The effects of strategic and market complementarity on acquisition performance: evidence from the U.S. commercial banking industry, 1989–2001

Most traditional research on mergers and acquisitions tends to focus on the role of similarity in explaining acquisition performance. While scholars have recently begun to examine acquisition complementarity, there is still little evidence concerning how complementarity influences acquisition performance. Further, previous research has not drawn the connections between related contexts and the potential benefits from complementarity. In this article, we move the study of acquisition complementarity forward by investigating the effects of strategic and market complementarity on acquisition performance in the context of related horizontal acquisitions. We also propose that two key attributes of acquirers—strategic focus and out‐of‐market acquisition experience—will moderate this relationship. We investigate our research questions in the context of all 2,204 acquisitions made by publicly traded U.S. commercial banks during the 12‐year period from 1989 to 2001. Our findings are generally supportive, suggesting complementarity is an important antecedent of acquisition performance, and raising important issues on the nature of acquisition research in general. Copyright © 2009 John Wiley & Sons, Ltd.     

无机化学教学之我见

针对本科无机化学的学科特点,论述了教师在课堂教学的过程中,应该注重与学生的互动,启发学生思维,将多媒体与传统教学相结合。采用有效的教学方法,建立良好的课堂气氛,能增强学生的学习热情,进而提高教学质量。     

International stock return predictability

We propose a new methodology for predicting international stock returns. Our Bayesian framework performs probabilistic selection of predictors that can shift at multiple unknown structural break dates. The approach generates significantly more accurate forecasts of international stock returns than a range of popular models that are economically meaningful for a risk-averse mean–variance investor. Allowing for regime-specific variable selection reduces considerably the international diversification of an unhedged U.S. investor’s portfolio.     

Contagion between real estate and financial markets: A Bayesian quantile-on-quantile approach

We study contagion between Real Estate Investment Trusts (REITs) and the equity market in the U.S. over four sub-samples covering January, 2003 to December, 2017, by using Bayesian nonparametric quantile-on-quantile (QQ) regressions with heteroskedasticity. We find that the spillovers from the REITs on to the equity market has varied over time and quantiles defining the states of these two markets across the four sub-samples, thus providing evidence of shift-contagion. Further, contagion from REITs upon the stock market went up during the global financial crisis particularly, and also over the period corresponding to the European sovereign debt crisis, relative to the pre-crisis period. Our main findings are robust to alternative model specifications of the benchmark Bayesian QQ model, especially when we control for omitted variable bias using the heteroskedastic error structure. Our results have important implications for various agents in the economy namely, academics, investors and policymakers.     

The effect of environmental regulations,top management commitment,and organizational learning on green product innovation: Evidence from automobile industry

This paper proposes and empirically examines a model to investigate the effect of environmental regulations, top management commitment (TMCO) and organizational learning toward green product innovation (GPI). The proposed theoretical model, grounded in dynamic capabilities view (DCV) and upper echelons theory, is analyzed by Partial least squares (PLS) method using the data from Indian automotive manufacturing firms. The findings indicate the importance of TMCO and organizational learning for implementing GPI (in response to regulations), and achieve desired performance. Further, organizational learning fully mediates between commitment of top management and GPI. The findings can be useful for managers in automotive manufacturing firms who are interested toward implementing GPI. The paper contributes to green innovation literature by empirically examining the role of TMCO and organizational learning for GPI.     

Past,present, and future of green product innovation

Firms are under constant pressure from various governmental and nongovernmental agencies to switch from conventional environmentally polluting products to green product innovations (GPIs). However, the relevant research pertaining to GPI has been published in a diverse set of journals that vary in their scope and readership and, therefore, the scholarly contribution to the topic remains largely fragmented. This study has utilised a systematic literature review approach to examine the literary corpus on GPI to paint a holistic picture of its different aspects. The content and thematic analysis of 85 studies resulted in the extraction of seven key research themes: organisational capabilities, organisational learning, institutional pressures, barriers, structural changes, benefits of GPI, and methodological choices. This study's findings further highlight the various gaps in the GPI literature and raise some research questions that warrant scholarly investigation in the future. Likewise, our study has important implications for practitioners who are likely to benefit from a holistic understanding of the different aspects of GPI. Similarly, policymakers can use this study's findings to introduce policy interventions, especially in countries where GPI adoption is low.     

The interaction effect of gender and ethnicity in loan approval: A Bayesian estimation with data from a laboratory field experiment

Microfinance targets women and uses loan provision as a tool for empowerment, which translates into better household nutrition, improved education, and a scale down of domestic violence. However, ethnic discrimination in microfinance may exist in countries with a segregated indigenous population. We assessed this possibility with a field experiment in Bolivia. The controlled laboratory experiment evaluated whether credit officers rejected microloan applications based on the interaction effect of ethnicity and gender of potential borrowers. Point estimates of a Bayesian mixed‐effects logistic regression, estimated with the experimental data, indicate that nonindigenous women have double the chance of loan approval, but indigenous women have only 1.5 times the chance of loan approval when compared with men. While the findings about gender are limited, the evidence for the interaction of gender and ethnicity is more robust and suggests the existence of positive taste‐based discrimination favorable for nonethnic women in Bolivia. We conclude that the affirmative actions towards women promoted by development agencies and microfinance institutions must not overlook ethnicity as an important factor for financial policies of sustainable development. In practice, these policies should be aimed at identifying and reducing both social desirability bias and the structural barriers to financial inclusion that indigenous women may face when trying to obtain access to a loan.     

Risk aversion,public disclosure,and partially informed outsiders

In this paper, we examine the impact of public disclosure and partially informed outsiders on a risk-averse insider’s trading behavior, market efficiency, and market depth. In our model, under disclosure requirements, except for the final auction, market depth is the same at every auction. When informed outsiders are risk-neutral, in contrast to the case of a risk-averse insider with no informed outsiders, the insider is more concerned about the uncertainty about future price risk. When the number of informed outsiders increases, market liquidity improves, and the insider increases the variance of her random component to conceal her trading strategy. However, since the insider is relatively more risk-averse, she pays less attention to doing this on her own. Besides, the order flow provided by informed outsiders and randomly added by the insider injects additional liquidity into the market. When informed outsiders are risk-averse, compared to risk-neutral informed outsiders, an insider is most concerned about trading risks brought by informed outsiders at the beginning of trading. Furthermore, whether the trader is an insider or informed outsider, the more risk-averse trader has lower expected profits. Moreover, outsiders’ greater risk aversion leads to a smaller market depth.     

Object detection based on visual memory: a feature learning and feature imagination process

ABSTRACT

Visual memory plays an important role for the human’s visual system to detect objects. The features of an object stored in the visual memory have much lower dimensions than the features contained within an image. We simulate the visual memory as a feature learning and feature imagination (FLFI) process to build an object detection algorithm. The method is constructed by a bottom-up feature learning and a top-down feature imagination. The proposed object detection method is tested using publicly available benchmark data sets, and the result indicates that it is fast and more robust.     

Bankruptcy prediction for small- and medium-sized companies using severely imbalanced datasets

Bankruptcy prediction is still important topic receiving notable attention. Information about an imminent bankruptcy threat is a crucial aspect of the decision-making process of managers, financial institutions, and government agencies. In this paper, we utilize a newly acquired dataset comprising financial parameters derived from the annual reports of small- and medium-sized companies. The data, which reveal the true ratio between bankrupt and non-bankrupt companies, are severely imbalanced and only contain a small fraction of bankrupt companies. Our solution to overcome this challenging scenario of imbalanced learning was to adopt three one-class classification methods: a least-squares approach to anomaly detection, an isolation forest, and one-class support vector machines for comparison with conventional support vector machines. We provide a comprehensive analysis of the financial attributes and identify those that are most relevant to bankruptcy prediction. The highest prediction performance in terms of the geometric mean score is 91%. The results are validated on two datasets from the manufacturing and construction industries.