Stock Market Volatility Analysis Using Temporal Convolutional Network in Financial Time Series Evaluation
Abstract
Proper evaluation and prediction of the volatility of the stock market is essential to carry out the proper risk management in investment and to be able to make strategic decisions in the market. The key aspect of trading strategies and risk mitigation is accurate volatility forecasting. Nonetheless, financial time series have some complexities inherent in them especially regarding the modeling of both the short-term and long-term volatility patterns. The classical approaches do not usually consider the dependencies across a long horizon, and nonlinear market dynamics, which results in defective predictive accuracy. Besides, traditional models are unable to support various and intricate multimodal data, which limits their predictive abilities in unstable market conditions. This paper will address the following challenges by introducing a new framework called SVT-TCN (Stock Volatility Trading using Temporal Convolutional Network). SVT-TCN takes advantage of the Temporal Convolutional Networks (TCNs) which are skillful in absorbing short-term and long-term temporal relationships in financial data. The framework improves predictive accuracy at a high level of 96.2% accuracy, 93.4% forecasting volatility and 92% efficient trading strategy by using the superior temporal pattern modeling capabilities of TCN. Comparative experiments show that SVT-TCN is better than traditional models of volatility prediction, as it is more robust and reliable. The results indicate a major advancement toward managing high-frequency market data, identifying intricate market patterns, and providing useful information to be acted upon by volatility-based trading systems.