Anti-Tipping off Perspective to Target Company Bank's Suspicious Transaction Report in Merger Activity

The purpose of this study is, to explain the legal concept and regulation of anti-tipping off in the banking sector, and to explore about the position of the Suspicious Transaction Report belonging to the target company bank in the merger activity based on anti-tipping off provisions. This is Normative legal Research with a statutory approach, a conceptual approach and a comparative approach. The background of the problem in this paper is, there is no strong legitimacy about definition and limitation of the meaning of the phrase ‘other parties’ in article 12 paragraph (1) Anti Money Laundering Law, which regulate about anti tipping off, wether the absorbing company bank is the 'other parties' of target company bank on the merger activity or not, when target company bank delivered it suspicious transaction reports to absorbing company bank prior to the merger. The results and discussion concluded that Anti-tipping off is a provision that prohibits tipping off. Tipping off itself is an action by a senior officer or Management or Employee of the Reporting Party (inter alia, Bank) to disclose facts related to a Suspicious Transaction Report that has been reported to Financial Intelligence Unit (in casu, PPATK). This is concrete and manifested in the provisions of Article 12 paragraph (1) of the Anti Money Laundering Law. Besides that, Viewed from the anti-tipping off perspective, all the rights owned by the target company Bank prior to the merger, ex officio, become the rights of the absorbing company, since the target company Bank legally merges into a part of the absorbing company. Based on that, it can be concluded that with respect to merger activities, the absorbing company banks are not ‘other Parties’ from the target company Bank.

Using Machine Learning to Analyze Merger Activity

An unprecedented amount of access to data, “big data (or high dimensional data),” cloud computing, and innovative technology have increased applications of artificial intelligence in finance and numerous other industries. Machine learning is used in process automation, security, underwriting and credit scoring, algorithmic trading and robo-advisory. In fact, machine learning AI applications are purported to save banks an estimated $447 billion by 2023. Given the advantages that AI brings to finance, we focused on applying supervised machine learning to an investment problem. 10-K SEC filings are routinely used by investors to determine the worth and status of a company–Warren Buffett is frequently cited to read a 10-K a day. We sought to answer–“Can machine learning analyze more than thousands of companies and spot patterns? Can machine learning automate the process of human analysis in predicting whether a company is fit to merge? Can machine learning spot something that humans cannot?” In the advent of rising antitrust discussion of growing market concentrations and the concern for decrease in competition, we analyzed merger activity using text as a data set. Merger activity has been traditionally hard to predict in the past. We took advantage of the large amount of publicly available filings through the Securities Exchange Commission that give a comprehensive summary of a company, and used text, and an innovative way to analyze a company. In order to verify existing theory and measure harder to observe variables, we look to use a text document and examined a firm’s 10-K SEC filing. To minimize over-fitting, the L2 LASSO regularization technique is used. We came up with a model that has 85% accuracy compared to a 35% accuracy using the “bag-of-words” method to predict a company’s likelihood of merging from words alone on the same period’s test data set. These steps are the beginnings of tackling more complicated questions, such as “Which section or topic of words is the most predictive?” and “What is the difference between being acquired and acquiring?” Using product descriptions to characterize mergers further into horizontal and vertical mergers could eventually assist with the causal estimates that are of interest to economists. More importantly, using language and words to categorize companies could be useful in predicting counterfactual scenarios and answering policy questions, and could have different applications ranging from detecting fraud to better trading.

On the relationship between gas content, star formation, and global H i asymmetry of galaxies on the star-forming main-sequence

ABSTRACT Observations have revealed that disturbances in the cold neutral atomic hydrogen (H i) in galaxies are ubiquitous, but the reasons for these disturbances remain unclear. While some studies suggest that asymmetries in integrated H i spectra (global H i asymmetry) are higher in H i-rich systems, others claim that they are preferentially found in H i-poor galaxies. In this work, we utilize the Arecibo Legacy Fast ALFA (ALFALFA) and extended GALEX Arecibo SDSS Survey (xGASS) surveys, plus a sample of post-merger galaxies, to clarify the link between global H i asymmetry and the gas properties of galaxies. Focusing on star-forming galaxies in ALFALFA, we find that elevated global H i asymmetry is not associated with a change in the H i content of a galaxy, and that only the galaxies with the highest global H i asymmetry show a small increase in specific star formation rate (sSFR). However, we show that the lack of a trend with H i content is because ALFALFA misses the ‘gas-poor’ tail of the star-forming main-sequence. Using xGASS to obtain a sample of star-forming galaxies that is representative in both sSFR and H i content, we find that global H i asymmetric galaxies are typically more gas-poor than symmetric ones at fixed stellar mass, with no change in sSFR. Our results highlight the complexity of the connection between galaxy properties and global H i asymmetry. This is further confirmed by the fact that even post-merger galaxies show both symmetric and asymmetric H i spectra, demonstrating that merger activity does not always lead to an asymmetric global H i spectrum.

Using competition tools during the COVID-19 pandemic: are merger control and state support substitutes or complements?

Many firms are facing financial difficulty as a result of COVID-19. However, we have not (yet) seen the predicted increase in merger activity or bankruptcies in some of the sectors most affected by the pandemic, including aviation. This may be, in part, a result of the substantial state support that has been provided to a number of companies in the sector. This article considers whether it is preferable to provide state aid to companies in order to allow them to continue operating, or should these ‘failing’ or ‘flailing’ firms be allowed to be acquired by others. It further considers whether there could be more alignment between these tools.

The Aggregate Implications of Mergers and Acquisitions

This paper develops a search and matching model of mergers and acquisitions (M&A) and uses it to evaluate the implications of merger activity for aggregate economic outcomes. The theory is consistent with a rich set of facts on US M&A, including sorting among merging firms, a substantial merger premium and serial acquisition. It provides a sharp link between these facts and the nature of merger gains. At the micro-level, both complementarities between merging firms and productivity improvements of target firms are important in generating gains. At the macro-level, the model suggests a significant beneficial impact of M&A on aggregate outcomes – the contribution to steady state output is 14% and 4% for consumption – which occurs through the reallocation of resources across firms and equilibrium effects on firm selection and new entrepreneurship. Nevertheless, the economy is not efficient, suggesting a scope for policy improvements – a simple flat tax on M&A can raise steady state consumption as much as 2% relative to the laissez-faire equilibrium. In short, the boundaries of the firm can matter for macroeconomic outcomes.

International Trade and the Propagation of Merger Waves

Cross-border merger activity is growing in importance. We map the global trade network each year from 1989 to 2016 and compare it to cross-border and domestic merger activity. Trade-weighted merger activity in trading partner countries has statistically and economically significant explanatory power for the likelihood that a given country will be in a merger wave state, at both the cross-border and domestic levels, even controlling for its own lagged merger activity. The role of trade as a channel for transmitting merger waves is confirmed using import tariff cuts and trade sanctions as instruments to mitigate endogeneity. Overall, the full trade network helps our understanding of merger waves and how merger activity propagate across borders.

Uncertainty, Investor Sentiment, and Innovation

We develop a theory of innovation waves, investor sentiment, and merger activity based on Knightian uncertainty. Uncertainty-averse investors are more optimistic on an innovation when they can make contemporaneous investments in multiple uncertain projects. Innovation waves occur when there is a critical mass of innovative companies, and are characterized by stronger investor sentiment, higher equity valuation, and hot initial public offering markets. Our approach to investor sentiment is not based on erroneous beliefs disjoint from economic fundamentals, but depends on uncertainty on the fundamentals. Our model can explain sector-specific booms uncorrelated with aggregate economic activity and the overall stock market.