Prediction of Volatility in Stock Commodities using Deep Learning
Keywords:
Deep Learning, Machine Learning, ARCH, GARCH, LSTM, Forecasting, Stock Commodities, S&P 500, Yahoo FinanceAbstract
Investors face risks throughout the investment process, and managing these risks depends on the volatility of stock commodities.
Understanding how to calculate and predict the volatility of stock commodities can greatly assist in forecasting future fluctuations. This research aims to assess the effectiveness of deep learning, specifically the long short-term memory (LSTM) model, in predicting volatility in stock commodities. Additionally, the research work aims to determine which model produces the best results for stock investors in forecasting volatility by comparing the GARCH model, ARCH model, and LSTM (deep learning model). The research work utilizes Yahoo Finance datasets for oil, gold, diesel, and the S&P 500 index to conduct the stock market analysis. The results of this research work show that the LSTM model achieved a high accuracy rate of 98.8%, outperforming the GARCH model at 94% and the ARCH model at 89%. Therefore, the predictive power of the LSTM model surpasses that of the ARCH and GARCH models, establishing the effectiveness of deep learning models in predicting volatility. Furthermore, the GARCH model
demonstrates superior predictive power compared to the ARCH model.
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