Avoiding the Pitfalls on Stock Market: Challenges and Solutions in Developing Quantitative Strategies

Abstract: Quantitative stock trading based on Machine Learning (ML) and Deep Learning (DL) has gained great attention in recent years thanks to the ever-increasing availability of financial data and the ability of this technology to analyze the complex dynamics of the stock market. Despite the plethora of approaches present in literature, a large gap exists between the solutions produced by the scientific community and the practices adopted in real-world systems. Most of these works in fact lack a practical vision of the problem and ignore the main issues afflicting fintech practitioners. To fill such a gap, we provide a systematic review of the main dangers affecting the development of an ML/DL pipeline in the financial domain. They include managing the stochastic and non-stationary characteristics of stock data, various types of bias, overfitting of models and devising impartial valuation methods. Finally, we present possible solutions to these critical issues.

Citation:

Bergianti, M.; Cioffo, N.; Del Buono, F.; Paganelli, M.; Porrello, A. "Avoiding the Pitfalls on Stock Market: Challenges and Solutions in Developing Quantitative Strategies" CEUR Workshop Proceedings, vol. 3486, ita, pp. 489 -494 , 2023, 2023

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