Output and Expected Returns in Central and Eastern European Countries

Jerzy Gajdka, Piotr Pietraszewski

Abstract


Theoretical background: Although some controversy remains, some aspects of the predictability of aggregate stock market returns in the United States and other industrialized countries appear to be relatively well established. Intertemporal asset pricing models based on the paradigm of investor rationality and market efficiency imply that various macro variables describing the state of the economy may forecast future returns on the aggregate stock market.

Purpose of the article: The aim of the article is to present the results of a preliminary study which set out to determine whether the ratio of the stock index to the aggregate output in the economy and future rates of return in the aggregate stock markets in Central and Eastern Europe are significantly related to each other over different time horizons.

Research methods: Heteroskedasticity and autocorrelation-consistent estimators with a small sample degrees of freedom adjustment were used in regressions to track overlapping data problem and small sample bias.

Main findings: The analysis of the key market indices has shown that they explain much of the variation in the long-horizon future cumulative returns, as well as in cumulative excess returns.


Keywords


stock return predictability; GDP; industrial production; CEE countries

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References


Ang, A., & Bekaert, G. (2007). Stock return predictability: Is it there? Review of Financial Studies, 20(3), 651–707.

Bansal, R., & Yaron, A. (2004). Risks for the long run: A potential resolution of asset pricing puzzles. Journal of Finance, 59, 1481–1509.

Campbell, J., & Shiller, R.J. (1988). The dividend-price ratio and expectations of future dividends and discount factors. Review of Financial Studies, 1(3), 195–228.

Campbell, J.Y., & Cochrane, J.H. (1999). By force of habit: A consumption-based explanation of aggregate stock market behaviour. Journal of Political Economy, 107, 205–251.

Campbell, J., & Shiller, R.J. (2001). Valuation ratios and the long-run stock market outlook: An update. Working Paper no. 8221, NBER.

Chang, Y.S., & Pak, D.H. (2018). Warren Buffett value indicator vs. GDP size – is the relationship superlinear? International Journal of Economics and Business Research, 15(2), 223–235. doi:10.2139/ssrn.2897317

Cochrane, J.H. (1991). Production-based asset pricing and the link between stock returns and economic fluctuations. Journal of Finance, 46, 207–234.

Cochrane, J.H. (2007). The dog that did not bark: A defense of return predictability. Review of Financial Studies, 21, 1533–1575.

Cochrane, J.H. (2011). Presidential address: Discount rates. Journal of Finance, 66(4), 1047–1108. doi:10.1111/j.1540-6261.2011.01671.x

Domian, D.L., & Reichestein, W.R. (2009). Long-horizon stock predictability: Evidence and applications. The Journal of Investing, 18(3), 12–20.

Fama, E.F., & French, K. (1988). Dividend yields and expected stock returns. Journal of Financial Economics, 22, 3–25.

Goyal, A., & Welch, I. (2003). Predicting the equity premium with dividend ratios. Management Science, 49, 639–654.

Harri, A., & Brorsen, B.W. (2009). The overlapping data problem. Quantitative and Qualitative Analysis in Social Sciences, 3(3), 78–115.

Hansen, L., & Hodrick, R. (1980). Forward exchange rates as optimal predictors of future spot rates: An econometric analysis. Journal of Political Economy, 88(5), 829–853.

Hodrick, R.J. (1992). Dividend yields and expected stock returns: Alternative procedures for inference and measurement. Review of Financial Studies, 5(3), 357–386.

Indrayono, Y. (2019). Predicting returns with financial ratios: Evidence from Indonesian Stock Exchange. Management Science Letters, 9(11), 1901–1908. doi:10.5267/j.msl.2019.6.003

Keimling, N. (2016). Predicting stock market returns using the Shiller CAPE – an improvement towards traditional value indicators? doi:10.2139/ssrn.2736423

Kirby, C. (1997). Measuring the predictable variation in stock returns. Review of Financial Studies, 10(3), 579–630.

Lettau, M., & Ludvigson, S. (2001). Consumption, aggregate wealth and expected stock returns. Journal of Finance, 56(3), 815–849. doi:10.1111/0022-1082.00347

Lleo, S., & Ziemba, W.T. (2019). Can Warren Buffett forecast equity market corrections? The European Journal of Finance, 25(4), 369–393. doi:10.1080/1351847X.2018.1521859

Nelson, C.R., & Kim, M.J. (1993). Predictable stock returns: The role of small sample bias. Journal of Finance, 48, 641–661.

Newey, W.K., & West, K.D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3), 703–708.

Rangvid, J. (2006). Output and expected returns. Journal of Financial Economics, 81(3), 595–624. doi:10.1016/j.jfineco.2005.07.010

Rapach, D.E., Wohar, M.E., & Rangvid, J. (2005). Macro variables and international stock return predictability. International Journal of Forecasting, 21(1), 137–166. doi:10.1016/j.ijforecast.2004.05.004

Robertson, D., & Wright, S. (2006). Dividends, total cash flow to shareholders, and predictive return regressions. The Review of Economics and Statistics, 88(1), 91–99.

Santos, T., & Veronesi, P. (2006). Labor income and predictable stock returns. Review of Financial Studies, 19(1), 1–44.

Sekuła, P. (2016). Strategia wartości – test na GPW w Warszawie. Annales Universitatis Mariae Curie-Skłodowska, sectio H – Oeconomia, 50(4), 414–421.

Shiller, R.J. (2014). Speculative asset prices. Cowles Foundation Discussion Paper, 1936. doi:10.2139/ssrn.2391284

Stambaugh, R.F. (1999). Predictive regressions. Journal of Financial Economics, 54, 375–421.

Trevino, R., & Robertson, F. (2002). P/E ratios and stock market returns. Journal of Financial Planning, 15(2), 76–84.




DOI: http://dx.doi.org/10.17951/h.2020.54.4.41-54
Date of publication: 2020-12-29 19:34:56
Date of submission: 2020-06-22 14:39:50


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