Directional stock price forecasting for bogle head investing using various machine learning algorithms

Agnij Moitra
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Agnij Moitra: AISS - Amity International School, Saket

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Abstract: Boglehead investing, founded on the principles of John C. Bogle is one of the classic time tested long term, low cost, and passive investment strategy. This paper uses various machine learning methods, and fundamental stock data in order to predict whether or not a stock would incur negative returns next year, and suggests a loss averted bogle-head strategy to invest in all stocks which are expected to not give negative returns over the next year. Results reveal that XGBoost, out of the 44 models trained, has the highest classification metrics for this task. Furthermore, this paper shall use various machine learning methods for exploratory data analysis, and SHAP values reveal that Net Income Margin, ROA, Gross Profit Margin and EBIT are some of the most important factors for this. Also, based on the SHAP values it is interesting to note that the current year has negligible contribution to the final prediction. Investors can use this as a heuristic guide for loss averted long term (1-year) stock portfolios.

Keywords: Stock picking; Stock market; Directional price forecasting; Machine learning (search for similar items in EconPapers)
Date: 2024-07-26
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Persistent link: https://EconPapers.repec.org/RePEc:hal:wpaper:hal-04663125

DOI: 10.13140/RG.2.2.23644.81288

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