Manipulation is an important issue for both developed and emerging stock markets. For the study of manipulation, it is critical to analyze investor behavior in the stock market. In this paper, an analysis of the full transaction records of over a hundred stocks in a one-year period is conducted. For each stock, a tradi…
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48 results for “manipulation analysis”
Study examines reasons for Nutek India's share price drop.
problem Deep fall in Nutek India Limited's share price.
method Analyzed transactions and market forces to identify manipulation.
result Identified intentional interference in supply and demand.
Study reveals widespread manipulation of meme coins, leading to significant economic losses.
problem Widespread manipulation of meme coins leading to economic losses.
method Cross-chain analysis of 34,988 tokens across Ethereum, BNB Smart Chain, Solana, and Base.
result 82.8% of high-return tokens show evidence of artificial growth strategies.
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.
Learning Unfair Trading: a Market Manipulation Analysis From the Reinforcement Learning Perspectiveq-fin.TR
Market manipulation is a strategy used by traders to alter the price of financial securities. One type of manipulation is based on the process of buying or selling assets by using several trading strategies, among them spoofing is a popular strategy and is considered illegal by market regulators. Some promising tools h…
We present a generative autoencoder that provides fast encoding, faithful reconstructions (eg. retaining the identity of a face), sharp generated/reconstructed samples in high resolutions, and a well-structured latent space that supports semantic manipulation of the inputs. There are no current autoencoder or GAN model…
Interpreting the Latent Space of GANs via Correlation Analysis for Controllable Concept Manipulationcs.CV
Method interprets GAN latent space via latent variable correlation analysis.
problem Understanding the inner workings of GANs.
method Analyzing correlation between latent variables and semantic contents in generated images.
result A method for controllable semantic content generation in GANs.
Paper proposes a new method to attack Bayesian forecasting models.
problem Lack of research on adversarial attacks against time series forecasting systems.
method Decision analysis based attacking strategy for Bayesian forecasting models.
result Demonstrates the vulnerability of Bayesian forecasting models to adversarial attacks.
Study online learner attacks by manipulating labels, revealing critical thresholds.
problem Adversarial attacks on online learners, especially label manipulation.
method Control-theoretical perspective, theoretical analysis, empirical study.
result Discontinuous transition in learner's accuracy occurs at a critical threshold.
Ethereum trends analyzed through blockchain transactions and Google searches.
problem Identifying market manipulation in crypto prices.
method Big data analysis of Ethereum transactions, smart contracts, and search volumes.
result Big players manipulate crypto markets after price drops.
Robotic clothing manipulation improved with fashion image analysis techniques.
problem Automated identification of clothing categories and landmarks for robotic tasks.
method Training data augmentation methods and rotation invariant convolutions.
result Our approach outperforms state-of-the-art models on unseen datasets.
Data augmentation affects feature importance, enhancing learning for neural networks.
problem Understanding the effect of data augmentation on feature importance and learning dynamics.
method Analyzing a two-layer convolutional neural network in a multi-view data model.
result Data augmentation alters feature importance, making certain features more likely to be learned.
A new method improves target selection for manipulating complex systems like the brain.
problem Improper incorporation of low-variance outcomes into latent space of predictive models.
method Developed a novel objective based on supervised variational autoencoders (SVAEs) for PPCA (Probabilistic Principal Component Analysis).
result gPCR (Generative Principal Component Regression) dramatically improves target selection in manipulation compared to standard PCR and SVAEs.
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.
With the ever-increasing reliance on data for data-driven applications in power grids, such as event cause analysis, the authenticity of data streams has become crucially important. The data can be prone to adversarial stealthy attacks aiming to manipulate the data such that residual-based bad data detectors cannot det…
Facial analysis models are increasingly used in applications that have serious impacts on people's lives, ranging from authentication to surveillance tracking. It is therefore critical to develop techniques that can reveal unintended biases in facial classifiers to help guide the ethical use of facial analysis technolo…
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.
Paper detects pump and dump schemes in cryptocurrencies.
problem Cryptocurrency market manipulation by communities.
method Analyzed communities, reported case studies, introduced real-time detection approach.
result Real-time detection approach outperforms current methods.
Manipulating video content is easier than ever. Due to the misuse potential of manipulated content, multiple detection techniques that analyze the pixel data from the videos have been proposed. However, clever manipulators should also carefully forge the metadata and auxiliary header information, which is harder to do …
In this paper, we introduce an unsupervised learning approach to automatically discover, summarize, and manipulate artistic styles from large collections of paintings. Our method is based on archetypal analysis, which is an unsupervised learning technique akin to sparse coding with a geometric interpretation. When appl…
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…
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.
The paper proposes a method to learn from both simulation and real-world data.
problem Training autonomous systems in simulation and applying them to real-world environments.
method Balancing samples from simulation and real-world data using a replay buffer.
result The method achieves better performance in real-world tasks compared to training only in simulation.
DIGIT is a low-cost tactile sensor for in-hand manipulation.
problem Difficulty in sensing contact forces limits robotic manipulation.
method DIGIT miniaturizes and improves a vision-based tactile sensor.
result DIGIT enables better control of interactions with the environment.
Volunteer labor can temporarily yield lower benefits to charities than its costs. In such instances, organizations may wish to defer volunteer donations to a later date. Exploiting a discontinuity in blood donations' eligibility criteria, we show that deferring donors reduces their future volunteerism. In our setting, …
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…
Paper presents a new port-Hamiltonian model for vehicle manipulators.
problem Complex mechanical systems' energy flow and conservation.
method Derives port-Hamiltonian dynamics from Hamiltonian reduction theory.
result Establishes mathematical equivalence with existing formulations.
Optimal benchmark design varies based on costs in financial manipulation.
problem Manipulation of price benchmarks in finance.
method Analyzes empirical pattern and cost structures to determine optimal benchmark design.
result The optimal benchmark depends on the relative sizes of fixed and variable costs.
New foundation for Shapley value immune to coalitional manipulations.
problem Coalitional manipulations in game theory.
method Revised Shapley value criteria for coalitional games.
result Shapley value is immune to coalitional manipulations under new criteria.
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…
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.
This paper focuses on an extension of the Limit Order Book (LOB) model with general shape introduced by Alfonsi, Fruth and Schied. Here, the additional feature allows a time-varying LOB depth. We solve the optimal execution problem in this framework for both discrete and continuous time strategies. This gives in partic…
Physics-informed GCRL tackles sparse feedback learning with hybrid dynamics.
problem Sparse feedback learning with high-dimensional, hybrid, or contact-dependent dynamics.
method Introduces physics-informed inductive biases into goal-conditioned value learning.
result Contact-rich manipulation tasks degrade existing Pi-GCRL methods.
A new method for robot manipulation tasks using imagined object goals.
problem Learning robot manipulation in sparse reward environments.
method Train objects to reach target positions, then use predictions to create a curriculum of tasks.
result Higher success rates in challenging learning scenarios compared to alternatives.
Learning Force Control for Contact-rich Manipulation Tasks with Rigid Position-controlled Robotscs.LG
This work tackles force control for contact-rich manipulation tasks with rigid robots using RL.
problem Challenges in working with real robotic hardware, especially position-controlled robots.
method Combines RL with traditional force control techniques, implementing parallel position/force control and admittance control.
result Validated methods on both simulation and real robot (UR3 e-series) for force control.
Study on costs of manipulating AMM-based price oracles.
problem Cost of manipulation in AMM-based on-chain price oracles.
method Analyzes the robustness of AMM-based oracles to strategic manipulation, considering different aggregation methods and market conditions.
result Manipulation costs depend on the total quote depth and can be minimized by optimal liquidity weights.
Many problems in image processing and computer vision (e.g. colorization, style transfer) can be posed as 'manipulating' an input image into a corresponding output image given a user-specified guiding signal. A holy-grail solution towards generic image manipulation should be able to efficiently alter an input image wit…
Recent work has shown that state-of-the-art classifiers are quite brittle, in the sense that a small adversarial change of an originally with high confidence correctly classified input leads to a wrong classification again with high confidence. This raises concerns that such classifiers are vulnerable to attacks and ca…
Investigates optimal execution under time-varying liquidity, preventing price manipulation.
problem Optimal execution with time-varying liquidity impacts and price manipulation prevention.
method Almgren-Chriss framework, deterministic time variation, well-posedness, second-order conditions, price manipulation prevention.
result Sufficient conditions for a unique solution and prevention of price manipulation.
Tool manipulation is vital for facilitating robots to complete challenging task goals. It requires reasoning about the desired effect of the task and thus properly grasping and manipulating the tool to achieve the task. Task-agnostic grasping optimizes for grasp robustness while ignoring crucial task-specific constrain…
New attack manipulates UCB algorithm, new defense algorithm reduces pseudo-regret.
problem Adversarial attacks on stochastic bandit algorithms.
method Introducing action-manipulation attacks and proposing a robust defense algorithm.
result Proposed defense algorithm reduces pseudo-regret to O(max{log T, A}).
Paper explores vulnerabilities in image authenticity detection methods, especially printing and scanning attacks.
problem Vulnerability of image authenticity detection models to printing and scanning attacks.
method Demonstrates and proposes a new machine learning model to counter these attacks.
result Proposed model outperforms state-of-the-art models when trained on images from a single printer.
A new reward shaping method balances learning efficiency and effectiveness for robot manipulation.
problem Efficient and effective learning in robot manipulations with system uncertainty.
method Dense2Sparse reward shaping method combining dense and sparse rewards.
result Dense2Sparse method achieves higher expected reward and better system uncertainty tolerance.
In financial markets, liquidity is not constant over time but exhibits strong seasonal patterns. In this article we consider a limit order book model that allows for time-dependent, deterministic depth and resilience of the book and determine optimal portfolio liquidation strategies. In a first model variant, we propos…
Research examines how strategic latency manipulation impacts Ethereum's network efficiency and decentralization.
problem Impact of artificial latency on Ethereum's network efficiency and decentralization.
method Comprehensive analysis of MEV-Boost auction system and empirical validation with a pilot.
result Increased profitability for node operators and significant systemic challenges like heightened network inefficiencies and centralization risks.
This work tackles real-world robotic reinforcement learning challenges.
problem Limited success of reinforcement learning in real-world robotics.
method Proposes a system for autonomous real-world learning without instrumentation.
result Demonstrates a complete system that learns without human intervention.