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.
Sell-side analysts' reports explain 10% of stock returns, with income statement analyses most impactful.
Trading strategy uses analyst coverage network to outperform markets.
Analysts use vague language in reports to convey useful information about future payoffs.
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.
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…
Improved earnings predictions through text-morphed earnings calls.
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.
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.
Improved stock price prediction using LSTM with customized loss function and analyst calls.
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…
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…
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…
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…
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…
TradingAgents uses LLM-powered multi-agent framework for financial trading.
The paper proposes a method to improve sales forecasts by selecting optimal reference classes.
Framework adds human knowledge to AI decisions to improve outcomes.
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.
Model earnings call transcripts for better stock price prediction.
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.
Develops an anytime-valid framework for optimal policy identification from logged contextual bandit data.
Automate residual plot assessment with R package and Shiny application
Predictive models that are developed in a regulated industry or a regulated application, like determination of credit worthiness, must be interpretable and rational (e.g., meaningful improvements in basic credit behavior must result in improved credit worthiness scores). Machine Learning technologies provide very good …
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…
I study the behavior and the performance of the long-term forecasts issued by financial analysts with respect to the Extrapolation Hypothesis. That hypothesis states that investors, extrapolating from the firms' recent performances, are too optimistic about growth and large firms and too pessimistic about value and sma…
FinRobot AI agent for equity research provides comprehensive insights.
A theorem for debiasing machine learning with finite sample guarantees.
Explains isometric immersions and their applications.
Combustion reaction kinetics models are used for the description of a special class of bursty Financial Time Series. The small number of parameters they depend upon enable financial analysts to predict the time as well as the magnitude of the jump of the value of the portfolio. Several Financial Time Series are analyse…
Study evaluates LLMs like ChatGPT and GPT-4 on financial analysis tasks.