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Zou, J., Lou, J., Wang, B., & Liu, S. (2023). A Novel Deep Reinforcement Learning Based Automated Stock Trading System Using Cascaded LSTM Networks. Expert Systems with Applications, 242, Article ID: 122801.
https://doi.org/10.1016/j.eswa.2023.122801

has been cited by the following article:

• TITLE: Flexible Stock Market Algorithm

  AUTHORS: Alexander Rubchinsky, Daria Chubarova

  KEYWORDS: Flexible Algorithm, Stock Market, Graph Decomposition, Clustering

  JOURNAL NAME: Technology and Investment, Vol.16 No.4, November 28, 2025

  ABSTRACT: 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.