Knowledge that Transforms

This study investigates the sources of bank productivity growth in China over the period 2002–2009. In order to perform this research, we propose an advanced index – input slack-based productivity index (ISP) – a model that disaggregates total factor productivity growth into each input productivity change. Funds, capital, and employees are chosen as the inputs, whereas loans and other earning assets are outputs in this study. Our results show that technological gains transcend the efficiency regressions and result in total factor productivity growth. More specifically, technical progress in capital productivity reveals the dominant force behind the total factor technical change and productivity improvement. In addition, this paper uses these disaggregation terms to find out the competitive advantages and disadvantages of input usages for each Chinese bank. These findings indicate that the ISP index provides more insights than traditional total factor productivity indices.

We use a new dataset to study how mutual fund flows depend on past performance across 28 countries. We show that there are marked differences in the flow-performance relationship across countries, suggesting that US findings concerning its shape do not apply universally. We find that mutual fund investors sell losers more and buy winners less in more developed countries. This is because investors in more developed countries are more sophisticated and face lower costs of participating in the mutual fund industry. Higher country-level convexity is positively associated with higher levels of risk taking by fund managers.

We study empirical mean-variance optimization when the portfolio weights are restricted to be direct functions of underlying stock characteristics such as value and momentum. The closed-form solution to the portfolio weights estimator shows that the portfolio problem in this case reduces to a mean-variance analysis of assets with returns given by single-characteristic strategies (e.g., momentum or value). In an empirical application to international stock return indexes, we show that the direct approach to estimating portfolio weights clearly beats a naive regression-based approach that models the conditional mean. However, a portfolio based on equal weights of the single-characteristic strategies performs about as well, and sometimes better, than the direct estimation approach, highlighting again the difficulties in beating the equal-weighted case in mean-variance analysis. The empirical results also highlight the potential for ‘stock-picking’ in international indexes using characteristics such as value and momentum with the characteristic-based portfolios obtaining Sharpe ratios approximately three times larger than the world market.

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 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 paper investigates the motive of option trading. We show that option trading is mostly driven by differences of opinion, a finding different from the current literature that attempts to attribute option trading to information asymmetry. Our conclusion is based on three pieces of empirical evidence. First, option trading around earnings announcements is speculative in nature and mostly dominated by small, retail investors. Second, around earnings announcements, the pre-announcement abnormal turnovers of options seem to predict the post-announcement abnormal stock returns. However, once we control for the pre-announcement stock returns, the predictability completely disappears, implying that option traders simply take cues from the stock market and turn around to speculate in the options market. Third, cross-section and time-series regressions reveal that option trading is also significantly explained by differences of opinion. While informed trading is present in stocks, it is not detected in options.

Volatility clustering, with autocorrelations of the hyperbolic decay rate, is unquestionably one of the most important stylized facts of financial time series. This paper presents a market microstructure model that is able to generate volatility clustering with hyperbolically decaying autocorrelations via traders with multiple trading frequencies, using Bayesian information updates in an incomplete market. The model illustrates that signal extraction, which is induced by multiple trading frequencies, can increase the persistence of the volatility of returns. Furthermore, we show that the volatility of the underlying time series of returns varies greatly with the number of traders in the market.

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.

The literature disagrees on the link between so-called busy boards (where many independent directors hold multiple board seats) and firm performance. Some argue that busyness certifies a director’s ability and that such directors are value enhancing. Others argue that “over-boarded” directors are ineffective and detract from firm value. We find evidence that (1) the disparate results in prior work stem from differences in both sample composition and empirical design, (2) on balance the results suggest a negative association between board busyness and firm performance, and (3) the inclusion of firm fixed effects dramatically affects the conclusions drawn from, and the explanatory power of, multivariate analyses. We also explore alternative empirical definitions of what constitutes a busy director and find that commonly used proxies for busyness perform well relative to more complex alternatives.

We use option prices to examine whether changes in stock return skewness and kurtosis preceding earnings announcements provide information about subsequent stock and option returns. We demonstrate that changes in jump risk premiums can lead to changes in implied skewness and kurtosis and are also associated with the mean and variability of the stock price response to the earnings announcement. We find that changes in both moments have strong predictive power for future stock returns, even after controlling for implied volatility. Additionally, changes in both moments predict call returns, while put return predictability is primarily linked to changes in skewness.