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Comparing the Effectiveness of Multiple Quantitative Trading Strategies

Published: 16 January 2019 Publication History

Abstract

Investment in stock has drawn worldwide attention from individuals and investment companies. There are many common practices of trading strategies. An easy but effective one is the buy and hold strategy strongly advocated by Warren Buffett, where investigators would buy one or a group of stocks and let time make money. A common strategy for individual investors was by reading the stock chart, based on their personal judgement, which often was not quite different from random guesses due to the lack of information and experience. Another recent strategy is to use machine-learning techniques to predict the stock market.
I am interested in investigating which of the above strategies were more effective in the current stock market. To gain an up-to-date view, I applied these strategies on 4 different popular stocks and observed their performance for 100 randomly chosen time frame in 2017 and 2018. By evaluating the return and risk of each strategy, my results provide guidance for quantitative trading for general investors. Specifically, if the stock market is stably increasing, the optimal strategy is to use the buy-and-hold strategy. If the stock market is comparatively stable, using a good machine learning strategy is expected to help. Overall, individual investors should devote more efforts in selecting a promising stock or portfolio than focusing too closely on daily price changes.

References

[1]
Fama, Eugene F., and Kenneth R. French. "Size, value, and momentum in international stock returns." Journal of financial economics 105, no. 3 (2012): 457--472.
[2]
Koijen, Ralph SJ, Hanno Lustig, and Stijn Van Nieuwerburgh. "The cross-section and time series of stock and bond returns." Journal of Monetary Economics 88 (2017): 50--69.
[3]
Fung, G. Pui Cheong, J. Xu Yu, and Wai Lam. "Stock prediction: Integrating text mining approach using real-time news." In Computational Intelligence for Financial Engineering, 2003. Proceedings. 2003 IEEE International Conference on, pp. 395--402. IEEE, 2003.
[4]
Si, Jianfeng, Arjun Mukherjee, Bing Liu, Qing Li, Huayi Li, and Xiaotie Deng. "Exploiting topic based twitter sentiment for stock prediction." In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), vol. 2, pp. 24--29. 2013.
[5]
Almenberg, Johan, and Anna Dreber. "Gender, stock market participation and financial literacy." Economics Letters 137 (2015): 140--142.
[6]
Brock, William, Josef Lakonishok, and Blake LeBaron. "Simple technical trading rules and the stochastic properties of stock returns." The Journal of finance 47, no. 5 (1992): 1731--1764.
[7]
LeBaron, Blake. "Technical trading rule profitability and foreign exchange intervention." Journal of international economics 49, no. 1 (1999): 125--143.
[8]
Chen, Mu-Yen, and Bo-Tsuen Chen. "A hybrid fuzzy time series model based on granular computing for stock price forecasting." Information Sciences 294 (2015): 227--241.
[9]
Li, Chuan, Wenfeng Qian, Calum J. Maclean, and Jianzhi Zhang. "The fitness landscape of a tRNA gene." Science 352, no. 6287 (2016): 837--840.
[10]
Malta, Tathiane M., Artem Sokolov, Andrew J. Gentles, Tomasz Burzykowski, Laila Poisson, John N. Weinstein, Bozena Kaminska et al. "Machine learning identifies stemness features associated with oncogenic dedifferentiation." Cell 173, no. 2 (2018): 338--354.
[11]
Cao, Song, Kan Chen, and Ram Nevatia. "Abstraction hierarchy and self annotation update for fine grained activity recognition." In Applications of Computer Vision (WACV), 2016 IEEE Winter Conference on, pp. 1--9. IEEE, 2016.
[12]
Wang, Xiaoyu, and Amir Mortazawi. "Medium wave energy scavenging for wireless structural health monitoring sensors." IEEE Trans. Microw. Theory Tech 62, no. 4/2 (2014): 1067--1073.
[13]
Madge, Saahil, and S. Bhatt. "Predicting Stock Price Direction using Support Vector Machines." Independent Work Report Spring (2015).
[14]
Wei, Wei, Li Jiang, and Qiujun Lan. "Stock Investment Strategy Driven by EIS Events Based on Decision Tree Model." In 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), pp. 663--668. IEEE, 2018.
[15]
Rather, Akhter Mohiuddin, Arun Agarwal, and V. N. Sastry. "Recurrent neural network and a hybrid model for prediction of stock returns." Expert Systems with Applications 42, no. 6 (2015): 3234--3241.
[16]
Hu, Yong, Kang Liu, Xiangzhou Zhang, Lijun Su, E. W. T. Ngai, and Mei Liu. "Application of evolutionary computation for rule discovery in stock algorithmic trading: A literature review." Applied Soft Computing 36 (2015): 534--551.

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  • (2022)Research on prediction model of optimal trading strategy-Taking bitcoin and gold as an exampleBCP Business & Management10.54691/bcpbm.v26i.193626(272-278)Online publication date: 19-Sep-2022

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    cover image ACM Other conferences
    ICCMS '19: Proceedings of the 11th International Conference on Computer Modeling and Simulation
    January 2019
    253 pages
    ISBN:9781450366199
    DOI:10.1145/3307363
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • University of Wollongong, Australia
    • College of Technology Management, National Tsing Hua University, Taiwan
    • Swinburne University of Technology
    • University of Technology Sydney

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 16 January 2019

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    Author Tags

    1. Stock market
    2. buy and hold
    3. machine learning
    4. trading strategy

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    ICCMS 2019
    ICCMS 2019: The 11th International Conference on Computer Modeling and Simulation
    January 16 - 19, 2019
    QLD, North Rockhampton, Australia

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    • (2022)Research on prediction model of optimal trading strategy-Taking bitcoin and gold as an exampleBCP Business & Management10.54691/bcpbm.v26i.193626(272-278)Online publication date: 19-Sep-2022

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