Fourier Analysis for Stock Price Forecasting: Assumption and Evidence

Abstract

The research addressed the relevant question whether the Fourier analysis really provides practical value for investors forecasting stock market price. To answer this question, the significant cycles were discovered using the Fourier analysis inside the price series of US stocks; then, the simulation of an agent buying and selling on minima and maxima of these cycles was made. The results were then compared to those of an agent operating chaotically. Moreover, the existing significant cycles were found using more precise methods, suggested in the research, and based on the results of an agent buying and selling on all possible periods and phases. It has been analysed whether these really existing cycles were in accordance with the significant cycles resulting from the Fourier analysis. It has been concluded that the Fourier analysis basically failed. Suchlike failures are expected on similar data series. In addition, momentum and level trading backtests have been used in a similar way. It has been found that the level trading does provide a certain practical value in comparison to the momentum trading method. The research also simplifies the complicated theoretical background for practitioners.

Regulatory Stress Tests and Bank Responses

“…Another stream of literature is related to the discussions on the calibration of methodologies of stress tests from macro and microprudential perspective (Andersen et al, 2019;Stádník et al, 2016;Witzany, 2017a). In the EU, EBA stress tests are run under the static balance sheet assumption.…”

“…Our contribution to the literature is twofold. First, our study contributes to the banking literature that specifically focuses on investigating the implications of regulatory policies on stress testing and capital requirements for the banking institutions (Ahnert et al, 2018;Bassett and Berrospide, 2018;Calem et al, 2017;Cohen and Scatigna, 2016;Cortés et al, 2018;Goldstein and Sapra, 2014;Gropp et al, 2018;Mésonnier and Monks, 2014;Pierret and Stex`ri, 2019;Schuermann, 2013;Stádník et al, 2016;Sutorova and Teply, 2013;Vozková and Teplý, 2018). We extend this literature by providing evidence, based on the novel identification strategy with the application of the causal inference methods that allow us to isolate the effects of regulatory stress test from other capital regulations and analyse the heterogeneity of treatment effect in time-dynamic settings.…”

Forecasting, Valuation and Portfolio Returns of Stock Market Evolution: Problems, Paradoxes and Efficient Information. Worldwide Implications and Romanian Evidence

“…Smales (2017) tackles the correlation between stock market returns and sentiment indices and reveals a strong relationship based on theoretical explanation of sentiment. Forecasting the stock market by Fourier analysis had no lead to robust and significant empirical results (Stadnik, Raudeliuniene, & Davidaviciene, 2016). Symitsi, Symeonidis, Kourtis, and Markellos (2018) analyse different covariance of forecasting models and come to the conclusion that the results are robust when it is used high-frequency data models.…”

Forecasting Cryptocurrency Time Series Using Fuzzy Transform, Fourier Transform and Fuzzy Inference System

“…On the other hand, Fourier transform is successfully being used in many hedge funds and investment banks for forecasting the prices of financial derivatives, usually options [10,11]. The algorithm is to take the discrete Fourier transform (DFT) of the specific derivative or asset and then take the inverse DFT (IDFT) to get the future price.…”