Copyright (c) 2009 All rights reserved. http://works.bepress.com/leon_zolotoy Recent documents in Leon Zolotoy en-us Sat, 05 Dec 2009 03:38:22 PST 3600 http://works.bepress.com/leon_zolotoy/7 http://works.bepress.com/leon_zolotoy/7 Thu, 03 Dec 2009 15:18:53 PST This paper uses cross country data to explore the role of cultural attributes in inequality of income distribution and poverty within countries. Using the Hofstede cultural dimensions, we found that individualism is negatively correlated with the Gini index and poverty ratio. We also found that the lower the status of women in a society, the higher the level of poverty in that society. Furthermore, we found that combing the cultural variables in the analysis significantly improves its ability to explain the differences in inequality and poverty in different countries. Leon Zolotoy http://works.bepress.com/leon_zolotoy/6 http://works.bepress.com/leon_zolotoy/6 Thu, 03 Dec 2009 15:09:04 PST What are the determinants of welfare caseload fluctuations? To address this crucial policy issue most current research uses either cross-sectional or time-series models. In this study we propose a new two-step latent factor approach. First, a latent-factor model is fitted to the data. Next, time-series techniques are applied to study the relation between the latent factor and control macroeconomic and demographic variables. As an application, we study the dynamics of welfare caseload in Israel over the period 1986- 2002. Our major findings are as follows. First, a single latent-factor model accounts for more than 80% of the variance in welfare caseload. Second we find strong evidence that the latent factor and control variables are cointegrated. Third, the cointegration relation has experienced a structural shift during 1994-1995 following new welfare legislations as well as changes in the Israeli labor market. Overall, our findings suggest that both economic and demographic conditions as well as welfare policy regulations play an important role in explaining welfare caseload trends. Leon Zolotoy http://works.bepress.com/leon_zolotoy/5 http://works.bepress.com/leon_zolotoy/5 Thu, 03 Dec 2009 15:05:36 PST In this paper we address the issue of modelling the relation between the stock prices and accounting earnings in the presence of potential divergence of opinions regarding the earnings data generating process. In our model the market's earnings expectation is defined as the weighted average of both the time-series and analysts' forecasts, with weights being estimated directly from stock returns. No assumptions are made on functional form of the earnings surprise-stock returns relation, which makes our model flexible enough to incorporate a variety of models discussed in the previous literature. The model is estimated semiparametrically following Hardle et al. [Annals of Statistics, 1993]. Our key findings are as follows. First, we find that investors use both the time- series models and analysts' forecasts to predict future earnings. Second, the proportion of investors using the random walk (analysts) forecasts is significantly higher (lower) for the stocks with low (high) proportion of institutional holdings. Third, we find the choice of a particular earnings forecasting model to be related to its' forecast accuracy, effect which is less pronounced for the institutional investors. Finally, we show how accounting for the dispersion of earnings forecasts leads to a substantial increase in the magnitude of the post-earnings announcement drift. Leon Zolotoy http://works.bepress.com/leon_zolotoy/4 http://works.bepress.com/leon_zolotoy/4 Thu, 03 Dec 2009 14:57:38 PST The post-earnings announcement drift is the tendency of cumulative abnormal re- turns to drift in the direction of earnings surprise for several weeks after the earnings news is released. We show that a standard approach of measuring abnormal returns by using pre-announcement estimates of market risk (betas) causes the magnitude of this phenomenon to be significantly underestimated. Our key findings are as follows. First, we find that stock beta tends to rise (fall) following the release of "bad" ("good") earnings news. Second, we find that by not taking into account post-announcement shifts in betas prior studies are likely to underestimate the magnitude of the drift. A 60-days cumulative abnormal returns on hedge portfolio appear to be approximately 1.4% higher when the "leverage effect" is incorporated in the estimates of market risk. Our results are robust with respect to different model specifications as well as different earnings surprise measures. Leon Zolotoy http://works.bepress.com/leon_zolotoy/3 http://works.bepress.com/leon_zolotoy/3 Thu, 03 Dec 2009 14:54:25 PST Previous studies reported firms management to release more"bad" news on Fridays compared to the rest of weekdays, potentially exploiting investors limited attention. In this study we examine whether this strategy was detected by investors. Our key findings are as follows. First, consistent with previous studies, we find that over the last two decades firms consistently reported more "bad" news on Friday than during the rest of trading days. Second, we report a structural shift in the earnings-return relation with stock returns becoming more sensitive to the Friday negative earnings news compared to similar announcements released during the rest of the week. Finally, we find this structural shift to be particularly pronounced for the firms with high financial visibility. Overall, our findings suggest that investors have learned about the firms management strategy to report "bad" news on Fridays. As a result, the benefits from following this strategy have disappeared over time. Leon Zolotoy http://works.bepress.com/leon_zolotoy/2 http://works.bepress.com/leon_zolotoy/2 Thu, 03 Dec 2009 14:47:43 PST In this paper we study the dynamic relationship between trading volume, volatility, and stock returns at international stock markets. We test a number of theoretical models which suggest that the trading volume and volatility can predict future behavior of stock returns. Our analysis uses both semi-nonparametric (Flexible Fourier Form) and parametric techniques. Our findings suggest that the main factor driving the magnitude of the return reversals is stock market volatility and not trading volume. First, apart from a direct effect on expected returns with mixed signs, we find no evidence of the trading volume affecting the serial correlation of stock market returns, as predicted by Campbell et al. (1993) and Wang (1994). Second, the stock market volatility has a negative and statistically significant impact on the serial correlation of the stock market returns, consistent with the "positive feedback" trading model of Sentana and Wadhwani (1992). Third, the lagged trading volume is positively related to the stock market volatility, supporting the "information flow" theory (Clark, 1973). Moreover, we find that taking into account both trading volume and volatility improves the accuracy of the out-of-sample forecasts of the stock market behavior. Leon Zolotoy http://works.bepress.com/leon_zolotoy/1 http://works.bepress.com/leon_zolotoy/1 Thu, 03 Dec 2009 14:44:30 PST We examine the mechanism of the pricing information transfer between Tokyo and the US stock markets using a sample of Japanese firms whose shares are listed on both markets. At the mean return level Tokyo emerges as the dominant market, while the US markets behave as the satellites. In terms of higher moments, we find significant symmetric volatility spillovers, and some evidence of cross-market skewness dynamics. Moreover, we find strong empirical evidence of the trading vol- ume affecting the dynamics of the information spillovers, but in an asymmetric way. This asymmetry suggests that the trading volume provides additional information to investors. Leon Zolotoy