Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry

We investigate informed trading activity in equity options prior to the announcement of corporate mergers and acquisitions (M&A), focusing on the target rms. We nd that the probability of informed trading in options during the period just prior to the deal announcement date is much higher than one would expect prior to any randomly chosen date. This is demonstrated by pervasive, unusual option trading volume, implied volatility and liquidity attributes. We document highly positive abnormal trading volumes, excess implied volatility, and higher bid-ask spreads, prior to M&A announcements, with stronger eects for OTM call options on the target company. The probability of option volume on a random day exceeding that of our strongly unusual trading (SUT) sample is trivial - about three in a trillion. These eects are accentuated in the sub-sample of announcements of cash oers for large target rms, particularly in a narrow window before the announcement date. We further document a decrease in the slope of the term structure of implied volatility and an average rise in percentage bid-ask spreads, prior to the announcements. We provide evidence that there is also unusual activity in volatility strategies on the acquirer. We also present a summary analysis of SEC cases involving options trading ahead of M&A announcements, and show that the SEC is more likely to investigate cases where the acquirer is headquartered outside the US, the target is relatively large, and experienced substantial positive abnormal returns after the announcement.

/pdf/informed-options-trading-prior-to-m-a-announcements-insider-3p0vwd1lbq.pdf

Informed Options Trading prior to M&A Announcements: Insider Trading?

Making sense of large graph datasets is a fundamental and challenging process that advances science, education and technology. We survey research on graph exploration and visualization approaches aimed at addressing this challenge. Different from existing surveys, our investigation highlights approaches that have strong potential in handling large graphs, algorithmically, visually, or interactively; we also explicitly connect relevant works from multiple research fields — data mining, machine learning, human-computer ineraction, information visualization, information retrieval, and recommender systems — to underline their parallel and complementary contributions to graph sensemaking. We ground our discussion in sensemaking research; we propose a new graph sensemaking hierarchy that categorizes tools and techniques based on how they operate on the graph data (e.g., local vs global). We summarize and compare their strengths and weaknesses, and highlight open challenges. We conclude with future research directions for graph sensemaking.

https://www.cc.gatech.edu/~dchau/papers/15-bigcomp-sensemaking.pdf

Scalable graph exploration and visualization: Sensemaking challenges and opportunities

Liquidity restrictions on investors, like the redemption gates and liquidity fees introduced in the 2016 money market fund (MMF) reform, are meant to improve financial stability. However, we find evidence that such liquidity restrictions exacerbated the run on prime MMFs during the COVID-19 crisis. Our results indicate that gates and fees could generate strategic complementarities among investors in crisis times. Severe outflows from prime MMFs led the Federal Reserve to intervene with the Money Market Mutual Fund Liquidity Facility (MMLF). Using MMLF microdata, we show how the provision of "liquidity of last resort"" stabilized prime funds."

Liquidity Restrictions, Runs, and Central Bank Interventions: Evidence from Money Market Funds

Finding patterns in graphs has become a vital challenge in many domains from biological systems, network security, to finance (e.g., finding money laundering rings of bankers and business owners). While there is significant interest in graph databases and querying techniques, less research has focused on helping analysts make sense of underlying patterns within a group of subgraph results. Visualizing graph query results is challenging, requiring effective summarization of a large number of subgraphs, each having potentially shared node-values, rich node features, and flexible structure across queries. We present VIGOR, a novel interactive visual analytics system, for exploring and making sense of query results. VIGOR uses multiple coordinated views, leveraging different data representations and organizations to streamline analysts sensemaking process. VIGOR contributes: (1) an exemplar-based interaction technique, where an analyst starts with a specific result and relaxes constraints to find other similar results or starts with only the structure (i.e., without node value constraints), and adds constraints to narrow in on specific results; and (2) a novel feature-aware subgraph result summarization. Through a collaboration with Symantec, we demonstrate how VIGOR helps tackle real-world problems through the discovery of security blindspots in a cybersecurity dataset with over 11,000 incidents. We also evaluate VIGOR with a within-subjects study, demonstrating VIGOR's ease of use over a leading graph database management system, and its ability to help analysts understand their results at higher speed and make fewer errors.

VIGOR: Interactive Visual Exploration of Graph Query Results

Using a strategic rational expectations equilibrium framework, we show that forcing a well-informed insider to disclose her trades in advance tends to increase welfare for both the insider and less-informed outsiders. Advance disclosure generates price risk for the insider, and to mitigate this risk, the insider trades less aggressively on her private information. Consequently, outsiders face lower adverse selection costs, which improves risk sharing and increases welfare. The drop in trading aggressiveness also causes market efficiency to decline. Furthermore, pretrade disclosure encourages excessive risk taking but may either encourage or discourage managerial effort.

Advance Disclosure of Insider Trading

Using a rational expectations equilibrium framework, I evaluate the effects of a short-sale prohibition in an economy with asymmetrically informed investors who are identical except for their infor...

Informed Trading with a Short-Sale Prohibition

The closed-end fund puzzle: Management fees and private information

Leveraged and inverse exchange-traded funds (ETFs) have been heavily criticized for exacerbating volatility in financial markets because it is thought that they mechanically rebalance their portfolios in the same direction as contemporaneous returns. We argue that these criticisms are likely exaggerated because they ignore the effects of capital flows on ETF rebalancing demand. Empirically, we find that capital flows substantially reduce the need for ETFs to rebalance when returns are large in magnitude and, therefore, mitigate the potential for these products to amplify volatility. We also show theoretically that flows can completely eliminate ETF rebalancing in the limit.

Are Concerns About Leveraged ETFs Overblown

Rule 10b5-1 enables insiders to preplan future trades before becoming informed. Within a strategic rational expectations equilibrium framework, I characterize an insider’s unique optimal trading plan, which balances portfolio diversification against exploitation of the rule’s selective termination option. Because the rule reduces adverse selection and provides insurance against bad outcomes, the rule generally improves welfare for both the insider, who later becomes informed, and uninformed outsiders, provided there exists a sufficient degree of information asymmetry. Eliminating the rule’s selective termination option results in an even greater welfare improvement under a large subset of parametric conditions.Received March 9, 2018; editorial decision January 11, 2019 by Editor Wei Jiang.

Cancellable Insider Trading Plans: An Analysis of SEC Rule 10b5-1*

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