Identification and Applicability of Time Cycles in the Indian Stock Market

Abstract

Time cycle patterns have been identified across asset classes in financial markets
(Hurst, 1972), and this is an area of great interest. This study aimed at testing some of the
methods; including Hurst's time cycle envelope and Dewey’s statistical data analytics, to
explore the possibility of using the same in stock trading in Indian markets profitably.
Considering the dynamic nature of stock markets and their dependencies on various factors,
the study tested one of the selected methods for the dataset to support the hypothesis that time
cycles are more of an academic subject than a tool to trade in stock markets.
The study used an exploratory study that follows the principles of quantitative
research. The study analyzed the Fast Fourier Transform and Goertzel general algorithms to
select the one with the least error in the detection of the dominant cycle in terms of buy and
sell signals.
A composite cycle was constructed to analyze and track the Nifty50 index for the
period between 01/05/2023 and July 31, 2023, which shows the principle of variation cycles
arrive earlier or later than the standard time cycle in a trendy market with absolute return of
5.03 % annualized return by Nifty 50 index and does not show value of using time cycles as
indicator of choice while other traditional indicators like MACD are showing 209.30%
annualized return in the same period.
This study's findings could be valuable to stock market traders who are trying to time
the market based on time cycle software and indicators. This research will provide traders
with a base on which they can further build additional indicators to be able to make better
trading decisions and improve profitability.