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Financial Economics (financial + economics)

Distribution by Scientific Domains
Business, Economics, Finance and Accounting75%

Selected Abstracts

Spurious Regressions in Financial Economics?

THE JOURNAL OF FINANCE, Issue 4 2003
Wayne E. Ferson
ABSTRACT Even though stock returns are not highly autocorrelated, there is a spurious regression bias in predictive regressions for stock returns related to the classic studies of Yule (1926) and Granger and Newbold (1974). Data mining for predictor variables interacts with spurious regression bias. The two effects reinforce each other, because more highly persistent series are more likely to be found significant in the search for predictor variables. Our simulations suggest that many of the regressions in the literature, based on individual predictor variables, may be spurious. [source]

The disposition effect and investment performance in the futures market

THE JOURNAL OF FUTURES MARKETS, Issue 6 2009
Hyuk Choe
This study examines whether the disposition effect (DE), i.e., the tendency of investors to ride losses and realize gains, exists in the Korean stock index futures market. Using a unique database, we find strong evidence for the DE and explain this in terms of investor characteristics. We also investigate the effect that the disposition bias has on investment performance. There are four main findings. First, individual investors are much more susceptible to the DE than institutional and foreign investors. Second, sophistication and trading experience tend to reduce the DE. Third, the DE is stronger in long positions than in short positions. Finally, there is a negative relationship between the DE and investment performance. This result is consistent with Odean (1998, Journal of Finance, 53, 1775,1798), but contrasts with Locke and Mann (2005, Journal of Financial Economics, 76, 401,444) who find no evidence of any contemporaneous measurable costs associated with the DE. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 29:496,522, 2009 [source]

Improved Estimates of Correlation Coefficients and their Impact on Optimum Portfolios

EUROPEAN FINANCIAL MANAGEMENT, Issue 3 2006
Edwin J. Elton
G11 Abstract To implement mean variance analysis one needs a technique for forecasting correlation coefficients. In this article we investigate the ability of several techniques to forecast correlation coefficients between securities. We find that separately forecasting the average level of pair-wise correlations and individual pair-wise differences from the average improves forecasting accuracy. Furthermore, forming homogenous groups of firms on the basis of industry membership or firm attributes (e.g. size) improves forecast accuracy. Accuracy is evaluated in two ways: First, in terms of the error in estimating future correlation coefficients. Second, in the characteristics of portfolios formed on the basis of each forecasting technique. The ranking of forecasting techniques is robust across both methods of evaluation and the better techniques outperform prior suggestions in the literature of financial economics. [source]

Non-Gaussian Ornstein,Uhlenbeck-based models and some of their uses in financial economics

JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 2 2001
Ole E. Barndorff-Nielsen
Non-Gaussian processes of Ornstein,Uhlenbeck (OU) type offer the possibility of capturing important distributional deviations from Gaussianity and for flexible modelling of dependence structures. This paper develops this potential, drawing on and extending powerful results from probability theory for applications in statistical analysis. Their power is illustrated by a sustained application of OU processes within the context of finance and econometrics. We construct continuous time stochastic volatility models for financial assets where the volatility processes are superpositions of positive OU processes, and we study these models in relation to financial data and theory. [source]