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This paper investigates the puzzle of mergers and acquisitions’ (M&A) long-term performance through the strategic management perspective. The authors measure the M&A capability construct and test its relationship with the long-term performance of the firms.

The study employs a natural language processing (NLP) methodology to quantify unstructured data from 564 annual reports and 2,602 M&A synopses from January 01, 2013 to December 31, 2016. The authors combine qualitative document analysis with a quantitative method using a multiple regression analysis model.

Among serial acquirers, M&A capability positively relates to long-term firm performance measured by both return on equity and price-to-book value. The authors also find that the size of the company and the number of acquisitions influence the M&A capability, confirming previous results in the literature.

Detailed M&A management plans are usually confidential and not fully reported. Future studies could employ enhanced artificial intelligence tools to measure the M&A capability construct beyond filing reports, encompassing interviews, social media posts, press releases and other unstructured data sources.

Firms can improve their M&A capability by understanding the underlying foundation of the construct provided in the research. Additionally, researchers can build on the methodology employed using advanced NLP tools to measure M&A capability.

Improving their M&A capability would allow firms to better choose their targets and conduct a superior integration process, which could prevent distressing mergers, unnecessary negative social impacts and culture disruption. As a result, the ensuing organization would be stronger, and the long-term performance would improve.

This study addresses gaps in the literature on M&A performance and provides a new empirical method to measure the M&A capability.

The purpose of this paper is to examine socially responsible investment (SRI) fund performance and investigate the factors influencing fund performance.

The study uses return data from the Morningstar database for 152 SRI funds from January 1995 to May 2015. The initial analysis includes the use of various risk-adjusted performance measures, including Sharpe ratio, Treynor ratio, Information ratio, Sortino ratio and M². The study also uses four factor models, including Jensen single-factor model, Fama–French three-factor model, Carhart four-factor model and Fama–French five-factor model to explain SRI fund returns. Finally, a cross-sectional regression analysis is applied to investigate the determinants of SRI fund returns.

The results show that, on average, the SRI funds provide comparable risk-adjusted returns relative to various benchmark market indices. Market factor is significant in explaining SRI fund returns. Examining each factor model, the results do not support Fama–French’s three-factor model as neither size nor value factor is significant. The author finds weak support for Carhart’s momentum factor along with the market factor. Finally, the Fama–French five-factor model shows market, size and operating profit factors explain SRI fund returns. The study also finds the fund performance is stronger for funds with the higher turnover ratio, the larger fund size and more managerial experience and lower for funds with higher expense ratio. Also, funds formed with negative screening perform better than positive or mixed screened funds.

SRI funds have received considerable attention from investors. This study contributes to the literature by examining SRI fund performance and investigating factors influencing their performance using multiple factor models and cross-sectional regression analysis. The findings are relevant for investors who demand responsible investment opportunities without sacrificing returns for nonfinancial screenings. Findings also suggest that investors should consider fund characteristics when selecting SRI funds.

The impact of founder CEOs on firm value continues to be debated in the finance literature. While earlier studies suggest that founding family ownership and founding CEO structure create less value than public ownership, later studies provide contradicting evidence. This study examines how founder CEOs affect firm value in the business group context while controlling for firm-specific variables and various CEO characteristics.

The authors use a sample of publicly listed Indian firms from 2010 to 2015 with 997 firm-year data observations. While 306 of these are in business groups, the remaining 691 are in a nonbusiness group. The authors also divide the sample into various sector subgroups, including materials (170), industrials (198), consumer (422) and others (198). They use two different models, including the fixed effect model (FEM) and pooled generalized method of moments (GMM) model to run regressions.

The authors find that firms with founder CEOs have lower firm value than those with nonfounder CEOs. These results show the importance of the role of founder CEOs in the Indian business groups. The authors further find a positive relationship between founder CEO and business group interaction variable, showing that an increase in founder CEO (or business group) increases the significance effect of business group (founder CEO) on firm performance. After separating the sample business and nonbusiness groups, the relationship between founder CEOs and firm value in both groups remains negative. Using various firm-specific control variables, the authors find that highly leveraged and smaller firms experience lower Tobin's Q. In contrast, firms with more investment in research and development perform better. Among CEO characteristics, the authors find that firms with highly educated CEOs do not perform well, while firms with older CEOs do better. Finally, they find that CEO tenure and duality are associated with lower firm performance.

This study adds value by providing evidence on the founder CEOs and firm performance in business groups from a fast-developing emerging market.

The purpose of this paper is to examine the performance of US mutual funds that invest primarily in emerging market equities and bonds.

The study adopts the Morningstar classification of mutual funds and uses the Lipper US Mutual Fund Database through FactSet to obtain monthly returns and various metrics for emerging market equity and bond mutual funds covering the period from January 2000 to May 2017. Several descriptive statistics for these funds are reported as well as various risk-adjusted performance measures. Alphas are computed for different sub-periods using different factor models to mitigate potential biases.

The results show that diversified emerging market funds generate some significant alphas for their investors during the study period. Emerging market bond funds, on the other hand, do not provide any significant positive alphas; mostly alphas are negative. An analysis of sub-period performance suggests that these funds do not consistently provide excess returns, showing great variations from one period to another.

The emerging market funds provide US investors with an alternative source of exposure for their portfolios. Emerging markets differ from developed markets on a wide range of market and economic characteristics, including size, liquidity, and regulation. This study contributes to the scarce literature on these types of funds and provides a comprehensive performance assessment against various benchmarks during a period that encompasses significant bear and bull markets across the world.