Price nonsynchronicity, idiosyncratic risk, and expected stock returns in China

We are the first to examine the pricing of relative idiosyncratic risk, or price nonsynchronicity, in the Chinese equity market. Using several tests, we investigate returns on more than 2700 companies in the period 1998 to 2018. Contrary to the U.S. evidence, price nonsynchronicity negatively predicts future returns in the cross-section. A value-weighted strategy going long (short) the quintile of least (most) synchronised stocks produces a negative monthly six-factor model alpha of 0.61%. Also, we demonstrate that the effect is driven by the low-idiosyncratic volatility anomaly. Once the absolute idiosyncratic risk is taken into account, the nonsynchronicity becomes irrelevant for future returns

Composite equity issuance and the cross-section of country and industry returns

Behavioural finance literature argues that stock issuance contains information on equity valuation. If so, does it predict the cross-section of both country and industry stock returns? To answer this question, we investigate data from 68 markets from 1976 to 2022. We find that composite equity issuance negatively correlates with future aggregate stock returns. An equal-weighted quintile of countries (industries) with the highest issuance underperforms those with the lowest by 0.34% (0.58%) per month. Established risk factors and other anomalies cannot subsume this cross-sectional pattern. Furthermore, the effect remains robust to many considerations. This documented issuance anomaly paves the way for an exploitable international investment strategy

Trade competitiveness and the aggregate returns in global stock markets

Using the change in the real effective exchange rate (REER) to reflect trade competitiveness, we examine its role in the cross-section of global equity returns. The changes in REER negatively affect stock market returns. The REER effect is robust after controlling for known risk factors and market characteristics. Furthermore, it remains pervasive across different periods and subsamples. Our findings support the conventional wisdom that appreciating currency harms trade values, consequently dampening a firm\u27s stock market performance

Seasonality in the Cross-Section of Cryptocurrency Returns

This study presents the first attempt to examine the cross-sectional seasonality anomaly in cryptocurrency markets. To this end, we apply sorts and cross-sectional regressions to investigate daily returns on 151 cryptocurrencies for the years 2016 to 2019. We find a significant seasonal pattern: average past same-weekday returns positively predict future performance in the cross-section. Cryptocurrencies with high same-day returns in the past outperform cryptocurrencies with a low same-day return. This effect is not subsumed by other established return predictors such as momentum, size, beta, idiosyncratic risk, or liquidity

Is geopolitical risk priced in the cross-section of cryptocurrency returns?

We examine the role of geopolitical risk in the cross-sectional pricing of cryptocurrencies. We calculate cryptocurrency exposure to changes in the geopolitical risk index and document that coins with the lowest geopolitical beta outperform those with high geopolitical beta. Our findings suggest that risk-averse investors require additional compensation as motivation to hold cryptocurrencies with low and negative geopolitical betas, and they are willing to pay a premium for assets with high and positive geopolitical betas. The effect cannot be explained by known return predictors and is robust to many considerations.Narodowym Centrum Nauki, NCN: 2021/41/B/HS4/02443National Science Center of Poland [2021/41/B/HS4/02443

The abundance of homoeologue transcripts is disrupted by hybridization and is partially restored by genome doubling in synthetic hexaploid wheat

Dataset S7. List of nonadditively expressed genes in F1 hybrids derived from AS2255 × AS60. (XLSX 72 kb

Extreme risk spillovers between China and major international stock markets

We examine the complex dependence structure and risk spillovers between the Chinese stock market and twelve major international markets. To this end, we employ three types of vine copulas and tests for the Granger causality in risk of Hong et al. (2009). The results indicate that the R-vine copula is the optimal model to characterize the high-dimensional dependence structure of the markets after China joined the WTO, which suggests obvious structural differences with varying degrees of mainly positive dependences. Moreover, we identify unilateral extreme risk spillovers from China to the United States, France, and Germany, and either from Japan to China. We also detect bilateral spillovers between China and the United States, Japan, as well as Australia

What drives the dependence between the Chinese and global stock markets?

By applying time-varying copulas and panel regression analysis, this study investigates the dependence between the Chinese and eleven international stock markets, as well as its determinants during the period 2002-2018. Our results indicate that the dependence magnitude between the Chinese stock market and major international markets varies with region. Furthermore, the dependence is negatively driven by both economic policy uncertainty differentials and interest rate differentials while positively affected by the global financial crisis and trade interdependence. Our findings are of great importance to international investors and policymakers

Forecasting the equity premium: Do deep neural network models work?

oai:ojs2.mf-journal.com:article/2This paper constructs deep neural network (DNN) models for equity-premium forecasting. We compare the forecasting performance of DNN models with that of ordinary least squares (OLS) and historical average (HA) models. The DNN models robustly work best and significantly outperform both OLS and HA models in both in- and out-of-sample tests and asset allocation exercises. Specifically, DNN models generate monthly out-of-sample R2 of 3.42% and an annual utility gain of 2.99% for a mean-variance investor from 2011:1 to 2016:12. Moreover, the forecasting performance of DNN models is enhanced by adding additional 14 variables selected from finance literature