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 examines dependence of extreme electricity prices in Australian markets.
Investor finds a fair outcome in complex financial markets.
Polymarket users exploit mispriced assets for profit.
A new uplift modeling approach uses binary treatment indicators more efficiently.
The paper examines insurance market dynamics and optimal regulation.
The game theory techniques are used to find the equilibrium of a market. Game theory refers to the ways in which strategic interactions among economic agents produce outcomes with respect to the preferences (or utilities) of those agents, where the outcomes in question might have been intended by none of the agents. Th…
We present a novel methodology for predicting future outcomes that uses small numbers of individuals participating in an imperfect information market. By determining their risk attitudes and performing a nonlinear aggregation of their predictions, we are able to assess the probability of the future outcome of an uncert…
Geometric theory explains substitutability in market outcomes based on production constraints.
We develop a model of how information flows into a market, and derive algorithms for automatically detecting and explaining relevant events. We analyze data from twenty-two "political stock markets" (i.e., betting markets on political outcomes) on the Iowa Electronic Market (IEM). We prove that, under certain efficienc…
Study improves trading decisions by predicting profit and loss outcomes.
The paper explores how mining costs, rewards, and blockchain security are interconnected.
This paper treats prediction markets as Bayesian inverse problems to quantify uncertainty and identify event outcomes.
The Artificial Prediction Market is a recent machine learning technique for multi-class classification, inspired from the financial markets. It involves a number of trained market participants that bet on the possible outcomes and are rewarded if they predict correctly. This paper generalizes the scope of the Artificia…
News novelty predicts negative stock market returns.
New algorithms learn stable matchings from uncertain user preferences.
The study aims to explore the strength of causal relationship between stock price search interest and real stock market outcomes on worldwide equity market indices. Such a phenomenon could also be mediated by investor behavior and extent of news coverage. The stock-specific internet search trends data and corresponding…
Study shows Bitcoin security tied to mining rewards and prices.
This paper examines the intra-day seasonality of transacted limit and market orders in the DEM/USD foreign exchange market. Empirical analysis of completed transactions data based on the Dealing 2000-2 electronic inter-dealer broking system indicates significant evidence of intraday seasonality in returns and return vo…
The paper analyzes how leverage affects manipulation in event-linked markets, offering new insights into regulation.
Model predicts market dynamics of competing technologies.
Prediction markets are used in real life to predict outcomes of interest such as presidential elections. This paper presents a mathematical theory of artificial prediction markets for supervised learning of conditional probability estimators. The artificial prediction market is a novel method for fusing the prediction …
New neural network method improves uplift modeling accuracy.
We test a historical price time series in a financial market (the NASDAQ 100 index) for a statistical property known as detailed balance. The presence of detailed balance would imply that the market can be modeled by a stochastic process based on a Markov chain, thus leading to equilibrium. In economic terms, a positiv…
Investor expectations shifted pessimistically during the 2020 stock market crash and recovery.
This paper measures and compares the tail risks of limit and market orders using Extreme Value Theory. The analysis examines realised tail outcomes using the Dealing 2000-2 electronic broking system based on completed transactions rather than the more common analysis of indicative quotes. In general, limit and market o…
This work explores the idea of a causal contextual multi-armed bandit approach to automated marketing, where we estimate and optimize the causal (incremental) effects. Focusing on causal effect leads to better return on investment (ROI) by targeting only the persuadable customers who wouldn't have taken the action orga…
RAGIC predicts stock intervals with risk considerations, improving prediction accuracy and coverage.
This paper describes recent development and test implementation of a continuous time recurrent neural network that has been configured to predict rates of change in securities. It presents outcomes in the context of popular technical analysis indicators and highlights the potential impact of continuous predictive capab…
The paper analyzes optimal dealer strategies in agent-based market models.
Study proposes new methods to convert betting odds into accurate probabilities for sports forecasting.
Investment disputes increase stock volatility, especially for companies with negative outcomes.
Unified survey of treatment effect heterogeneity and uplift modeling methods.
Decentralized prediction markets use AMMs to pool and withdraw liquidity, improving financial properties.
Study shows how high-budget agents can manipulate prediction markets.
Model shows bailout stigma affects firm funding and market performance.
The persistence of racial inequality in the U.S. labor market against a general backdrop of formal equality of opportunity is a troubling phenomenon that has significant ramifications on the design of hiring policies. In this paper, we show that current group disparate outcomes may be immovable even when hiring decisio…
Market competition depends on computational complexity, P != NP makes it impossible.
Study shows how competition affects learning in matching markets, proving it's possible to balance stability, fairness, and regret.
Study examines how market dynamics affect emissions trading prices and abatement efforts.
Market crowd trading behavior and volume impact stock prices in China.
In this paper I empirically investigate prediction markets for binary options. Advocates of prediction markets have suggested that asset prices are consistent estimators of the "true" probability of a state of the world being realized. I test whether the market reaches a "consensus." I find little evidence for converge…
We consider a network of interacting agents and we model the process of choice on the adoption of a given innovative product by means of statistical-mechanics tools. The modelization allows us to focus on the effects of direct interactions among agents in establishing the success or failure of the product itself. Mimic…
Investment strategy developed using causal discovery algorithms in equity markets.
We study the behavior of simple models for financial markets with widely spread frequency either in the trading activity of agents or in the occurrence of basic events. The generic picture of a phase transition between information efficient and inefficient markets still persists even when agents trade on widely spread …
Nearly one-half of all trades in financial markets are executed by high-speed, autonomous computer programs -- a type of trading often called high-frequency trading (HFT). Although evidence suggests that HFT increases the efficiency of markets, it is unclear how or why it produces this outcome. Here we create a simple …
In this article we propose a study of market models starting from a set of axioms, as one does in the case of risk measures. We define a market model simply as a mapping from the set of adapted strategies to the set of random variables describing the outcome of trading. We do not make any concavity assumptions. The fir…
An explicit formula is derived for the value of weak information in a discrete time model that works for a wide range of utility functions including the logarithmic and power utility. We assume a complete market with a finite number of assets and a finite number of possible outcomes. Explicit calculations are performed…
K-means algorithm improves financial market risk prediction accuracy.