Predicting financial asset prices with neural network: a comparative study of neural network’s effectiveness in financial decision-making

Abstract

Investing requires deep knowledge of complex financial markets, making it incredibly tedious to predict inflation and deflation. Predictive conventional models like ARIMA and GARCH do not accurately capture the non-linearity and volatility presented in financial datasets. This research examines the different forms of predictive assets, real estate, stocks, commodities, bonds, and cryptocurrency using Long Short-Term Memory (LSTM) Neural networks. The primary focus of this research is to assess the valuable prediction capabilities of LSTM across assets and its integration with financial decision-making. According to the empirical results, deep learning LSTM models give better outcomes with equities and gold, with the R2 indicator reaching over 99% alongside a low RMSE. LSTMs had an over 100% MPE prediction error rate for other assets during the test phase, making it harder to predict intensely volatile assets. The model's verification transfers residual autocorrelation, showing that it can enhance forecasting performance with detailed macroeconomic indicators and sentiment analysis data. Studies show that LSTMs are effective in high-frequency markets with non-linear price changes, but they require special attention to balance interpretability and overfitting. Despite the progress that has been achieved in utilizing neural networks for financial forecasting, hybrid models integrated with XAI are recommended to improve efficiency and real-world applicability. These results contribute to the growing domain of AI-powered finance by offering additional means for many investors, analysts, and decision-makers who wish to utilize data for market speculation.