Top Management Team Characteristics and Accrual-Based Earnings Management

This study examines how top management team (TMT) knowledge and average tenure affect accrual-based earnings management by investigating 4791 Taiwanese listed companies from 2006 to 2010. TMT members with more knowledge (higher education level, more accounting expertise, and greater prior top management experience) and longer average tenure have better performances and higher reputations, and are more aware of the litigation costs of earnings manipulations; therefore, they reduce managers' incentives to manage earnings (incentive-reduction effect). On the other hand, these TMT members are also likely to become entrenched and engage in more earnings manipulations (entrenchment-enhancing effect). The empirical results show that firms' TMT knowledge and average tenure are negatively associated with discretionary accruals, suggesting that the incentive-reduction effect is stronger than the entrenchment-enhancing effect, which makes TMT members less likely to engage in earnings management. Moreover, the above results are robust when employing different earnings management measures and suspect firm analyses, as well as considering endogeneity issues. Finally, the study suggests that the presence of a founding family may reduce the influences of TMT knowledge and average tenure on earnings management.     

A logistic regression point of view toward loss given default distribution estimation

We propose a new procedure to estimate the loss given default (LGD) distribution. Owing to the complicated shape of the LGD distribution, using a smooth density function as a driver to estimate it may result in a decline in model fit. To overcome this problem, we first apply the logistic regression to estimate the LGD cumulative distribution function. Then, we convert the result into the LGD distribution estimate. To implement the newly proposed estimation procedure, we collect a sample of 5269 defaulted debts from Moody’s Default and Recovery Database. A performance study is performed using 2000 pairs of in-sample and out-of-sample data-sets with different sizes that are randomly selected from the entire sample. Our results show that the newly proposed procedure has better and more robust performance than its alternatives, in the sense of yielding more accurate in-sample and out-of-sample LGD distribution estimates. Thus, it is useful for studying the LGD distribution.     

Predicting issuer credit ratings using a semiparametric method

This paper proposes a prediction method based on an ordered semiparametric probit model for credit risk forecast. The proposed prediction model is constructed by replacing the linear regression function in the usual ordered probit model with a semiparametric function, thus it allows for more flexible choice of regression function. The unknown parameters in the proposed prediction model are estimated by maximizing a local (weighted) log-likelihood function, and the resulting estimators are analyzed through their asymptotic biases and variances. A real data example for predicting issuer credit ratings is used to illustrate the proposed prediction method. The empirical result confirms that the new model compares favorably with the usual ordered probit model.     

Predicting the Loss Given Default Distribution with the Zero-Inflated Censored Beta-Mixture Regression that Allows Probability Masses and Bimodality

Journal of Financial Services Research - We propose a new procedure to predict the loss given default (LGD) distribution. Studies find empirical evidence that LGD values have a high concentration...     

Auditor selection within a business group: evidence from Taiwan

Although auditor selection is well documented in the literature, it is unclear whether group characteristics affect firms’ auditor selection decisions. Generally, a business group is the result of diversification by the core firm. Major decisions of the business group, such as auditor selection, are made by the core firm and influenced by the business group’s characteristics. Using operational and ownership linkages perspectives, this study investigates the determinants of a business group’s member firm engaging the same auditor as its core firm. We employ the input–output relationship of products along a supply chain to construct product vertical relatedness measures between member firms and the core firm, and establish logistic regression models to test our hypotheses. Using a sample of publicly listed business groups in Taiwan from 2000 to 2010, our results suggest that a member firm is more likely to engage the same auditor as its core firm when (1) the core firm engages a Big N auditor, (2) the core firm’s auditor is an industry specialist for both the core firm and its member firm, (3) the degree of vertical relatedness increases, or (4) the controlling shareholders’ deviation of voting rights from cash flow rights increases (hereafter deviation). On the other hand, the likelihood of a member firm engaging the same auditor as its core firm when induced by higher deviation could be offset by the influence of stronger business vertical linkages.     

Forecasting forward defaults: a simple hazard model with competing risks

A forward default prediction method based on the discrete-time competing risk hazard model (DCRHM) is proposed. The proposed model is developed from the discrete-time hazard model (DHM) by replacing the binary response data in DHM with the multinomial response data, and thus allowing the firms exiting public markets for different causes to have different effects on forward default prediction. We show that DCRHM is a reliable and efficient model for forward default prediction through maximum likelihood analysis. We use actual panel data-sets to illustrate the proposed methodology. Using an expanding rolling window approach, our empirical results statistically confirm that DCRHM has better and more robust out-of-sample performance than DHM, in the sense of yielding more accurate predicted number of forward defaults. Thus, DCRHM is a useful alternative for studying forward default losses on portfolios.     

Predicting Recurrent Financial Distresses with Autocorrelation Structure: An Empirical Analysis from an Emerging Market

The dynamic logit model (DLM) with autocorrelation structure (Liang and Zeger Biometrika 73:13–22, 1986) is proposed as a model for predicting recurrent financial distresses. This model has been applied in many examples to analyze repeated binary data due to its simplicity in computation and formulation. We illustrate the proposed model using three different panel datasets of Taiwan industrial firms. These datasets are based on the well-known predictors in Altman (J Financ 23:589–609, 1968), Campbell et al. (J Financ 62:2899–2939, 2008), and Shumway (J Bus 74:101–124, 2001). To account for the correlations among the observations from the same firm, we consider two different autocorrelation structures: exchangeable and first-order autoregressive (AR1). The prediction models including the DLM with independent structure, the DLM with exchangeable structure, and the DLM with AR1 structure are separately applied to each of these datasets. Using an expanding rolling window approach, the empirical results show that for each of the three datasets, the DLM with AR1 structure yields the most accurate firm-by-firm financial-distress probabilities in out-of-sample analysis among the three models. Thus, it is a useful alternative for studying credit losses in portfolios.     

Assessing bankruptcy prediction models via information content of technical inefficiency

We use a stochastic frontier model with firm-specific technical inefficiency effects in a panel framework (Battese and Coelli in Empir Econ 20:325–332, 1995) to assess two popular probability of bankruptcy (PB) measures based on Merton model (Merton in J Financ 29:449–470, 1974) and discrete-time hazard model (DHM; Shumway in J Bus 74:101–124, 2001). Three important results based on our empirical studies are obtained. First, a firm with a higher PB generally has less technical efficiency. Second, for an ex-post bankrupt firm, its PB tends to increase and its technical efficiency of production tends to decrease, as the time to its bankruptcy draws near. Finally, the information content about firm’s technical inefficiency provided by PB based on DHM is significantly more than that based on Merton model. By the last result and the fact that economic-based efficiency measures are reasonable indicators of the long-term health and prospects of firms (Baek and Pagán in Q J Bus Econ 41:27–41, 2002), we conclude that PB based on DHM is a better credit risk proxy of firms.