Forecasting of Financial Time Series for Market Efficiency and Predictive Patterns using Deep Learning Model

Abstract: Due to the still-evolving global financial markets, the requirement of financial time series (FTS) forecasting, in FM operation and management, has recently become more evident. The financial markets by nature are dynamic and can be described as volatile, nonlinear and intricate dependencies, which pose a difficult challenge to the traditional forecasting viewpoints. This paper will examine the empirical efficiency of deep learning (DL) models in predicting financial time series in order to comprehend the market efficiency and release of predicting strategy. Historical information drawn from stock prices, macroeconomic indicators, and trading volumes is used to train and evaluate the models. The study assesses LSTM networks in particular, comparing them with conventional methods and emphasizing their far greater ability to capture sequential dependencies. This study shows that deep-learning models not only offer a higher level of predictability but also suggest pathways for behavior demonstrating inefficiency of the market, therefore aiding both theoretical and practical actions taken concerning financial decision making.

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