An Analytical Confidence Interval for the Treynor Index: Formula, Conditions and Properties

The Treynor index, a well-known, widely-used measure of portfolio performance, is the ratio of the mean excess rate of return of the portfolio to the portfolio's beta. We derive an analytical formula that is designed to yield rigorous confidence intervals on the index. Necessary and sufficient conditions for the Treynor index to be statistically different from zero, are provided. We illustrate our approach with detailed examples and include simulations to help analysts choose the number of periods to study. Finally, some interesting properties of the interval and some sensitivity results are provided.     

A Joint Latent-Class Model: Combining Likert-Scale Preference Statements With Choice Data to Harvest Preference Heterogeneity

In addition to choice questions (revealed and stated choices), preference surveys typically include other questions that provide information about preferences. Preference-statement data include questions on the importance of different attributes of a good or the extent of agreement with a particular statement. The intent of this paper is to model and jointly estimate preference heterogeneity using stated-preference choice data and preference-statement data. The starting point for this analysis is the belief that the individual has preferences, and both his/her choices and preference statements are manifestations of those preferences. Our modeling contribution is linking the choice data and preference-statement data in a latent-class framework. Estimation is straightforward using the E-M algorithm, even though our model has hundreds of preference parameters. Our estimates demonstrate that: (1) within a preference class, the importance anglers associate with different Green Bay site characteristics is in accordance with their responses to the preference statements; (2) estimated across-class utility parameters for fishing Green Bay are affected by the preference-statement data; (3) estimated across-class preference-statement response probabilities are affected by the inclusion of the choice data; and (4) both data sets influence the number of classes and the probability of belonging to a class as a function of the individual’s type.     

Confidence interval for the total advertising impact and its mean duration under Koyck models

The estimates of total advertising impact and of its mean duration in distributed lag models are important in budgeting and planning decisions. The typical approach to assessing the short and long term impact of advertising is to perform a sequential test, one on the short term coefficient and one on the lag parameter. However, estimate of the effect of advertising on sales involves the ratio of estimated regression parameters. The estimates of these parameters are known to be correlated. Generation of confidence intervals on such key estimates is possible, using information that is typically discarded. This paper develops and illustrates a method for developing these confidence intervals which specifically accounts for the correlation between the standard regression estimates. Examples are used to illustrate that the conclusion from standard sequential testing procedures can be erroneous. An empirical application then demonstrates the application of the suggested procedure. Finally, we provide insights on the length of this interval as the sample size and other characteristics are varied.     

WINDOW DRESSING IN BOND MUTUAL FUNDS

We examine portfolio credit quality holding and daily return patterns in a large sample of bond mutual funds and document evidence of window dressing. Using portfolio credit quality holdings data, we find that bond funds on average hold significantly more government bonds during disclosure than nondisclosure, presumably to present a safer portfolio to shareholders. Multiple‐index market models estimated with daily returns data corroborate these findings. We detect differences in factor loadings on days surrounding disclosure dates that indicate systematic tilting of the portfolio toward higher quality instruments.     

Effects of economic conditions and policy interventions on OECD broadband adoption

The positive effects of broadband networks and services on productivity and economic growth are well established. Looking at broadband as an engine of economic prosperity, the OECD and its member states are seeking to foster its widespread adoption. However, which public policies best promote the adoption of broadband remains controversial. This article contributes in two ways to this discussion. It offers a comprehensive discussion of the factors that influence broadband adoption and uses an econometric approach that is well-suited to overcome the challenges of modelling broadband adoption. This framework allows drawing more robust and nuanced policy recommendations.     

Using Angler Characteristics and Attitudinal Data to Identify Environmental Preference Classes: A Latent-Class Model

A latent-class model of environmental preference groups is developed and estimated with only the answers to a set of attitudinal questions. Economists do not typically use this type of data in estimation. Group membership is latent/unobserved. The intent is to identify and characterize heterogeneity in the preferences for environmental amenities in terms of a small number of preference groups. The application is to preferences over the fishing characteristics of Green Bay. Anglers answered a number of attitudinal questions, including the importance of boat fees, species catch rates, and fish consumption advisories on site choice. The results suggest that Green Bay anglers separate into a small number of distinct classes with varying preferences and willingness to pay for a PCB-free Green Bay. The probability that an angler belongs to each class is estimated as function of observable characteristics of the individual. Estimation is with the expectation–maximization (E–M) algorithm, a technique new to environmental economics that can be used to do maximum-likelihood estimation with incomplete information. As explained, a latent-class model estimated with attitudinal data can be melded with a latent-class choice model. Edward Morey and Jennifer Thacher are equal authors and rotate authorship across articles.     

Predicting corporate governance in emerging markets

This paper investigates what predicts corporate governance in emerging markets. Specifically, we examine what predicts governance changes and the level of governance itself. To conduct this study, we utilize a unique dataset from AllianceBernstein that consists of monthly firm-level corporate governance ratings for 24 emerging market countries for almost seven years. Since the AllianceBernstein ratings are time-series data, they allow us to determine the direction of change in a firm’s corporate governance, and the timing of these changes. Using these data, we find two main results. First, as firms grow they are more likely to improve their governance. Second, the level of political risk where the firm resides is negatively and significantly related to the level of firm governance but positively and significantly related to changes in firm governance. Hence, firm governance is better in countries with lower political risk but firms are more likely to improve their governance in countries with higher political risk.     

Designing an Interactive Visualization to Explore Eye-movement Data

For many user applications large data sets may be collected passively and unobtrusively in the flow of their activity, and on scales ranging from the individual to increasingly larger communities. Large data sets, however, bring a concomitant need for tools to help understand what the data indicates. With the emergence of smart eyewear and the availability of sophisticated but affordable eye-tracking devices, eye movement data becomes a source of detailed information about a user’s focus and indirectly about their cognition and attention. Visualizing this usefully in terms meaningful for diagnosis however, remains a challenge. In this paper we report a new data representation from significant data sets generated by a gaze-controlled digital reading application for second language speakers. Current tools provide data sets aimed primarily towards statistical analysis of patterns: our focus is on end-user exploration of data sets in domain terms, so that practical implications can be readily identified. The visualization of horizontal eye movement data allows rapid diagnosis of problem areas in texts, informing educators immediately of individual or wider issues. The general applicability of this visualization to other applications is discussed.     

AN EMPIRICAL EXAMINATION OF FINANCIAL LIBERALIZATION AND THE EFFICIENCY OF EMERGING MARKET STOCK PRICES

The efficient markets hypothesis in finance suggests that as equity markets are liberalized and made more open to the public, equity prices should reflect the increased availability of information and be more efficiently priced. In this paper, we examine whether emerging market equity prices have become more efficient after financial liberalization. Using two sets of financial liberalization dates, a battery of econometric tests, and data from sixteen countries and three composite portfolios, we find that in spite of theory suggesting the opposite, liberalization does not seem to have improved the efficiency of emerging markets. In fact, most of our statistical tests indicate that the markets were already efficient before the actual liberalization.