A Comprehensive Review of Stock Price Prediction and Quantitative Trading Models: Data Science Approaches

Yixuan Liu

doi:10.47297/wspceWSP2516-251921.20261002

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A Comprehensive Review of Stock Price Prediction and Quantitative Trading Models: Data Science Approaches

10卷第2期

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A Comprehensive Review of Stock Price Prediction and Quantitative Trading Models: Data Science Approaches
A Comprehensive Review of Stock Price Prediction and Quantitative Trading Models: Data Science Approaches
Jinan New Channel School, Jinan, Shandong 250000, China
2026年10卷第2期页码：103-107
纸质出版：2026-04-30
DOI：10.47297/wspceWSP2516-251921.20261002
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Yixuan Liu. (2026). A Comprehensive Review of Stock Price Prediction and Quantitative Trading Models: Data Science Approaches. 创意经济（英文版）, 10(2), 103-107.

Liu Yixuan.A Comprehensive Review of Stock Price Prediction and Quantitative Trading Models: Data Science Approaches[J].Creative Economy,2026,10(02):103-107.
Yixuan Liu. (2026). A Comprehensive Review of Stock Price Prediction and Quantitative Trading Models: Data Science Approaches. 创意经济（英文版）, 10(2), 103-107. DOI： 10.47297/wspceWSP2516-251921.20261002.

Liu Yixuan.A Comprehensive Review of Stock Price Prediction and Quantitative Trading Models: Data Science Approaches[J].Creative Economy,2026,10(02):103-107. DOI： 10.47297/wspceWSP2516-251921.20261002.

摘要

Abstract

The rapid expansion of financial data and the increasing automation of trading have significantly transformed modern financial markets. Traditional statistical models often struggle to capture the complex nonlinear relationships and dynamic structural changes that characterise stock price movements. In response

machine learning methods have become increasingly prominent in financial forecasting research. However

many existing studies focus primarily on predictive accuracy while overlooking the critical gap between statistical prediction and economically workable trading strategies.This paper provides a comprehensive methodological review of stock price prediction models and their transformation into quantitative trading strategies. The study examines key approaches used in the literature for evaluating predictive models

including feature analysis

model performance diagnostics

calibration testing

and robustness validation. Particular attention is given to identifying the drivers of model predictions and assessing whether predictive signals remain stable across different market conditions.In addition

the paper discusses the methodological challenges involved in translating predictive signals into executable trading strategies. Issues such as signal filtering

risk management

transaction costs

and strategy robustness are analyzed to highlight the limitations of purely accuracy-based evaluations.The paper proposes an integrated analytical framework that links model diagnosis

strategy construction

and performance evaluation into a unified research process. By emphasising interpretability

robustness testing

and economic relevance

the framework aims to bridge the gap between predictive modelling and practical quantitative investment. The findings provide methodological guidance for researchers and practitioners seeking to develop more reliable and sustainable stock market forecasting systems.

关键词

Keywords

references

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