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.
Study shows human advisors use context to improve student outcomes in algorithm-assisted advising.
Develops a test to assess if human experts add value to predictions.
New formulas estimate life insurance benefits with less computation.
We investigate the relationship between market efficiency of rice futures transaction in Osaka and the Japanese government intervention in rice distributions by directly buying and selling rice during the interwar period, from the middle 1910s to 1939, considering the context of "discretion versus rules." We use a time…
Study validates Libor model for insurance benefits calculation.
The paper shows how expert knowledge can improve treatment effect estimation.
A new method calibrates forecasts without sacrificing expertise.
We present a methodology for obtaining explicit solutions to infinite time horizon optimal stopping problems involving general, one-dimensional, Itô diffusions, payoff functions that need not be smooth and state-dependent discounting. This is done within a framework based on dynamic programming techniques employing var…
We analyze an optimal stopping problem with random maturity under a nonlinear expectation with respect to a weakly compact set of mutually singular probabilities . The maturity is specified as the hitting time to level of some continuous index process at which the payoff process is even allowed to have…
FinHEAR combines LLMs with human expertise for better financial decision-making.
Evidence acquisition costs influence disclosure behavior and preference.
Within the context of traditional life insurance, a model-independent relationship about how the market value of assets is attributed to the best estimate, the value of in-force business and tax is established. This relationship holds true for any portfolio under run-off assumptions and can be used for the validation o…
FinRobot AI agent for equity research provides comprehensive insights.
We analyze expenditure patterns of discretionary funds by Brazilian congress members. This analysis is based on a large dataset containing over million expenses made publicly available by the Brazilian government. This dataset has, up to now, remained widely untouched by machine learning methods. Our main contribut…
This paper presents a generic Bayesian framework that enables any deep learning model to actively learn from targeted crowds. Our framework inherits from recent advances in Bayesian deep learning, and extends existing work by considering the targeted crowdsourcing approach, where multiple annotators with unknown expert…
Novel CNN-based gaze scanpath comparison distinguishes experts from novices in dental radiograph interpretation.
PTBCC improves accuracy in multi-class annotation aggregation by learning from prototype confusion matrices.
We present an interactive version of an evidence-driven state-merging (EDSM) algorithm for learning variants of finite state automata. Learning these automata often amounts to recovering or reverse engineering the model generating the data despite noisy, incomplete, or imperfectly sampled data sources rather than optim…
UCFE benchmarks LLMs in financial tasks with human feedback.
Robinhood users react strongly to overnight price changes and big losers, trading quickly after extreme losses.
The paper examines how macroeconomic control tools lost effectiveness, leading to a 'dark ages' period.
Fund2Persona creates personalized financial advisor personas from fund data, improving investment advice.
A popular approach for large scale data annotation tasks is crowdsourcing, wherein each data point is labeled by multiple noisy annotators. We consider the problem of inferring ground truth from noisy ordinal labels obtained from multiple annotators of varying and unknown expertise levels. Annotation models for ordinal…
Many efficient algorithms with strong theoretical guarantees have been proposed for the contextual multi-armed bandit problem. However, applying these algorithms in practice can be difficult because they require domain expertise to build appropriate features and to tune their parameters. We propose a new method for the…
DPBD simplifies labeling functions through interactive demonstrations.
The study identifies extremal dependence in financial markets using a bootstrap-based testing procedure.
As part of Basel II's incremental risk charge (IRC) methodology, this paper summarizes our extensive investigations of constructing transition probability matrices (TPMs) for unsecuritized credit products in the trading book. The objective is to create monthly or quarterly TPMs with predefined sectors and ratings that …
Advocates for user-friendly RL problem descriptions to improve usability and generalization.
Equivalences are known between problems of singular stochastic control (SSC) with convex performance criteria and related questions of optimal stopping, see for example Karatzas and Shreve [SIAM J. Control Optim. 22 (1984)]. The aim of this paper is to investigate how far connections of this type generalise to a non co…
Paper proposes learnable topological features for efficient phylogenetic inference.
Algorithm improves learning by integrating diverse agents' behaviors.
RL algorithms with medical integration improve personalized treatment recommendations.
Study identifies Bitcoin arbitrageurs and their trading strategies.
A new method combines experts' opinions to train regression models with noisy labels.
The goal of imitation learning (IL) is to learn a good policy from high-quality demonstrations. However, the quality of demonstrations in reality can be diverse, since it is easier and cheaper to collect demonstrations from a mix of experts and amateurs. IL in such situations can be challenging, especially when the lev…
New method learns robot skills from data, matching or outperforming existing methods.
We propose a probabilistic model to aggregate the answers of respondents answering multiple-choice questions. The model does not assume that everyone has access to the same information, and so does not assume that the consensus answer is correct. Instead, it infers the most probable world state, even if only a minority…
Paper tackles medical question similarity using domain-relevant embeddings.
We present a learning-based system for rapid mass-scale material synthesis that is useful for novice and expert users alike. The user preferences are learned via Gaussian Process Regression and can be easily sampled for new recommendations. Typically, each recommendation takes 40-60 seconds to render with global illumi…
Data Science is currently a popular field of science attracting expertise from very diverse backgrounds. Current learning practices need to acknowledge this and adapt to it. This paper summarises some experiences relating to such learning approaches from teaching a postgraduate Data Science module, and draws some learn…
Predicts stock volatility using Twitter data and random forests.
Learning algorithms normally assume that there is at most one annotation or label per data point. However, in some scenarios, such as medical diagnosis and on-line collaboration,multiple annotations may be available. In either case, obtaining labels for data points can be expensive and time-consuming (in some circumsta…
Paper tackles unobserved confounding in human-AI collaborations.
Graph representation learning improves with domain knowledge.
Fund2Persona creates personalized financial advisor personas from fund data, improving investment advice and manager interpretation.
Media seems to have become more partisan, often providing a biased coverage of news catering to the interest of specific groups. It is therefore essential to identify credible information content that provides an objective narrative of an event. News communities such as digg, reddit, or newstrust offer recommendations,…
NASIB adapts NAS to varying computation resources efficiently.
Enhances model compression with multi-teacher knowledge distillation.