article
 
[15]  Karpoff, J. (1987). The relation between price changes and trading volume: A survey. Journal of Financial and Quantitative Analysis, 22, 109-125.
In article      View Article
 
[16]  Yermack, David (2015). Is Bitcoin a real currency? An economic appraisal. In Handbook of digital currency. Elsevier, 31-43.
In article      View Article
 
[17]  Bianchi, Danielle (2020), Cryptocurrencies as an Asset Class? An Empirical Assessment. The Journal of Alternative Investments, 23 (2), 1-105.
In article      View Article
 
[18]  Shen, D., A. Urquhart, and P. Wang (2020). A three-factor pricing model for cryptocurrencies. Finance Research Letters, 34, 1-12.
In article      View Article
 
[19]  Boxiang Jia, John W.Goodell, and Dehua Shen. (2022) Momentum or reversal: Which is the appropriate third factor for cryptocurrencies? Finance Research Letters, 45.
In article      View Article
 
[20]  Sockin, Michael and Wei Xiong (2020). A model of cryptocurrencies. Tech. rep., Working paper.
In article      View Article
 
[21]  Cong, Lin William, Zhiguo He, and Jiasun Li (2021). Decentralized Mining in Centralized Pools. The Review of Financial Studies, 34(3), 1191-1235.
In article      View Article
 
[22]  Kristoufek, Ladislav (2020). Bitcoin and its mining on the equilibrium path. Energy Economics, 85(1), 104588.
In article      View Article
 
[23]  Mueller, Peter (2020). “Cryptocurrency Mining: Asymmetric Response to Price Movement” SSRN Working Paper, (November 18, 2020). Accessed online on September 9, 2021, at https://ssrn.com/abstract=3733026.
In article      View Article
 
[24]  Dwyer, G.P. (2015). The economics of Bitcoin and similar private digital currencies. Journal of Financial Stability 17, 81-91.
In article      View Article
 
[25]  Cheah, E.T. and Fry, J. (2015). Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Economics Letters 130, 32-36.
In article      View Article
 
[26]  Fama E. F., French K. R. (1989). Business conditions and expected returns on stocks and bonds, Journal of Financial Economics 25, 23-49.
In article      View Article
 
[27]  Li, Y., Ng, D., and Swaminathan, B. (2013). Predicting market returns using aggregate implied cost of capital, Journal of Financial Economics, 110 (2), 419-436.
In article      View Article
 

Published with license by Science and Education Publishing, Copyright © 2022 Frederick Adjei and Mavis Adjei

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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Normal Style
Frederick Adjei, Mavis Adjei. Trading Volume and Cryptocurrency Returns. Journal of Finance and Accounting. Vol. 10, No. 1, 2022, pp 23-27. http://pubs.sciepub.com/jfa/10/1/4
MLA Style
Adjei, Frederick, and Mavis Adjei. "Trading Volume and Cryptocurrency Returns." Journal of Finance and Accounting 10.1 (2022): 23-27.
APA Style
Adjei, F. , & Adjei, M. (2022). Trading Volume and Cryptocurrency Returns. Journal of Finance and Accounting, 10(1), 23-27.
Chicago Style
Adjei, Frederick, and Mavis Adjei. "Trading Volume and Cryptocurrency Returns." Journal of Finance and Accounting 10, no. 1 (2022): 23-27.
Share
[1]  Gervais, S., Kaniel, R. and Mingelgrin, D.H. (2001). The high-volume return premium. The Journal of Finance, 51, 877-919.
In article      View Article
 
[2]  Chaim, P., and M.P. Laurini (2019). Is Bitcoin a bubble? Physica A: Statistical Mechanics and its Applications 517, 222-232.
In article      View Article
 
[3]  Smales, L. A. (2019). Bitcoin as a safe haven: Is it even worth considering? Finance Research Letters 30, 385-393.
In article      View Article
 
[4]  Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters 148, 80-82.
In article      View Article
 
[5]  Phillip, Andrew, Jennifer S.K. Chan and Peiris, Shelton (2018). A new look at Cryptocurrencies. Economics Letters, Elsevier, 163(C), 6-9.
In article      View Article
 
[6]  Liu, Y. and Tsyvinski, A. (2020). Risks and Returns of Cryptocurrency. The Review of Financial Studies, 34(6), 2689-2727.
In article      View Article
 
[7]  Borri, N. and Shakhnov, K. (2020). Regulation spillovers across cryptocurrency markets. Finance Research Letters, 36:101333.
In article      View Article
 
[8]  Ying. C. C. (1966). Stock Market Prices and Volumes of Sales. Ecommetrica, 34, 676-686.
In article      View Article
 
[9]  Rutledge. D. J. S. (1984). Trading Volume and Price Variability: New Evidence on the Price Effects of Speculation. In Peck A. E. (ed.), Selected Writings on Futures Markets: Research Directions in Commodity Markets (237-251). Chicago: Chicago Board of Trade.
In article      
 
[10]  Osbome. M. F. M. (1959). Brownian Motion in the Stock Market. Operations Research, 1, 145-173.
In article      View Article
 
[11]  Harris Lawrence and Eitan Gurel (1986). Price and Volume Effects Associated with Changes in the S&P 500 List: New Evidence for the Existence of Price Pressures, The Journal of Finance, 41 (4), 815-829.
In article      View Article
 
[12]  Lee, S.B. and Rui, O. M. (2002). The dynamic relationship between stock return and trading volume: Domestic and cross-country evidence. Journal of Banking and Finance, 26, 51-78.
In article      View Article
 
[13]  Pisedtasalasai, A. and Gunasekarage, A. (2007). Causal and dynamic relationships among stock returns, return volatility and trading volume: Evidence from emerging markets in South-East Asia. Asia-Pacific Financial Markets, 14, 277-297.
In article      View Article
 
[14]  Grobys Klaus and Niranjan Sapkota (2019). Cryptocurrencies and momentum, Economics Letters, 180 (C), 6-10.
In article      View Article
 
[15]  Karpoff, J. (1987). The relation between price changes and trading volume: A survey. Journal of Financial and Quantitative Analysis, 22, 109-125.
In article      View Article
 
[16]  Yermack, David (2015). Is Bitcoin a real currency? An economic appraisal. In Handbook of digital currency. Elsevier, 31-43.
In article      View Article
 
[17]  Bianchi, Danielle (2020), Cryptocurrencies as an Asset Class? An Empirical Assessment. The Journal of Alternative Investments, 23 (2), 1-105.
In article      View Article
 
[18]  Shen, D., A. Urquhart, and P. Wang (2020). A three-factor pricing model for cryptocurrencies. Finance Research Letters, 34, 1-12.
In article      View Article
 
[19]  Boxiang Jia, John W.Goodell, and Dehua Shen. (2022) Momentum or reversal: Which is the appropriate third factor for cryptocurrencies? Finance Research Letters, 45.
In article      View Article
 
[20]  Sockin, Michael and Wei Xiong (2020). A model of cryptocurrencies. Tech. rep., Working paper.
In article      View Article
 
[21]  Cong, Lin William, Zhiguo He, and Jiasun Li (2021). Decentralized Mining in Centralized Pools. The Review of Financial Studies, 34(3), 1191-1235.
In article      View Article
 
[22]  Kristoufek, Ladislav (2020). Bitcoin and its mining on the equilibrium path. Energy Economics, 85(1), 104588.
In article      View Article
 
[23]  Mueller, Peter (2020). “Cryptocurrency Mining: Asymmetric Response to Price Movement” SSRN Working Paper, (November 18, 2020). Accessed online on September 9, 2021, at https://ssrn.com/abstract=3733026.
In article      View Article
 
[24]  Dwyer, G.P. (2015). The economics of Bitcoin and similar private digital currencies. Journal of Financial Stability 17, 81-91.
In article      View Article
 
[25]  Cheah, E.T. and Fry, J. (2015). Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Economics Letters 130, 32-36.
In article      View Article
 
[26]  Fama E. F., French K. R. (1989). Business conditions and expected returns on stocks and bonds, Journal of Financial Economics 25, 23-49.
In article      View Article
 
[27]  Li, Y., Ng, D., and Swaminathan, B. (2013). Predicting market returns using aggregate implied cost of capital, Journal of Financial Economics, 110 (2), 419-436.
In article      View Article