Comparing Firm Failure Predictions Between Logit,KMV, and ZPP Models: Evidence from Taiwan’s Electronics Industry

This paper applies the Taiwan electronics industry data to detect the discriminatory powers of Logit, KMV, and zero-price probability (ZPP) models that represent respectively the regressive fitting model, the option-based pricing model, and the GARCH time series simulation model. In our circumstances, according to cumulative accuracy profile, receiver operating characteristic, and even Brier score, the KMV performs the worst. The disadvantages for KMV are that the equity market exists some nonlinear characteristics, the unknown market value of asset affected by the change of capital structure is not exogenous, and the failure point is difficult to be estimated correctly. Besides, KMV is however too simple to model the fluctuation of the equity value as the GARCH does. On the other hand, the Logit performs above average. To preclude over-fitting and keep model parsimonious, two significant factors are extracted from as many as forty financial variables for the logistic regression on binary failure data. The result of Logit training has perfect discrimination. However, for the post-sample data, the fitting to categorical but not ordinal data makes Logit have the divergent failure predicted probabilities and highest Briser Score. In practical, ZPP GARCHNorm uses just equity value to predict firm failure but it performs remarkably well supposing that downward price trend or volatility persistence in stock price changes is appropriately caught. It implies that the distorted signals such as overreaction of traders and insider trading would definitely impair the ZPP GARCHNorm. Nevertheless, the larger type I error than type II error in all models indicates that the prediction of non-failed firms should be more examined further than that of failed firms.     

Efficiency of Microfinance Institutions: A Data Envelopment Analysis

This study examines the cost efficiency of 39 microfinance institutions across Africa, Asia and the Latin America using non-parametric data envelopment analysis. Our findings show non-governmental microfinance institutions particularly; under production approach, are the most efficient and this result is consistent with their fulfillment of dual objectives: alleviating poverty and simultaneously achieving financial sustainability. However, bank-microfinance institutions also outperform in the measure of efficiency under intermediation approach. This result reflects that banks are the financial intermediaries and have access to local capital market. It may be possible that bank-microfinance institutions may outperform the non-governmental microfinance institutions in the long run.     

Return dispersion and expected returns

This paper proposes a two-factor asset-pricing model that incorporates market return and return dispersion. Consistent with this model, we find that stocks with higher sensitivities to return dispersion have higher average returns, and that return dispersion carries a significant positive price of risk. In particular, the return dispersion factor dominates the book-to-market factor in explaining cross-sectional expected returns. The return dispersion model outperforms the CAPM, MVM, IVM, and FF-3M when using a set of 5×5 test portfolios constructed from NYSE and AMEX stock returns from August 1963 to December 2005. Return dispersion continues to play an important role in explaining the cross-sectional variation of expected returns, even when market volatility, idiosyncratic volatility, size, book-to-market factors, and a momentum factor are included. This study sheds some light on the ability of return dispersion to explain expected returns beyond the standard asset-pricing factors. Our finding suggests that return dispersion captures two dimensions of systematic risk: the business cycle and fundamental economic restructuring.     

The two-fund separation theorem revisited

This paper resolves two issues regarding the traditional capital asset pricing model with one risk-free asset which seem to have been overlooked in the literature. First, it provides an elementary and complete proof of the two-fund separation theorem which accounts for the fact that asset demand may become undefined if the limiting slopes of the investor’s indifference curves are finite. Second, it shows that an additional limiting condition on investors’ risk aversions is generally necessary to guarantee existence of an equilibrium. Moreover, a generalized existence result is formulated which includes investors who in equilibrium may not invest in risky assets and a simple condition ensuring positive equilibrium asset prices is given.     

Modelling the volatility of commodities prices using a stochastic volatility model with random level shifts

We use the approach of Qu and Perron (Econom J 16(3):309–339, 2013) for the modeling and inference of volatility of a set of commodity prices in the presence of random level shifts of unknown timing, magnitude and frequency. Our approach contributes to the study of commodities in several aspects. First, we test for the presence of a genuine long-memory process in the volatility of commodities. Second, we determine that the random level shifts are certainly the main source of variation in the commodity price volatility. Finally, we estimate the volatility and its components as latent variables, thereby making it possible to evaluate their level of correlation with macroeconomic variables in small open economies such as Latin-American countries where the dependence on commodity price volatility is high. We use six commodity series: agriculture, livestock, gold, oil, industrial metals and a general commodity index. All series cover the period from January 1983 until December 2013 in daily frequency. The results show that although the occurrence of a level shift is rare, (about once every 1.5 or 1.8 years), this component clearly contributes most to the variation in the volatility. Furthermore, isolating the level shift component from the overall volatility indicates a strong relationship of this component with a set of business cycle indicators of several Latin American countries.     

Linking thoughts to feelings: Investigating cognitive appraisals and consumption emotions in a mixed-emotions context

While emotions have been shown to have significant influence on various consumer behaviors, the cognitive appraisals linked to consumption emotions have not been fully explored. This research investigates how individuals' cognitive interpretations of situations correspond to the emotions they experience in these contexts. Using analysis of variance and multiple discriminant analysis, our results show a systematic relationship between cognitive appraisals and 10 consumption emotions. The author's findings offer theoretical insights into these consumption emotions, the appraisal/consumption emotion correspondence, basisversus subordinate-level category differences in emotions, and mixed consumption emotions. The findings provide a practical framework for academics and practitioners interested in better understanding and managing consumption emotions. Julie A. Ruth (Ph.D., University of Michigan) is an assistant professor of marketing in the School of Business—Camden at Rutgers University. Her research interests include affect and emotions, consumer relationships, and consumer response to brand strategies such as brand alliances and sponsorships. Frédéric F. Brunel (Ph.D., University of Washington) is an assistant professor of marketing in the School of Management at Boston University. His research interests include consumer perceptions of product design and aesthetics, consumer attitude and affect, and gender and sociocultural issues in consumption. Cele C. Otnes (Ph.D., University of Tennessee) is an associate professor of business administration in the College of Commerce and Business Administration at the University of Illinois, Urbana. Her research interests include consumer rituals, affect and consumer ambivalence, and gift exchange.     

Food assistance programs and food insecurity: implications for Canada in light of the mixing problem

In light of concerns about high rates of food insecurity, some have suggested that it might be time for Canada to implement national food assistance programs like those provided in the US, namely the Supplemental Nutrition Assistance Program (SNAP) and the National School Lunch Program (NSLP). In this paper, we assess how adopting these types of assistance programs would change the food insecurity rate in Canada among households with children. Using data from the Current Population Survey (CPS), we first evaluate the causal impact of these programs on food insecurity rates in the US using the Canadian definition of food security. Following other recent evaluations of food assistance programs, we use partial identification methods to address the selection problem that arises because the decision to take up the program is not random. We then combine these estimated impacts for the US with data from the Canadian Community Health Survey (CCHS) to predict how SNAP and NSLP would impact food insecurity rates in Canada. Partial identification methods are used to address the “mixing problem” that arises if some eligible Canadian households would participate in SNAP and others would not. The strength of the conclusions depends on the strength of the identifying assumptions. Under the weakest assumptions, we cannot determine whether food insecurity rates would rise or fall. Under our strongest nonparametric assumptions, we find that food insecurity would fall by at least 16% if SNAP were implemented and 11% if NSLP were implemented.