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Polynomial Moving Regression Band Stocks Trading System

Author

Listed:
  • Gil Cohen

    (The Management Department, Western Galilee College, Acre 2412101, Israel)

Abstract

In this research, we attempted to fit a trading system based on polynomial moving regression bands (MRB) to Nasdaq100 stocks from 2017 till the end of March 2024. Since stocks movement does not follow a linear behavior, we used multiple degree polynomial regression models to identify the stocks’ trends and two standard deviations from the regression model to generate the trading signals. This way, the MRB was transformed into a momentum indicator designed to identify strong uptrends that can be used by a fully automated trading system. Our results indicate that the behavior of Nasdaq100 stocks can be tracked using all three examined polynomial models and can be traded profitably using fully automated systems based on those models. The best performing model was the model that used a four-degree polynomial MRB achieving the highest average net profit (USD 162.73). Regarding the risks involved, the third model has the lowest loss in dollar value (USD −95.52), and the highest minimum percent of profitable trades (41.51%) and profit factor (0.55) that indicates that this strategy is relatively less risky than the other two strategies.

Suggested Citation

  • Gil Cohen, 2024. "Polynomial Moving Regression Band Stocks Trading System," Risks, MDPI, vol. 12(10), pages 1-15, October.
    • Handle: RePEc:gam:jrisks:v:12:y:2024:i:10:p:166-:d:1501479
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    References listed on IDEAS

    as
    1. Sharmin Islam & Md. Shakil Sikder & Md. Farhad Hossain & Partha Chakraborty, 2021. "Predicting the daily closing price of selected shares on the Dhaka Stock Exchange using machine learning techniques," SN Business & Economics, Springer, vol. 1(4), pages 1-16, April.
    2. Edson Kambeu, 2019. "Trading volume as a predictor of market movement: An application of Logistic regression in the R environment," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 8(2), pages 57-69, April.
    3. Zezheng Zhang & Matloob Khushi, 2020. "GA-MSSR: Genetic Algorithm Maximizing Sharpe and Sterling Ratio Method for RoboTrading," Papers 2008.09471, arXiv.org.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    polynomial; trading; stocks; systems;
    All these keywords.

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