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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,694 papers · 148 categories

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1122 · Aug 202119922001200920172026
9 results for takeovers

Two machine learning methods detect insider trading from investor activity data.

problem Detecting insider trading from trading activity data is challenging.
method Two unsupervised machine learning methods: clustering and group identification.
result Identifies potential insider trading rings around price sensitive events.

Toehold purchase, defined here as purchase of one share in a firm by an investor preparing a tender offer to acquire majority of shares in it, reduces by one the number of shares this investor needs for majority. In the paper we construct mathematical models for the toehold and no-toehold strategies and compare the exp…

2012-04-10abs ↗pdf ↗

Optimizes control interventions in real-world networks using deep-learning and network science.

problem Optimizing control over socioeconomic networks subject to constraints.
method Integrates optimization tools from deep-learning with network science.
result Characterizes vulnerability of corporate networks to takeovers.

AI uses KGs to assess economic impact of selective lockdowns on Italian companies.

problem Impact of selective lockdowns on Italian companies' economic stability.
method Automated Reasoning and Knowledge Graphs to analyze company networks.
result Identifies strategic companies at risk of takeover during lockdowns.

This review is about the convenience, the benefits, as well as the destructive capacities of money. It deals with various aspects of money creation, with its value, and its appropriation. All sorts of money tend to get corrupted by eventually creating too much of them. In the long run, this renders money worthless and …

2008-11-19abs ↗pdf ↗

Paper predicts M&A deal success using ML and DL techniques.

problem Predicting the success of M&A deals to avoid costly mistakes.
method Data preprocessing with ML techniques, feedforward neural networks, and sentiment scores integration.
result Methodology outperforms benchmark models in preliminary tests.