A machine learning approach to support decision in insider trading detection

• URL: http://arxiv.org/abs/2212.05912v1
• Date: Tue, 6 Dec 2022 12:06:11 GMT
• Title: A machine learning approach to support decision in insider trading detection
• Authors: Piero Mazzarisi, Adele Ravagnani, Paola Deriu, Fabrizio Lillo, Francesca Medda, Antonio Russo
• Abstract summary: We propose two complementary unsupervised machine learning methods to support market surveillance. The first one uses clustering to identify, in the vicinity of a price sensitive event such as a takeover bid, discontinuities in the trading activity of an investor. The second unsupervised approach aims at identifying (small) groups of investors that act coherently around price sensitive events, pointing to potential insider rings.
• Score: 1.304892050913381
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Identifying market abuse activity from data on investors' trading activity is very challenging both for the data volume and for the low signal to noise ratio. Here we propose two complementary unsupervised machine learning methods to support market surveillance aimed at identifying potential insider trading activities. The first one uses clustering to identify, in the vicinity of a price sensitive event such as a takeover bid, discontinuities in the trading activity of an investor with respect to his/her own past trading history and on the present trading activity of his/her peers. The second unsupervised approach aims at identifying (small) groups of investors that act coherently around price sensitive events, pointing to potential insider rings, i.e. a group of synchronised traders displaying strong directional trading in rewarding position in a period before the price sensitive event. As a case study, we apply our methods to investor resolved data of Italian stocks around takeover bids.

Related papers

• When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments [55.19252983108372]
  We have developed a multi-agent AI system called StockAgent, driven by LLMs. The StockAgent allows users to evaluate the impact of different external factors on investor trading. It avoids the test set leakage issue present in existing trading simulation systems based on AI Agents.
  arXiv  Detail & Related papers  (2024-07-15T06:49:30Z)
• Language Models Can Reduce Asymmetry in Information Markets [100.38786498942702]
  We introduce an open-source simulated digital marketplace where intelligent agents, powered by language models, buy and sell information on behalf of external participants. The central mechanism enabling this marketplace is the agents' dual capabilities: they have the capacity to assess the quality of privileged information but also come equipped with the ability to forget. To perform well, agents must make rational decisions, strategically explore the marketplace through generated sub-queries, and synthesize answers from purchased information.
  arXiv  Detail & Related papers  (2024-03-21T14:48:37Z)
• Identifying key players in dark web marketplaces [58.720142291102135]
  This paper aims to identify the key players in Bitcoin transaction networks linked to dark markets. We show that a large fraction of the traded volume is concentrated in a small group of elite market participants. Our findings suggest that understanding the behavior of key players in dark web marketplaces is critical to effectively disrupting illegal activities.
  arXiv  Detail & Related papers  (2023-06-15T20:30:43Z)
• Mastering Pair Trading with Risk-Aware Recurrent Reinforcement Learning [10.566829415146426]
  CREDIT is a risk-aware agent capable of learning to exploit long-term trading opportunities in pair trading similar to a human expert. CREDIT is the first to apply bidirectional GRU along with the temporal attention mechanism to fully consider the temporal correlations embedded in the states. It helps our agent to master pair trading with a robust trading preference that avoids risky trading with possible high returns and losses.
  arXiv  Detail & Related papers  (2023-04-01T18:12:37Z)
• Protecting Retail Investors from Order Book Spoofing using a GRU-based Detection Model [0.0]
  This paper proposes a method to detect illicit activity and inform investors on spoofing attempts. Our framework is based on a highly extendable Gated Recurrent Unit (GRU) model.
  arXiv  Detail & Related papers  (2021-10-08T14:23:41Z)
• Trade When Opportunity Comes: Price Movement Forecasting via Locality-Aware Attention and Iterative Refinement Labeling [11.430440350359993]
  We propose LARA, a novel price movement forecasting framework with two main components. LA-Attention extracts potentially profitable samples through masked attention scheme. RA-Labeling refines the noisy labels of potentially profitable samples. LARA significantly outperforms several machine learning based methods on the Qlib quantitative investment platform.
  arXiv  Detail & Related papers  (2021-07-26T05:52:42Z)
• REST: Relational Event-driven Stock Trend Forecasting [76.08435590771357]
  We propose a relational event-driven stock trend forecasting (REST) framework, which can address the shortcoming of existing methods. To remedy the first shortcoming, we propose to model the stock context and learn the effect of event information on the stocks under different contexts. To address the second shortcoming, we construct a stock graph and design a new propagation layer to propagate the effect of event information from related stocks.
  arXiv  Detail & Related papers  (2021-02-15T07:22:09Z)
• Adversarial trading [91.3755431537592]
  We show that adversarial samples can be implemented in a trading environment and have a negative impact on certain market participants. This could have far reaching implications for financial markets either from a trading or a regulatory point of view.
  arXiv  Detail & Related papers  (2020-12-16T16:08:22Z)
• Taking Over the Stock Market: Adversarial Perturbations Against Algorithmic Traders [47.32228513808444]
  We present a realistic scenario in which an attacker influences algorithmic trading systems by using adversarial learning techniques. We show that when added to the input stream, our perturbation can fool the trading algorithms at future unseen data points.
  arXiv  Detail & Related papers  (2020-10-19T06:28:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.