A semiparametric random effects approach to unobserved heterogeneity in count data models of organizational founding

The paper discusses a semiparametric random-effects approach to the problem of unobserved population heterogeneity in organizational research based on models for pooled cross-sectional time series count data. The analytical value of this approach rests in its ability to produce estimates of the structural parameters that do not depend on any specific assumption about the distribution of the heterogeneity components in the population. The practical value of the method proposed is illustrated in an empirical application to processes of organizational founding, and to the relation between density dependence and unobserved heterogeneity in spatially distributed organizational populations. The empirical evidence produced suggests that future studies of organizational founding at the population level will have to account for variation in observed as well as unmeasured (or unobservable) variables.     

The impact of investor structure on stock price crash sensitivity: Evidence from China's stock market

Stock price crash sensitivity refers to the conditional probability of a stock crash when the market collapses. It focuses on individual stocks' sensitivity to the market crash and can affect stock pricing significantly. Although the crash sensitivity of China's stock market is very high as a whole (Weigert, 2016), different individual stocks show varying degrees of crash sensitivity. This paper, adopting the perspective of institutional investors, explores the reasons for the difference in crash sensitivity in China's stock market, and finds that: First, institutional investors' shareholdings is positively related to firms' stock price crash sensitivity. However, after dividing institutional investors into professional (represented by financial institutions) and non-professional institutional investors (represented by general legal persons), we find that only professional institutional investors' shareholdings is negatively related to firms' stock price crash sensitivity. Second, the impact of professional institutional investors on the crash sensitivity is influenced by stock liquidity and media sentiment: when the stock liquidity of listed companies is good or the media sentiment is strong, the negative impact of professional institutional investors on the crash sensitivity is accordingly high. This paper, by highlighting the investor structure, attempts a pioneering exploration of the influencing factors of the difference in stock price crash sensitivity in China. Our empirical results enrich research on stock price crash sensitivity and the heterogeneity of institutional investors. They can also serve to guide regulatory authorities' development of institutional investors and efforts to maintain market stability.     

Forecasting stock price volatility: New evidence from the GARCH-MIDAS model

This paper introduces a combination of asymmetry and extreme volatility effects in order to build superior extensions of the GARCH-MIDAS model for modeling and forecasting the stock volatility. Our in-sample results clearly verify that extreme shocks have a significant impact on the stock volatility and that the volatility can be influenced more by the asymmetry effect than by the extreme volatility effect in both the long and short term. Out-of-sample results with several robustness checks demonstrate that our proposed models can achieve better performances in forecasting the volatility. Furthermore, the improvement in predictive ability is attributed more strongly to the introduction of asymmetry and extreme volatility effects for the short-term volatility component.     

Corporate governance and stock price crash risk: Evidence from Taiwan

Managers are likely to withhold negative news to protect their own interests. When they can no longer withhold such news, extreme negative returns and a stock price crash (SPC) follow. This study explores whether a favorable corporate governance (CG) mechanism helps reduce SPC risk. The findings reveal that CG affects SPC risk. Moreover, the effects of institutional ownership, board size, and disclosure violation frequency are particularly significant in family-owned businesses. We also examine the effectiveness of CG evaluation (CGE) in Taiwan and discover that companies with high rankings are substantially less likely to encounter an SPC. This study verifies that CGE can be considered an indicator of SPC risk.     

Stock price reactions to stock dividend announcements: A case from a sluggish economic period

The 2007 financial crisis and the Great Recession that followed resulted in a loss of confidence among investors, and regaining their full trust and confidence has been a challenge for companies. Although economic growth has been volatile throughout the postwar World War II period, recent growth (2008–2015) has been remarkably weaker than in the previous low-growth period (1974–1995). The 2006–2015 period is often characterized by sluggish economic growth. This study investigates stock price reactions to stock dividend announcements, 30 days before and after the announcement dates, of publicly traded companies in the period 2006–2012. We use an event study methodology for 460 events and daily stock price data for companies in the CRSP historical data set. The study shows a significant reaction in stock prices around the event date. On average, stock prices reacted positively to stock dividend announcements. However, compared to previous findings of abnormal returns (5.9%), results from this study show small abnormal returns (about 1.81%) attributable to stock dividend announcements that are cumulative of the announcement day and up to 3-day post-announcement days. Our estimates are even lower than the 2.01% stock price reaction obtained in the 1987–1996 period.     

Using neural network to forecast stock index option price: a new hybrid GARCH approach

This study aims to apply a new hybrid approach to estimate volatility in neural network option-pricing model. The analytical results also indicate that the new hybrid method can be used to forecast the prices of derivative securities. Owing to combines the grey forecasting model with the GARCH to improve the estimated ability, the empirical evidence shows that the new hybrid GARCH model outperforms the other approaches in the neural network option-pricing model.     

A股市盈率特征分析——上海证券交易所的数据验证

利用历史数据计算按不同板块 (全部样本股票、行业划分、每股收益大小、流通股本大小、流通股比例、流通市值大小 )划分股票市场 ,各板块市盈率水平的具体数值 ,并讨论各不同板块的市盈率特征。研究结果表明 :板块的市盈率水平与每股收益、流通股本、流通市值具有反相关关系 ;同时 ,板块的市盈率水平和流通股比例没有明显的相关性规律。从多个角度讨论单个股票的市盈率与其直接影响变量———股票价格和每股收益之间的相互关系。用数学模型论述股票价格和每股收益在影响市盈率变化方面存在的差异 ,并用实际数据验证股票价格、每股收益、每股收益的倒数对市盈率的影响程度。研究结果证实 :决定股票市场市盈率水平及其变化的主要因素是上市公司的业绩而不是股票价格。     

How oil price and exchange rate affect stock price in China using Bayesian Quantile_on_Quantile with GARCH approach

We revisit the links of real exchange rate, oil price and stock market price for China using Bayesian Multivariate Quantile_on_Quantile with GARCH approach over the period of September 14, 2001 to June 17, 2022 (a total of 4051 days). Results indicate both the links between stock price and oil price and between stock price and exchange rate varying under different combinations of quantiles. GARCH model also indicate that yesterday news and persistence measures varying with current conditional variance under different quantiles. We further estimate half-life of a shock to our whole markets and find out the half-life of a shock range from 0.415 to 4.015 days. Result not found in previous study. Our study has important policy implications for the investors, practitioners, and the government.     

Measuring correlations of integrated but not cointegrated variables: A semiparametric approach

Many macroeconomic and financial variables are integrated of order one (or I(1)) processes and are correlated with each other but not necessarily cointegrated. In this paper, we propose to use a semiparametric varying coefficient approach to model/capture such correlations. We propose two consistent estimators to study the dependence relationship among some integrated but not cointegrated time series variables. Simulations are used to examine the finite sample performances of the proposed estimators.     

Index futures trading and stock price volatility: Evidence from Denmark and France

This study uses an EGARCH methodology to investigate the impact of index futures trading on the price volatility of two European stock markets. The results show that index futures trading has changed the distribution of stock returns in Denmark and France, however, it has not increased stock price volatility. There is evidence that futures trading has dampened stock price fluctuations in France. The results further show that stocks in Denmark and France exhibit strong volatility persistence and asymmetry, especially during the post-futures period.     

论中国股票市场的投资价

从动态一般均衡的角度看,中国股票市场存在巨大泡沫,没有投资价值.但是,股票市场的内在规律又决定了中国股票市场未来几年将在巨大泡沫的基础上走向巨大的牛市,中国股票市场面临非常好的投资机会.这有利于中国政府抓住机会解决股票市场中长期存在的规范、发展问题,同时也为广大投资者、上市公司、中介机构带来了难得的历史机遇.     

论中国股票市场的投资价值

从动态一般均衡的角度看，中国股票市场存在巨大泡沫，没有投资价值。但是，股票市场的内在规律又决定了中国股票市场未来几年将在巨大泡沫的基础上走向巨大的牛市，中国股票市场面临非常好的投资机会。这有利于中国政府抓住机会解决股票市场中长期仔在的规范、发展问题，同时也为广大投资、上市公司、中介机构带来了难得的历史机遇。     

Inference robustness of ARIMA models under non-normality —Special application to stock price data

Wold's decomposition theorem [Wold] states that every weakly stationary stochastic process can be written as a linear combination of orthogonal shocks. For practical reasons, however, it is desirable to employ models which use parameters parsimoniously.Box andJenkins [1970] show how parsimony can be achieved by representing the linear process in terms of a small number of autoregressive and moving average terms (ARIMA-models). The Gaussian hypothesis assumes that the shocks follow a normal distribution with fixed mean and variance. In this case the process is characterized by first and second order moments. The normality assumption seems reasonable for many kinds of series. However, it was pointed out byKendall [1953],Mandelbrot [1963, 1967],Fama [1965],Mandelbrot andTaylor [1967] that particularly for stock price data the distribution of the shocks appears leptokurtic: In this paper we investigate the sensitivity of ARIMA models to non-normality of the distribution of the shocks. We suppose that the distribution function of the shocks is a member of the symmetric exponential power family, which includes the normal as well as leptokurtic and platikurtic distributions. A Bayesian approach is adopted and the inference robustness of ARIMA models with respect to

1. the estimation of parameters
2. the forecasts of future observations is discussed.

     

基于数据挖掘技术的股票市场预测分析实例研究

本文主要研究数据挖掘技术在股票市场价格预测中的应用,重点采用数据挖掘分类和聚类算法对大盘走势和个股走势进行分析研究,采用实体数据进行挖掘分析,总结得出有益于投资者的结论。     

Modelling oil price expectations: Evidence from survey data

Using Consensus Forecast survey data on WTI oil price expectations for 3- and 12-month horizons over the period November 1989 to December 2008, we find that the rational expectation hypothesis is rejected and that none of the traditional extrapolative, regressive and adaptive processes fits the data by itself. We suggest a mixed expectation model defined as a linear combination of these traditional processes, which we interpret as the aggregation of individual mixing behavior and of heterogenous groups of agents using these simple processes. This approach is consistent with the economically rational expectations theory. We show that the target oil price included in the regressive component of this model depends on the long-run marginal cost of crude oil production and on short term macroeconomic fundamentals whose effects are subject to structural changes. For the two horizons, estimation results provide evidence for our mixed expectation model incorporating this break-dependent target price.     

Non-linearities in cross-country growth regressions: a semiparametric approach

In this paper we employ an additive semiparametric partially linear model to uncover the way that initial output and schooling levels affect growth rates. Our results based on marginal integration allow for graphical representation of the non-linearities that characterize the effects that these variables have on growth rates and suggest the presence of multiple regimes (equilibria). Our findings seem to be in agreement with those of Durlauf and Johnson ( 1995 ) and Hansen ( 1996 ) who used a different data set. Copyright © 1999 John Wiley & Sons, Ltd.     

Modeling microstructure price dynamics with symmetric Hawkes and diffusion model using ultra-high-frequency stock data

This study examines the theoretical and empirical perspectives of the symmetric Hawkes model of the price tick structure. Combined with the maximum likelihood estimation, the model provides a proper method of volatility estimation specialized in ultra-high-frequency analysis. Empirical studies based on the model using the ultra-high-frequency data of stocks in the S&P 500 are performed. The performance of the volatility measure, intraday estimation, and the dynamics of the parameters are discussed. A new approach of diffusion analogy to the symmetric Hawkes model is proposed with the distributional properties very close to the Hawkes model. As a diffusion process, the model provides more analytical simplicity when computing the variance formula, incorporating skewness and examining the probabilistic property. An estimation of the diffusion model is performed using the simulated maximum likelihood method and shows similar patterns to the Hawkes model.     

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.     

Volatility estimation for stock index options: A GARCH approach

The objective of this paper is to compare the mispricing of option valuation models when alternate techniques are applied to the volatility estimation. Akgiray (1989) shows that out-of-sample forecasts of return variances of stock indices based on a GARCH model are superior predictors of the actual ex-post variances in comparison to forecasts generated using standard rolling regression methods. A second objective of this study is to examine if Akgiray's results carry over to option valuation. Although we find that the implied volatility technique results in the least mispricing, within the class of forecasts using only historic returns data, the use of GARCH models will also significantly reduce model mispricing.     

A note on stock market seasonality: The impact of stock price volatility on the application of dummy variable regression model

This article provides both statistical analysis and empirical evidence that the dummy variable regression models extensively employed in the market seasonality literature may wind-up misleading results. We show that the estimates of the said model tend to reject the null hypothesis incorrectly once the stock returns exhibit higher volatility for the specified period under examination. Our empirical results suggest that the so-called “January effect” could be attributed to the application of inappropriate statistical method.