Optimizing trend analytics and forecasting models for diverse financial instruments

Abstract

The financial market is a complex and dynamic system, where the prediction of newlinefuture trends and behaviours can be challenging. With the increasing availability newlineof financial data and the advancements in computational techniques, there has newlinebeen a growing interest in developing accurate and robust forecasting models for newlinefinancial markets using nature-inspired optimization strategies. In recent years, newlinesoft computing and deep learning techniques have emerged as powerful tools for newlineimproving the accuracy and reliability of financial market forecasting. Soft newlinecomputing techniques such as fuzzy logic, genetic algorithms, and neural newlinenetworks can capture the complex and nonlinear relationships between the input newlinevariables and the output variables, while deep learning techniques such as newlineconvolutional neural networks and long short-term memory networks can handle newlinelarge amounts of sequential data and capture the temporal dependencies in newlinefinancial time series data. newlineHowever, the success of these techniques in financial market forecasting newlinedepends on several factors, including the appropriate selection of input features, newlinethe optimization of model parameters, and the careful evaluation of model newlineperformance. Therefore, the development of optimized soft computing and deep newlinelearning techniques for financial market forecasting requires a deep understanding newlineof both the underlying financial theories and the computational techniques. newlineThis Ph.D. thesis aims to contribute to the development of optimized soft newlinecomputing and deep learning techniques for enhancing financial market newlineforecasting. The thesis focuses on two main objectives: (1) the development of newlinenovel soft computing and deep learning algorithms for financial market newlineforecasting, and (2) the evaluation and comparison of these algorithms in terms of newlinetheir accuracy, efficiency, and robustness. The thesis then reviews the principles newlineand algorithms of nature-inspired optimization strategies, including genetic newlinealgorithms, particle swarm optimization, ant colony optimization, and artificial newlineb