Long-term valuation effects of stock splits

This study attempts to determine if stock splits affect the long-term stock performance of forms, and to explain cross-sectional variation in this performance proxy among firms. The consequences of both higher percentage transaction cost following a stock split and an investor overreaction hypothesis are expected to render negative effects on stock values. The consequences of any earnings and dividend signaling accompanying splits are expected to have a positive impact on stock values. The results of the analysis suggest that the cumulative abnormal returns (CARs) are positive and statistically significant through the eleventh month after a stock split. The CARs then decrease nearly monotonically through the thirty-sixth month after the split (CAR=?8.23%). This indicates that initially the signaling effects dominate, but later the consequences of investors' downward revisions of previous expectations and the increase in percentage transaction cost dominate. The cross-sectional results indicate that firms with higher earnings-growth rates exhibit higher CARs, and firms with higher share prices just before the split exhibit lower CRRs.     

Overnight stock returns,intraday returns,and firm-specific investor sentiment

In this study, we propose a new index for measuring firm-specific investor sentiment using overnight and intraday stock returns. We use actual equity data to construct the firm-level investor sentiment index and find that the new index has characteristics expected of a sentiment measure. In addition, we propose a novel sentiment-weighted trading strategy and apply it to momentum and short-term reversal strategies. We find that the sentiment-weighted trading strategy generates better performance in momentum and short-term reversal strategies. The sentiment-weighted trading strategy’s superior performance is evidence that our firm-level investor sentiment index possesses predictive powers with regard to future returns.     

Predicting the portfolio risk of high-dimensional international stock indices with dynamic spatial dependence

The spatial dependence of assets, which relates to similarities in economic, political, or cultural systems and other aspects, has been confirmed through empirical research; however, spatial dependence has rarely been applied to financial risk measurement. To fill this gap in the literature, a dynamic spatial GARCH-copula (sGC) model is proposed in this paper to evaluate the portfolio risk of international stock indices. In this model, a spatial GARCH is used as the marginal distribution and vine copula is adopted as the joint distribution of indices. Then, the proposed model is applied empirically to assess portfolio risk. Results show that, first, the proposed risk prediction model with spatial dependence outperforms a model neglecting spatial effects per the Kupiec test, Z test and Christoffersen test. Risk prediction during periods of economic stability is also more accurate than during times of crisis. Second, risk measures for models with spatial dependence are higher than those without such dependence but lower than for vine copula models. Third, models including either spatial dependence or vine copulas alone exhibit relatively poor performance. Fourth, the model involving extreme value theory (EVT) generates the greatest value at risk to pass the Kupiec test, Z test and Christoffersen test; however, this model is not suitable for characterizing international indices with EVT based on negative values of the shape parameters of estimates. Findings offer important implications for personal investors, institutional investors, and national regulatory authorities.     

Can idiosyncratic volatility help forecast stock market volatility?

This paper examines the predictive power of idiosyncratic volatility in the context of daily stock market volatility dynamics. Specifically, the relative performance of various models of market volatility is considered with respect to whether idiosyncratic volatility is excluded or included as an explanatory variable in such models. Using high frequency data covering the thirty stocks within the Dow Jones Industrial Average (DJIA) index, the results indicate that the inclusion of idiosyncratic volatility leads to significant in-sample and out-of-sample improvements in the fit of all the volatility models considered. These results are shown to be relatively robust to the loss function adopted by the forecaster, with reasonable forecast accuracy improvements available to such forecasters.     

Predicting stock market crises using daily stock market valuation and investor sentiment indicators

The purpose of this paper is to develop a daily early warning system for stock market crises using daily stock market valuation and investor sentiment indicators. To achieve this goal, we use principal components analysis to propose a comprehensive index of daily market indicators that reflects stock market valuation and investor sentiment. Based on the comprehensive index, we employ a logit model with Ensemble Empirical Mode Decomposition to develop a daily early warning system for stock market crises. Finally, we apply the proposed system to the early warning for stock market crises in China. The in-sample forecasting results show that investor sentiment and the forecast horizon by Ensemble Empirical Mode Decomposition improve the forecasting performance of conventional early warning systems. The out-of-sample forecasting results indicate that the proposed warning system still has a good performance.     

The relationship between the valuation effect of equity financing and firm-specific characteristics

The findings of this study indicate that although the overall announcement effect of equity offerings is negative on the issuing firms' common stock prices, 23 percent of the sample firms have a positive two-day announcement period stock abnormal return (CAR). Further tests show that firms in the positive CAR group have significantly higher research and development (R&D) and profitability than firms in the negative CAR group, while the average leverage ratio of firms in the positive CAR group are significantly lower. Results of the regression analysis suggest a positive relation between the price effect and R&D as well as the profitability ratio. It also is found that only in the sample of large R&D firms is the magnitude of R&D significantly associated with the magnitude of announcement effect.     

Robust results on the sharing of firm-specific information: Incentives and welfare effects

Contrary to much of the existing literature, we obtain robust and clear-cut results for the incentives and welfare effects of information sharing when information is firm-specific. We show that firms’ incentives to share this type of information are aligned with social welfare. Whenever revealing information is the dominant strategy (such as for Cournot firms revealing costs or Cournot and Bertrand firms revealing demand), it is socially beneficial. Only cost information in Bertrand competition will not be revealed but this is socially desirable, too. These findings are independent of distributional assumptions on random shocks and signals and hold for general asymmetric oligopoly with any mixture of substitute, complementary and independent goods.     

Spin-offs to stock markets as a complementary form of entrepreneurship: Contrasting US,UK and Japanese experiences

This study explores the impact of the national institutional environment on the listing of firms on stock exchanges in Japan, the US, and the UK. In particular, the study compares the incidence of: (1) independent firm initial public offerings (IPOs); and (2) the subsidiaries of established corporations being spun-off to stock markets. An empirical analysis is conducted on a sample of 9118 IPOs extracted from the Securities Data Company New Issue Database. The results show that Japan and the UK are more active in incubating new innovative ventures within large corporations and spinning them to the stock markets than their general entrepreneurial activity would suggest. These results direct our attention to different forms of industrial renewal in different institutional environments.     

The New Keynesian Phillips Curve and staggered price and wage determination in a model with firm-specific labor

We develop a DSGE model with firm-specific labor where wage and price setting are subject to Calvo-type staggering. This is in general an intractable problem due to complicated intertemporal dependencies between price and wage decisions. However, the problem is significantly simplified if we, in line with empirical evidence, assume that prices can be changed whenever wages are. We show that the price- and wage-setting relationships are substantially altered by the introduction of firm-specific labor. Specifically, the inflation response is substantially dampened, whereas the wage inflation response is increased as compared to models with freely mobile labor. These distinctive features of the model with firm-specific labor are supported by empirical evidence from a structural VAR.     

Valuation of stock loans with jump risk

A stock loan is a special loan with stocks as collateral, which offers the borrowers the right to redeem the stocks on or before the maturity (Xia and Zhou, 2007, Dai and Xu, 2011). We investigate pricing problems of both infinite- and finite-maturity stock loans under a hyper-exponential jump diffusion model. In the infinite-maturity case, we derive closed-form formulas for stock loan prices and deltas by solving the related optimal stopping problem explicitly. Moreover, we obtain a sufficient and necessary condition under which the optimal stopping time is finite with probability one. In the finite-maturity case, we provide analytical approximations to both stock loan prices and deltas by solving an ordinary integro-differential equation as well as a complicated non-linear system. Numerical experiments demonstrate that the approximation methods for both prices and deltas are accurate, fast, and easy to implement.     

Panel estimators and the identification of firm-specific efficiency levels in parametric,semiparametric and nonparametric settings

The paper analyzes a number of competing approaches to modeling efficiency in panel studies. The specifications considered include the fixed effects stochastic frontier, the random effects stochastic frontier, the Hausman–Taylor random effects stochastic frontier, and the random and fixed effects stochastic frontier with an AR(1) error. I have summarized the foundations and properties of estimators that have appeared elsewhere and have described the model assumptions under which each of the estimators have been developed. I discuss parametric and nonparametric treatments of time varying efficiency including the Battese–Coelli estimator and linear programming approaches to efficiency measurement. Monte Carlo simulation is used to compare the various estimators and to assess their relative performances under a variety of misspecified settings. A brief illustration of the estimators is conducted using U.S. banking data.     

Hypothesis testing in nonparametric models of production using multiple sample splits

Several tests of model structure developed by Kneip et al. (J Bus Econ Stat 34:435–456, 2016) and Daraio et al. (Econ J 21:170–191, 2018) rely on comparing sample means of two different efficiency estimators, one appropriate under the conditions of the null hypothesis and the other appropriate under the conditions of the alternative hypothesis. These tests rely on central limit theorems developed by Kneip et al. (Econ Theory 31:394–422, 2015) and Daraio et al. (Econ J 21:170–191, 2018), but require that the original sample be split randomly into two independent subsamples. This introduces some ambiguity surrounding the sample-split, which may be determined by choice of a seed for a random number generator. We develop a method that eliminates much of this ambiguity by repeating the random splits a large number of times. We use a bootstrap algorithm to exploit the information from the multiple sample-splits. Our simulation results show that in many cases, eliminating this ambiguity results in tests with better size and power than tests that employ a single sample-split.

     

A Hotelling model with a ceiling on the stock of pollution

Environmental agreements such as the Kyoto Protocol aim to stabilize the amount of carbon in the atmosphere, which is mainly caused by the burning of nonrenewable resources such as coal. We characterize the solution to the textbook Hotelling model when there is a ceiling on the stock of emissions. We consider both increasing and decreasing demand for energy. We show that when the ceiling is binding, both the low-cost nonrenewable resource and the high-cost renewable resource may be used jointly. A key implication is that if energy demand were to decline in the long run, we may supplement energy supply through ‘clean’ renewables to meet the environmental standard, but then revert back to using only ‘dirty’ fossil fuels in the future when the ceiling has become non-binding. That is, the much heralded societal ‘transition’ to clean energy resources may be short-lived.     

Momentum strategies with stock index exchange-traded funds

Previously reported momentum profits may not be available to individual investors who have more trading constraints. Therefore, I examine the profitability of momentum strategies with international iShares and US sector exchange-traded funds (ETFs) traded on the NYSE. The index ETFs provide individual investors easy access to international stock markets and US sectors for asset allocations. Using cross-sectional momentum strategies, in contrast to prior research, I find that momentum profits are insignificant for the late 1990s–2014 period. Few country and industry ETFs yield positive results using time series momentum, and the overall performance is worse than the buy-and-hold strategy. Time series momentum offers significant profits during the 2008 global financial crisis, but the profits decline sharply for the post-crisis period.     

Forecasting the direction of the US stock market with dynamic binary probit models

Several empirical studies have documented that the signs of excess stock returns are, to some extent, predictable. In this paper, we consider the predictive ability of the binary dependent dynamic probit model in predicting the direction of monthly excess stock returns. The recession forecast obtained from the model for a binary recession indicator appears to be the most useful predictive variable, and once it is employed, the sign of the excess return is predictable in-sample. The new dynamic “error correction” probit model proposed in the paper yields better out-of-sample sign forecasts, with the resulting average trading returns being higher than those of either the buy-and-hold strategy or trading rules based on ARMAX models.     

The effects of employee stock ownership on stock liquidity: Evidence from the Korean market

This study investigates the relationship between the level of employee stock ownership (ESO) and stock liquidity. Using Korean ESO data, we find that ESO is positively associated with various liquidity measures. Stock-owning employees tend to mitigate information asymmetry to increase their benefits from the transparent market. We also find stronger effects when the firm is not an affiliate of a chaebol family group, and is less monitored by financial analysts, foreign investors, and outside directors. Furthermore, we employ various robustness tests to mitigate potential endogeneity concerns.     

证券民事赔偿新规及其对证券监管的影响

文章针对最高人民法院于2003年1月9日颁布的《关于审理证券市场因虚假陈诉引发的民事赔偿案件的若干规定》中关于案件受理前置条件的规定,进行了探讨,并认为虽然还存在有争议的地方,但却全面的影响了证券市场的监管。     

Estimating the determinants of stock price changes

I develop an approach for estimating the determinants of stock price changes that uses all eligible trade data and other observable parameters of market activity. This approach backs out the unobserved continuous price change distribution from the observable discrete price changes, and does not constrain the determinants to be proportions of the traded bid-ask spread. I show that theoretically impermissible results and skewed estimates of cost components are obtained when the model used for estimating the determinants of stock price changes does not attempt to uncover the mapping between the observed price changes and the underlying unobserved continuous price change process, and does not effectively use all eligible trade data.     

On the cointegration of international stock indices

This paper investigates the cointegration relationship among a group of international stock indices in light of new developments of econometric methods. Kasa (1992) first documented strong evidence for cointegration relations among five national stock indices, which suggests that there exists a common trend among those stock indices. Using Johansen multivariate cointegration test, we find that his findings are persistent in a sample of longer periods and more countries. In order to investigate whether these results are driven by statistical biases related to the sample size, we apply to our tests the Johansen’s small sample correction factor. The results still point toward the existence of a cointegration relationship but the evidence becomes much weaker. We next examine the empirical patterns emerged from different lag specifications and argue that Kasa’s findings are more likely due to the size distortion in extreme long lag VAR models. Indeed, when we employ a newly developed non-parametric test that does not require estimation VAR models, the null hypothesis of no cointegration cannot be rejected for the original sample of Kasa’s five-country stock indices from 1974 to 1990, nor for the extended period from 1970 to 2003.     

Cross-region risk spillover between the stock and stock index futures markets under exogenous shocks

By integrating the stock and futures markets of mainland China and Hong Kong into the same financial system, we explore the cross-region risk spillovers between the stock market and stock index futures market under the impact of exogenous events. We find evidence of significant risk spillovers between the two stock markets, and confirm that exogenous shocks, including the adjustments of regulatory policies of mainland China and 2019 Hong Kong Protest, can significantly affect the volatility spillover across assets and markets. Our findings can potentially help regulators and investors understand the cross-region risk conduction and assess portfolio risk after exogenous event.