Who makes acquisitions? CEO overconfidence and the market's reaction

Does CEO overconfidence help to explain merger decisions? Overconfident CEOs over-estimate their ability to generate returns. As a result, they overpay for target companies and undertake value-destroying mergers. The effects are strongest if they have access to internal financing. We test these predictions using two proxies for overconfidence: CEOs’ personal over-investment in their company and their press portrayal. We find that the odds of making an acquisition are 65% higher if the CEO is classified as overconfident. The effect is largest if the merger is diversifying and does not require external financing. The market reaction at merger announcement (-90$-90$ basis points) is significantly more negative than for non-overconfident CEOs (-12$-12$ basis points). We consider alternative interpretations including inside information, signaling, and risk tolerance.     

An Alternative Testable Form of the Consumption CAPM

This paper develops a consumption-oriented model of asset prices in a multigood economy that is, in principle, testable even when aggregate consumption of goods and their market prices are only partially observable. Previous studies show that, when there are m consumption goods, equilibrium expected excess returns on securities are functions of their covariances with $m+1$ variables—aggregate consumption expenditure and market prices of consumption goods. Without making any further assumptions, the present model shows that a similar equilibrium relationship can be expressed in terms of covariances of asset returns with the following $m+1$ variables: market prices of k consumption goods and aggregate consumption of $m+1?k$ goods. Because the author's result provides researchers with some flexibility in choosing the set of $m+1$ variables that measure riskiness of securities, it should lead to more powerful tests of the model.     

The Timing and Stock Selection Abilities of Bank Funds: Evidence Based on Meta-Analysis

This study investigates the risk-adjusted investment performance of the equity portfolios of bank trust departments, over the 1975–1992 period, attributable to their micro stock selection and macro market timing abilities. This paper first employs a widely known parametric statistical procedure developed by Henriksson and Merton to test jointly for the presence of either superior stock selection or market timing abilities. The paper then utilizes an alternative technique, called meta-analysis, to further examine the regression results obtained under the Henriksson–Merton model. The meta-analysis essentially eliminates such study artifacts as sampling and measurement errors through cumulation of results across studies. The findings of the joint test, based on the Henriksson–Merton model, do not support either superior stock selection abilities or market timing skills on the part of bank equity fund managers: selectivity measures are positive and timing measures are generally negative, but both measures are statistically insignificant. In contrast, the evidence based on the meta-analysis suggest that the managers of bank equity investment funds possess superior stock selection abilities and somewhat negative timing skills. Therefore, the results of this study suggest that, even though bank trust departments, like other categories of institutional investors, are unable to outperform a passive buy and hold investment strategy through timing the equity market, they are able to improve their investment performance through superior stock selection abilities.     

Assessing Goodness-of-Fit of Asset Pricing Models: The Distribution of the Maximal R2

The development of asset pricing models that rely on instrumental variables together with the increased availability of easily-accessible economic time-series have renewed interest in predicting security returns. Evaluating the significance of these new research findings, however, is no easy task. Because these asset pricing theory tests are not independent, classical methods of assessing goodness-of-fit are inappropriate. This study investigates the distribution of the maximal $R^2$ when k of m regressors are used to predict security returns. We provide a simple procedure that adjusts critical $R^2$ values to account for selecting variables by searching among potential regressors.     

Information Monopoly and Commitment in Intermediary-Firm Relationships

A bank may use the private information that it acquires through monitoring to hold up borrowers. This information monopoly of the bank may inefficiently distort the borrowers investment decisions in environments where moral hazard is prevalent. The paper analyses how this problem is resolved within bank-firm relationships. In the benchmark case when the bank can contractually commit to future actions, the optimal contract turns out to be ambiguous in nature. When commitment contracts cannot be written, firms have an incentive to develop multiple banking relationships in order to decrease the inside banks bargaining power. However, with costly monitoring, this may defeat the initial purpose for contracting with a financial intermediary, namely information production. The paper argues that when contractual commitment is not feasible, bank size may serve as an alternative commitment device that prevents the bank from holding up borrowers in the future.     

A new approach to assessing model risk in high dimensions

A central problem for regulators and risk managers concerns the risk assessment of an aggregate portfolio defined as the sum of d individual dependent risks $X_i$. This problem is mainly a numerical issue once the joint distribution of $\left(X_1\text{,}{X}_2\text{,}\dots \text{,}{X}_d\right)$ is fully specified. Unfortunately, while the marginal distributions of the risks $X_i$ are often known, their interaction (dependence) is usually either unknown or only partially known, implying that any risk assessment of the portfolio is subject to model uncertainty.Previous academic research has focused on the maximum and minimum possible values of a given risk measure of the portfolio when only the marginal distributions are known. This approach leads to wide bounds, as all information on the dependence is ignored. In this paper, we integrate, in a natural way, available information on the multivariate dependence. We make use of the Rearrangement Algorithm (RA) of Embrechts et al. (2013) to provide bounds for the risk measure at hand. We observe that incorporating the information of a well-fitted multivariate model may, or may not, lead to much tighter bounds, a feature that also depends on the risk measure used. In particular, the risk of underestimating the Value-at-Risk at a very high confidence level (as used in Basel II) is typically significant, even if one knows the multivariate distribution almost completely.Our results make it possible to determine which risk measures can benefit from adding dependence information (i.e., leading to narrower bounds when used to assess portfolio risk) and, hence, to identify those situations for which it would be meaningful to develop accurate multivariate models.     

R2

Even with hindsight, the ability to explain stock price changes is modest. $R^2$s were calculated for the returns of large stocks as explained by systematic economic influences, by the returns on other stocks in the same industry, and by public firm-specific news events. The average adjusted $R^2$ is only about .35 with monthly data and .20 with daily data. There is little relation between explanatory power and either the firm's size or its industry. There is little improvement in $R^2$ from eliminating all dates surrounding news reports in the financial press. However, the sample kurtosis is quite different when such news events are eliminated, thereby revealing a mixture of return distributions. Non-news dates also indicate the presence of a distributional mixture, perhaps due to traders acting on private information.     

Patterns of Productivity in the Finance Literature: A Study of the Bibliometric Distributions

This study finds a bibliometric regularity in the finance literature that the number of authors publishing n papers is about $1/n^c$ of those publishing one paper. We find that the finance literature conforms very well to the inverse square law $(c=2)$ if data are taken from a large collection of journals. When applied to individual finance journals, we find that values of c range from 1.95 to 3.26. We also find that top-rated journals have higher concentrations among their contributors. This implies that the phenomenon “success breeds success” is more common in higher quality publications.