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.
Market-GAN adds context control to financial market data generation.
Understanding how funding and 4H context regulate crypto markets.
Study improves keyword forecasting in earnings-call prediction markets.
Adaptive algorithms minimize regret in matching markets with contextual arm preferences.
MacroHFT uses memory and context-aware reinforcement learning to improve HFT performance.
Novel framework for systemic risk analysis in financial markets.
Improved crypto market forecasting using historical price reactions to tweets.
Contextualizing financial news improves stock price predictions.
The study identifies and analyzes different market regimes in equity markets using advanced signal processing techniques.
In this study, we establish a network structure of the Korean stock market, one of the emerging markets, with its minimum spanning tree through the correlation matrix. Base on this analysis, it is found that the Korean stock market doesn't form the clusters of the business sectors or of the industry categories. When th…
Study finds users mostly use recent market and decision information to guess market direction.
Article examines NFT market microstructure and trading risks.
Motivated by the work of Segal and Segal on the Black-Scholes pricing formula in the quantum context, we study a quantum extension of the Black-Scholes equation within the context of Hudson-Parthasarathy quantum stochastic calculus. Our model includes stock markets described by quantum Brownian motion and Poisson proce…
We analyze the ideal gas like models of markets and review the different cases where a `savings' factor changes the nature and shape of the distribution of wealth. These models can produce similar distribution of wealth as observed across varied economies. We present a more realistic model where the saving factor can v…
Study compares ML algorithms for predicting stock market directional bias.
Word embeddings are a powerful approach for capturing semantic similarity among terms in a vocabulary. In this paper, we develop exponential family embeddings, a class of methods that extends the idea of word embeddings to other types of high-dimensional data. As examples, we studied neural data with real-valued observ…
We treat a fairly broad class of financial models which includes markets with proportional transaction costs. We consider an investor with cumulative prospect theory preferences and a non-negativity constraint on portfolio wealth. The existence of an optimal strategy is shown in this context in a class of generalized s…
Paper tackles overfitting in RL for trade execution.
IndexGAN predicts stock trends using GAN with expert knowledge and news context.
Market research is generally performed by surveying a representative sample of customers with questions that includes contexts such as psycho-graphics, demographics, attitude and product preferences. Survey responses are used to segment the customers into various groups that are useful for targeted marketing and commun…
Stock market prediction is one of the most attractive research topic since the successful prediction on the market's future movement leads to significant profit. Traditional short term stock market predictions are usually based on the analysis of historical market data, such as stock prices, moving averages or daily re…
Paper generalizes Hardy-Rogers maps for market equilibrium analysis in duopoly markets.
Generative model predicts NFT collection transactions based on early history.
We consider the problem of portfolio optimization in the presence of market impact, and derive optimal liquidation strategies. We discuss in detail the problem of finding the optimal portfolio under Expected Shortfall (ES) in the case of linear market impact. We show that, once market impact is taken into account, a re…
Develops a new framework to measure network connectedness across and within markets.
A novel algorithm for actively trading stocks is presented. While traditional expert advice and "universal" algorithms (as well as standard technical trading heuristics) attempt to predict winners or trends, our approach relies on predictable statistical relations between all pairs of stocks in the market. Our empirica…
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…
Combining neural networks and multiscale decomposition for financial market analysis.
We analyze impermanent loss in AMMs and show G3Ms are simplest.
Motivated by applications to bond markets, we propose a multivariate framework for discrete time financial markets with proportional transaction costs and a countable infinite number of tradable assets. We show that the no-arbitrage of second kind property (NA2 in short), recently introduced by Rasonyi for finite-dimen…
In the context of an incomplete market with a Brownian filtration and a fixed finite time horizon, this paper proves that for general dynamic convex risk measures, the buyer's and seller's risk indifference prices of a contingent claim are bounded from below and above by the dynamic lower and upper hedging prices, resp…
Study extends Lévy models to capture market propagation delays.
Market making is one of the most important aspects of algorithmic trading, and it has been studied quite extensively from a theoretical point of view. The practical implementation of so-called "optimal strategies" however suffers from the failure of most order book models to faithfully reproduce the behaviour of real m…
Study market-to-book ratios using Stochastic Portfolio Theory.
AHEAD improves financial market efficiency through ad-hoc auctions.
This work models market regimes using CTMSTOU and simulates trading policies.
This paper examines the uniform properties of AMMs in cryptocurrency markets.
In this paper we continue our systematic analysis of the operatorial approach previously proposed in an economical context and we discuss a {\em mixed} toy model of a simplified stock market, i.e. a model in which the price of the shares is given as an input. We deduce the time evolution of the portfolio of the various…
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…
QCML improves bond similarity learning in illiquid markets.
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…
Paper establishes MLE consistency for market microstructure models.
Study identifies key trades predicting market movements.
Develops a stochastic approach to financial market delays.
The paper explores states of financial markets using correlation matrices and their dynamics.
This paper investigates the time-varying risk-premium relation of the Chinese stock markets within the framework of cross-sectional momentum and contrarian effects by adopting the Capital Asset Pricing Model and the French-Fama three factor model. The evolving arbitrage opportunities are also studied by quantifying the…
In this paper, reinforcement learning is applied to the problem of optimizing market making. A multi-agent reinforcement learning framework is used to optimally place limit orders that lead to successful trades. The framework consists of two agents. The macro-agent optimizes on making the decision to buy, sell, or hold…
Study market efficiency under partial information using SDEs and optimization.