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The Hurst exponent is a statistical measure used to evaluate the long-term memory or autocorrelation of a time series, indicating whether a system exhibits trending behavior, mean-reverting characteristics, or randomness. A Hurst exponent greater than 0.5 signifies the existence of long-range dependence, implying that previous trends are prone to persisting into subsequent periods. Conversely, a Hurst exponent below 0.5 indicates mean-reverting behavior, where trends are likely to reverse, and an exponent near 0.5 suggests a random walk with no discernible trend.
Reference [1] proposed using the Hurst exponent to time the market. Specifically, the authors calculated the moving Hurst exponent for rolling windows of 100 and 150 days. The timing signals are generated as follows,
- If (H100 − H150)n > 0 and (H100 − H150)n+1 < 0, then the signal is BUY.
- If (H100 − H150)n < 0 and (H100 − H150)n+1 > 0, then the signal is SELL.
The authors pointed out,
The results of our study suggest that the Moving Hurst (MH) indicator offers a valuable approach to forecasting and managing volatility in Indian equity markets. Our analysis shows that MH provides a more effective means of capturing profitable trading opportunities compared to traditional indicators like Moving Averages (MA). It also shows how MH is a less lagging indicator than MA. For not consecutive buy/sell signals, an argument is made that for a current buy/sell, there might be a sell/buy indicator in the past or the future which was not included in the moving window frame. By incorporating the principles of chaos theory and fractal analysis, this new indicator presents a unique perspective for market analysis. Our analysis shows that MH provides a more effective means of capturing profitable trading opportunities compared to traditional indicators like Moving Averages(MA). By incorporating the principles of chaos theory and fractal analysis, this new indicator presents a unique perspective for market analysis.
In short, using the Hurst exponent as a timing indicator proved to be effective. We note that the research was conducted in the Indian stock market. However, it can be readily applied to any stock market.
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References
[1] Shah, Param, Ankush Raje, and Jigarkumar Shah, Patterns in the Chaos: The Moving Hurst Indicator and Its Role in Indian Market Volatility. Journal of Risk and Financial Management 17: 390, 2024
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