Stock market dispersion, the business cycle and expected factor returns

Angelidis, Timotheos; Σάκκας, Αθανάσιος; Tessaromatis, Nikolaos

doi:10.1016/j.jbankfin.2015.04.025

Cited by 40 publications

(42 citation statements)

References 55 publications

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“…Despite its modest magnitude at peak (0.02%), the impact of stock market dispersion on the unemployment rate is relatively long-lived, with the responses lasting beyond the 20-month horizon. Our result is consistent with previous studies that use U.S. data (e.g., Loungani, Rush, andTave 1990, andmore recently Angelidis, Sakkas, andTessaromatis 2015) in the sense that unemployment significantly depends on the lags of stock market dispersion.…”

Section: Stock Market Dispersion and The Unemployment Ratesupporting

confidence: 93%

“…Fortin and Araar () study the effects of stock market dispersion using Canadian data. Recently, Angelidis, Sakkas, and Tessaromatis () find that returns dispersion can help predict variation in economic activity using a Probit regression with data from G‐7 countries. While also exploring the panel nature of the data, their approach is different from our panel VAR approach that highlights both the cross‐sectional heterogeneity and the dynamic nature of cross‐country interdependencies.…”

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confidence: 99%

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International Effects of Stock Market Dispersion

2020

Southern Economic Journal

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We study the extent to which stock market dispersion is related to unemployment and output growth for 16 countries over 20 years. Using panel vector‐auto‐regressions and panel dynamic regressions, we find increases in stock market dispersion across industries to induce future increases in unemployment and future decreases in industrial production (IP) growth. Moreover, the responses of unemployment and IP growth following a positive shock to stock market dispersion are persistent and are robust to various controls, sample periods, and estimation methods. Our article provides cross‐country evidence in support of the hypothesis that shifts in demand across industries negatively affect employment.

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Section: Stock Market Dispersion and The Unemployment Ratesupporting

confidence: 93%

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confidence: 99%

International Effects of Stock Market Dispersion

2020

Southern Economic Journal

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“…The results are consistent with findings in Hwang and Satchell (2005) and Byun (2016) that adding information in the cross-sectional dispersion helps better specify the daily volatility process. They are also in line with the evidence in Angelidis et al (2015), Connolly and Stivers (2006), and Stivers (2003), which document a positive relation between dispersion measures and future market-level volatility. The implication is that a higher dispersion measure is linked to a higher market volatility, indicating a deterioration of financial conditions.…”

Section: Introductionsupporting

confidence: 87%

“…Low cross-sectional dispersion implies that investors, both retail and institutional, follow each other in and out of the same stocks due to information cascade or behavioral biases especially during extreme market conditions (Chang et al, 2000;Choi and Sias, 2009;Christie and Huang, 1995;Lakonishok et al, 1992;Li et al, 2017;Yao et al, 2014). This underscores another inherent link between return dispersion and market volatility (Angelidis et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Cross-sectional return dispersion and volatility prediction

Fei

Liu

Wen

2019

Pacific-Basin Finance Journal

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We use intraday and daily data to examine the impact of cross-sectional return dispersion on volatility forecasting in the Chinese equity market. We adopt the GARCH, GJR-GARCH, and HAR models and, by augmenting them with return dispersion measures, provide empirical evidence that the return dispersion exhibits substantial information in describing the volatility dynamics by generating significantly lower forecasting errors at market and industry levels.Furthermore, the information content of the return dispersion tends to offer economic gain to a mean-variance utility investor. The findings are robust with respect to alternative volatility proxies, subsample analysis, and alternative market-wide stock indices. JEL code: G11, G17.

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“…5 Liu et al [42] showed that a rectification by the factor 1/s 1/2 is necessary. 6 In wavelet applications, it is common to raise the squared local amplitude to a further power in order to accentuate contrast in the corresponding heat map; see, e.g. Percival and Walden [47].…”

Section: Morlet Waveletsmentioning

confidence: 99%

Frequency aspects of information transmission in a network of three western equity markets

Schmidbauer

Rösch

Uluceviz

2017

Physica A: Statistical Mechanics and its Applications

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractCycles in the behavior of stock markets have been widely documented. There is an increasing body of literature on whether stock markets anticipate business cycles or its turning points. Several recent studies assert that financial integration impacts positively on business cycle comovements of economies.We consider three Western equity markets, represented by their respective stock indices: DJIA (USA), FTSE 100 (UK), and Euro Stoxx 50 (euro area). Connecting these three markets together via vector autoregressive processes in index returns, we construct "propagation values" to measure and trace, on a daily basis, the relative importance of a market as a volatility creator within the network, where volatility is due to a return shock in a market.A cross-wavelet analysis reveals the joint frequency structure of pairs of the propagation value series, in particular whether or not two series tend to move in the same direction at a given frequency. Our main findings are: (i) From 2001 onwards, the daily propagation values of markets have been fluctuating much less than before, and high frequencies have become less pronounced; (ii) the European markets are in phase at business cycle frequency, while the US market is not in phase with either European market; (iii) in 2008, the euro area has taken over the leading role. This approach not only provides new insight into the time-dependent interplay of equity markets, but it can also replicate certain findings of traditional business cycle research, and it has the advantage of using only readily available stock market data.

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Stock market dispersion, the business cycle and expected factor returns

Cited by 40 publications

References 55 publications

International Effects of Stock Market Dispersion

International Effects of Stock Market Dispersion

Cross-sectional return dispersion and volatility prediction

Frequency aspects of information transmission in a network of three western equity markets

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