Lagged country returns and international stock return predictability during business cycle recession periods

Wen, Yi-Chieh; Li, Bin

doi:10.1080/00036846.2020.1752899

Cited by 8 publications

(3 citation statements)

References 52 publications

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“…Many studies find that stock return predictability is sensitive to business cycles, as the variation in economic conditions will influence investor behavior and will eventually drive stock returns (Andrei et al , 2019; Racicot and Théoret, 2019; Wen and Li, 2020). Moreover, the relationship between higher moments contained in hedge funds and future returns has an asymmetric effect over the business cycles (Gregoriou et al , 2020; Racicot and Théoret, 2019).…”

Section: Resultsmentioning

confidence: 99%

Higher moments and US industry returns: realized skewness and kurtosis

Chen

Worthington

2021

RAF

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Purpose The purpose of this paper is to examine the relationships between the higher moments of returns (realized skewness and kurtosis) and subsequent returns at the industry level, with a focus on both empirical predictability and practical application via trading strategies. Design/methodology/approach Daily returns for 48 US industries over the period 1970–2019 from Kenneth French’s data library are used to calculate the higher moments and to construct short- and medium-term single-sort trading strategies. The analysis adjusts returns for common risk factors (market, size, value, investment, profitability and illiquidity) to confirm whether conventional asset pricing models can capture these relationships. Findings Past skewness positively relates to subsequent industry returns and this relationship is unexplained by common risk factors. There is also a time-varying effect in which the predictive role of skewness is much stronger over business cycle expansions than recessions, a result consistent with varying investor optimism. However, there is no significant relationship between kurtosis and subsequent industry returns. The analysis confirms robustness using both value- and equal-weighted returns. Research limitations/implications The calculation of realized moments conventionally uses high-frequency intra-day data, regrettably unavailable for industries. In addition, the chosen portfolio-sorting method may omit some information, as it compares only average group returns. Nonetheless, the close relationship between skewness and future returns at the industry level suggests variations in returns unexplained by common risk factors. This enriches knowledge of market anomalies and questions yet again weak-form market efficiency and the validity of conventional asset pricing models. One suggestion is that it is possible to significantly improve the existing multi-factor asset pricing models by including industry skewness as a risk factor. Practical implications Given the relationship between skewness and future returns at the industry level, investors may predict subsequent industry returns to select better-performing funds. They may even construct trading strategies based on return distributions that would generate abnormal returns. Further, as the evaluation of individual stocks also contains industry information, and stocks in industries with better performance earn higher returns, risks related to industry return distributions can also shed light on individual stock picking. Originality/value While there is abundant evidence of the relationships between higher moments and future returns at the firm level, there is little at the industry level. Further, by testing whether there is time variation in the relationship between industry higher moments and future returns, the paper yields novel evidence concerning the asymmetric effect of stock return predictability over business cycles. Finally, the analysis supplements firm-level results focusing only on the decomposed components of higher moments.

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Section: Resultsmentioning

confidence: 99%

Higher moments and US industry returns: realized skewness and kurtosis

Chen

Worthington

2021

RAF

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“…Alessi and Detken (2011) found that the global measures of liquidity are the best indicators of asset price cycles as opposed to domestic monetary and credit conditions. Additionally, the cross-country influence between stock prices may arise from at least two channels: the impact of monetary policy conducted abroad (Bhattarai et al, 2021;Kolasa & Wesołowski, 2020) and the link between stock prices in different countries (Wen & Li, 2020;Tang et al, 2019), among other channels.…”

Section: Datamentioning

confidence: 99%

The Interconnectedness of Stock Prices, Money, and Credit Across Time and Frequency From 1970 to 2016

Ryczkowski

Zinecker

2022

Technological and Economic Development of Economy

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This article applies continuous wavelet and maximal overlap discrete wavelet transforms to study the co-movements and the direction of causality between money (M3 and M2), bank credit, credit from all sectors vs. stock price dynamics across time and frequency for 12 developed countries. The time-varying linkages were significant typically for credit rather than money developments. Particularly, these linkages appeared during phases of house and stock price boom and bust at a business cycle frequency. The discovered lead-lag patterns give relatively much time for monetary policy authorities to support their financial stability objectives by imposing restrictions on credit. Closing the channel through which credit and stock prices interact may counteract the rising financial imbalances. This finding holds also for the euro area where the monetary pillar is based on broad money. The new application of wavelets allowed discovering the varying linkages that were not necessarily evident in the standard methods of analysing data.

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“…Predicting stock market returns has long been of interest to academics, policy makers and practitioners in finance. Numerous studies have examined the predictability of stock market returns around the world, yet the robustness of predictions remains unresolved (see Li et al, 2020;2022;Wen and Li, 2020). The need to predict changes in stock returns in international financial market has become essentially important in the wake of growing financial globalization, as robust predictions enable investors to minimize their risks and losses.…”

Section: Introductionmentioning

confidence: 99%

Does gold–platinum price ratio predict stock returns? International evidence

Singh

2022

IJMF

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PurposeThe purpose of this study is to investigate the relationship between gold–platinum price ratio (GP) and stock returns in international stock markets. The study addresses three empirical questions: (1) Does GP have robust predictive power in international stock markets? (2) Does GP outperform other macroeconomic variables in international stock markets? (3) What is the relationship between GP and stock market returns during economic recessions?Design/methodology/approachThe study mainly uses OLS regressions to perform empirical tests for a comprehensive set of 17 advanced international stock markets and overall world market. The monthly data is used for the period January 1978 to July 2019, 499 observations for each market.FindingsThe study finds that the first-difference of GP (ΔGP), not the initial-level of GP, has strong predictive power for stock returns, both in short- and long-time horizons. The results remain robust after controlling for a number of macroeconomic predictors. The out-of-sample test results are significant, confirming the robustness of the predictive power of ΔGP.Originality/valueThis study is the first to examine the ability of the ΔGP to predict stock returns, and provide novel evidence on the relationship between ΔGP and international stock markets. The study draws on behavioral finance theory, specifically the myopic loss aversion, the herd effect and the limited attention theory, to explain the predictability of stock returns in international stock markets.

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Lagged country returns and international stock return predictability during business cycle recession periods

Cited by 8 publications

References 52 publications

Higher moments and US industry returns: realized skewness and kurtosis

Higher moments and US industry returns: realized skewness and kurtosis

The Interconnectedness of Stock Prices, Money, and Credit Across Time and Frequency From 1970 to 2016

Does gold–platinum price ratio predict stock returns? International evidence

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