The article considers one of the most famous examples of socio-economic systems characterized by significant uncertainty—the S&P-500 stock market, where shares of 500 largest US companies are traded. The flexible algorithm for daily trading has been developed. It is based on known fixed data about cost of shares in previous days as well as on some previously calculated values. Each day, one of the two algorithms for tomorrow trading is selected, depending on assumptions about the cost changes of some stocks at the next day. It can be similar to the change on the previous day or the opposite of it. The validity of the assumptions made is confirmed by the experimental results of trading using the proposed algorithm for 20 years from 1995 to 2014.
References
[1]
Aldridge, I. (2020). A Practical Guide to Algorithmic Strategies and Trading Systems. Wiley.
[2]
Bruni, R. (2017). Stock Market Index Data and Indicators for Day Trading as a Binary Classification Problem. DatainBrief,10, 569-575. https://doi.org/10.1016/j.dib.2016.12.044
[3]
Chan, E. P. (2021). QuantitativeTrading:HowtoBuildYourOwnAlgorithmicTradingBusiness(WileyTrading) (2nd ed.). Wiley.
[4]
Chen, J., & Tsang, P. K. (2021). DetectingRegimeChangeinComputationalFinanceDataScience,MachineLearningandAlgorithmicTrading. Chapman & Hall.
[5]
Clenow, A. F. (2023). FollowingtheTrend: Diversified Managed Futures Trading (2nd ed.). Wiley. https://doi.org/10.1002/9781394320516
[6]
Cover, T. M. (1991). Universal Portfolios. Mathematical Finance, 1, 1-29.
[7]
Dashore, P. et al. (2010). Fuzzy Rule Based System to Characterize the Decision-Making Process in Share Market. InternationalJournalonComputerScienceandEngineering,2, 1973-1979.
[8]
Donadio, S., & Ghosh, S. (2019). LearnAlgorithmicTrading:Buildand Deploy Algorithmic Trading Systems and Strategies Using Pythonand Advanced Data Analysis. Packt Publishing.
[9]
Fazli, S., & Jafari, H. (2012). Developing a Hybrid Multi-Criteria Model for Investment in Stock Exchange. Management Science Letters, 2, 457-468. https://doi.org/10.5267/j.msl.2012.01.011
[10]
Fung, S. P. Y. (2021). Online Two-Way Trading: Randomization and Advice. TheoreticalComputerScience,856, 41-50. https://doi.org/10.1016/j.tcs.2020.12.016
[11]
Georgieva, P. V., Popchev, I. P., & Stoyanov, S. N. (2015). A Multi-Step Procedure for Asset Allocation in Case of Limited Resources. CyberneticsandInformationTechnologies,15, 41-51. https://doi.org/10.1515/cait-2015-0040
[12]
Guo, Y. (2020). Stock Trading Based on Principal Component Analysis and Clustering Analysis. IOPConferenceSeries:MaterialsScienceandEngineering,740, Article ID: 012129. https://doi.org/10.1088/1757-899x/740/1/012129
[13]
Jallo, D., Budai, D., Boginski, V., Goldengorin, B., & Pardalos, P. M. (2013). Network-Based Representation of Stock Market Dynamics: An Application to American and Swedish Stock Markets. In B. Goldengorin, et al. (Eds.), Models, Algorithms, and Technologies for Network Analysis (pp. 93-106). Springer. https://doi.org/10.1007/978-1-4614-5574-5_5
[14]
Jing, D., Imeni, M., Edalatpanah, S. A., Alburaikan, A., & Khalifa, H. A. E. (2023). Optimal Selection of Stock Portfolios Using Multi-Criteria Decision-Making Methods. Mathematics,11, 415-435. https://doi.org/10.3390/math11020415
[15]
Kissell, R. (2014). The Science of Algorithmic Trading and Portfolio Management. Elsevier Inc.
[16]
Mayanja, F., Mataramvura, S., & Charles, W. M. (2013). A Mathematical Approach to a Stocks Portfolio Selection: The Case of Uganda Securities Exchange (Use). JournalofMathematicalFinance,3, 487-501. https://doi.org/10.4236/jmf.2013.34051
[17]
Mohammed Almasani, S. A., Finaev, V. I., Abdo Qaid, W. A., & Tychinsky, A. V. (2017). The Decision-Making Model for the Stock Market under Uncertainty. InternationalJournalofElectricalandComputerEngineering,7, 2782-2790. https://doi.org/10.11591/ijece.v7i5.pp2782-2790
[18]
Pakpahan, E. E. et al. (2019). Investment Decision Making Methods on Stock Market. DLSUBusiness&EconomicsReview,28, 104-108.
[19]
Prado, L. (2018). AdvancesinFinancialMachineLearning. Wiley.
[20]
Rubchinsky, A. (2018). Graph Dichotomy Algorithm and Its Applications to Analysis of Stocks Market. In V. Kalyagin, P. Pardalos, O. Prokopyev, & I. Utkina (Eds.), Computational Aspects and Applications in Large-Scale Networks (pp. 75-111). Springer International Publishing. https://doi.org/10.1007/978-3-319-96247-4_6
[21]
Rubchinsky, A., & Baikova, K. (2023). Algorithm of Trading on the Stock Market, Providing Satisfactory Results. In B. Goldengorin, & S. Kuznetsov (Eds.), Data Analysis and Optimization (pp. 331-347). Springer. https://doi.org/10.1007/978-3-031-31654-8_20
[22]
Ruke, A., Gaikwad, S., Yadav, G., Buchade, A., Nimbarkar, S., & Sonawane, A. (2024). Predictive Analysis of Stock Market Trends: A Machine Learning Approach. In 20244thInternationalConferenceonDataEngineeringandCommunicationSystems(ICDECS) (pp. 1-6). IEEE. https://doi.org/10.1109/icdecs59733.2023.10503557
[23]
Seth, S. (2023). BasicsofAlgorithmicTrading:ConceptsandExamples. UtradeAlgos.
[24]
Ziemba, W. T., Lleo, S., & Zhitlukhin, M. (2018). StockMarketCrashes.PredictableandUnpredictableandWhattoDoaboutThem. World Scientific.
[25]
Zou, J., Lou, J., Wang, B., & Liu, S. (2023). A Novel Deep Reinforcement Learning Based Automated Stock Trading System Using Cascaded LSTM Networks. ExpertSystemswithApplications,242, Article ID: 122801. https://doi.org/10.1016/j.eswa.2023.122801