The comparative efficiency performance of small and large US commercial banks in the pre- and post-deregulation eras

The purposes of this paper are twofold: first, to employ a flexible non-parametric approach to contrast the productive efficiency of a sample of small and large banks in order to examine the relationship between size and productive performance in the banking industry. Second, to investigate whether the relative efficiency performance of small and large banks has changed following the changes in the banking environment in the 1980s and to contrast the rate of technological change achieved by these two groups of banks over this time period. The findings based on group-specific frontiers suggest that in the pre-deregulation environment small banks were more efficient than the large banks while in the deregulated environment small and large banks were equally efficient. Moreover, the dispersion in the efficiency measures of the small banks is found to have increased substantially while that of the large banks changed little over the sample period.     

Determinants of dividend payout and dividend propensity in an emerging market,Iran: an application of the LASSO

Accurate prediction of dividends is important for market participants such as investors, firm managers, and monitoring authorities, as they can, respectively, invest, manage dividend decisions, and monitor dividend policies more effectively. We identify the most relevant variables for predicting the dividend payout of the firms in an emerging market, Iran, using the least absolute shrinkage and selection operator (LASSO). The advantages of the LASSO include: enhancing the prediction accuracy of the dividend model, improving interpretation of the results, and applicability to high-dimensional data. We obtain several results. First, some fundamental determinants of dividends in the industrialized economies such as market-to-book ratio and current ratio, do not play a role in deciding dividends in Iran. Second, LASSO-selected variables outperform the variables commonly used in the literature in terms of model fit and prediction accuracy. Third, business risk, leverage, return on assets and effective tax rate are the most important predictors of dividend propensity of the Iranian firms. Fourth, if the support vector machine algorithm, an often-used classification method, is combined with LASSO-selected variables, it can better discriminate between dividend-paying and dividend non-paying firms than other methods such as logistic regression and linear discriminant analysis.

Abbreviations: LASSO: Least Absolute Shrinkage and Selection Operator; TSE: Tehran Stock Exchange; RMSE: Root Mean Squared Errors; MAE: Mean Absolute Errors; ROC: Receiver Operating Characteristics; GMM: Generalized Method of Moments; MENA: Middle East and North Africa region; AIC: Akaike Information Criterion; BIC: Bayesian Information Criterion; LARS: Least Angel Regression; OLS: Ordinary Least Squares; AUC: Area Under Curve; BS: Brier Score ; OA: Overall Accuracy; LDA: Linear Discriminant Analysis; SVM: Support Vector Machine algorithm; LR: Logistic Regression.     

Market Risk,Interest Rate Risk,and Interdependencies in Insurer Stock Returns: A System‐GARCH Model

We examine market risk, interest rate risk, and interdependencies in returns and return volatilities across three insurer segments within a System‐GARCH framework. Three main results are obtained: market risk is greatest for accident and health (A&H) insurers, followed by life (Life) and property and casualty (P&C) insurers; interest rate sensitivity is negative and greatest for Life insurers; and interdependencies in returns are significant with the magnitude being strongest between P&C and A&H insurers. The implication is that greatest diversification benefits arise between Life and the other segments of the insurance industry. Market risk and interest rate risk for diversified firms are smaller than those for nondiversified firms for both product and geographic diversification.     

Convergence and risk-return linkages across financial service firms

We examine the risk and return linkages across US commercial banks, securities firms, and life insurance companies during the 1991–2001 period. After controlling for changes in the broader stock market, interest rates, and foreign currency values, we find that return and risk interdependencies across these financial firms are significant and size-varying; larger institutions display stronger volatility transmission linkages, while smaller ones exhibit more prominent return-related linkages. The tighter link in risk among large financial institutions (FIs) suggests stronger convergence, employment of common models of risk measurement and risk management, and more intense inter-industry competition, particularly between large banks and large securities firms, compared to smaller institutions. Lack of risk spillover among smaller FIs confirms the intuition that they typically assume more localized and idiosyncratic risk. The co-movement of stock returns among smaller FIs has been helped by the effects of locally based factors, such as economic conditions and state regulations, on all such institutions, and a less diversified product set. Differences in spillover patterns between large and smaller institutions have implications on investment choices and mergers and acquisitions in the industry. Introduction of the Gramm-Leach-Bliley Act (1999) has had dissimilar effects on the riskiness of large versus smaller life insurance and securities firms, and an insignificant effect on commercial banks.     

Interest Rate Risk and Equity Values of Life Insurance Companies: A GARCH–M Model

The importance of managerial decisions related to interest‐sensitive cash flows has received considerable attention in the insurance literature. Consistent with the interest‐sensitive nature of insurer assets and liabilities, empirical research has shown that insurer insolvency is significantly related to interest rate volatility. We investigate the interest rate sensitivity of monthly stock returns of life insurers based on a generalized autoregressive conditionally heteroskedastic in the mean (GARCH–M) model. We examine three different portfolios (equally weighted, risk‐based, and size‐based) with binary variables to explicitly account for varying interest rate strategies adopted by the Federal Reserve System. Results based on data for the period 1975 through 2000 indicate that life insurer equity values are sensitive to long‐term interest rates and that interest sensitivity varies across subperiods and across risk‐based and size‐based portfolios. The results complement insolvency research that links insurer financial performance to changes in interest rates.     

Measuring technical and scale inefficiencies in the beer industry: Nonparametric and parametric evidence

Two alternative approaches of efficiency measurement, nonparametric and statistical, are employed to calculate three types of efficiency indexes for the U.S. beer industry over the period 1950–1986. The results indicate that the beer industry was operating at a high level of pure technical efficiency over that period. The mean value of this efficiency measure is 93.7 percent based on the nonparametric approach and 87.5 percent based on the statistical approach. The two approaches yield dissimilar values and sources for overall technical inefficiency. The overall technical efficiency index computed under the nonparametric approach stands at 91.10 percent and the observed inefficiency is found to be more due to pure technical inefficiency than to scale inefficiency. Using the statistical approach, the beer industry is found to be less overall technically efficient (68.42 percent) than indicated by the nonparametric methodology and the observed inefficiency is found to be primarily contributed to by scale inefficiency.     

The Association Between Market and Exchange Rate Risks and Accounting Variables: A GARCH Model of the Japanese Banking Institutions

This study has two purposes. First, it estimates the market, interest rate, and exchange rate sensitivities (betas) of the Japanese banking institutions. Second, it investigates the relationship between the market-based measures of risk and accounting-based financial ratios. We extend the literature in three important ways. First, we employ a multi-factor GARCH model to estimate the betas. This framework incorporates non-linearities in the bank stock return modeling and allows for time-varying risk premia. Second, we investigate the determinants of market and exchange rate risk in terms of bank financial ratios. To this end, we regress the beta measures derived from the GARCH model against the corporate decision variables to determine the direction and the magnitude of the impact of the latter on the market and exchange rate risk exposures. Third, by using data on the Japanese banking institutions, we provide a comparison of the bank interest rate and exchange rate sensitivities and the strength of the links between the risk measure and the corporate decision variables between the U.S. and the Japanese banking institutions. This comparison sheds light on the robustness of the results concerning interest rate and exchange rate risk, and their determinants, across the two countries. Several interesting results are obtained. First, empirical results indicate that interest rate is only occasionally significant while market and exchange rate variables are significant for all the banks in the sample. Second, market and exchange rate risk measures do impound information in the financial ratios with the explanatory power of the market beta model being higher than that of the exchange rate beta model. Third, the association of the market-based risk measures and the financial ratios is weaker for the Japanese banks than those found for their U.S. counterparts in the existing literature.JEL Classification: G21, F37