Deep Learning Algorithms for Stock Market Trend Prediction in Financial Risk Management

Abstract

This article discusses how the hybrid deep learning algorithms can be used in predicting the trend in a stock market and manage financial risk. The proposed architecture serves as a combination of Convolutional Neural Networks (CNN) and the Long Short-term Memory (LSTM) networks whereby it takes advantage of both models. The historical data of the stock market should be used to extract important features, including price trends and technical indicators by CNNs, and the long-term dependencies of the time-series data can be captured to forecast future movement with LSTMs. Hybrid model is developed to forecast the trends (up/down) in stock prices or to categorize the trend of the market movement, helping in making an informed decision of the investment. In addition to this, the management of financial risk is also incorporated into the model with the incorporation of the key financial risks analysis metrics that include Value-at-Risk (VaR) and Conditional VaR which quantitatively estimates the financial risk being undertaken. Accuracy, precision, recall, and risk-adjusted measures are used to assess the performance of the model and the results are also back-tested into historical coloration of the stock market data to show whether the model performs under actual conditions. This is expected to increase the forecasting abilities of stock market models and at the same time reduce the risk exposure. The results are indicators of how the CNNs and LSTMs can provide a powerful framework of predicting and managing the risks better in financial markets to foretell the trend and implement more arranging and intelligent business procedures in terms of investments.