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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.

168,695 papers · 148 categories

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22436586 · Oct 202519922001200920172026
48 results for financial manipulation

New method uses statistical physics to detect financial market manipulation.

problem Detecting financial market manipulation activities like spoofing and layering.
method Modeling order book dynamics as particle motion and using momentum measure.
result Method outperforms conventional Z-score-based anomaly detection.

Framework detects covert financial market manipulation using LOB representations.

problem Detecting covert financial market manipulation (spoofing) from complex anomaly patterns in multilevel prices.
method Cascaded contrastive representation learning of LOB data.
result Transformer-based architectures achieve state-of-the-art results in detection performance.

RL agent outperforms model-based approach in detecting price manipulation.

problem Detecting and exploiting price manipulation opportunities.
method Compared model-free RL with model-based approach in a market with Almgren-Chriss framework.
result RL consistently outperforms model-based approach, especially with noisy parameter estimates.

We provide direct evidence of market manipulation at the beginning of the financial crisis in November 2007. The type of manipulation, a "bear raid," would have been prevented by a regulation that was repealed by the Securities and Exchange Commission in July 2007. The regulation, the uptick rule, was designed to preve…

2011-12-14abs ↗pdf ↗

Study reveals risks of investing in new crypto-tokens in decentralized exchanges.

problem Risks associated with investing in newly created tokens in decentralized exchanges.
method Analysis of financial impact, market dynamics, profitability, and liquidity manipulations.
result Significant market liquidity trapped in honeypots, reducing market efficiency and misleading investors.

New attacks inflate earnings while reducing fraud scores, potentially millions at stake.

problem Manipulating financial reports to hide distress and gain.
method Maximum Violated Multi-Objective (MVMO) attacks that adapt search direction.
result Inflation of earnings by 100-200% while reducing fraud scores by 15% in 50% of cases.

Online trading platforms manipulate profits and losses, causing 82% of retail traders to lose money.

problem Manipulation of online trading platforms leading to financial losses for retail traders.
method Independent recording of trade details using REST API responses, comparison with broker reviews.
result 82% of retail traders lose money due to platform technical issues.

We develop an option pricing model based on a tug-of-war game. This two-player zero-sum stochastic differential game is formulated in the context of a multi-dimensional financial market. The issuer and the holder try to manipulate asset price processes in order to minimize and maximize the expected discounted reward. W…

2014-10-07abs ↗pdf ↗

Framework detects and ranks suspicious market manipulation using temporal convolutions and expert assessment.

problem Detecting and deterring rogue agents in financial markets.
method Weakly supervised learning, expert assessment, similarity search.
result Promising preliminary results in detecting and ranking suspicious market manipulation.

Algorithms are increasingly common components of high-impact decision-making, and a growing body of literature on adversarial examples in laboratory settings indicates that standard machine learning models are not robust. This suggests that real-world systems are also susceptible to manipulation or misclassification, w…

2018-11-27abs ↗pdf ↗

Researchers show how to manipulate Partial Dependence plots to deceive explanations of predictive models.

problem The robustness and trustworthiness of Partial Dependence (PD) explanations are compromised.
method Data poisoning using genetic and gradient algorithms to manipulate PD plots.
result PD explanations can be fooled and manipulated to mislead understanding of predictive models.

Investigates cross-impact kernels for financial asset prices.

problem Understanding and parameterizing cross-impact kernels for financial asset prices.
method Examined martingale-admissible and no-statistical-arbitrage-admissible kernels, determined their overlap, and provided calibration formulas.
result Identified the overlap between martingale-admissible and no-statistical-arbitrage-admissible kernels and provided formulas for their calibration.

A Markov-chain model is developed for the purpose estimation of the cure rate of non-performing loans. The technique is performed collectively, on portfolios and it can be applicable in the process of calculation of credit impairment. It is efficient in terms of data manipulation costs which makes it accessible even to…

2018-05-30abs ↗pdf ↗

Optimal execution strategy for merger & acquisition contracts with price impact.

problem Optimal execution and pricing of financial derivatives in M&A deals.
method Indifference utility arguments, considering linear and nonlinear contracts.
result Linear contracts are more expensive and vulnerable to manipulation.

AI learns market manipulation through simulation, suggesting regulation.

problem Regulating AI to prevent market manipulation.
method Used a genetic algorithm in an artificial market simulation.
result AI discovered market manipulation as an optimal strategy.

Prime Match protects client stock trades from market price manipulation.

problem Protecting client stock trades from market price manipulation.
method Prime Match uses a two-round secure linear comparison protocol to match orders without revealing information.
result Prime Match reduces market impact and maintains client privacy.

The paper analyzes how leverage affects manipulation in event-linked markets, offering new insights into regulation.

problem Manipulation and insider information in leveraged event-linked markets.
method Develops a two-axis manipulation taxonomy and analyzes leverage's effects on market-price and outcome manipulation.
result Leverage scales market-price manipulation linearly but shifts the cost-benefit threshold for outcome manipulation.

New model improves neural network robustness against input manipulations.

problem Improving neural network robustness against input manipulations.
method Causal view and deep causal manipulation augmented model (deep CAMA) with data augmentation and test-time fine-tuning.
result Deep CAMA shows superior robustness against unseen manipulations compared to traditional models.

This paper studies poisoning attacks in episodic RL and discovers their effectiveness depends on reward bounds.

problem Understanding security threats to RL algorithms through poisoning attacks.
method Examined two types of poisoning attacks: reward and action manipulation, in bounded and unbounded reward settings.
result The effectiveness of poisoning attacks depends on reward bounds, with different attack costs and success rates.

In this work we propose a model that can manipulate individual visual attributes of objects in a real scene using examples of how respective attribute manipulations affect the output of a simulation. As an example, we train our model to manipulate the expression of a human face using nonphotorealistic 3D renders of a f…

2019-01-28abs ↗pdf ↗

Prediction markets can be manipulated by traders who can move contract settlements, harming price discovery.

problem Manipulation of settlement times in prediction markets leads to unfair wealth transfer and harms price discovery.
method Developed a model showing how settlement manipulation transfers wealth and harms price discovery, and observed real-world effects on Polymarket's Bitcoin contract.
result Manipulators capture significant profits from retail traders, especially when settlement times are short.

Manipulating data, such as weighting data examples or augmenting with new instances, has been increasingly used to improve model training. Previous work has studied various rule- or learning-based approaches designed for specific types of data manipulation. In this work, we propose a new method that supports learning d…

2019-10-28abs ↗pdf ↗

Deep semi-supervised anomaly detection improves fraud detection in financial markets.

problem Detecting fraud in high-frequency financial data with limited labeled examples.
method Evaluation of Deep Semi-Supervised Anomaly Detection (Deep SAD) on proprietary limit order book data.
result Deep SAD significantly improves fraud detection accuracy with minimal labeled data.

Adaptive financial dataflow system improves model robustness in dynamic markets.

problem Static historical data leads to poor performance in dynamic financial markets.
method Drift-aware dataflow system with adaptive control and optimization.
result Enhanced model robustness and improved risk-adjusted returns.