Research on momentum strategy and contrarian strategy in AI stock prediction

Research Article
Open access

Research on momentum strategy and contrarian strategy in AI stock prediction

Yinuo Zhao 1*
  • 1 Renmin University of China    
  • *corresponding author zhaoyn202211@163.com
Published on 26 December 2023 | https://doi.org/10.54254/2755-2721/29/20231207
ACE Vol.29
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-259-6
ISBN (Online): 978-1-83558-260-2

Abstract

The emergence of ChatGPT has significantly enhanced the recognition and acceptance of artificial intelligence concept stocks within the Chinese stock market. Nevertheless, the short- and long-term fluctuations in the prices of AI companies remain uncertain. Therefore, the purpose of this research is to determine optimal strategy for evaluating the suitability of the contrarian strategy versus the momentum strategy in predicting the stock prices of AI concept stocks in the Chinese stock market. Based on a cross-comparison of the Chinese financial data sources iFinD and Wind Economic Database (EDB), this study collects the price data of AI concept stocks over the past six months, starting from the date of ChatGPT's publication. This study employ Python to model stock price movements for both the momentum and reversal strategies. The goodness of fit is evaluated by comparing the modeled stock prices with the actual stock prices. This study demonstrates that the momentum strategy exhibits greater explanatory power than the contrarian strategy, accurately predicting 84.21% of artificial intelligence concept stocks. However, other studies suggest that while AI concept stocks continue to rise, momentum strategies remain effective, whereas when market sentiment cools down, contrarian strategies become more suitable for Chinese AI concept stocks. Hence, in China, the effectiveness of these strategies may vary depending on the prevailing market conditions.

Keywords:

momentum strategy, contrarian strategy, China AI concept stocks, python simulation

Zhao,Y. (2023). Research on momentum strategy and contrarian strategy in AI stock prediction. Applied and Computational Engineering,29,125-132.
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References

[1]. Jegadeesh N., Titman S.Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency[J]. Journal of Finance, 1993(1): 65~91.

[2]. De Bondt W.F.M., Thaler R.Does the Stock Market Overreact? [J]. Journal of Finance, 1985(3): 793~805.

[3]. Fama E.F., French K.R..A Five-factor Asset Pricing Model[J]. Journal of Financial Economics, 2015(1): 1~22.

[4]. Chui A C W, Titman S, John Wei. Individualism and Momentum around the World. The Journal of Finance, 2010(1).

[5]. Barberis, Nicholas, Shleifer, Andrei and Vishny, Robert 1998, “A Model of Investor Sentiment”, Journal of Financial Economics, Vol.49, pp.307~343.

[6]. Hong, H. and Stein, J.C., 1999, “A Unified Theory of Underreaction, Momentum Trading and Overreaction in Asset Markets”, The Journal of Finance, Vol.54, pp. 2143~2184.

[7]. Tan Xiaofen, Empirical Research and Theoretical Explanation of Momentum Effect and Reversal Effect in China's A-Share Market_China Soft Science 2012 Vol.8, p. 45-57.

[8]. Daniel K., Hirshleifer D., Subrahmanyam A. Inverstor psychology and security market under and overreaction[J]. Journal of Finance, 1998(6):1839~1885.

[9]. Song Guanghui, Analysis of stock price momentum effect and reversal effect based on evolutionary game model,_Accounting Monthly_2019_Vol.9, p156-163.

Cite this article

Zhao,Y. (2023). Research on momentum strategy and contrarian strategy in AI stock prediction. Applied and Computational Engineering,29,125-132.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

ISBN：978-1-83558-259-6(Print) / 978-1-83558-260-2(Online)
Editor：Alan Wang, Marwan Omar, Roman Bauer
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.29
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Jegadeesh N., Titman S.Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency[J]. Journal of Finance, 1993(1): 65~91.

[2]. De Bondt W.F.M., Thaler R.Does the Stock Market Overreact? [J]. Journal of Finance, 1985(3): 793~805.

[3]. Fama E.F., French K.R..A Five-factor Asset Pricing Model[J]. Journal of Financial Economics, 2015(1): 1~22.

[4]. Chui A C W, Titman S, John Wei. Individualism and Momentum around the World. The Journal of Finance, 2010(1).

[5]. Barberis, Nicholas, Shleifer, Andrei and Vishny, Robert 1998, “A Model of Investor Sentiment”, Journal of Financial Economics, Vol.49, pp.307~343.

[6]. Hong, H. and Stein, J.C., 1999, “A Unified Theory of Underreaction, Momentum Trading and Overreaction in Asset Markets”, The Journal of Finance, Vol.54, pp. 2143~2184.

[7]. Tan Xiaofen, Empirical Research and Theoretical Explanation of Momentum Effect and Reversal Effect in China's A-Share Market_China Soft Science 2012 Vol.8, p. 45-57.

[8]. Daniel K., Hirshleifer D., Subrahmanyam A. Inverstor psychology and security market under and overreaction[J]. Journal of Finance, 1998(6):1839~1885.

[9]. Song Guanghui, Analysis of stock price momentum effect and reversal effect based on evolutionary game model,_Accounting Monthly_2019_Vol.9, p156-163.

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