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.
Adaptive volatility method improves probabilistic financial forecasting.
The study compares M6 competitors' performance to industry benchmarks and discusses incentives for investment managers.
Investment challenge study finds luck and strategy equally important.
This paper won 1st place in forecasting and investment challenges, improving on meta-learning and parametric models.
We compute for all orientable irreducible geometric 3-manifolds certain complexity functions that approximate from above Matveev's natural complexity, known to be equal to the minimal number of tetrahedra in a triangulation. We can show that the upper bounds on Matveev's complexity implied by our computations are sharp…
Proposes CoPO, a new policy optimization method for competitive games.
Kaggle competitions offer valuable insights for business forecasting.
The origin of economic crises is a key problem for economics. We present a model of long-run competitive markets to show that the multiplicity of behaviors in an economic system, over a long time scale, emerge as statistical regularities (perfectly competitive markets obey Bose-Einstein statistics and purely monopolist…
New framework promotes reproducible, domain-agnostic reinforcement learning algorithms.
The real estate is a pillar industry of China's national economy. Due to changes in policy and market conditions, the real estate companies are facing greater pressures to survive in a competitive environment. They must improve their financial competitiveness. Based on the conceptual framework of financial competitiven…
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…
Study examines machine learning competitions' impact on AI development.
Bayesian rating system for large competitions improves prediction and efficiency.
MineRL Competition reduced reinforcement learning sample needs.
This paper evaluates financial competitiveness of Indian real estate companies using entropy method.
In this paper, the optimal pricing strategy in Avellande-Stoikov's for a monopolistic dealer is extended to a general situation where multiple dealers are present in a competitive market. The dealers' trading intensities, their optimal bid and ask prices and therefore their spreads are derived when the dealers are info…
Model predicts growth competition on curved surfaces.
Market competition depends on computational complexity, P != NP makes it impossible.
The M5 competition tackles overdispersed retail sales forecasting with GAMLSS.
Model predicts competition between similar products in sales.
The NIPS 2018 Adversarial Vision Challenge is a competition to facilitate measurable progress towards robust machine vision models and more generally applicable adversarial attacks. This document is an updated version of our competition proposal that was accepted in the competition track of 32nd Conference on Neural In…
Improved forecasting in daily time series competition using a correlator method.
Data competitions rely on real-time leaderboards to rank competitor entries and stimulate algorithm improvement. While such competitions have become quite popular and prevalent, particularly in supervised learning formats, their implementations by the host are highly variable. Without careful planning, a supervised lea…
We introduce an irreversible discrete multiplicative process that undergoes Bose-Einstein condensation as a generic model of competition. New players with different abilities successively join the game and compete for limited resources. A player's future gain is proportional to its ability and its current gain. The the…
Politicians world-wide frequently promise a better life for their citizens. We find that the probability that a country will increase its {\it per capita} GDP ({\it gdp}) rank within a decade follows an exponential distribution with decay constant . We use the Corruption Perceptions Index (CPI) and the Global …
We present a broad agenda for meaningful banking regulation reform aiming the creation of evolutive competitive environment to maximize the effectiveness of international financial system through the introduction of fair competition process among the banks in free market capitalism. We assume that the international fin…
FLAIR measures LP competitiveness in AMMs, improving LP performance evaluations.
Kaggle chronicles 15 years of competitions, innovation, and data science.
A competition increases financial transaction models' robustness against attacks.
This work bridges competitive learning with gradient-based learning for faster feature extraction.
The authors propose a parametric model called the arena model for prediction in paired competitions, i.e. paired comparisons with eliminations and bifurcations. The arena model has a number of appealing advantages. First, it predicts the results of competitions without rating many individuals. Second, it takes full adv…
Study predicts individual treatment effects in ride-sharing competitions.
Two-layer model studies reinsurance contracts and competition between insurer and reinsurers.
Indirect competition emerged from the complex organization of human societies, and knowledge of the existing network topology may aid in developing effective strategies for success. Here, we propose an agent-based model of competition with systems co-existing in a `small-world' social network. We show that within the r…
This paper presents competitive algorithms for a novel class of online optimization problems with memory. We consider a setting where the learner seeks to minimize the sum of a hitting cost and a switching cost that depends on the previous decisions. This setting generalizes Smoothed Online Convex Optimization. The…
The paper analyzes optimal stock position-building strategies in competitive markets.
In this model study of the commodity market, we present some evidence of competition of commodities for the status of money in the regime of parameters, where emergence of money is possible. The competition reveals itself as a rivalry of a few (typically two) dominant commodities, which take the status of money in turn…
Competition aims to develop sample-efficient reinforcement learning methods.
To accelerate research on adversarial examples and robustness of machine learning classifiers, Google Brain organized a NIPS 2017 competition that encouraged researchers to develop new methods to generate adversarial examples as well as to develop new ways to defend against them. In this chapter, we describe the struct…
Adaptive learning rate improves FTRL's performance in online learning.
We study the growth dynamics of the size of manufacturing firms considering competition and normal distribution of competency. We start with the fact that all components of the system struggle with each other for growth as happened in real competitive bussiness world. The detailed quantitative agreement of the theory w…
We study a portfolio optimization problem for competitive agents with CRRA utilities and a common finite time horizon. The utility of an agent depends not only on her absolute wealth and consumption but also on her relative wealth and consumption when compared to the averages among the other agents. We derive a closed …
Study liquidity provision with exogenous competition using a reference market maker.
We develop a probabilistic consumer choice framework based on information asymmetry between consumers and firms. This framework makes it possible to study market competition of several firms by both quality and price of their products. We find Nash market equilibria and other optimal strategies in various situations ra…
Combines gradient-based and competitive learning for unsupervised feature extraction.
State-space models win a forecasting competition for unstable data.
LLMs can collude in market divisions, maximizing profits.
Model analyzes competitive pricing strategies in large markets of perishable products.