U.S. bank M&As in the post-Dodd–Frank Act era: Do they create value?

We analyze the impact of the Dodd–Frank Act on the shareholder wealth gains using a sample of 640 completed U.S. M&As announced between 1990 and 2014. Our results indicate a positive DFA effect on announcement period abnormal returns in small bank mergers. In fact, mergers with combined firm assets of less than $10 billion create more shareholder value after the DFA, than ever before. This positive announcement effect in small deals appears to be linked with merger-related compliance cost savings and profitability improvements. By examining long-run abnormal returns, we find that the documented DFA effect on small deals announcement abnormal returns does not disappear overtime. Finally, we do not find such effects for non-U.S. bank M&As over the same period

Market concentration and bank M&As: Evidence from the European sovereign debt crisis

Using a sample of 312 bank M&As announced between 1998 and 2016 in the EU-27 countries, this paper investigates the impact of market concentration and the European sovereign debt crisis on the way investors react to these corporate events. In Western European countries, we find results which contrast the conventional wisdom that acquiring banks lose around the merger announcement date. In fact, since 2009, acquiring banks shareholders gain approximately $34 million around the announcement, a$56 million improvement compared to the pre-crisis period. These documented shareholder gains are also accompanied by significant improvements in post-merger profitability. Markedly, we link this superior performance of the post-2008 acquirers with the degree of market concentration in the Western European region. Finally, results for the Eastern European countries indicate that the crisis did not have a significant impact on the quality of bank M&As in the region

U.S. bank M&As in the post-Dodd-Frank Act era: Do they create value?

The Dodd-Frank Act has produced a new wave of bank M&As. This consolidation trend is mainly driven by mergers of small banks, since small banks feel the need to merge in order to absorb the compliance costs of the new regulation. We document that the $10 billion asset-size threshold has become the ceiling of the optimal scale for bank combinations, given that banks below this$10 billion mark avoid several regulatory hurdles imposed by the Dodd-Frank Act. Results for these “less than $10 billion mergers” suggest significant value creation for both firms’ shareholders: Bidders experience large anticipated wealth gains during the passage of the legislation since the market had ex-ante identified these bids. Consequently, at the deal announcement date, bidders experience insignificant returns, targets experience large abnormal returns and the combined abnormal returns are statistically positive. Finally, bidders experience positive abnormal returns at the deal completion date. On the contrary, results for larger bank mergers indicate a redistribution of wealth from the bidder to the target firm

Textual Information and IPO Underpricing: A Machine Learning Approach

This study examines the predictive power of textual information from S-1 filings in explaining IPO underpricing. Our empirical approach differs from previous research, as we utilize several machine learning algorithms to predict whether an IPO will be underpriced, or not. We analyze a large sample of 2,481 U.S. IPOs from 1997 to 2016, and we find that textual information can effectively complement traditional financial variables in terms of prediction accuracy. In fact, models that use both textual data and financial variables as inputs have superior performance compared to models using a single type of input. We attribute our findings to the fact that textual information can reduce the ex-ante valuation uncertainty of IPO firms, thus leading to more accurate estimates

EU Regulation and open market share repurchases: new evidence

This paper re-examines the impact of the EU Market Abuse Directive (MAD) on the market reaction around share repurchase announcements. We use a unique hand-collected dataset of firms listed on the Athens Stock Exchange, and we find evidence that contrasts with previous conclusions for large European economies. The implementation of the MAD is followed by a significant increase in announcement abnormal returns, which is more pronounced in initial repurchase programs. Our results remain robust to a series of robustness tests. We attribute our findings to cross-country differences in institutional framework and pre-MAD existing national laws. Collectively, our results support the notion that EU directives do not have a uniform effect across Member States. Thus, the impact of such reforms should also be examined in individual capital market studies

Machine Learning in U.S. Bank Merger Prediction: A Text-Based Approach

This paper investigates the role of textual information in a U.S. bank merger prediction task. Our intuition behind this approach is that text could reduce bank opacity and allow us to understand better the strategic options of banking firms. We retrieve textual information from bank annual reports using a sample of 9,207 U.S. bank-year observations during the period 1994-2016. To predict bidders and targets, we use textual information along with financial variables as inputs to several machine learning models. Our key findings suggest that: (1) when textual information is used as a single type of input, the predictive accuracy of our models is similar, or even better, compared to the models using only financial variables as inputs, and (2) when we jointly use textual information and financial variables as inputs, the predictive accuracy of our models is substantially improved compared to models using a single type of input. Therefore, our findings highlight the importance of textual information in a bank merger prediction task

Using textual analysis to identify merger participants: Evidence from the U.S. banking industry

In this paper, we use the sentiment of annual reports to gauge the likelihood of a bank to participate in a merger transaction. We conduct our analysis on a sample of annual reports of listed U.S. banks over the period 1997 to 2015, using the Loughran and McDonald’s lists of positive and negative words for our textual analysis. We find that a higher frequency of positive (negative) words in a bank’s annual report relates to a higher probability of becoming a bidder (target). Our results remain robust to the inclusion of bank-specific control variables in our logistic regressions

Real-Time Nanoparticle–Cell Interactions in Physiological Media by Atomic Force Microscopy

Particle–cell interactions in physiological media are important in determining the fate and transport of nanoparticles and biological responses to them. In this work, these interactions are assessed in real time using a novel atomic force microscopy (AFM) based platform. Industry-relevant CeO2 and Fe2O3 engineered nanoparticles (ENPs) of two primary particle sizes were synthesized by the flame spray pyrolysis (FSP) based Harvard Versatile Engineering Nanomaterials Generation System (Harvard VENGES) and used in this study. The ENPs were attached on AFM tips, and the atomic force between the tip and lung epithelia cells (A549), adhered on a substrate, was measured in biological media, with and without the presence of serum proteins. Two metrics were used to assess the nanoparticle cell: the detachment force required to separate the ENP from the cell and the number of bonds formed between the cell and the ENPs. The results indicate that these atomic level ENP–cell interaction forces strongly depend on the physiological media. The presence of serum proteins reduced both the detachment force and the number of bonds by approximately 50% indicating the important role of the protein corona on the particle cell interactions. Additionally, it was shown that particle to cell interactions were size and material dependent