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Exploring Statistical Arbitrage Opportunities Using Machine Learning Strategy

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Abstract

Arbitrage opportunity exploration is important to ensure the profitability of statistical arbitrage. Prior studies that concentrate on cointegration model and other predictive models suffer from various problems in both prediction and transaction. To prevent these problems, we propose a novel strategy based on machine learning to explore arbitrage opportunities and further predict whether they will make a profit or not. The experiment is conducted in the context of Chinese financial markets with high-frequency data of CSI 300 exchange traded fund (ETF) and CSI 300 index futures (IF) from 2012 to 2020. We find that machine learning strategy can explore more arbitrage opportunities with lower risks, which outperforms cointegration strategy in different aspects. Besides, we compare different algorithms and find that LSTM achieve better performance in predicting the positive arbitrage samples and obtaining higher ROI and Sharpe ratio. The profitability of machine learning strategy validate the mean reversion and price discovery function of asset price between spot market and futures market, which further substantiate the market efficiency. Our empirical results provide practical significance to the development of quantitative finance.

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References

  • Abreu, D., & Brunnermeier, M. K. (2002). Synchronization risk and delayed arbitrage. Journal of Financial Economics, 66(2–3), 341–360

    Article  Google Scholar 

  • Ahn, D. H., Boudoukh, J., Richardson, M., & Whitelaw, R. F. (2002). Partial adjustment or stale prices? Implications from stock index and futures return autocorrelations. The Review of Financial Studies, 15(2), 655–689

    Article  Google Scholar 

  • Attari, M., Mello, A. S., & Ruckes, M. E. (2005). Arbitraging arbitrageurs. The Journal of Finance, 60(5), 2471–2511

    Article  Google Scholar 

  • Baker, M., & Savaşoglu, S. (2002). Limited arbitrage in mergers and acquisitions. Journal of Financial Economics, 64(1), 91–115

    Article  Google Scholar 

  • Balvers, R., Wu, Y., & Gilliland, E. (2000). Mean reversion across national stock markets and parametric contrarian investment strategies. The Journal of Finance, 55(2), 745–772

    Article  Google Scholar 

  • Barboza, F., Kimura, H., & Altman, E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, 405–417

    Article  Google Scholar 

  • Basak, S., & Croitoru, B. (2006). On the role of arbitrageurs in rational markets. Journal of Financial Economics, 81(1), 143–173

    Article  Google Scholar 

  • Basak, S., Kar, S., Saha, S., Khaidem, L., & Dey, S. R. (2019). Predicting the direction of stock market prices using tree-based classifiers. The North American Journal of Economics and Finance, 47, 552–567

    Article  Google Scholar 

  • Brogaard, J., Hendershott, T., & Riordan, R. (2014). High-frequency trading and price discovery. The Review of Financial Studies, 27(8), 2267–2306

    Article  Google Scholar 

  • Broussard, J. P., & Vaihekoski, M. (2012). Profitability of pairs trading strategy in an illiquid market with multiple share classes. Journal of International Financial Markets, Institutions and Money, 22(5), 1188–1201

    Article  Google Scholar 

  • Chaboud, A. P., Chiquoine, B., Hjalmarsson, E., & Vega, C. (2014). Rise of the machines: Algorithmic trading in the foreign exchange market. The Journal of Finance, 69(5), 2045–2084

    Article  Google Scholar 

  • Chakravarty, S., Gulen, H., & Mayhew, S. (2004). Informed trading in stock and option markets. The Journal of Finance, 59(3), 1235–1257

    Article  Google Scholar 

  • Chatzis, S. P., Siakoulis, V., Petropoulos, A., Stavroulakis, E., & Vlachogiannakis, N. (2018). Forecasting stock market crisis events using deep and statistical machine learning techniques. Expert Systems with Applications, 112, 353–371.

    Article  Google Scholar 

  • Chaudhuri, K., & Wu, Y. (2003). Random walk versus breaking trend in stock prices: Evidence from emerging markets. Journal of Banking & Finance, 27(4), 575–592

    Article  Google Scholar 

  • Chen, Y. L., & Gau, Y. F. (2010). News announcements and price discovery in foreign exchange spot and futures markets. Journal of Banking & Finance, 34(7), 1628–1636

    Article  Google Scholar 

  • Chiu, M. C., & Wong, H. Y. (2015). Dynamic cointegrated pairs trading: Mean–variance time-consistent strategies. Journal of Computational and Applied Mathematics, 290, 516–534

    Article  Google Scholar 

  • De Moura, C. E., Pizzinga, A., & Zubelli, J. (2016). A pairs trading strategy based on linear state space models and the Kalman filter. Quantitative Finance, 16(10), 1559–1573

    Article  Google Scholar 

  • Do, B., & Faff, R. (2010). Does simple pairs trading still work? Financial Analysts Journal, 66(4), 83–95

    Article  Google Scholar 

  • Fama, E. F. (1970). Efficient capital markets: Of theory and empirical work. The Journal of Finance, 25(2), 383–417.

    Article  Google Scholar 

  • Fama, E. F., & French, K. R. (1988). Permanent and temporary components of stock prices. Journal of Political Economy, 96(2), 246–273.

    Article  Google Scholar 

  • Gatev, E., Goetzmann, W. N., & Rouwenhorst, K. G. (2006). Pairs trading: Performance of a relative-value arbitrage rule. The Review of Financial Studies, 19(3), 797–827

    Article  Google Scholar 

  • Gârleanu, N., & Pedersen, L. H. (2013). Dynamic trading with predictable returns and transaction costs. The Journal of Finance, 68(6), 2309–2340

    Article  Google Scholar 

  • Hogan, S., Jarrow, R., Teo, M., & Warachka, M. (2004). Testing market efficiency using statistical arbitrage with applications to momentum and value strategies. Journal of Financial Economics, 73(3), 525–565.

    Article  Google Scholar 

  • Huck, N. (2015). Pairs trading: does volatility timing matter? Applied Economics, 47(57), 6239–6256

    Article  Google Scholar 

  • Huck, N. (2019). Large data sets and machine learning: Applications to statistical arbitrage. European Journal of Operational Research, 278(1), 330–342

    Article  Google Scholar 

  • Jordà, Ã., & Taylor, A. M. (2012). The carry trade and fundamentals: Nothing to fear but FEER itself. Journal of International Economics, 88(1), 74–90

    Article  Google Scholar 

  • Kao, C. (1999). Spurious regression and residual-based tests for cointegration in panel data. Journal of Econometrics, 90(1), 1–44.

    Article  Google Scholar 

  • Krauss, C., Do, X. A., & Huck, N. (2017). Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500. European Journal of Operational Research, 259(2), 689–702

    Article  Google Scholar 

  • Kozhan, R., & Tham, W. W. (2012). Execution risk in high-frequency arbitrage. Management Science, 58(11), 2131–2149

    Article  Google Scholar 

  • McMillan, D. G., & Speight, A. E. (2006). Nonlinear dynamics and competing behavioral interpretations: Evidence from intra-day FTSE‐100 index and futures data. Journal of Futures Markets: Futures, Options, and Other Derivative Products, 26(4), 343–368

    Article  Google Scholar 

  • Neely, C. J., & Weller, P. A. (2013). Lessons from the evolution of foreign exchange trading strategies. Journal of Banking & Finance, 37(10), 3783–3798

    Article  Google Scholar 

  • Papantonis, I. (2016). Cointegration-based trading: evidence on index tracking & market-neutral strategies. Managerial Finance, 42(5), 449–471

    Article  Google Scholar 

  • Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Systems with Applications, 42(1), 259–268.

    Article  Google Scholar 

  • Qian, B., & Rasheed, K. (2007). Stock market prediction with multiple classifiers. Applied Intelligence, 26(1), 25–33

    Article  Google Scholar 

  • Ross, S. A. (1976). The Arbitrage Theory of Capital Asset Pricing. Journal of Economic Theory, 13, 341–360

    Article  Google Scholar 

  • Schnaubelt, M., Fischer, T. G., & Krauss, C. (2020). Separating the signal from the noise–financial machine learning for Twitter. Journal of Economic Dynamics and Control, 114, 103895

    Article  Google Scholar 

  • Schultz, P., & Shive, S. (2010). Mispricing of dual-class shares: Profit opportunities, arbitrage, and trading. Journal of Financial Economics, 98(3), 524–549

    Article  Google Scholar 

  • Tsay, R. S. (1998). Testing and modeling multivariate threshold models. Journal of the American Statistical Association, 93(443), 1188–1202.

    Article  Google Scholar 

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Funding

This work was supported by National Natural Science Foundation of China (Nos. 71532013, 71431008 and 71572050).

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Authors and Affiliations

  1. School of Management, Harbin Institute of Technology, Harbin, China

    Baoqiang Zhan

  2. School of Management, Guangdong University of Technology, Guangzhou, China

    Shu Zhang & Helen S. Du

  3. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China

    Xiaoguang Yang

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  1. Baoqiang Zhan
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  2. Shu Zhang
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  3. Helen S. Du
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  4. Xiaoguang Yang
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Correspondence to Baoqiang Zhan.

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Zhan, B., Zhang, S., Du, H.S. et al. Exploring Statistical Arbitrage Opportunities Using Machine Learning Strategy. Comput Econ 60, 861–882 (2022). https://doi.org/10.1007/s10614-021-10169-8

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