Short interest and aggregate stock returns

Rapach, David E.; Ringgenberg, Matthew C.; Zhou, Guofu

doi:10.1016/j.jfineco.2016.03.004

Cited by 482 publications

(256 citation statements)

References 58 publications

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“…Following recent related studies (see, e.g., Rapach et al, 2010Rapach et al, , 2016 It is instructive to begin by reviewing the construction of the GBVX. 5 The theoretical underpinning of the GBVX is rooted in the state-preference asset pricing framework of Debreu (1959) and Arrow (1964).…”

Section: Model Specificationmentioning

confidence: 99%

“…10 These 14 predictors, which are also the subject of interest in recent studies by Rapach et al (2010Rapach et al ( , 2016 and Neely et al (2014) on stock return predictability, are as follows:…”

Section: Other Predictorsmentioning

confidence: 99%

“…Welch and Goyal (2008) show that in addition to these 14 conventional predictors, the Percent Equity Issuing (labelled as "eqis"), which is the ratio of equity issuing activity to the fraction of total issuing activity, has strong predictive ability on monthly stock excess returns. Nonetheless, we follow recent literature on U.S. stock return predictability (see, e.g., Rapach et al, 2010Rapach et al, , 2016Neely et al, 2014) and exclude the monthly "eqis" variable from our final analysis since its monthly data (which are available from Jeffrey Wurgler's website at http://people.stern.nyu.edu/jwurgler/) only extend to 2008. Our (unreported) experiment reveals that "eqis" yields strong explanatory power (its in-sample 2 R = 2.75%) in predicting the U.S. equity premium over a narrower sample period that covers from 2000:09 to 2008:04.…”

Section: Log Earnings-price Ratio (Ep)mentioning

confidence: 99%

“…Nonetheless, Neely et al (2014) and Rapach et al (2016) (2008), we proxy CPI using the lagged consumer price index for all urban consumers, since the monthly report on inflation information for month t is typically released in the subsequent month t+1.…”

Section: Default Return Spread (Dfr)mentioning

confidence: 99%

“…Some studies find little evidence of the role of conventional predictors such as dividend yield and earnings-price ratio in predicting the equity premium out-of-sample (see, in particular, the study by Welch and Goyal, 2008), whereas others find that new predictors such as technical indicators and short interest play a strong role (Neely et al, 2014;Rapach et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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A new government bond volatility index predictor for the U.S. equity premium

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2018

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Section: Model Specificationmentioning

confidence: 99%

Section: Other Predictorsmentioning

confidence: 99%

Section: Log Earnings-price Ratio (Ep)mentioning

confidence: 99%

Section: Default Return Spread (Dfr)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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What can we learn from the return predictability over the business cycle?

Liu

Pan

Wang

2020

Journal of Forecasting

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In this paper, we forecast stock returns using time-varying parameter (TVP) models with parameters driven by economic conditions. An in-sample specification test shows significant variation in the parameters. Out-of-sample results suggest that the TVP models outperform their constant coefficient counterparts. We also find significant return predictability from both statistical and economic perspectives with the application of TVP models. The out-of-sample R 2 of an equal-weighted combination of TVP models is as high as 2.672%, and the gains in the certainty equivalent return are 214.7 basis points. Further analysis indicates that the improvement in predictability comes from the use of information on economic conditions rather than simply from allowing the coefficients to vary with time.

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Forecasting US stock market volatility: How to use international volatility information

Zhang

Wang

2020

Journal of Forecasting

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This paper aims to accurately forecast US stock market volatility by using international market volatility information flows. The results show the significant ability of the combined international volatility information to predict US stock volatility. The predictability is found to be both statistically and economically significant. Furthermore, in this framework, we compare the performance of a large set of approaches dealing with multivariate information.Dynamic model averaging (DMA) and dynamic model selection (DMS) perform better than a wide variety of competing strategies, including the heterogeneous autoregressive (HAR) benchmark, kitchen sink model, popular forecast combinations, principal component analysis (PCA), partial least squares (PLS), and the ridge, lasso, and elastic net shrinkage methods. A wide range of extensions and robustness checks reduce the concern regarding data mining. DMA and DMS are also able to significantly forecast international stock market volatilities.

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Short interest and aggregate stock returns

Cited by 482 publications

References 58 publications

A new government bond volatility index predictor for the U.S. equity premium

A new government bond volatility index predictor for the U.S. equity premium

What can we learn from the return predictability over the business cycle?

Forecasting US stock market volatility: How to use international volatility information

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