Search Results for « Quantitative investing »

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.

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. JEL: C38, C53, G17, G11, C45

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.

The rapid expansion of multimodal financial data and the inherent non-stationarity of markets have exposed critical limitations of traditional linear quantitative frameworks. In response, the integration of Deep Reinforcement Learning and Large Language Models has catalyzed a paradigm shift from manual feature engineering to autonomous financial intelligence. To systematically synthesize these cross-disciplinary advancements, this paper reconceptualizes modern quantitative investment not as a collection of isolated predictive tasks, but as an Four-Step Multi-Factor Investing Framework organized along four computationally interconnected stages: Factor Mining, Factor Combination, Risk Control, and Portfolio Management. Through this unified framework, we trace the evolutionary trajectory of each stage, showing how technical innovations propagate through the pipeline: factor mining has progressed from stochastic symbolic search to LLM-driven semantic reasoning; factor combination has shifted from simple linear weighting to synergistic aggregation that resolves multicollinearity; risk control has evolved from static exposure limits to the active prediction of forward-looking tail-risk indicators; and portfolio management has advanced from static convex solvers to DRL orchestrated dynamic allocation under real-world market frictions. We further provide a structured taxonomy of multimodal datasets and benchmark simulation environments, and present a systematic comparative summary of state-of-the-art performance across diverse market regimes. This survey establishes a structural blueprint for evaluating cross-modal AI models in finance, identifies critical bottlenecks including semantic hallucinations and Out-of-Distribution fragility, and outlines open research directions toward fully autonomous multi-agent trading systems.

This research studied quantitative investing strategies of famous investors in the Stock Exchange of Thailand from 2002 to 2016. This study found that the Graham’s net nets, Dreman’s contrarian, Fisher’s super stock, O’Neil’s CANSLIM, Slater’s zulu principle, Neff’s Cheapo, O’Shaughnessy’s tiny titans, Greenblatt’s magic formula, Carlisle’s acquirer’s multiple and Piotroski’s F-score strategies beat the market (SET TRI). It also found that the Benjamin Graham’s net nets strategy which used the market capitalization of less than two thirds of net current assets value (NCAV) criterion produced the highest return among the strategies used. However, the Tobias Carlisle’s Acquirer’s multiple strategy which used EBIT to enterprise value (EBIT/EV) to sort stocks for 30 stocks yielded the highest risk-adjusted return.