Recent Advancements and Application of Signal Processing in Financial Data Analysis

Large and complex datasets produced by stock exchanges, trading platforms, economic indicators, and other sources are what define the financial industry. Making sense of this abundance of data and forecasting the future is a very difficult task. Techniques for signal processing, which have historically been connected to fields like engineering and telecommunications, have become useful instruments in addressing these issues in recent years.Financial data analysis has become a major application for signal processing techniques in recent years. An overview of the most current developments and uses of signal processing in the financial industry is given in this study. It looks at how techniques for signal processing, which were first created for industries like telecommunications and engineering, have been modified to deal with the particular difficulties presented by financial data. The benefits of using signal processing in financial research are covered in the study, including enhanced data extraction, pattern detection, and predictive modeling. Additionally, it outlines a few particular uses for these methods in fields including fraud detection, risk management, and algorithmic trading. Through the utilization of signal processing, scholars and financial experts can acquire more profound understanding of the workings of financial markets and to make decisions.