We examine how various aspects of corporate governance structures affect the capital allocation inefficiency that drives the value discounts of diversified firms. Diversified firms with more effective internal or external governance mechanisms experience more efficient investment allocations at both the firm and segment levels and show less of a diversification discount. The efficiency of the investment allocation process is better for diversified firms with high board independence, low board busyness, high institutional ownership, high outside director ownership, high CEO equity-based pay, high audit quality, and strong shareholder rights. The results hold after controlling for other potential influences. Our evidence suggests that corporate governance considerations are important in assessing the relation between investment efficiency and firm value for diversified firms.

This paper documents that classified boards substantially reduce the cost of debt. The evidence is not consistent with the argument that bondholders benefit from board classification because they are concerned about hostile takeovers. Instead, the results suggest that the lessened concern for takeovers associated with a classified board structure reduces managerial risk-taking, and increases managerial incentive for financial disclosure, with both effects inuring to bondholders’ benefit. Consistent with prior literature, classified boards on average are associated with a lower firm performance. However, under the circumstances that the agency conflict between shareholders and bondholders is severe, the performance effect of classified boards appears benign.

This study reappraises banks’ productivity by using 42 Taiwanese banks during 1999–2007 as observations. It introduces an input-oriented generalized metafrontier Malmquist productivity index (I-gMMPI), while considering the latent effect of risk-taking behavior in the analytic framework. We learn that public and private banks should face separate short-term technological frontiers, while the econometric model considering risk input can portray banks’ operating frontiers better. Moreover, neglecting the influence of risk input would bring about distortions of efficiency, technology and TFP dynamic estimations for banks; meanwhile, the degree of scale economies would also be overestimated. The paper concludes that neglecting the risk-taking essence in bank performance evaluation is equal to allowing banks to barter risk-bearing as the term for further output growth. Consequently, the potential cost is that banks may excessively aggrandize their scales of business, implying the possibility of another financial crisis.

This paper investigates whether the empirical linkages between stock returns and trading volume differ over the fluctuations of stock markets, i.e., whether the return–volume relation is asymmetric in bull and bear stock markets. Using monthly data for the S&P 500 price index and trading volume from 1973M2 to 2008M10, strong evidence of asymmetry in contemporaneous correlation is found. As for a dynamic (causal) relation, it is found that the stock return is capable of predicting trading volume in both bear and bull markets. However, the evidence for trade volume predicting returns is weaker.

We examine how strategic interaction in an industry influences the earnings expectations of financial analysts with regard to new product strategies. We find that following announcement of new products, analysts revise earnings forecasts upward more for announcing firms competing in low-strategic interaction industries than for firms competing in high-strategic interaction industries. For value-enhancing (value-reducing) product strategies, earnings forecast revisions are more favorable for rivals competing under a high (low) degree of strategic interaction than for rivals competing under a low (high) degree of strategic interaction. Overall empirical evidence indicates that the nature of strategic competition within the industry is important in assessing the market expectations of earnings for new product announcers and their rivals.

In this paper we investigate the effects of informed trading (PIN) and information uncertainty in determining price momentum. We find that trading strategies based on buying high-uncertainty good-news stocks and shorting high-uncertainty bad-news stocks work well when limited to high-PIN stocks, while stocks with low-PIN do not exhibit price continuations, even when the uncertainty level of those stocks is high. In contrast, momentum returns are always significant for high-PIN stocks, irrespective of information uncertainty. Overall, we show that the informed trading effect is both independent of and stronger than that of information uncertainty in determining price momentum.

This paper examines the implications of market microstructure for foreign exchange markets. We argue that the usual order flow model needs to be recast in broader terms to incorporate the transaction costs of liquidity and the limitation of price discovery through order flows that involve low trading density currencies. Using a daily data set, we find that order flows are inadequate when it comes to explaining the changes in the low trading density currencies. Alternatively, within the high trading density, both order flows and bid–ask spreads significantly affect the foreign exchange rate returns. Our findings suggest that the order flow model is better at incorporating these microstructure effects except for some currencies with a very high level of trading density.

In many credit risk and pricing applications, credit transition matrix is modeled by a constant transition probability or generator matrix for Markov processes. Based on empirical evidence, we model rating transition processes as piecewise homogeneous Markov chains with unobserved structural breaks. The proposed model provides explicit formulas for the posterior distribution of the time-varying rating transition generator matrices, the probability of structural break at each period and prediction of transition matrices in the presence of possible structural breaks. Estimating the model by credit rating history, we show that the structural break in rating transitions can be captured by the proposed model. We also show that structural breaks in rating dynamics are different for different industries. We then compare the prediction performance of the proposed and time-homogeneous Markov chain models.

This paper reexamines the explanatory power of Taylor rule fundamentals for real exchange rate determination. We assume the agents know the time-varying parameters in central bank policy rules. The empirical results suggest that a monetary policy rule with regime switching is better able to explain the real Deutschemark/dollar exchange rate from 1976 to 1998 compared with a fixed-regime monetary policy rule. The findings show the importance of accounting for the expectation formation effect in changing policy rules as emphasized by the Lucas critique. Ignoring these effects can undermine the value of the rational expectations models.

Using a unique dataset, we document that only those closed funds for which no new fund is subsequently launched continuously deliver positive abnormal returns. This suggests the existence of an optimal fund scale. In spite of the potential diseconomies of scale, a non-trivial proportion of closed funds have new funds cloned—the scale motive would not be a complete explanation for the closure. When managers of closed funds clone new funds, they receive greater public attention and thus can attract more fund flows and charge higher fees. Furthermore, better-performing closed fund managers attract more fund flows to their new siblings, making the closure an effective mechanism to extract economic rents. Overall, we find that closing and cloning is an attractive strategy for funds seeking to increase their management fees and funds with more managers in place. Aspects of the closed fund family also affect the launch decision of new siblings.