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AlphaMLDigger: A Novel Machine Learning Solution to Explore Excess Return on Investment

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  • Jimei Shen
  • Zhehu Yuan
  • Yifan Jin

Abstract

How to quickly and automatically mine effective information and serve investment decisions has attracted more and more attention from academia and industry. And new challenges have arisen with the global pandemic. This paper proposes a two-phase AlphaMLDigger that effectively finds excessive returns in a highly fluctuated market. In phase 1, a deep sequential natural language processing (NLP) model is proposed to transfer Sina Microblog blogs to market sentiment. In phase 2, the predicted market sentiment is combined with social network indicator features and stock market history features to predict the stock movements with different Machine Learning models and optimizers. The results show that the ensemble models achieve an accuracy of 0.984 and significantly outperform the baseline model. In addition, we find that COVID-19 brings data shift to China's stock market.

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  • Jimei Shen & Zhehu Yuan & Yifan Jin, 2022. "AlphaMLDigger: A Novel Machine Learning Solution to Explore Excess Return on Investment," Papers 2206.11072, arXiv.org, revised Dec 2022.
    • Handle: RePEc:arx:papers:2206.11072
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    References listed on IDEAS

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    1. Herbert A. Simon, 1955. "A Behavioral Model of Rational Choice," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 69(1), pages 99-118.
    2. Grossman, Sanford J & Stiglitz, Joseph E, 1980. "On the Impossibility of Informationally Efficient Markets," American Economic Review, American Economic Association, vol. 70(3), pages 393-408, June.
    3. Evangelos Vasileiou, 2021. "Behavioral finance and market efficiency in the time of the COVID-19 pandemic: does fear drive the market?," International Review of Applied Economics, Taylor & Francis Journals, vol. 35(2), pages 224-241, March.
    4. Qinkai Chen, 2021. "Stock Movement Prediction with Financial News using Contextualized Embedding from BERT," Papers 2107.08721, arXiv.org.
    5. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    6. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    7. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    8. Gabriele Ranco & Darko Aleksovski & Guido Caldarelli & Miha Grčar & Igor Mozetič, 2015. "The Effects of Twitter Sentiment on Stock Price Returns," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-21, September.
    9. Rui Dias & Nuno Teixeira & Veronika Machova & Pedro Pardal & Jakub Horak & Marek Vochozka, 2020. "Random walks and market efficiency tests: evidence on US, Chinese and European capital markets within the context of the global Covid-19 pandemic," Oeconomia Copernicana, Institute of Economic Research, vol. 11(4), pages 585-608, December.
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