Evaluating Random Forest and LSTM Architectures in Volatility Forecasting for the Magnificent Seven

DOI

10.2991/978-94-6239-801-6_149How to use a DOI?

Keywords

Volatility Forecasting; Deep Learning; Statistical Safety Margin; Non-Linear Regime Switches; Capital Preservation

Abstract

Volatility prediction is still required for the whole of financial risk control, and due to its instability, changes in stock prices can be quite sudden. The standard linear model cannot explain the pronounced fluctuations in the concentrated stock market. This paper examines the prediction accuracy of machine learning and deep learning methods for the stock prices of the Magnificent Seven indices based on these seven indices. A large amount of data, such as macroeconomic and liquidity indices, is to be used with both the LSTM model and the Random Forest model for comparison; according to the experiment, the LSTM model has shown good performance in capturing the time-series characteristics of financial data. The decrease in the out-of-sample Root Mean Square Error (RMSE) of the proposed model compared with that of Random Forest is 24.3%. To know how big the predicted volatility Figures are in reality, they are then used in a dynamic allocation plan to determine the exposure coefficient of volatility. Backtesting shows that, in the event of high market volatility at the beginning of 2026, there will be a 6.8% increase in the Sharpe ratio and a 15.2% decrease in the maximum drawdown. In short, it has been determined that the deep learning model can improve the accuracy of volatility forecasting and offer some support for portfolio risk management in the concentrated growth equity market.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Bo Wen
PY  - 2026
DA  - 2026/10/23
TI  - Evaluating Random Forest and LSTM Architectures in Volatility Forecasting for the Magnificent Seven
BT  - Proceedings of the 2026 2nd International Conference on Financial Innovation and Marketing Management (FIMM 2026)
PB  - Atlantis Press
SP  - 1423
EP  - 1430
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-801-6_149
DO  - 10.2991/978-94-6239-801-6_149
ID  - Wen2026
ER  -