The authors fit standard factor models and an LSTM to U.S. sector returns and report that the five-factor model and LSTM each look best in different sectors.
A Consolidated Volatility Prediction with Back Propagation Neural Network and Genetic Algorithm
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abstract
This paper provides a unique approach with AI algorithms to predict emerging stock markets volatility. Traditionally, stock volatility is derived from historical volatility,Monte Carlo simulation and implied volatility as well. In this paper, the writer designs a consolidated model with back-propagation neural network and genetic algorithm to predict future volatility of emerging stock markets and found that the results are quite accurate with low errors.
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Regression and Forecasting of U.S. Stock Returns Based on LSTM
The authors fit standard factor models and an LSTM to U.S. sector returns and report that the five-factor model and LSTM each look best in different sectors.