Review of Pacific Basin Financial Markets and Policies

Using a multi-dimensional model of corporate transparency developed by DeBoskey and Gillett (2011) based on disclosure information, intermediary information, earnings quality information, and insider information, this study extends their findings to examine whether corporate transparency has significant power to explain cross-sectional variation in market liquidity (measured by bid-ask spreads and market depth) and analyst forecast properties (measured by analyst forecast accuracy and analyst forecast dispersion). We find that: (i) market depth and analyst forecast dispersion are significantly associated with public disclosure information transparency; (ii) market depth, forecast dispersion and forecast error are significantly associated with intermediary information transparency; and (iii) relative bid-ask spreads is signficantly associated with earnings quality information. Our findings offer an extended view of the impact of corporate transparency on a set of criterion variables expanded beyond firm-level ones to include market participants such as dealers/specialists who rely to some extent on company-provided information.

Financial development is recognized as an absorptive capacity in the relationship between foreign direct investment (FDI) and economic growth. Therefore, FDI effect on economic growth is contingent with the level of financial development. However, existing studies also show that financial development dampens economic growth through the “too much finance harms economic growth” hypothesis. Hence, there is a question of how far financial development should be developed to optimize the benefits of FDI on economic growth. The novelty of this study is that it reexamines the role of financial development in FDI-growth relationship by including the interaction term between FDI and the nonlinearity of financial development on economic growth in the period following the 2007–2008 Global Financial Crisis. Interestingly, our results demonstrate that the nonlinear relationship of financial development on economic growth is a U-shaped curve by using data from the 2009–2013 period, for 65 developing countries, which contrast the findings from previous studies. The absorptive capacity effects work nonlinearly, in that FDI accelerates growth after reaching a certain level of financial development, and that the positive effect originates from a minimum level. The study thus suggests that the level of financial development needs to be increased since it serves as a form of absorptive capacity enabling the positive growth effects of FDI in the recipient countries.

Income classification shifting involves misclassifying core expenses into non-core items to boost core earnings. Managers engage in classification shifting because they believe they can manage the perceptions of investors and financial analysts. We examine analysts’ earnings forecasts to determine whether analysts can identify classification shifting ex post and how they respond to shifted income statement components. Analysts play a role as information intermediaries between firms and investors. We find that analysts respond less to increased core earnings from classification shifting. However, analysts fail to gauge the full impact of classification shifting, leading to more optimistically biased and less accurate forecasts.

This study is the first to examine the impact of two recent key events on technical, cost and profit efficiencies of Chinese commercial banks — the 2007 Global Financial Crisis (GFC) and the subsequent Chinese Government’s 2009 fiscal stimulus package. Stochastic frontier analysis together with univariate and multivariate tests are used on a sample of 143 banks, including the big five, joint stock, city, rural and foreign, over the 2006–2013 period. Overall, there is substantial scope for improvement — cost efficiency could improve by up to 50%; technical and profit efficiencies by up to 30%. The effect of the crisis and the stimulus package on various overall and segment efficiencies is, at best, inconsistent and inconclusive.

In two-pass regression-tests of asset-pricing models, cross-sectional correlations in the errors of the first-pass time-series regression lead to correlated measurement errors in the betas used as explanatory variables in the second-pass cross-sectional regression. The slope estimator of the second-pass regression is an estimate for the factor risk-premium and its significance is decisive for the validity of the pricing model. While it is well known that the slope estimator is downward biased in presence of uncorrelated measurement errors, we show in this paper that the correlations seen in empirical return data substantially suppress this bias. For the case of a single-factor model, we calculate the bias of the OLS slope estimator in the presence of correlated measurement errors with a first-order Taylor-approximation in the size of the errors. We show that the bias increases with the size of the errors, but decreases the more the errors are correlated. We illustrate and validate our result using a simulation approach based on empirical data commonly used in asset-pricing tests.

We develop a simultaneous determination model of capital structure and stock returns. Specifically, we incorporate the managerial investment autonomy theory into the structural equation modeling with confirmatory factor analysis to jointly determine the capital structure and stock return. Besides attributes introduced in previous studies, we introduce indicators affecting a firm’s financing decision, such as managerial entrenchment, macroeconomic factors, government financial policy, and pricing factors. Empirical results show that stock returns, asset structure, growth, industry classification, uniqueness, volatility, financial rating, profitability, government financial policy, and managerial entrenchment are major factors of the capital structure.