Forecasting Equity using LSTM Value-at-Risk Estimation
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Abstract
A deep learning hybrid approach (LSTM-VaR) is proposed for risk-based stock value prediction by comparing the relationship and temporal sequence of stock value data. By utilizing time in its predictions, the model can improve accuracy and reduce volatility in stock price projections. It can anticipate changes in stock market indices and develop a reliable strategy for projecting future costs while calculating normal fluctuations of indices.
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