Abstract
Using the sample time period 1950–2020, the historical average stock return correlation of a portfolio of firms is a strong positive predictor of that same portfolio’s future average stock return correlation. The prediction improves when more prior monthly returns are used. The prediction holds after controlling for macroeconomic factors. The prediction also is much better for portfolios comprised of small firms relative to large firms. The prediction holds not only for the CRSP market of firms, but also for various-sized randomly chosen sub-samples of that market.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availibility
The datasets generated during and/or analyzed during the current study are available in the CRSP monthly returns file which can be obtained with a subscription to Wharton Research Data Services from the University of Pennsylvania.
Notes
Excess returns (returns less the risk-free rate).
Obtained from the Federal Reserve Bank of St. Louis website: https://fred.stlouisfed.org/.
I examine other macroeconomic factors which could plausibly be thought to explain future average return correlation but find no statistically significant associations. The other factors I examine are the volatility index (VIX) created by the Chicago Board of Options Exchange, the economic policy uncertainty index (EPU) created by Baker et al. (2016) and the change in total assets of the federal reserve.
The algorithm was designed to pick out groups of firms with low historical average correlation and these same groups subsequently realized low future average correlation out-of-sample.
See Mech (1993) for a more complete explanation.
Whether I use 12,24,36,48 or 60 prior returns in measuring \(corr_{t-1}\) the results of the DF test remain qualitatively the same.
I couldn’t take more than 250 random samples and repeat the analysis as this exceeded my computing power.
Obtained from their website: https://www.policyuncertainty.com/.
The coefficients on each of those variables separately were close to being significant at the 10% level (untabulated analysis).
References
Baker SR, Bloom N, Davis SJ (2016) Measuring economic policy uncertainty. Q J Econ 131(4):1593–1636
Box GE, Jenkins GM, Reinsel GC, Ljung GM (2015) Time series analysis: forecasting and control. John Wiley & Sons
Campbell JY, Viceira LM, Viceira LM et al. (2002). Strategic asset allocation: portfolio choice for long-term investors
Chan K, Chen N-F (1991) Structural and return characteristics of small and large firms. J Fin 46(4):1467–1484
Chaudhuri SE, Lo AW (2015). Spectral analysis of stock-return volatility, correlation, and beta. In 2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE), pp. 232–236
Clarke RG, De Silva H, Thorley S (2006) Minimum-variance portfolios in the us equity market. J Portf Manag 33(1):10–24
Cortes GS, Gao GP, Silva FB, Song Z (2022) Unconventional monetary policy and disaster risk: Evidence from the subprime and covid-19 crises. J Int Money Financ 122:102543
Dedola L, Georgiadis G, Gräb J, Mehl A (2021) Does a big bazooka matter? quantitative easing policies and exchange rates. J Monet Econ 117:489–506
DeJong DV, Collins DW (1985) Explanations for the instability of equity beta: Risk-free rate changes and leverage effects. J Financ Quant Anal 20(1):73–94
Dickey DA, Fuller WA (1979) Distribution of the estimators for autoregressive time series with a unit root. J Am Stat Assoc 74(366a):427–431
Duffee GR (1995) Stock returns and volatility a firm-level analysis. J Financ Econ 37(3):399–420
Engle RF (2016) Dynamic conditional beta. J Financ Economet 14(4):643–667
Ibbotson RG, Kaplan PD, Peterson JD (1997) Estimates of small-stock betas are much too low. J Portf Manag 23(4):104
Kim D (1993) The extent of nonstationarity of beta. Rev Quant Financ Acc 3(2):241–254
Lee C-F, Lee A, Lee J (2021). Handbook of investment analysis, portfolio management, and financial derivatives
Levy RA (1971) On the short-term stationarity of beta coefficients. Financ Anal J 27(6):55–62
Markowitz H (1952) Portfolio selection. J Financ 7:77–91
Mech TS (1993) Portfolio return autocorrelation. J Financ Econ 34(3):307–344
Pollet JM, Wilson M (2010) Average correlation and stock market returns. J Financ Econ 96(3):364–380
Reinganum MR (1982) A direct test of roll’s conjecture on the firm size effect. J Financ 37(1):27–35
Roenfeldt RL, Griepentrog GL, Pflaum CC (1978) Further evidence on the stationarity of beta coefficients. J Financ Quantit Analy 13(1):117–121
Roll R (1981) A possible explanation of the small firm effect. J Financ 36(4):879–888
Theobald M (1981) Beta stationarity and estimation period: some analytical results. J Financ Quant Analy 16(5):747–757
Acknowledgements
None.
Funding
None.
Author information
Authors and Affiliations
Contributions
I am the sole author and the only one who contributed.
Corresponding author
Ethics declarations
Conflict of interest
There is no conflict of interest between this article’s content and any third party
Ethical approval
This article does not contain any studies with human participants performed by the author.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Ross, J. Does prior stock return correlation predict future stock return correlation?. SN Bus Econ 3, 176 (2023). https://doi.org/10.1007/s43546-023-00551-z
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s43546-023-00551-z