An Empirical Analysis of the Bias and Rationality of Profit Forecasts Published in New Issue Prospectuses

Our study sets out to assess the accuracy of profit forecasts made by IPOs in Hong Kong. We use a variety of measures and tests to examine the accuracy, bias, rationality, and superiority of earnings estimates. The results show that forecast accuracy compares favourably with the findings from the developed economies of Australia, Britain, Canada, and New Zealand. Forecasts are shown to be superior to the predictions from time series models. IPOs tend to underforecast in the sense that actual profits exceed the forecasts. The rationality tests show mixed results. Cross-sectional analyses of forecast accuracy have poor explanatory power although the Big Six reporting accountants are associated with smaller forecast errors.     

Earnings Forecasts and the Information Contained in Spinoff Announcements

This study examines spinoff announcements in conjunction with financial analysts’ forecasts of earnings. The analysis shows that spinoff announcement abnormal returns are significantly related to the firm's information environment as proxieci by financial analysts’ earnings prediction errors. The findings also indicate that analysts significantly increase their short-term earnings forecasts in response to spinoffs, but do not significantly revise their long-term earnings forecasts. However, the earnings revisions are not significantly different across prediction error groups, which confirms that spinoff-related abnormal returns cannot be attributed solely to expected performance gains.     

Thailand-Japan＇s Volume of Trade Influencing on Socio-economic and Well-Being Development Using Maximum Entropy Bootstrap

This paper is preliminary information on some empirical findings based on an analysis of Thailand＇s macroeconomic data. The purpose of the study is to quantify social-economic well-being development with special emphasis on poverty. The maximum entropy bootstrap was presented that did not require the property of stationarity. Moreover, the methodology was stratified both the ergodic theorem and the central limit theorem. Thailand＇s GINI index and Thailand-Japan＇s volume of trade data cover the 14-year period from 1998 to 2011. There is a statistically significant negative non-linear relationship between endogenous Thailand＇s GINI index and exogenous Thailand-Japan＇s volume of trade. The results confirmed that every lagged one percent increase in Thailand-Japan＇s volume of trade influenced by a decrease of Thailand＇s GINI index coefficient 0.039 percent with the asymmetric around 0-almost closed to range condition -0.034824348 at 2.5%.     

Competitive sprawl

Sprawl and low urban density are often considered challenges to the vitality and efficiency of metropolitan areas. Utilizing a difference equation approach to endogenize density choices, this paper examines how competition between new cities causes development to occur too quickly compared to welfare maximizing development. Early development causes land to be sold in larger lots to a smaller number of buyers, causing lower population density. Competition in a timing game among developers causes early development, lower population density and lower capital density. I would like to thank James Peck, Don Haurin, Bruce Weinberg, David Hineline and an anonymous referee for comments on earlier drafts.     

GMM estimation of a maximum entropy distribution with interval data

We develop a generalized method of moments (GMM) estimator for the distribution of a variable where summary statistics are available only for intervals of the random variable. Without individual data, one cannot calculate the weighting matrix for the GMM estimator. Instead, we propose a simulated weighting matrix based on a first-step consistent estimate. When the functional form of the underlying distribution is unknown, we estimate it using a simple yet flexible maximum entropy density. Our Monte Carlo simulations show that the proposed maximum entropy density is able to approximate various distributions extremely well. The two-step GMM estimator with a simulated weighting matrix improves the efficiency of the one-step GMM considerably. We use this method to estimate the U.S. income distribution and compare these results with those based on the underlying raw income data.     

Forecasting the business cycle using survey data

Regular business survey data are published as percentages of firms predicting higher, equal or lower values of some reference variable. Time series of such percentages do not fit production data too well. Univariate models often produce forecasts which are just as accurarate. Still, surveys contain anticipative judgement which, when combined with univariate modeling and proper filtering, may produce a good indicator for business cycle turning points. The way survey data are transformed so as to fit statistics on production seems not to be of much importance. A case study of the Finnish forest industry is offered as an example.     

The fragility of a discipline when a model has monopoly status

We consider the consequences of a scientific literature with only one model of an important phenomenon. The falsification of the model would mean falsification of the science. Scientists who would prefer not to have their discipline falsified will be tempted to find ad hoc explanations to excuse the failure. To test this hypothesis we propose a study of the economic forecasts of the comparative Soviet and American growth rates in the years before a public choice model of central planning was a viable alternative to the public interest model. JEL Code A11, B23 Earlier versions of the paper were presented at the University of Manitoba Economics Department Retreat in October 2005 and at the Center for Study of Public Choice Wednesday Seminar in November 2005. We thank the participants for their suggestions. All the remaining errors are our responsibility.     

Measuring the Impact of Corporate Investment Announcements on Share Prices: The Spanish Experience

We bring together three disparate strands of literature to develop a comprehensive empirical framework to examine the efficiency of security analysts' earnings forecasts in Singapore. We focus specifically on how the increased uncertainty and the negative market sentiment during the period of the Asian crisis affected the quality of earnings forecasts. While we find no evidence of inefficiencies in the pre-crisis period, our results suggest that after the onset of the crisis, analysts (1) issued forecasts that were systematically upward biased; (2) did not fully incorporate the (negative) earnings-related news; and (3) predicted earnings changes which proved too extreme.     

The disparity between long-term and short-term forecasted earnings growth

We find the disparity between long-term and short-term analyst forecasted earnings growth is a robust predictor of future returns and long-term analyst forecast errors. After adjusting for industry characteristics, stocks whose long-term earnings growth forecasts are far above or far below their implied short-term forecasts for earnings growth have negative and positive subsequent risk-adjusted returns along with downward and upward revisions in long-term forecasted earnings growth, respectively. Additional results indicate that investor inattention toward firm-level changes in long-term earnings growth is responsible for these risk-adjusted returns.     

Accuracy, unbiasedness and efficiency of professional macroeconomic forecasts: An empirical comparison for the G7

In this paper, we use survey data to analyze the accuracy, unbiasedness and efficiency of professional macroeconomic forecasts. We analyze a large panel of individual forecasts that has not previously been analyzed in the literature. We provide evidence on the properties of forecasts for all G7-countries and for four different macroeconomic variables. Our results show a high degree of dispersion of forecast accuracy across forecasters. We also find that there are large differences in the performances of forecasters, not only across countries but also across different macroeconomic variables. In general, the forecasts tend to be biased in situations where the forecasters have to learn about large structural shocks or gradual changes in the trend of a variable. Furthermore, while a sizable fraction of forecasters seem to smooth their GDP forecasts significantly, this does not apply to forecasts made for other macroeconomic variables.