Reevaluating Stock Price Prediction: The Influence of Machine Learning on Forecast Accuracy

Yingqian Cao

2024

Abstract

Due to the complexity and volatility of financial markets, traditional methods have often failed, prompting the adoption of advanced machine learning techniques that leverage vast datasets and sophisticated algorithms for improved forecast accuracy. This research aims to compare traditional and advanced machine learning techniques in predicting equity premiums, a crucial metric in financial economics. Using a comprehensive dataset encompassing various economic and financial indicators, this study initially implemented a rolling linear regression model as a baseline, followed by more sophisticated models, including Bagged Trees. Model performance was assessed using Out-of-Sample (OOS) R-squared (R2), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The results showed that the Bagged Trees model exhibited the most reliable performance, the Rolling Linear Regression model followed, with the Long Short-Term Memory (LSTM) model being the least effective. The inherent unpredictability of equity premiums is attributed to limited access to comprehensive market information, the noisy and complex nature of financial markets, and the over-fitting tendency of advanced models. To enhance predictive accuracy, future research should consider integrating alternative data sources, employing advanced noise-filtering techniques, developing hybrid models, applying robust regularization methods and creating dynamic models that adapt to evolving market conditions.

Paper Citation

in Harvard Style

Cao Y. (2024). Reevaluating Stock Price Prediction: The Influence of Machine Learning on Forecast Accuracy. In Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI; ISBN 978-989-758-726-9, SciTePress, pages 85-91. DOI: 10.5220/0013207000004568

in Bibtex Style

@conference{ecai24,
author={Yingqian Cao},
title={Reevaluating Stock Price Prediction: The Influence of Machine Learning on Forecast Accuracy},
booktitle={Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI},
year={2024},
pages={85-91},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013207000004568},
isbn={978-989-758-726-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI
TI - Reevaluating Stock Price Prediction: The Influence of Machine Learning on Forecast Accuracy
SN - 978-989-758-726-9
AU - Cao Y.
PY - 2024
SP - 85
EP - 91
DO - 10.5220/0013207000004568
PB - SciTePress