Research predicts XRP price anomalies using graph topologies.
problem Forecasting extreme price movements in XRP cryptoasset.
method Analyzed topological features of XRP transaction graphs.
result Topological features indicate extreme price surges.
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48 results for “price anomalies”
DeepTrust uses NLP to quickly identify and verify financial anomalies on Twitter.
problem Unreliable information in financial markets leading to unexpected price changes.
method Machine learning for anomaly detection, NLP for information retrieval and reliability assessment.
result DeepTrust outperforms baseline classifiers in identifying financial anomalies.
A scalable system detects price anomalies in online marketplaces to improve customer experience.
problem Inaccurate prices on online marketplaces lead to poor customer experience and revenue loss.
method MoatPlus uses unsupervised statistical features and an ensemble of models to generate upper price bounds.
result Our approach improves precise anchor coverage by up to 46.6% in high-vulnerability item subsets.
Online retailers execute a very large number of price updates when compared to brick-and-mortar stores. Even a few mis-priced items can have a significant business impact and result in a loss of customer trust. Early detection of anomalies in an automated real-time fashion is an important part of such a pricing system.…
Paper finds linear laws in Bitcoin price changes, aiding anomaly detection.
problem Detecting anomalies in Bitcoin price changes.
method Time embedding of autocorrelation function, binary series generation, stepped time windows.
result Linear laws became more complex before major market events, suggesting price manipulation.
Study finds anomalies in high-frequency S&P 500 price changes.
problem Anomalies in high-frequency S&P 500 price changes.
method Using NBBO event-time data, the study forms pairs of backward and forward price increments, standardizes them, and estimates expected responses on a fine grid of push magnitudes.
result Persistent structural shift in expected responses: near zero for short lags, pronounced tails for long lags, indicating correlation between larger historical pushes and nonzero responses.
The Rank Effect for Commoditiesq-fin.GN
We uncover a large and significant low-minus-high rank effect for commodities across two centuries. There is nothing anomalous about this anomaly, nor is it clear how it can be arbitraged away. Using nonparametric econometric methods, we demonstrate that such a rank effect is a necessary consequence of a stationary rel…
We uncover a new anomaly in asset pricing that is linked to the remuneration: the more a company spends on salaries and benefits per employee, the better its stock performs, on average. Moreover, the companies adopting similar remuneration policies share a common risk, which is comparable to that of the value premium. …
Midterm stock price prediction is crucial for value investments in the stock market. However, most deep learning models are essentially short-term and applying them to midterm predictions encounters large cumulative errors because they cannot avoid anomalies. In this paper, we propose a novel deep neural network Mid-LS…
In this paper we address three main objections of behavioral finance to the theory of rational finance, considered as anomalies the theory of rational finance cannot explain: Predictability of asset returns, The Equity Premium, (The Volatility Puzzle. We offer resolutions of those objections within the rational finance…
The paper explains how to predict returns based on firm characteristics.
problem Predicting returns based on firm characteristics in equilibrium models.
method Reverse-engineering equilibrium construction process with linear demands in characteristics.
result Linear expressions for returns are derived from scaled net aggregate demands and their variations.
We study several aspects of the so-called low-vol and low-beta anomalies, some already documented (such as the universality of the effect over different geographical zones), others hitherto not clearly discussed in the literature. Our most significant message is that the low-vol anomaly is the result of two independent…
Detects anomalies in stock and crypto data with high accuracy.
problem Identifying rare or unexpected events in time series data.
method Uses signature or randomized signature methods for anomaly detection.
result Achieves F1 scores up to 88% in identifying pump and dump attempts.
Correlated anomaly detection (CAD) from streaming data is a type of group anomaly detection and an essential task in useful real-time data mining applications like botnet detection, financial event detection, industrial process monitor, etc. The primary approach for this type of detection in previous researches is base…
Detecting Crypto Pump-and-Dump Schemes: A Thresholding-Based Approach to Handling Market Noiseq-fin.ST
Detects crypto pump-and-dump schemes with a thresholding-based model.
problem Detecting genuine anomalies from minor trading fluctuations.
method Combining threshold-based criteria with EWMA and volatility measures.
result Balances high true-positive detection with minimal noise.
We report on the occurrence of an anomaly in the price impacts of small transaction volumes following a change in the fee structure of an electronic market. We first review evidence for the existence of a master curve for price impact on the Johannesburg Stock Exchange (JSE). On attempting to re-estimate a master curve…
New method separates model and non-model risks for more practical asset pricing.
problem Asset pricing under model-uncertainty.
method Binary model-risks and constraints over preferences; unique model-risk pricing formula.
result Unique model-risk pricing formula with dynamically conserved constant.
This note investigates the causes of the quality anomaly, which is one of the strongest and most scalable anomalies in equity markets. We explore two potential explanations. The "risk view", whereby investing in high quality firms is somehow riskier, so that the higher returns of a quality portfolio are a compensation …
This paper examines how investors mislearn factor risk premia under structural breaks in a misspecified Bayesian framework.
problem Investors' mislearning of factor risk premia under structural breaks in asset pricing models.
method Proposes a minimal Bayesian framework to study how investors learn under a misspecified model that underestimates structural breaks.
result Elevated mislearning is associated with stronger long-horizon returns and Sharpe ratios, consistent with an equilibrium premium for acute model uncertainty.
Time Series Analysis of Electricity Price and Demand to Find Cyber-attacks using Stationary Analysiseess.SP
With developing of computation tools in the last years, data analysis methods to find insightful information are becoming more common among industries and researchers. This paper is the first part of the times series analysis of New England electricity price and demand to find anomaly in the data. In this paper time-se…
On Possible Influence of Space Weather on Agricultural Markets: Necessary Conditions and Probable Scenariosphysics.ao-ph
We present the results of study of a possible relationship between the space weather and terrestrial markets of agricultural products. It is shown that to implement the possible effect of space weather on the terrestrial harvests and prices, a simultaneous fulfillment of three conditions is required: 1) sensitivity of …
Study finds on-chain data can proxy off-chain cryptocurrency pricing.
problem Develop methods to proxy off-chain cryptocurrency pricing using on-chain data.
method Graphical models, mutual information, and ensemble machine learning.
result A significant amount of pricing information is contained in on-chain data, but precise prices are hard to recover except on short time scales.
We develop a theoretical trading conditioning model subject to price volatility and return information in terms of market psychological behavior, based on analytical transaction volume-price probability wave distributions in which we use transaction volume probability to describe price volatility uncertainty and intens…
On Bitcoin Price Predictionq-fin.ST
This paper examines Bitcoin's price predictability, finding inefficiencies under certain conditions.
problem Predictability of Bitcoin's price movements.
method Theoretical reviews, empirical analyses, machine learning approaches, time series modeling.
result Bitcoin's market tends toward efficiency but shows exploitable inefficiencies under specific conditions.
Improves anomaly detection with contaminated unlabeled data.
problem Weakness in existing semi-supervised anomaly detection methods when unlabeled data contain anomalies.
method Integrates positive-unlabeled learning with deep anomaly detection models.
result Achieves better detection performance on various datasets.
The paper explains stock market predictability through a model of heterogeneous beliefs.
problem Understanding and predicting stock market behavior based on news and investor beliefs.
method A discrete-time model of heterogeneous beliefs where some agents receive noisy signals about asset fundamentals.
result Momentum and reversal in stock prices arise from investors' incorrect beliefs about signal accuracy and fundamental values.
The true probability of a European call option to achieve positive return is investigated under the Black-Scholes model. It is found that the probability is determined by those market factors appearing in the BS formula, besides the growth rate of stock price. Our numerical investigations indicate that the biases of BS…
The paper argues for using more degrees of freedom in empirical financial analysis to improve conclusions.
problem Improving trustworthiness of financial analysis conclusions.
method Using more degrees of freedom and forking paths in multiple testing.
result Forking paths raises the bar for significance in multiple testing.
End-to-end anomaly detection framework using labeled anomalies.
problem Limited deep learning for anomaly detection and inefficiency of existing methods.
method Deviation learning neural network with labeled anomalies and prior probability.
result Significantly better anomaly scoring than state-of-the-art methods.
Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Datacs.LG
Deep RL detects anomalies from few labeled examples and large unlabeled data.
problem Anomaly detection with limited labeled data and large unlabeled data.
method Deep reinforcement learning to optimize detection of labeled and unlabeled anomalies.
result Significantly outperforms state-of-the-art methods on 48 real-world datasets.
We propose a supervised anomaly detection method for data with inexact anomaly labels, where each label, which is assigned to a set of instances, indicates that at least one instance in the set is anomalous. Although many anomaly detection methods have been proposed, they cannot handle inexact anomaly labels. To measur…
Recent semi-supervised anomaly detection methods that are trained using small labeled anomaly examples and large unlabeled data (mostly normal data) have shown largely improved performance over unsupervised methods. However, these methods often focus on fitting abnormalities illustrated by the given anomaly examples on…
A new method assigns anomaly scores to features for better interpretation.
problem Interpreting anomaly scores from feature attributions.
method Proposes a characteristic function to attribute anomaly scores using Shapley value.
result Demonstrates the potential utility of the proposed attribution methods.
Topological data analysis reveals complex financial-ratio-stock return relationships.
problem Understanding the complex associations between financial ratios and stock returns.
method Topological data analysis (TDA) using the Ball Mapper algorithm.
result Interdependencies between financial ratios are often non-monotonic, offering new insights.
Ensemble learning improves anomaly detection for milder symptoms.
problem Difficulty in detecting incipient anomalies due to similarity to normal conditions.
method Utilize uncertainty information from ensemble learning to identify misclassified incipient anomalies.
result Ensemble learning methods show improved performance on incipient anomaly detection.
New anomaly estimator reduces bias in MLE for normally distributed data.
problem Bias in Maximum Likelihood Estimation of structured anomalies.
method Derive a new anomaly estimator using a mixture model.
result New estimator is asymptotically unbiased regardless of anomaly family size.
We propose the development of a prediction market for forecasting prices for "toxic assets" to be transferred from Irish banks to the National Asset Management Agency (NAMA). Such a market allows market participants to assume a stake in a security whose value is tied to a future event. We propose that securities are cr…
TPA-AD detects axle-box bearing anomalies using pseudo anomalies near normal boundaries.
problem Detecting axle-box bearing anomalies with only normal training data.
method Two-stage approach: pseudo anomalies, contrastive learning, KNN.
result Improves anomaly detection separability and sensitivity to degradation.
Paper proposes RAN for better anomaly detection in time series data.
problem Anomaly detection algorithms often fail to accurately detect anomalies due to incomplete reconstruction of anomaly data.
method RAN uses adversarial learning and latent vector-constrained Autoencoder to ensure consistent reconstruction of anomaly data.
result RAN outperforms other algorithms in detecting meaningful anomalies with higher AUC-ROC scores.
Method detects insider trading using trading data and dimensionality reduction.
problem Identifying insider trading in large datasets.
method Unsupervised machine learning, principal component analysis, autoencoders.
result Identifies suspicious trading behavior based on reconstruction errors.
A new method combines generative and feature-based approaches for unsupervised anomaly detection.
problem Identifying subtle anomalies in test samples compared to a normative distribution.
method A generative cold-diffusion pipeline trained to restore synthetically-corrupted images, combined with a novel synthetic anomaly generation procedure and ensembling restorations.
result Surpasses prior state-of-the-art for unsupervised anomaly detection in three Brain MRI datasets.
Paper tackles anomaly detection and RCA in dynamical systems using ICODE Networks.
problem Anomalies in dynamical systems impact performance and reliability.
method Proposes ICODE Networks for anomaly detection, RCA, and type classification.
result Demonstrates the ability to accurately detect anomalies, classify types, and pinpoint origins.
Study identifies high-density anomalies in normal data regions.
problem Detecting anomalies in normal data regions.
method Introduces non-parametric algorithmic frameworks for unsupervised detection.
result IPP framework yields the best detection results.
ReVol normalizes stock price features to mitigate distribution shifts, improving prediction accuracy.
problem Distribution shifts in stock price data hinder accurate prediction.
method ReVol uses normalization, attention-based estimation, and geometric Brownian motion.
result ReVol achieves an average improvement of more than 0.03 in IC and over 0.7 in SR.
Improves relevancy of black-box anomaly detectors with user feedback.
problem Users often ignore many detected anomalies, requiring a method to identify and prioritize relevant ones.
method Uses user feedback to adjust anomaly selection process based on identified anomaly types.
result Significant improvements in precision and recall over various anomaly detectors.
Paper introduces an unsupervised tensor-based anomaly detection method for spatiotemporal data.
problem Challenges in detecting anomalies in spatiotemporal data, especially in urban traffic monitoring and medical imaging.
method Formulates anomaly detection as a regularized robust low-rank + sparse tensor decomposition, incorporating spatiotemporal smoothness and local dependencies.
result Demonstrates improved anomaly detection performance on both synthetic and real data.
FAMDAD detects anomalies in mixed data using kurtosis-weighted Factor Analysis.
problem Detecting anomalies in high-dimensional mixed data.
method kurtosis-weighted Factor Analysis of Mixed Data (FAMDAD).
result Anomalies are highly separable in the first and last few dimensions of the FAMDAD embedding.
Anomaly Awarenesscs.LG
Anomaly Awareness detects anomalies in particle physics and computer vision.
problem Detect anomalies in complex data sets.
method Modifies cost function to learn normal events and anomalies.
result Effective at identifying new anomalies not previously seen.