Cross-sectional return dispersion and the equity premium

Maio, Paulo F.

doi:10.1016/j.finmar.2015.09.001

Cited by 64 publications

(70 citation statements)

References 72 publications

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“…Following Stivers and Sun (2010) and Maio (2014) we create an equally weighted measure of dispersion based on 100 portfolios formed using all stocks from the G7 stock markets. We also calculate a capitalization weighted measure of portfolio based RD.…”

Section: Data and Dispersion Measuresmentioning

confidence: 99%

“…The sample period for the ADS equation ends on December 2009 because the data for the G7 countries are not available after 2009. 10 We focus on a yearly forecasting horizon following the evidence in Maio (2014) showing that the predicting ability of RD is stronger for holding periods greater than one or six months. Using monthly data we also find consistent but generally weaker results compared to annual returns.…”

Section: Rd As a Predictor Of The State Of The Economymentioning

confidence: 99%

“…Using monthly portfolio returns to measure RD, Maio (2014) finds a negative and statistically significant relation for the US market. The negative relation between RD and future returns is consistent with the evidence in Guo and Savickas (2008) for the G7 countries using idiosyncratic volatility instead of RD.…”

Section: Introductionmentioning

confidence: 98%

“…6 Gomes, Kogan and Zhang (2003) present a theoretical link between RD, the economy, future market returns and volatility. Empirical evidence on the predictive ability of RD for US stock returns are provided by Garcia, Garcia-Mantilla and Martellini (2013) and Maio (2014) and for the value and momentum premia by Stivers andSun (2010, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Evidence on the relation between RD and multiple horizon returns are provided in Maio (2014). Using monthly portfolio returns to measure RD, Maio (2014) finds a negative and statistically significant relation for the US market.…”

Section: Introductionmentioning

confidence: 99%

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Stock market dispersion, the business cycle and expected factor returns

Angelidis

Σάκκας

Tessaromatis

2015

Journal of Banking & Finance

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We provide evidence using data from the G7 countries suggesting that return dispersion may serve as an economic state variable in that it reliably predicts time-variation in economic activity, market returns, the value and momentum premia and market volatility.A relatively high return dispersion predicts a deterioration in business conditions, a higher value premium, a smaller momentum premium and lower market returns.Dispersion based market and factor timing strategies outperform out-of-sample buy and hold strategies. The evidence are robust to alternative specifications of return dispersion and are not driven by US data. Return dispersion conveys incremental information relative to idiosyncratic risk.

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Section: Data and Dispersion Measuresmentioning

confidence: 99%

Section: Rd As a Predictor Of The State Of The Economymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stock market dispersion, the business cycle and expected factor returns

Angelidis

Σάκκας

Tessaromatis

2015

Journal of Banking & Finance

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Cross‐sectional return dispersion and stock market volatility: Evidence from high‐frequency data

Niu

Demirer

Suleman

et al. 2023

Journal of Forecasting

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This paper investigates whether the cross‐sectional variance (CSV) of stock returns and its asymmetric components contain incremental information to predict stock market volatility under a high‐frequency, heterogeneous autoregressive (HAR) model framework. We present novel evidence that CSV is a powerful predictor of future realized volatility, both in‐ and out‐of‐sample, even after controlling for the well‐established predictors obtained from intraday data. Further analysis suggests that distinguishing between positive and negative CSV components in the forecasting model enhances the predictive capability of volatility models at all out‐of‐sample forecasting horizons, with the asymmetric HAR‐type‐ACSV model consistently outperforming all alternative HAR‐type variations. We argue that the asymmetries in the predictive relation between CSV and volatility are largely driven by the disagreement among market participants that spikes during bad times. Finally, economic analysis shows that incorporating CSV in the forecasting model can generate sizeable economic gains for a mean–variance investor, suggesting that out‐of‐sample predictive ability of CSV can be exploited in forward looking investment strategies to enhance investment returns.

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Does Option‐Implied Cross‐Sectional Return Dispersion Forecast Realized Cross‐Sectional Return Dispersion? Evidence From the G10 Currencies

Grobys

Heinonen

2016

Journal of Futures Markets

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This study employs option‐price data to back out the implied cross‐sectional return variance in the G10 currencies. It investigates the relation of implied cross‐sectional return dispersion in the currency market and subsequent realized cross‐sectional return dispersion. We find that implied cross‐sectional return variance, based on option‐price data with 1‐ and 3‐month maturity, outperforms past cross‐sectional return variance in forecasting future cross‐sectional return variance. © 2016 Wiley Periodicals, Inc. Jrl Fut Mark 37:3–22, 2017

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Cross-sectional return dispersion and the equity premium

Cited by 64 publications

References 72 publications

Stock market dispersion, the business cycle and expected factor returns

Stock market dispersion, the business cycle and expected factor returns

Cross‐sectional return dispersion and stock market volatility: Evidence from high‐frequency data

Does Option‐Implied Cross‐Sectional Return Dispersion Forecast Realized Cross‐Sectional Return Dispersion? Evidence From the G10 Currencies

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