Narrow Results

This paper examines the impact of profitability, stock price performance and growth opportunity on the capital structure of firms in Singapore, Taiwan and Hong Kong. In contrast to Kayhan and Titman (2007), it is found that firms in these three Chinese-dominated economies strongly prefer debt to equity or internal fund financing. They also take advantage of stock price appreciation by issuing more shares. An adjustment model for debt ratios is estimated. The results suggest that the leverage ratios of these firms slowly adjust toward their target levels. Deviations from the target due to the pecking order and market timing effects are found to be significant.

We extend the parametric portfolio policies that exploit firm characteristics to optimize portfolios of stocks and are thus based on asset selection. In addition to this, our extension exploits market indicators for market timing purposes (i.e. optimal allocations between stocks and a risk-free asset). We demonstrate the mechanics of the proposed technique in simulation studies. Specifically, we show that the extended approach is able to produce portfolios based on selection and timing that outperform portfolios that only apply selection, when the applied market indicators have sufficient predictive power. In purely demonstrative empirical applications, we illustrate how investors can use our optimization approach using common market indicators.

While most papers in finance literature investigate how the stock market reacts to announcements of corporate events, very few study the opposite, how namely, the manager responds to the information from outside investors. In this paper, we examine this issue, using open market share repurchases. Open market share repurchase offers flexibility for the manager to decide whether or not to buy back shares. Therefore, the manager may refer to the opinions of outside investors and make the decision, based on actual buyback activities. We propose learning, over-confidence and timing hypotheses to interpret the behavior of the managerial response to initial market reaction on the share repurchase announcement. Empirically, if a repurchase announcement abnormal return is low, then the manager tends to achieve the repurchase announced ratio by purchasing more shares. In addition, the investor will positively react to this repurchase in the long run. These empirical findings are consistent with the market timing hypothesis, which implies that managers know the true value of their firms better than the market at the moment of the share repurchase announcement.

We compare valuation effects associated with debt issuance under SEC Rule 144A versus issuance in the public market. We find that nonconvertible debt Rule 144A issuers experience an incremental positive announcement effect when compared to their counterparts issuing in the public market. We attribute this to the timing of issuance under favorable market conditions and a reduction in informational asymmetry due to lender specialization. When compared to their public counterparts, convertible Rule 144A bond issuers often experience negative announcement effects and inferior post-issuance stock performance. We argue that this negative market reaction arises from a stronger opportunistic market-timing signal.

Overwhelming evidence indicates that firms time market conditions to issue equity. I investigate the motivations for security issuances in hot and cold markets. While it is commonly believed that firms tend to exploit overvaluations to issue equity and overinvest in so-called 'hot' markets, which often results in lower future returns, I show that security issuances in certain periods with lower adverse selection costs are motivated by fundamentals such as capital expenditures and financial constraints, and that these issuances can create shareholder wealth. In contrast, firms that issue equity during periods of high sentiment experience a decline in future returns.

The initial public offering (IPO) filing volume is positively related to changes in market volatility, especially when market returns are at ‘normal’ levels. This is consistent with the view that filing with the Securities and Exchange Commission (SEC) gives would-be issuers an ‘option’ on market valuations. Creating this option is attractive not only when market valuations rise but also when volatility increases, and the effect of volatility is more pronounced when market returns are neither high nor low. We therefore identify a distinct type of ‘window of opportunity’ for firms attempting to go public, characterized not particularly by strong stock valuations but by increased volatility in valuations.

In this chapter, we investigate whether hedge fund managers possessing advanced professional education, as represented by the CFA@ designation, have better managerial ability and fund performance compared to managers lacking such education. We define managerial ability in terms of market, volatility, and liquidity timing ability. In general, we find that CFA designated managers have better volatility and liquidity timing ability and earn better risk-adjusted returns than non-CFA managers. We also find that the CFA designation does not contribute to the fund flow–performance relation. The rise of high frequency and algorithmic trading may partially explain these findings.

Dynamic capital structure theory suggests that firms have target debt ratios and other factors such as financial deficit, market timing, implied debt ratio may lead observed capital structures to deviate from the targets. Because of adjustment costs, firms will not make adjustment immediately and completely when there are deviations. The decisions whether to adjust the debt ratios and the adjustment magnitude will be based on the tradeoffs between the costs and benefits of the adjustment. Based on the latest achievements of capital structure theory and empirical research, this paper constructs a comprehensive model to pursue the long-term dynamic path of capital structure adjustment of listed firms in China. Firstly, we estimate a Tobit regression model to predict the target debt ratios and construct a variable — leverage deficit as an independent variable in the second step regression which is scaled by the difference between the target debt ratios and the observed debt ratios; then we use the partial adjustment model to examine how capital structure can be influenced through dynamic adjustments. The results indicate that financial deficit, market timing and stock price changes indeed lead to deviations between the observed debt ratios and target debt ratios. In particular, stock price changes have the strongest influence on capital structure changes. We find also that their effects are partially reversed over long horizons. These empirical results suggest that firms' capital structures tend to move towards their target debt ratios over time although their histories strongly influence their capital structures.

In this chapter, we look at how estimation is performed on an important finance valuation model to address the problem of risk pricing and also to apply the model to evaluate investment performances.

A key foundation of finance is the tradeoff between risk and return. Assets returning higher ex-ante (or conditional expected) return should also bear higher ex-ante risk, and vice versa. Finance theory and models seek to establish the exact relationship between this return and risk. One of the earliest finance models is the Sharpe-Lintner capital asset pricing model (CAPM) that links expected return to only systematic risk. The unsystematic or residual risk can be diversified away and is, therefore, not priced. Systematic risk in the form of market risk is priced. For any asset, this market risk is reflected in the sensitivity of the asset return to the market factor movement. Higher sensitivity implies a higher beta or systematic risk. When we refer to risk, it is usually quantified in terms of standard deviation instead of variance since the former is expressed in terms of the same unit as the underlying variable. We shall begin our study of CAPM with the statistical market model.