Algorithm trading, trend and cyclical volatility and stock returns: a band-pass filter and PMG-ARDL approach

Elahe Rezazade ¹ ( PhD Student, Department of Accounting, Fi.C., Islamic Azad University, Firoozkooh, Iran, elahe.rezazadeh@iau.ac.ir )
Mehdi Fathabadi ² ( Department of Economics, Fi.C., Islamic Azad University, Firoozkooh, Iran, fathabadi.mehdi@iau.ac.ir )
ُSeyedeh Atefeh Hosseini ³ ( Department of Accounting, Fi.C., Islamic Azad University, Firoozkooh, Iran, hosseini.atefeh@iau.ac.ir )
kazem Haronkolaei ⁴ ( Department of Accounting, Fi.C., Islamic Azad University, Firoozkooh, Iran, Kazem.haron@iau.ac.ir )

Submited date : 2025-06-07 Accepted date : 2025-08-12

Keywords: Algorithm Trading, Trend and cyclical volatility, Stock return, Band-pass Filter, PMG-ARDL,

Abstract :

This study investigated the effects of Algorithmic Trading (AT), trend and cyclical variability on daily stock price returns for 15 listed companies from May 9, 2020, to June 9, 2024. Initially, the unit root test results revealed that 12 return series were stationary, for which an ARMA model was estimated. Conversely, a random walk model was applied to the remaining three series. For the 12 stationary series, a GARCH(1,1) model was estimated, while an IGARCH(1,1) model was used for the 3 random walk series to obtain conditional variance as a measure of volatility. The volatility was subsequently decomposed into trend and cyclical components using the Cristiano-Fitzgerald (CF) band-pass filter. Following this, the effects of AT as well as total, trend and cyclical volatility on stock returns were analyzed using a PMG-ARDL model. First, the cross-sectional dependence test confirmed the presence of dependence across all series. Thereafter, the Pesaran unit root test (CIPS) demonstrated that all variables were integrated of order zero (I(0)). The Kao co-integration test further substantiated the existence of a long-run relationship between variables. The results indicate that AT has a negative and statistically insignificant effect on price returns in the long run, while showing a negative and statistically significant effect in the short run. Additionally, total and trend volatility exert a positive and significant impact on returns in both the long-run and short-run models. Conversely, cyclical volatility negatively affects returns in the long-run model, while its impact is insignificant in the short-run. These findings suggest that as cyclical volatility increases and persists, stock prices tend to decline.

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