Applications of the Fourier Transform as a Financial Indicator for Cyclical Asset Prices

This paper explores how mathematical tools used to study sound and signals can also reveal patterns in financial markets. By applying the Fourier Transform, a method that breaks down complex data into repeating cycles, the study identifies rhythmic trends in stock prices and builds a trading indicator based on them. Using historical market data, the strategy is tested for accuracy and risk-adjusted returns, showing strong performance across major stocks and indexes. The work highlights how mathematical analysis can deepen our understanding of financial behavior and guide data-driven investment decisions.