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Huang, Y., Capretz, L. F., & Ho, D. (2021). Machine Learning for Stock Prediction Based on Fundamental Analysis. In 2021 IEEE Symposium Series on Computational Intelligence (pp. 1-10). IEEE.
https://doi.org/10.1109/ssci50451.2021.9660134

has been cited by the following article:

• TITLE: Performance of Simple Deterministic Stock Trading Strategies Using an Agent-Based Modelling Approach

  AUTHORS: Kristian Nikolai Jæger Hansen

  KEYWORDS: Trading Strategy Evaluation, Quantitative Finance, Oslo Stock Exchange (OSE)

  JOURNAL NAME: Theoretical Economics Letters, Vol.14 No.5, October 29, 2024

  ABSTRACT: This paper evaluates four different deterministic trading strategies using an Agent-Based Modelling approach. The evaluated strategies were buy-and-hold, Moving Average, Momentum, and Mean Reversion. Data from eight stocks at the Oslo Stock Exchange were deployed for all strategies. The study analyses the performance of each strategy from 2018 to 2023 to make a profit in the stock market. In addition, a sensitivity analysis was conducted to determine the effect on returns based on changing trading costs and the agents’ access to historical data. The findings indicate considerable variability in each strategy’s returns. The sensitivity analysis shows that each trading strategy is sensitive to higher trading costs. The effect of access to historical data did not improve the Sharpe Ratio across the different strategies.