Approval of shareholder-sponsored proposals: Evidence from Canada

Little is known about shareholder voting at firms incorporated outside of the United States. Proposals sponsored at such firms and the voting patterns and factors associated with these proposals should conceivably be similar to those in the U.S. if the legal and governance structures of the countries are similar. We examine 264 shareholder proposals sponsored at Canadian firms between 2001 and 2005 in order to determine if differences created by the Canadian governance system, being more voluntary than that of the U.S. system, lead to differences in shareholder voting. We find many similarities between voting at the Canadian firms and those found in the literature for their U.S. counterparts, including some types of frequently submitted proposals and factors impacting the level of shareholder approval. However, unlike the concurrent literature on U.S. firms, we find very few majority approved proposals and a much lower overall level of affirmative voting returns.     

International Cross-Listing and Order Flow Migration: Evidence from an Emerging Market

Policymakers in emerging markets are increasingly concerned about the consequences for the domestic equity market when companies list stock abroad. We show that the effects of cross-listing depend on the quality of intermarket information linkages. We investigate these issues with unique data from the Mexican equity market. The impact of cross-listing is complex—balancing the costs of order flow migration against the benefits of increased intermarket competition. These effects are exacerbated by equity investment barriers that induce segmentation of the domestic equity market. Consequently, the benefits and costs of cross-listing are not evenly spread over all classes of shareholders.     

Return Behavior in Emerging Stock Markets

This article investigates the behavior of stock returns in thetwenty stock markets represented in the International FinanceCorporation's Emerging Markets Data Base. The aim is to testfor return anomalies and predictability. Using statistical methodologiesthat have identified seasonal and size-based return differences,as well as general return predictability in industrial markets,we find that these emerging markets display few of the sameanomalies. In particular, we find limited evidence of turn-of-the-tax-yeareffects and small-firm effects. We do find, however, evidenceof return predictability.     

Session Topic: Individual Investors and Mutual Funds

Summary . The modern theory of finance suggests that most investors should put part or all of their money into a “market portfolio” mixed with borrowing or lending. Empirical evidence generally supports the theory, but there are some unanswered questions about the composition of the best market portfolio, about the apparent attractiveness of low risk stocks relative to high risk stocks, and about ways of minimizing transaction costs. Attempts to create a fund based on these principles and to make it available to a large number of investors have uncovered some important problems. Legal costs due to government regulation, the costs of managing a fund, and especially the costs of selling it are all much higher than one might expect. Despite these problems, efforts to create such funds seem destined for eventual success.     

Pricing futures on geometric indexes: A discrete time approach

Several futures contracts are written against an underlying asset that is a geometric, rather than arithmetic, index. These contracts include: the US Dollar Index futures, the CRB-17 futures, and the Value Line geometric index futures. Due to the geometric averaging, the standard cost-of-carry futures pricing formula is improper for pricing these futures contracts. We assume that asset prices are lognormally distributed, and capital markets are complete. Using the concepts of equivalent martingale measure and the risk-neutral valuation relationships in conjunction with discrete time methodology, we derive closed-form pricing formulas for these contracts. Our pricing formulas are consistent with the ones obtained via a continuous time paradigm.

Substituting one risk increase for another: A method for measuring risk aversion

This paper defines the rate of substitution of one stochastic change to a random variable for another. It then focuses on the case where one of these changes is an nth degree risk increase, and the other is an m  th degree risk increase, where n>m?1$n>m?1$. The paper shows that the rate of substitution for these two risk increases can be used to provide a broader definition and two additional characterizations of the nth degree Ross more risk averse partial order. The implications for local intensity measures of nth degree risk aversion are also examined. The analysis organizes the existing results as well as generates new ones.     

Evaluating the performance of a predictive modeling approach to identifying members at high-risk of hospitalization

Abstract

Aims: To evaluate the risk-of-hospitalization (ROH) models developed at Blue Cross Blue Shield of Louisiana (BCBSLA) and compare this approach to the DxCG risk-score algorithms utilized by many health plans.

Materials and Methods: Time zero for this study was December 31, 2016. BCBSLA members were eligible for study inclusion if they were fully insured; aged 80?years or younger; and had continuous enrollment starting on or before June 1, 2016, through time zero. Up to 2?years of historical claims data from time zero per patient was included for model development. Members were excluded if they had cancer, renal failure, or were admitted for hospice. The Blue Cross ROH models were developed using (1) regularized logistic regression and (2) random decision forests (a tree ensemble learning classification method). All models were generated using Scikit-learn: Machine Learning in Python. Prognostic capabilities of DxCG risk-score algorithms were compared to those of the Blue Cross models.

Results: When stratifying by the top 0.1% of members with the highest ROH, the Blue Cross logistic regression model had the highest area under the receiving operator characteristics curve (0.862) based on the result of 10-fold cross-validation. The Blue Cross random decision forests model had the highest positive predictive value (49.0%) and positive likelihood ratio (61.4), but sensitivity, specificity, negative predictive values, and negative likelihood ratios were similar across all four models.

Limitations: The Blue Cross ROH models were developed and evaluated using BCBSLA data, and predictive power may fluctuate if applied to other databases.

Conclusions: The predictability of the Blue Cross models show how member-specific, regional data can be used to accurately identify patients with a high ROH, which may allow healthcare workers to intervene earlier and subsequently reduce the healthcare burden for patients and providers.     

The Impact of FSP FAS 157-4 on Commercial Banks

In September 2006, the Financial Accounting Standards Board issued Statement of Financial Accounting Standards No. 157, “Fair Value Measurements” (SFAS 157). Later in 2008, U. S. banks were mired in a credit crisis. Many economists and bankers suggested that one cause of the 2008 credit crisis was the requirements of SFAS 157 and a movement began to have its provisions amended or rescinded. The result of this movement was the passage of FSP FAS 157-4. The purposes of this study are to chronicle the events that lead to the release of FSP FAS 157-4, to report on the impact of FSP FAS 157-4 for the quarter ending March 31, 2009 for a sample of banks and to investigate the characteristics of the banks that early adopted FSP FAS 157-4 as compared to the non-adopters. The results did not find the predicted income increasing effect and indicated that total asset size and financial asset size were significant factors in the early adoption decision.