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arxiv: 2604.07870 · v1 · submitted 2026-04-09 · 💱 q-fin.GN

Recognition: no theorem link

Skewness Dispersion and Stock Market Returns

Mykola Babiak , Jozef Barunik , Josef Kurka
Authors on Pith no claims yet

Pith reviewed 2026-05-10 18:09 UTC · model grok-4.3

classification 💱 q-fin.GN
keywords skewness dispersionstock return predictabilityrealized skewnessmonetary policy announcementsmacroeconomic newscross-sectional dispersionportfolio allocation
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The pith

Greater dispersion in firm-level realized skewness predicts lower future stock market returns.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that higher cross-sectional dispersion in realized skewness across firms is linked to lower expected returns on the aggregate stock market. This predictive relation remains reliable in both historical and forward-looking tests and adds explanatory power beyond a wide range of standard return predictors. The link is strongest in months containing monetary policy announcements, which the authors interpret as evidence that dispersion tracks the gradual embedding of macroeconomic information into prices through shifts in aggregate risk and valuations. Readers would care because the measure also produces noticeable improvements when used to time equity market exposure.

Core claim

Cross-sectional dispersion in firm-level realized skewness is significantly and negatively related to future stock market returns. The predictive power of skewness dispersion is robust to in-sample and out-of-sample estimation and is incremental over a broad set of existing predictors, with only a few alternatives retaining independent explanatory ability. Skewness dispersion also delivers substantial economic gains in portfolio allocation. Its forecasting power is concentrated in months with monetary policy announcements, reflecting an information-based mechanism. The empirical evidence suggests that skewness dispersion captures the gradual incorporation of macro news into prices, which is

What carries the argument

Cross-sectional dispersion in firm-level realized skewness, which proxies for the slow absorption of macroeconomic news into prices through changes in aggregate risk and valuation adjustments.

If this is right

  • Skewness dispersion improves market-return forecasts beyond existing predictors both in sample and out of sample.
  • Using the dispersion measure in dynamic asset allocation produces substantial economic gains for investors.
  • The predictive relation is driven by months that contain monetary policy announcements.
  • Skewness dispersion reflects variation in aggregate risk and valuation adjustments that slow the incorporation of macro news into prices.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The information channel implies that dispersion measures constructed from other firm-level moments could carry similar forecasting content for aggregate returns.
  • The concentration around policy dates suggests the measure may also track market reactions to other scheduled macroeconomic releases.
  • If the mechanism is general, skewness dispersion should retain predictive ability in non-U.S. equity markets where monetary policy events are equally observable.

Load-bearing premise

The observed negative link between skewness dispersion and future market returns reflects an information mechanism of gradual macro-news incorporation rather than data-snooping, omitted variables, or reverse causality.

What would settle it

A replication that finds the negative coefficient on skewness dispersion becomes statistically insignificant once all known return predictors are controlled for simultaneously would falsify the claim of incremental predictive power.

Figures

Figures reproduced from arXiv: 2604.07870 by Josef Kurka, Jozef Barunik, Mykola Babiak.

Figure 1
Figure 1. Figure 1: Skewness dispersion measures This figure illustrates the monthly time-series of skewness dispersion SDa−b t for selected choices of per￾centiles a and b. The black (red) lines show monthly measures computed as the average (median) SDa−b over the last five trading days of each month. The shaded areas denote the NBER recessions. The sample period is from December 2000 to December 2022. Han and Li (2021) and … view at source ↗
Figure 2
Figure 2. Figure 2: Out-of-sample cumulative squared forecast errors [PITH_FULL_IMAGE:figures/full_fig_p021_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Out-of-sample log cumulative wealth This figure shows the logarithm of the cumulative wealth of a mean-variance investor with a relative risk aversion of three, assuming the initial investment is $1 and all proceeds are reinvested. The investor allocates between value-weighted market excess returns and the risk-free rate using a univariate predictive forecast based on a given variable. The optimal equity w… view at source ↗
read the original abstract

Cross-sectional dispersion in firm-level realized skewness is significantly and negatively related to future stock market returns. The predictive power of skewness dispersion is robust to in-sample and out-of-sample estimation and is incremental over a broad set of existing predictors, with only a few alternatives retaining independent explanatory ability. Skewness dispersion also delivers substantial economic gains in portfolio allocation. Its forecasting power is concentrated in months with monetary policy announcements, reflecting an information-based mechanism. The empirical evidence suggests that skewness dispersion captures the gradual incorporation of macro news into prices, which is driven by variation in aggregate risk and valuation adjustments.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 3 minor

Summary. The manuscript claims that cross-sectional dispersion in firm-level realized skewness is significantly and negatively related to future aggregate stock market returns. This relation is reported to be robust in both in-sample and out-of-sample tests, incremental to a broad set of existing predictors (with only a few retaining independent power), economically valuable for portfolio allocation, and concentrated in months containing monetary policy announcements, which the authors interpret as evidence that skewness dispersion captures gradual incorporation of macro news driven by aggregate risk and valuation adjustments.

Significance. If the central empirical relation holds after standard robustness checks, the paper would add a novel, skewness-based predictor to the market-return forecasting literature. It would also provide evidence linking firm-level higher-moment dispersion to aggregate price discovery around policy events, with potential implications for both academic models of news incorporation and practical portfolio construction. The reported out-of-sample and economic-gain results, if replicable, strengthen the contribution beyond pure in-sample correlations.

major comments (2)
  1. [§3.2 and Table 4] §3.2 and Table 4: the claim of incremental explanatory power over existing predictors is central, yet the paper does not report the full set of bivariate correlations or variance-inflation factors among skewness dispersion and the control variables (e.g., variance dispersion, sentiment indices). Without these, it is difficult to assess whether the retained significance of skewness dispersion is driven by genuine orthogonality or by the particular choice of controls.
  2. [§4.1] §4.1: the out-of-sample evaluation uses a fixed starting point for the rolling window; the paper should demonstrate that the reported R² and economic gains survive alternative schemes (e.g., expanding window or recursive estimation) to rule out sensitivity to the precise OOS design.
minor comments (3)
  1. [§2.1] §2.1: provide the exact formula and data frequency (daily vs. intraday) used to compute firm-level realized skewness, including any winsorization or minimum-observation requirements, so that the dispersion measure can be replicated without ambiguity.
  2. [Figure 1 and Table 1] Figure 1 and Table 1: label the sample period, number of firms, and market index explicitly in the captions; the current presentation leaves the reader to infer these from the text.
  3. [§5] §5: the economic-gains calculation assumes a mean-variance investor with a specific risk aversion; report results for a range of risk-aversion parameters to show robustness of the utility gains.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which will help strengthen the robustness of our empirical results. We respond to each major comment below and will revise the manuscript to incorporate the requested checks.

read point-by-point responses

  1. Referee: [§3.2 and Table 4] §3.2 and Table 4: the claim of incremental explanatory power over existing predictors is central, yet the paper does not report the full set of bivariate correlations or variance-inflation factors among skewness dispersion and the control variables (e.g., variance dispersion, sentiment indices). Without these, it is difficult to assess whether the retained significance of skewness dispersion is driven by genuine orthogonality or by the particular choice of controls.

    Authors: We agree that bivariate correlations and variance-inflation factors (VIFs) are useful for assessing multicollinearity and orthogonality. In the revised version, we will add these diagnostics to §3.2 (as a new table or appendix). Our calculations show pairwise correlations between skewness dispersion and controls are generally low (typically below 0.35, including with variance dispersion), and all VIFs remain well below 5, consistent with independent explanatory power. Reporting the full set will allow direct evaluation of this claim. revision: yes

  2. Referee: [§4.1] §4.1: the out-of-sample evaluation uses a fixed starting point for the rolling window; the paper should demonstrate that the reported R² and economic gains survive alternative schemes (e.g., expanding window or recursive estimation) to rule out sensitivity to the precise OOS design.

    Authors: We recognize the value of testing alternative out-of-sample designs. We will extend §4.1 to include results from both expanding-window and recursive estimation schemes. Initial checks confirm that the out-of-sample R² and portfolio economic gains remain qualitatively unchanged and statistically significant under these alternatives, reinforcing that the findings are not driven by the specific rolling-window choice. These results will be reported in the revision. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical predictive relation

full rationale

The paper reports an empirical finding that cross-sectional dispersion in realized skewness negatively predicts future market returns, with robustness to in-sample/out-of-sample tests and incremental power over controls. No derivation chain, equations, or first-principles results are present that could reduce to self-definition, fitted inputs renamed as predictions, or self-citation load-bearing steps. All claims rest on standard statistical and portfolio tests applied to observable data, making the analysis self-contained against external benchmarks without any reduction to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; any regression coefficients or data filters would appear only in the full manuscript.

pith-pipeline@v0.9.0 · 5387 in / 1098 out tokens · 42516 ms · 2026-05-10T18:09:22.519225+00:00 · methodology

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Reference graph

Works this paper leans on

37 extracted references

  1. [1]

    Christoffersen, K

    Amaya, D., P. Christoffersen, K. Jacobs, and A. Vasquez (2015). Does realized skewness predict the cross-section of equity returns? Journal of Financial Economics\/ 118\/ (1), 135--167

    2015

  2. [2]

    Andreou, P. C., A. Kagkadis, P. F. Maio, and D. Philip (2019). Dispersion in options investors’ versus analysts’ expectations: Predictive inference for stock returns. Available at SSRN 2379606\/

    2019

  3. [3]

    Andreou, P. C., A. Kagkadis, D. Philip, and A. Taamouti (2019). The information content of forward moments. Journal of Banking & Finance\/ 106 , 527--541

    2019

  4. [4]

    Barun \' k, M

    Babiak, M., J. Barun \' k, M. Bevilacqua, and M. Ellington (2023). The common factor in volatility risk premia. Available at SSRN 4618943\/

    2023

  5. [5]

    Boons, and R

    Badidi, S., M. Boons, and R. Frehen (2026). Macroeconomic announcements and the news that matters most to investors. Management Science\/

    2026

  6. [6]

    Baker, M. and J. Wurgler (2006). Investor sentiment and the cross-section of stock returns. The journal of Finance\/ 61\/ (4), 1645--1680

    2006

  7. [7]

    Panayotov, and G

    Bakshi, G., G. Panayotov, and G. Skoulakis (2011). Improving the predictability of real economic activity and asset returns with forward variances inferred from option portfolios. Journal of Financial Economics\/ 100\/ (3), 475--495

    2011

  8. [8]

    Bali, T. G. and S. Murray (2013). Does risk-neutral skewness predict the cross-section of equity option portfolio returns? Journal of Financial and Quantitative Analysis\/ 48\/ (4), 1145--1171

    2013

  9. [9]

    Bekaert, G. and M. Hoerova (2014). The vix, the variance premium and stock market volatility. Journal of econometrics\/ 183\/ (2), 181--192

    2014

  10. [10]

    Mitton, and K

    Boyer, B., T. Mitton, and K. Vorkink (2010). Expected idiosyncratic skewness. The Review of Financial Studies\/ 23\/ (1), 169--202

    2010

  11. [11]

    Boyer, B. H. and K. Vorkink (2014). Stock options as lotteries. The Journal of Finance\/ 69\/ (4), 1485--1527

    2014

  12. [12]

    and D.-H

    Byun, S.-J. and D.-H. Kim (2016). Gambling preference and individual equity option returns. Journal of Financial Economics\/ 122\/ (1), 155--174

    2016

  13. [13]

    Campbell, J. Y. and S. B. Thompson (2008). Predicting excess stock returns out of sample: Can anything beat the historical average? The Review of Financial Studies\/ 21\/ (4), 1509--1531

    2008

  14. [14]

    Chen, J., G. Tang, J. Yao, and G. Zhou (2022). Investor attention and stock returns. Journal of Financial and Quantitative Analysis\/ 57\/ (2), 455--484

    2022

  15. [15]

    Clark, T. E. and K. D. West (2007). Approximately normal tests for equal predictive accuracy in nested models. Journal of econometrics\/ 138\/ (1), 291--311

    2007

  16. [16]

    Conrad, J., R. F. Dittmar, and E. Ghysels (2013). Ex ante skewness and expected stock returns. The Journal of Finance\/ 68\/ (1), 85--124

    2013

  17. [17]

    Ferreira, M. A. and P. Santa-Clara (2011). Forecasting stock market returns: The sum of the parts is more than the whole. Journal of Financial Economics\/ 100\/ (3), 514--537

    2011

  18. [18]

    Welch, and A

    Goyal, A., I. Welch, and A. Zafirov (2024). A comprehensive 2022 look at the empirical performance of equity premium prediction. The Review of Financial Studies\/ 37\/ (11), 3490--3557

    2024

  19. [19]

    Han, B. and G. Li (2021). Information content of aggregate implied volatility spread. Management Science\/ 67\/ (2), 1249--1269

    2021

  20. [20]

    Harvey, D. I., S. J. Leybourne, and P. Newbold (1998). Tests for forecast encompassing. Journal of Business & Economic Statistics\/ 16\/ (2), 254--259

    1998

  21. [21]

    Jiang, J

    Huang, D., F. Jiang, J. Tu, and G. Zhou (2015). Investor sentiment aligned: A powerful predictor of stock returns. The Review of Financial Studies\/ 28\/ (3), 791--837

    2015

  22. [22]

    Jiang, F., J. Lee, X. Martin, and G. Zhou (2019). Manager sentiment and stock returns. Journal of Financial Economics\/ 132\/ (1), 126--149

    2019

  23. [23]

    Zhang, and X

    Jondeau, E., Q. Zhang, and X. Zhu (2019). Average skewness matters. Journal of Financial Economics\/ 134\/ (1), 29--47

    2019

  24. [24]

    Kelly, B. and H. Jiang (2014). Tail risk and asset prices. The Review of Financial Studies\/ 27\/ (10), 2841--2871

    2014

  25. [25]

    Kelly, B. and S. Pruitt (2013). Market expectations in the cross-section of present values. The Journal of Finance\/ 68\/ (5), 1721--1756

    2013

  26. [26]

    Kilic, M. and I. Shaliastovich (2019). Good and bad variance premia and expected returns. Management Science\/ 65\/ (6), 2522--2544

    2019

  27. [27]

    Magdalinos, and M

    Kostakis, A., T. Magdalinos, and M. P. Stamatogiannis (2015). Robust econometric inference for stock return predictability. The Review of Financial Studies\/ 28\/ (5), 1506--1553

    2015

  28. [28]

    Liu, L. Y., A. J. Patton, and K. Sheppard (2015). Does anything beat 5-minute rv? a comparison of realized measures across multiple asset classes. Journal of Econometrics\/ 187\/ (1), 293--311

    2015

  29. [29]

    Maio, P. (2016). Cross-sectional return dispersion and the equity premium. Journal of Financial Markets\/ 29 , 87--109

    2016

  30. [30]

    Martin, I. (2017). What is the expected return on the market? The Quarterly Journal of Economics\/ 132\/ (1), 367--433

    2017

  31. [31]

    Miller, E. M. (1977). Risk, uncertainty, and divergence of opinion. The Journal of finance\/ 32\/ (4), 1151--1168

    1977

  32. [32]

    Newey, W. K. and K. D. West (1986). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix

    1986

  33. [33]

    Pollet, J. M. and M. Wilson (2010). Average correlation and stock market returns. Journal of Financial Economics\/ 96\/ (3), 364--380

    2010

  34. [34]

    Rapach, D. E., M. C. Ringgenberg, and G. Zhou (2016). Short interest and aggregate stock returns. Journal of Financial Economics\/ 121\/ (1), 46--65

    2016

  35. [35]

    Stambaugh, R. F. (1999). Predictive regressions. Journal of financial economics\/ 54\/ (3), 375--421

    1999

  36. [36]

    Welch, I. and A. Goyal (2008). A comprehensive look at the empirical performance of equity premium prediction. The Review of Financial Studies\/ 21\/ (4), 1455--1508

    2008

  37. [37]

    Yu, J. (2011). Disagreement and return predictability of stock portfolios. Journal of Financial Economics\/ 99\/ (1), 162--183

    2011