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.
Improved earnings predictions through text-morphed earnings calls.
Improved stock price prediction using LSTM with customized loss function and analyst calls.
Model earnings call transcripts for better stock price prediction.
Sell-side analysts' reports explain 10% of stock returns, with income statement analyses most impactful.
A good clustering can help a data analyst to explore and understand a data set, but what constitutes a good clustering may depend on domain-specific and application-specific criteria. These criteria can be difficult to formalize, even when it is easy for an analyst to know a good clustering when they see one. We presen…
Trading strategy uses analyst coverage network to outperform markets.
Analysts use vague language in reports to convey useful information about future payoffs.
A good clustering can help a data analyst to explore and understand a data set, but what constitutes a good clustering may depend on domain-specific and application-specific criteria. These criteria can be difficult to formalize, even when it is easy for an analyst to know a good clustering when she sees one. We presen…
Analyst reports contain valuable information for investment decisions.
AI system analyzes financial analyst recommendations and track records for portfolio construction.
Adaptive data analysis is frequently criticized for its pessimistic generalization guarantees. The source of these pessimistic bounds is a model that permits arbitrary, possibly adversarial analysts that optimally use information to bias results. While being a central issue in the field, still lacking are notions of na…
Generative AI boosts analyst reports but increases forecast errors.
Study earnings calls to predict stock price movements, finding them more predictive than traditional data.
An analytic process is iterative between two agents, an analyst and an analytic toolbox. Each iteration comprises three main steps: preparing a dataset, running an analytic tool, and evaluating the result, where dataset preparation and result evaluation, conducted by the analyst, are largely domain-knowledge driven. In…
Study examines how uncertainty visualization affects analyst trust in automated classification systems.
Research builds an index measuring analysts' perception of informational asymmetry.
This study uses NLP to predict stock performance based on analyst reports.
Human analysts that use anomaly detection systems in practice want to retain the use of simple and explainable global anomaly detectors. In this paper, we propose a novel human-in-the-loop learning algorithm called GLAD (GLocalized Anomaly Detection) that supports global anomaly detectors. GLAD automatically learns the…
Study uses neural networks to predict firm earnings, outperforming benchmarks and analysts.
In many applications, an anomaly detection system presents the most anomalous data instance to a human analyst, who then must determine whether the instance is truly of interest (e.g. a threat in a security setting). Unfortunately, most anomaly detectors provide no explanation about why an instance was considered anoma…
LLMs outperform human analysts in predicting earnings direction.
We analyze the relation between earning forecast accuracy and expected profitability of financial analysts. Modeling forecast errors with a multivariate Gaussian distribution, a complete characterization of the payoff of each analyst is provided. In particular, closed-form expressions for the probability density functi…
AutoYara generates effective Yara rules faster than humans.
HSR reduces analyst earnings forecast errors by lowering travel friction.
Requirements elicitation requires extensive knowledge and deep understanding of the problem domain where the final system will be situated. However, in many software development projects, analysts are required to elicit the requirements from an unfamiliar domain, which often causes communication barriers between analys…
Optimizes task allocation for financial analysts to balance work efficiency and well-being.
Analysis of an organization's computer network activity is a key component of early detection and mitigation of insider threat, a growing concern for many organizations. Raw system logs are a prototypical example of streaming data that can quickly scale beyond the cognitive power of a human analyst. As a prospective fi…
New trading model uses self-organization for real-world complexity.
Anomaly detectors are often used to produce a ranked list of statistical anomalies, which are examined by human analysts in order to extract the actual anomalies of interest. Unfortunately, in realworld applications, this process can be exceedingly difficult for the analyst since a large fraction of high-ranking anomal…
NAS-Navigator automates neural network architecture search with visual steering.
Machine learning improves earnings forecasting accuracy and speed.
The efficient market hypothesis has been considered one of the most controversial arguments in finance, with the academia divided between who claims the impossibility of beating the market and who believes that it is possible to gain over the average profits. If the hypothesis holds, it means, as suggested by Burton Ma…
In critical applications of anomaly detection including computer security and fraud prevention, the anomaly detector must be configurable by the analyst to minimize the effort on false positives. One important way to configure the anomaly detector is by providing true labels for a few instances. We study the problem of…
Chiseling finds valid subgroups interactively, improving on existing methods.
Requirements elicitation can be very challenging in projects that require deep domain knowledge about the system at hand. As analysts have the full control over the elicitation process, their lack of knowledge about the system under study inhibits them from asking related questions and reduces the accuracy of requireme…
Adversarial attacks can manipulate ML-aided visualizations, tricking analysts.
FinBERT model identifies key speakers in earnings calls, boosting stock returns.
TradingAgents uses LLM-powered multi-agent framework for financial trading.
The paper proposes a method to improve sales forecasts by selecting optimal reference classes.
Detects systematic anomalies in consumer complaints using NLP.
Sparse generalized additive models (GAMs) are an extension of sparse generalized linear models which allow a model's prediction to vary non-linearly with an input variable. This enables the data analyst build more accurate models, especially when the linearity assumption is known to be a poor approximation of reality. …
Framework adds human knowledge to AI decisions to improve outcomes.
Anomaly detection has numerous applications and has been studied vastly. We consider a complementary problem that has a much sparser literature: anomaly description. Interpretation of anomalies is crucial for practitioners for sense-making, troubleshooting, and planning actions. To this end, we present a new approach c…
We study methods for simultaneous analysis of many noisy experiments in the presence of rich covariate information. The goal of the analyst is to optimally estimate the true effect underlying each experiment. Both the noisy experimental results and the auxiliary covariates are useful for this purpose, but neither data …
In many contexts, it can be useful for domain experts to understand to what extent predictions made by a machine learning model can be trusted. In particular, estimates of trustworthiness can be useful for fraud analysts who process machine learning-generated alerts of fraudulent transactions. In this work, we present …
Model predicts stock price direction with high accuracy using analyst ratings and technical indicators.
In this note we would like to present "an analysts' point of view" on the Nash-Kuiper theorem and in particular highlight the very close connection to some aspects of turbulence -- a paradigm example of a high-dimensional phenomenon.
CB-APM uses analyst consensus as a bottleneck to interpret stock returns.