1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

On robust estimation of the location parameter

Stock market efficiency is an important concept, for understanding the working of the capital markets particularly in emerging stock market such as India. The efficiency of the emerging markets assumes greater importance as the trend of investments is accelerating in these markets as a result of regulatory reforms and removal of other barriers for the international equity investments. There is enough evidence on market efficiency and day of the week effect in the developed markets, however, the same is not true for the emerging stock markets. This study provides empirical evidence on weak form efficiency and the day of the week effect in Bombay Stock Exchange over a period of 1987-1994. The results provide evidence of day of the week effect and that the stock market is not weak form efficient. The day of the week effect observed on the BSE pose interesting buy and hold strategy issues.

Evidence on Weak Form Efficiency and Day of the Week Effect in the Indian Stock Market

We present evidence on the day-of-the-week effect on a stock market with a particular settlement procedure: the Paris Bourse. A strong and persistent negative return is found on Tuesdays. A similar result was previously found for Japan and Australia. The settlement procedure is shown to have an effect on the distribution of daily returns consistent with forward pricing theory; it cannot explain the negative Tuesday return. After adjusting for this settlement effect, the mean return on Mondays is the largest in the week, a result at odds with the evidence in the U.S.A.

Day-of-the-week effect on the Paris Bourse

After describing the various concepts of efficiency (information, valuation, full-insurance and functional) with special reference to the Italian stock market, the paper analyzes the impact of particular dates and periods of the civil year and stock exchange calendars on stock price changes to test the existence of information inefficiencies. The analysis is based on the Milan Stock Exchange's ‘MIB storico’ stock index with reference to the period 2 January 1975–22 August 1989. The events tested for systematic anomalies include weekend and public holidays, the end of the calendar and stock exchange months, and the end of the year. The results obtained are in line with those found for the U.S. market, with evidence of anomalous changes, though not all are stable over time.

The italian stock market: Efficiency and calendar anomalies☆

(1988). Calendar Anomalies: Abnormal Returns at Calendar Turning Points. Financial Analysts Journal: Vol. 44, No. 6, pp. 28-39.

Calendar Anomalies: Abnormal Returns at Calendar Turning Points

This paper presents fast algorithms for calculating mean-variance efficient frontiers when the investor can sell securities short as well as buy long, and when a factor and/or scenario model of covariance is assumed. Currently, fast algorithms for factor, scenario, or mixed (factor and scenario) models exist, but (except for a special case of the results reported here) apply only to portfolios of long positions. Factor and scenario models are used widely in applied portfolio analysis, and short sales have been used increasingly as part of large institutional portfolios. Generally, the critical line algorithm (CLA) traces out mean-variance efficient sets when the investor's choice is subject to any system of linear equality or inequality constraints. Versions of CLA that take advantage of factor and/or scenario models of covariance gain speed by greatly simplifying the equations for segments of the efficient set. These same algorithms can be used, unchanged, for the long-short portfolio selection problem provided a certain condition on the constraint set holds. This condition usually holds in practice.

Portfolio Optimization with Factors, Scenarios, and Realistic Short Positions

It has been known for some time that the returns of small firms often differ from those of large firms, and that asset pricing theories, including the Capital Asset Pricing Model and Arbitrage Pricing Theory, cannot account for the difference. Small-capitalization stocks have provided higher average returns than large-capitalization stocks, and the outperformance has been strongest in the month of January. Various researchers have sought to explain this size effect as the result of differential transaction costs, liquidity, informational uncertainty and year-end tax-loss selling. Others have suggested that small stocks outperform because they tend to have lower PIE ratios. A multifactor analysis "disentangles" the effect of firm size from related factors that may influence return. These factors include cross-sectional effects, such as firm neglect and low PIE, and calendar effects, such as tax-loss selling. Disentangling provides "pure" returns to size that avoid the confounding associated with proxy effects. For instance, disentangling reveals the January small-firm seasonal to be a mere surrogate for the rebound that follows the abatement of tax-loss selling. An analysis of pure returns reveals that the size effect is buffeted by economic forces. There are times when small stocks outperform the market, and other times when they lag. But while the payoffs to the size effect are not at all regular to the naked eye, they are predictable in a broader empirical framework that incorporates macroeconomic drivers such as interest rates and industrial production. An examination of various forecasting models, including univariate and multivariate time-series techniques, indicates that one that imposes a Bayesian random-walk prior belief on the coefficients of a vector autoregressive model provides the best results.

Forecasting the Size Effect

This article proposes to augment portfolio theory and mean-variance optimization to incorporate investors’ aversion to leverage. The authors suggest a specification for leverage aversion that captures the unique risks of leverage. They also introduce mean-variance leverage-efficient frontiers, compare them with conventional mean-variance-efficient frontiers, and develop a mean-variance leverage-efficient region. Their analysis shows that leverage aversion can have a large effect on an investor’s portfolio choice.

/pdf/leverage-aversion-efficient-frontiers-and-the-efficient-1tdtcvuaxi.pdf

Leverage Aversion, Efficient Frontiers,and the Efficient Region

a dividend discount model (DDM) is but a small part of the security pricing story. An examination of the relation between DDM expected returns and 25 equity attributes, using multivariate regression and data from over 1000 stocks during the mid-1982 to mid1987 period, reveals whether certain equity attributes tend to be favored by DDM models. Low PIE, saleslprice, yield, zero yield, beta, sigma and trends in analysts' earnings estimates were all positively correlated with DDM expected return. Coskewness, small size, residual-return reversal and high booklprice were negatively associated. Neglect and earnings surprise were uncorrelated with DDM expected return, although they were associated with anomalous returns. A DDM strategy cannot be replicated with equity attributes alone, nor can all anomalous returns be captured with a DDM. An examination of actual security returns over the five-year period shows that a DDM strategy would have produced positive but insignificant returns. At times, the DDM was perverse, with DDM expected returns negatively correlated with actual returns. In a bivariate regression pitting the DDM against low P/E, low P/E provided a higher payoff and was significant in more quarters than the DDM; low PIE appears to subsume some portion of the DDM, rather than the reverse. A full multivariate regression considering the DDM simultaneously with 25 equity attributes showed the DDM to be insignificant, while many equity attributes-sales/price, neglect, relative strength, residual-return reversal, trends in analysts' estimates and earnings surprise-provided statistically significant abnormal performance. Equity attributes such as P/E are not mere proxies for value. Many attributes are better predictors of return than the DDM. In fact, the results suggest that DDM value is nothing more than an additional equity attribute and, like other attributes, may be amenable to

Bruce I. Jacobs

Papers

Calendar Anomalies: Abnormal Returns at Calendar Turning Points

Portfolio Optimization with Factors, Scenarios, and Realistic Short Positions

Forecasting the Size Effect

Leverage Aversion, Efficient Frontiers,and the Efficient Region

On the Value of 'Value'