Long-term adjusted volatility: Powerful capability in forecasting stock market returns

Rui Qiu, Jing Liu and Yan Li

International Review of Financial Analysis, 2023, vol. 86, issue C

Abstract: We develop the long-term adjusted volatility (LV_ADJ) by removing the interference information of short-term volatility from the simple long-term volatility and examine the role of LV_ADJ in the predictability of stock market returns. Using a sample from January 2000 to December 2019 and considering 19 popular predictors, LV_ADJ positively predicts the next-month returns of S&P 500 and the univariate model with LV_ADJ has the best forecasting performance with adjusted in-sample r-squared of 3.825%, out-of-sample r-squared of 3.356%, return gains of 5.976%, CER gains of 4.708 and Sharpe ratio gains of 0.394. The predictive information of LV_ADJ is independent of that obtained from the 19 popular predictors. Furthermore, we find that LV_ADJ also has predictive power for long-term (3–12 months) stock returns, and can forecast returns of industry portfolios and characteristic portfolios.

Keywords: Long-term adjusted volatility; Short-term volatility; Return predictability; Out-of-sample forecasting (search for similar items in EconPapers)
JEL-codes: C22 G11 G12 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:86:y:2023:i:c:s1057521923000467

DOI: 10.1016/j.irfa.2023.102530

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