Evolution of price effects after one-day abnormal returns in the US stock market

This paper provides a comprehensive analysis of price effects after one-day abnormal returns and their evolution in the US stock market, using Dow Jones Index over the period 1890–2018. We utilise several statistical tests and econometric methods (the modified cumulative abnormal return approach, regression analysis with dummy variables, R/S analysis (Hurst, 1951), and the trading simulation approach). The results suggest that a strong momentum effect between 1940 and 1980 after a day of positive abnormal returns was present in the US stock market, and it was exploitable for profit. However, after the 1980s this has since disappeared. Overall, price effects after one-day abnormal returns during the analysed period tend to be unstable in terms of their strength and direction (momentum or contrarian effect). Nowadays, the evidence for the price effects after one-day abnormal returns in the US stock market is weak. Our results, therefore, are consistent with the Adaptive Market Hypothesis (Lo, 2004).     

Sensitivity of US equity returns to economic policy uncertainty and investor sentiments

This paper examines the sensitivity of major US sectoral returns to economic policy uncertainty and investor sentiments. Our analysis is based on weekly frequency and ranges from January 1995 to December 2015 covering a span of 20 years. Considering existing, however limited evidence of non-linear structure exhibited by investor sentiments and economic policy uncertainty and on the basis of our non-linear diagnostics, we use novel technique of non-parametric causality in quantiles approach proposed by Balcilar, Gupta, and Pierdzioch (2016). Our results highlight that economic policy uncertainty and investor sentiments act as driving factors for US sectoral returns. The nature of relationship is reported as asymmetrical for stock returns and symmetrical for variance of returns with an exception of Healthcare sector for economic policy uncertainty and bullish market sentiments. Our study carries implications for portfolio diversification and policy makers for forecasting market efficiency and economic trends.     

Does US news impact Asian emerging markets? Evidence from nonparametric causality-in-quantiles test

This paper aims to analyze whether US news on inflation and unemployment causes returns and volatility of seven emerging Asian stock markets from 1994 to 2014, by employing the causality-in-quantile approach. We find evidence that US news affect returns and/or volatility of all the seven stock markets considered, with these effects clustered around the tails of the conditional distribution of returns and volatility when they are either in bear or bull modes. In general, our results highlight the importance of modeling nonlinearity and studying entire conditional distributions of stock returns and volatility to draw correct inferences.     

The evolution of scale economies in US banking

Continued consolidation of the US banking industry and a general increase in the size of banks have prompted some policymakers to consider policies that discourage banks from getting larger, including explicit caps on bank size. However, limits on the size of banks could entail economic costs if they prevent banks from achieving economies of scale. This paper presents new estimates of returns to scale for US banks based on nonparametric, local‐linear estimation of bank cost, revenue, and profit functions. We report estimates for both 2006 and 2015 to compare returns to scale some 7 years after the financial crisis and 5 years after enactment of the Dodd–Frank Act with returns to scale before the crisis. We find that a high percentage of banks faced increasing returns to scale in cost in both years, including most of the 10 largest bank holding companies. Also, while returns to scale in revenue and profit vary more across banks, we find evidence that the largest four banks operate under increasing returns to scale.     

The Value-relevance of Geographic Segment Earnings Disclosures Under SFAS 14

This study examines whether geographic segment earnings as reported under the requirements of SFAS 14 provide value-relevant information. The FASB recently issued SFAS 131, which drastically changes the segment reporting requirements for US firms. Firms are required to disclose segment information by operating segment. For those firms that define operating segments along industry lines, disclosure of geographic segment earnings is no longer required. If geographic segment earnings provide value-relevant information, a potentially valuable source of information may be lost.
In this study, geographic segment earnings coefficients are estimated by (1) regressing unexpected security returns on unexpected geographic segment earnings and (2) regressing leading-period returns on current geographic segment earnings. Leading period returns involve extending the return interval to include the returns for prior years. The results show that unexpected geographic segment earnings relate differentially to unexpected security returns. For the leading-period returns model, little significant evidence is found for the market's differential valuation of geographic segment earnings coefficients for one- and two-year return intervals. When the return intervals extend to three years or more, significant evidence is found that the market values geographic segment earnings differently, which suggests that such disclosures reflect information used by market participants in setting security prices. The FASB may want to reconsider or amend its segment reporting requirements.     

Accounting earnings and firm valuation: the French case

Considering that the level of the association between stock returns and accounting earnings provides a measure of the extent to which earnings summarize the information which is useful for firm valuation, this paper analyses the contemporaneous association between stock returns and earnings changes or earnings level of individual French stocks and portfolios for periods of one, two and five years between 1981 and 1990. The empirical findings are as follows. (a) Stock returns are more linked to earnings changes than to earnings levels indicating that earnings provide more information about changes in firm value than about firm value. (b) Earnings prepared in accordance with the French accounting principles are not less value-relevant than those prepared in accordance with US or UK GAAP. (c) A cross-sectionally and time-aggregated data procedure provides a large increase in the explanatory power of earnings for returns which is consistent with a noise-in-earnings effect probably induced by accounting measurement and valuation principles and with a recognition lag effect due to the fact that value-relevant events are not integrated into earnings exactly when they occur. These two effects are shown to be the major causes of the low association between earnings and returns generally observed in studies based on short period data for individual stocks.     

Price effects after one-day abnormal returns in developed and emerging markets: ESG versus traditional indices

This paper examines price effects related to one day abnormal returns on the stock market indices of both developed and emerging countries while accounting for differences between environmental, social, governance (ESG), and conventional indices. Using daily data from MSCI family indices from 2007 to 2020 and various methods to avoid methodological bias, the following hypotheses are tested: after one-day abnormal returns, specific price effects (momentum/contrarian) appear (H1) in cases of positive (H1.1) and negative (H1.2) returns, price effects after one-day abnormal returns are stronger in the case of traditional indices as compared to ESG indices (H2), price effects after one-day abnormal returns are stronger during the crisis period (H3), a dynamic trigger approach is more appropriate for defining abnormal returns than a static approach (H4), price effects after one-day abnormal returns are stronger in emerging markets as compared to developed ones (H5). The results are mixed in the case of H1 and provide no evidence in favor of H2-H5. They also show no significant differences between ESG and conventional indices. The types of detected price effects are the same for the cases of ESG and conventional indices; their power is different in some cases. Overall, a strong contrarian effect is observed in the US stock market after one-day abnormal returns; a trading strategy constructed based on this observation could generate profits from trading. The main results offer additional evidence against the Efficient Market Hypothesis and provide implications that can assist practitioners in beating the market.     

Short-term institutions’ information advantage and overvaluation

Prior literature has documented that institutions which trade more frequently are better able to forecast future returns and have an informational advantage. This study examines a proximate explanation for the differences in performance based on institutions’ investment horizon – short-term institutions are better informed because they are better able to identify overvalued stocks that are short-sale constrained and overvalued in the context of Miller’s (1977) overvaluation hypothesis. Analysis is conducted on 6330 unique firms from 1996 to 2014 using the calendar-time portfolio approach where abnormal returns are estimated from the Fama-French-Carhart four-factor regression model. The results provide evidence that stocks which are extremely overvalued due to short-sale constraints have the greatest decline in short-term institutional ownership, consistent with the notion that short-term institutions are able to correctly assess the components for stock overvaluation.     

Abnormal returns, risk, and options in large data sets

Large data sets in finance with millions of observations have become widely available. Such data sets enable the construction of reliable semi-parametric estimates of the risk associated with extreme price movements. Our approach is based on semi-parametric statistical extreme value analysis, and compares favorably with the conventional finance normal distribution based approach. It is shown that the efficiency of the estimator of the extreme returns may benefit from high frequency data. Empirical tail shapes are calculated for the German Mark—US Dollar foreign exchange rate, and we use the semi-parametric tail estimates in combination with the empirical distribution function to evaluate the returns on exotic options.     

Relationship between oil,stock prices and exchange rates: A vine copula based GARCH method

In this paper, we apply a vine copula approach to investigate the dynamic relationship between energy, stock and currency markets. Dependence modeling using vine copulas offers a greater flexibility and permits the modeling of complex dependency patterns for high-dimensional distributions. Using a sample of more than 10 years of daily return observations of the WTI crude oil, the Dow Jones Industrial average stock index and the trade weighted US dollar index returns, we find evidence of a significant and symmetric relationship between these variables. Considering different sample periods show that the dynamic of the relationship between returns is not constant over time. Our results indicate also that the dependence structure is highly affected by the financial crisis and Great Recession, over 2007–2009. Finally, there is evidence to suggest that the application of the vine copula model improves the accuracy of VaR estimates, compared to traditional approaches.     

Tornado activity,house prices,and stock returns

In this paper we investigate the effects of tornado activity on house prices and stock returns in the US. First, using geo-referenced and metropolitan statistical area (MSA)-level data, we find tornado activity to be responsible for a significant drop in house prices. Spillover tornado effects between adjacent MSAs are also detected. Furthermore, our granular analysis provides evidence of tornadoes having a negative impact on stock returns. However, only two sectors seem to contribute to such a negative effect (i.e., consumer discretionary and telecommunications). In a macro-analysis, which relies on aggregate data for the South, West, Midwest and Northeast US regions, we then show that tornado activity generates a significant drop in house prices only in the South and Midwest. In these regions, tornadoes are also responsible for a drop in income. Tornado activity is finally found to positively (negatively) affect stock returns in the Midwest (South). If different sectors are examined, a more heterogeneous picture emerges.     

Forecasting risk in the US Dollar exchange rate under volatility shifts

Recent evidence suggests that volatility shifts (i.e. structural breaks in volatility) in returns increases kurtosis which significantly contributes to the observed non-normality in market returns. In this paper, we endogenously detect significant shifts in the volatility of US Dollar exchange rate and incorporate this information to estimate Value-at-Risk (VaR) to forecast large declines in the US Dollar exchange rate. Our out-of-sample performance results indicate that a GARCH model with volatility shifts produces the most accurate VaR forecast relative to several benchmark methods. Our contribution is important as changes in US Dollar exchange rate have a substantial impact on the global economy and financial markets.     

Anomalies in emerging markets: The case of Mexico

In this article we explore the relationship between 19 of the most common anomalies reported for the US market and the cross-section of Mexican stock returns. We find that 1-month stock returns in Mexico are robustly predicted only by 3 of the 19 anomalies: momentum, idiosyncratic volatility, and the lottery effect. Momentum has a positive relation with future 1-month returns, while idiosyncratic volatility and the lottery effect have a negative relation. For longer horizons of 3 and 6 months, only the 3 most important factors in the US market predict returns: size, book-to-market, and momentum.     

Mean–variance econometric analysis of household portfolios

We investigate households' portfolio choice using a microeconometric approach derived from mean–variance optimization. We assume that households have heterogeneous expectations on the distribution of excess returns and that they cannot take short positions in risky assets. Assuming two such assets, we derive an explicit solution of the model characterized by four possible portfolio regimes, which are analyzed using two structural probit and tobit specifications with three latent state variables. Both specifications are estimated by weighted maximum likelihood on a cross‐section of US households drawn from the 2004 SCF. The tobit specification is simulated in order to evaluate the regressors' effects on regime probabilities and asset demands. We also assess to what extent the predicted state variables are consistent with the self‐reported expected returns and risk aversion elicited from the SCF questionnaire. Copyright © 2009 John Wiley & Sons, Ltd.     

The maturity structure of term premia with time-varying expected returns

This paper analyzes the maturity structure of term premia using McCulloch’s US Treasury yield curve data from 1953–91, allowing expected returns to vary across time. One, 3, 6, and 12 month holding period returns on maturities up to 5 years are projected on 3 ex ante variables to compute time-varying expected returns, and simulations are employed to generate distributions of conditionally expected return premia. The likelihood of expected returns monotonically increasing in maturity (as implied by the liquidity preference hypothesis) is relatively high when the yield curve is steep and interest rates are high, and with longer holding periods, but low in other cases. The hypothesis that intermediate maturity bonds have the highest expected returns (a “hump-shaped” maturity-return pattern) around the onset of recessions does not receive much support.     

The Value Relevance of Earnings and Cash Flows: Empirical Evidence for Japan

The Japanese equity market is one of the largest in the world. In recent years, fund managers worldwide have substantially increased their exposure to the Japanese capital markets. In spite of the Japanese capital market's rapid growth and its increasing importance in the international financial world, there has been limited empirical evidence linking security returns to earnings and cash flows. This study extends the growing empirical literature on the association of earnings and cash flows with security returns by using a Japanese dataset consisting of 6,662 firm-year observations for the period 1984–93. We hypothesize that (i) earnings and cash flows are jointly associated with stock returns, and (ii) the association between cash flows (earnings) and security returns increases (decreases) when earnings are transitory. This study provides empirical evidence (i) that cash flows (earnings) have information content beyond earnings (cash flows) in explaining security returns, and (ii) that cash flows (earnings) play a more (less) important role in the marketplace when earnings are transitory. Moreover, results show that the explanatory power of our Japanese models is similar to the evidence provided in prior US studies, indicating that Japanese investors utilize earnings and cash flows in their pricing of equities as their US counterparts.     

Time–frequency quantile dependence between Bitcoin and global equity markets

In this paper, we examine return dependence between Bitcoin and stock market returns using a novel quantile cross-spectral dependence approach. The results suggest a right-tail (high return) dependence between Bitcoin and the stock markets in the long term and that said dependence decreases significantly from yearly to monthly investment horizons. Furthermore, right-tail dependence between Bitcoin and the US stock market is the strongest compared with other stock markets. We also extract information on the time-varying and time–frequency structure of co-movements between Bitcoin and the stock markets using wavelet-coherence analysis, the results of which suggest that the co-movement between Bitcoin and the US stock market is positive, whereas, for other stock markets, it is negative at certain frequencies and time periods. Overall, the findings highlight additional risk-management capabilities of Bitcoin according to different stock markets.     

Cross-sectional quantile regression for estimating conditional VaR of returns during periods of high volatility

Evaluating value at risk (VaR) for a firm’s returns during periods of financial turmoil is a challenging task because of the high volatility in the market. We propose estimating conditional VaR and expected shortfall (ES) for a given firm’s returns using quantile regression with cross-sectional (CSQR) data about other firms operating in the same market. An evaluation using US market data between 2000 and 2020 shows that our approach has certain advantages over a CAViaR model. Identification of low-risk firms and a reduction in computing times are additional advantages of the new method described.     

Oil price uncertainty and movements in the US government bond risk premia

In this paper, we analyze the predictability of the movements of bond premia of US Treasury due to oil price uncertainty over the monthly period 1953:06 to 2016:12. For our purpose, we use a higher order nonparametric causality-in-quantiles framework, which in turn, allows us to test for predictability over the entire conditional distribution of not only bond returns, but also its volatility, by controlling for misspecification due to uncaptured nonlinearity and structural breaks, which we show to exist in our data. We find that oil uncertainty not only predicts (increases) US bond returns, but also its volatility, with the effect on the latter being stronger. In addition, oil uncertainty tends to have a stronger impact on the shortest and longest maturities (2- and 5-year), and relatively weaker impact on bonds with medium-term (3- and 4-year) maturities. Our results are robust to alternative measures of oil market uncertainty and bond market volatility.     

Multifactor risk loadings and abnormal returns under uncertainty and learning

We explore the time variation of factor loadings and abnormal returns in the context of a four-factor model. Our methodology, based on an application of the Kalman filter and on endogenous uncertainty, overcomes several limitations of competing approaches used in the literature. Besides taking learning into account, it does not rely on any conditioning information, and it only imposes minimal assumptions on the time variation of the parameters. Our estimates capture both short- and long-term fluctuations of risk loadings and abnormal returns, also showing marked variation across US industry portfolios. The results from mean-variance spanning tests indicate that our baseline model yields accurate predictions and can therefore improve pricing and performance measurement.