Machine Learning Models for Predicting Stock Market Volatility
DOI:
https://doi.org/10.29070/bt1yt736Keywords:
Machine learning, stock market volatility, LSTM, Random Forest, volatility forecasting, financial riskAbstract
The stock market is predictably unpredictable in some ways, but it is how financial experts handle each part of the system that determines the overall success of the market. Understanding how unpredictable, or how volatile, the market is will allow one to estimate the required risk making it a volatile market and execute plans based on that market estimation. Though there is a time and place for the ARCH and GARCH models, linear models usually fail as the market is complex, dynamic and non-linear. Predicting complex financial models is possible and ML (machine learning) models, a type of computational intelligence, should be applied. In this paper, I will focus on various ML methods to predict stock market volatility using historical stock market data and technical indicators I constructed to train the various ML models I created. I then evaluated the accuracy of these models using Mean Average Error (MAE) and Root Mean Squared Error (RMSE). I compared both LSTM ML model and GARCH model, where I concluded both work best for market prediction but LSTM is the most adaptive for market changes. The research shows how machine learning tools come with great predictive accuracy while forecasting volatility, with high efficiency in risk management in current financial markets.
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