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.
Analyzes stock trends and e-commerce user behavior using Twitter data.
LISBET automates social behavior analysis using machine learning.
The paper analyzes how behavioral investors make portfolio decisions using Markowitz Stochastic Dominance criteria.
COHORTNEY groups web users based on activity patterns.
Study explores factors influencing saving behavior among Dhaka employees.
Young investors, especially students, dominate Indonesian stock exchanges.
Empirical study shows carriers ignore past shippers' behavior, focusing only on current actions.
Over the last few years, traffic data has been exploding and the transportation discipline has entered the era of big data. It brings out new opportunities for doing data-driven analysis, but it also challenges traditional analytic methods. This paper proposes a new Divide and Combine based approach to do K means clust…
Gold prices show seasonal behavior, with January and July having opposite returns.
New method learns behavioral representations from mobility data.
A new oscillator measures trending behavior of financial instruments.
This paper presents a stochastic behavior analysis of a kernel-based stochastic restricted-gradient descent method. The restricted gradient gives a steepest ascent direction within the so-called dictionary subspace. The analysis provides the transient and steady state performance in the mean squared error criterion. It…
HA-SME models SGD dynamics with Hessian info for better escaping behaviors.
The Affective Behavior Analysis in-the-wild (ABAW) 2020 Competition is the first Competition aiming at automatic analysis of the three main behavior tasks of valence-arousal estimation, basic expression recognition and action unit detection. It is split into three Challenges, each one addressing a respective behavior t…
New framework analyzes pre-stock jump trading behaviors using multivariate time series analysis.
Herd behavior is an important economic phenomenon, especially in the context of the recent financial crises. In this paper, herd behavior in global stock markets is investigated with a focus on intercontinental comparison. Since most existing herd behavior indices do not provide a comparative method, we propose a new h…
Paper uses RL for high-level character control in 3D environments.
Study evaluates five LLMs for financial report analysis, revealing performance differences and variability.
This study uses Twitter to analyze traveler behavior in Manhattan.
This study investigates the potential effects of different Dynamic Message Signs (DMSs) on driver behavior using a full-scale high-fidelity driving simulator. Different DMSs are categorized by their content, structure, and type of messages. A random forest algorithm is used for three separate behavioral analyses; a rou…
We analyze the Bombay stock exchange (BSE) price index over the period of last 12 years. Keeping in mind the large fluctuations in last few years, we carefully find out the transient, non-statistical and locally structured variations. For that purpose, we make use of Daubechies wavelet and characterize the fractal beha…
Study uses contrastive learning to analyze market order behavior.
We discuss social network analysis from the perspective of economics. We organize the presentaion around the theme of externalities: the effects that one's behavior has on others' well-being. Externalities underlie the interdependencies that make networks interesting. We discuss network formation, as well as interactio…
Most multi-class classifiers make their prediction for a test sample by scoring the classes and selecting the one with the highest score. Analyzing these prediction scores is useful to understand the classifier behavior and to assess its reliability. We present an interactive visualization that facilitates per-class an…
Theoretical analysis improves understanding of Deep Q-Learning's behavior.
Empirical analysis serves as an important complement to theoretical analysis for studying practical Bayesian optimization. Often empirical insights expose strengths and weaknesses inaccessible to theoretical analysis. We define two metrics for comparing the performance of Bayesian optimization methods and propose a ran…
We use a generalization of the Gibbons-Hawking ansatz to study the behavior of certain non-compact Calabi-Yau manifolds in the large complex structure limit. This analysis provides an intermediate step toward proving the metric collapse conjecture for toric hypersurfaces and complete intersections.
We consider a novel application of inverse reinforcement learning with behavioral economics constraints to model, learn and predict the commenting behavior of YouTube viewers. Each group of users is modeled as a rationally inattentive Bayesian agent which solves a contextual bandit problem. Our methodology integrates t…
We perform a rescaling analysis to analyze the future behavior of a class of -symmetric vacuum spacetimes. We show that on the universal cover, there is -convergence to a spatially homogeneous spacetime that does not satisfy the vacuum Einstein equations.
In this paper we have analyzed scaling properties and cyclical behavior of the three types of stock market indexes (SMI) time series: data belonging to stock markets of developed economies, emerging economies, and of the underdeveloped or transitional economies. We have used two techniques of data analysis to obtain an…
Multimodal analysis assesses job interview performance and provides feedback.
This paper uses SDEs to analyze GANs training and long-run behavior.
In manifold learning, algorithms based on graph Laplacians constructed from data have received considerable attention both in practical applications and theoretical analysis. In particular, the convergence of graph Laplacians obtained from sampled data to certain continuous operators has become an active research topic…
This paper investigates asymptotic behaviors of gradient descent algorithms (particularly accelerated gradient descent and stochastic gradient descent) in the context of stochastic optimization arising in statistics and machine learning where objective functions are estimated from available data. We show that these alg…
Empirical time series of inter-event or waiting times are investigated using a modified Multifractal Detrended Fluctuation Analysis operating on fluctuations of mean detrended dynamics. The core of the extended multifractal analysis is the non-monotonic behavior of the generalized Hurst exponent -- the fundament…
The paper analyzes optimal execution strategies for traders with inventory processes influenced by Brownian motion.
Improved probabilistic forecasts using behavioral transformations.
Stochastic stability is a popular solution concept for stochastic learning dynamics in games. However, a critical limitation of this solution concept is its inability to distinguish between different learning rules that lead to the same steady-state behavior. We address this limitation for the first time and develop a …
Paper develops a framework for learning interpretable representations of sequential decision behavior.
In this paper we have analyzed scaling properties of time series of stock market indices (SMIs) of developing economies of Western Balkans, and have compared the results we have obtained with the results from more developed economies. We have used three different techniques of data analysis to obtain and verify our fin…
We develop a framework for analyzing extreme values in correlated financial data.
Paper finds significant impact of stock market swings on equity risk premium predictability.
The paper provides theoretical guarantees for behavior cloning using generative models.
Two-stream model recognizes affect from audio and video.
Study examines how balancing methods affect model behavior in imbalanced classification problems.
Study evaluates clustering methods for Google Trends data.
MLDS dataset reveals hidden model behavior via weight-space analysis.
The 1/3 Financial Rule helps prevent household bankruptcy through balanced spending, savings, and debt repayment.