LLMs simulate financial markets, revealing consistent trading strategies and market dynamics.
problem Testing financial theories with AI trading agents.
method Simulated stock market with LLMs using a persistent order book and varied strategies.
result LLMs can simulate different trading strategies and market dynamics.
The problem of hedging and pricing sequences of contingent claims in large financial markets is studied. Connection between asymptotic arbitrage and behavior of the α~-~quantile price is shown. The large Black-Scholes model is carefully examined.
In the context of large financial markets we formulate the notion of \emph{no asymptotic free lunch with vanishing risk} (NAFLVR), under which we can prove a version of the fundamental theorem of asset pricing (FTAP) in markets with an (even uncountably) infinite number of assets, as it is for instance the case in bond…
The main result of the paper is a version of the fundamental theorem of asset pricing (FTAP) for large financial markets based on an asymptotic concept of no market free lunch for monotone concave preferences. The proof uses methods from the theory of Orlicz spaces. Moreover, various notions of no asymptotic arbitrage …
Large financial dataset tracks FOMC communications and their impact.
problem Understanding how FOMC communications influence financial markets.
method Constructed a large annotated dataset of FOMC speeches, minutes, and transcripts. Developed a hawk-dove classification task. Evaluated various models on the dataset and used RoBERTa-large for monetary policy stance measurement.
result Monetary policy stance measures derived from FOMC documents predict market performance.
LLMs improve financial analysis by processing large data sets.
problem Traditional financial analysis methods struggle with large data volumes.
method Integrating LLMs for enhanced data processing and analysis.
result LLMs offer new capabilities for real-time financial decision-making.
FinSMART uses reinforcement learning to analyze financial sentiment, outperforming existing methods.
problem Limited adaptability of financial sentiment analysis to evolving market conditions.
method Market-aligned reinforcement learning framework that optimizes sentiment signals using realized market outcomes.
result Significantly outperforms existing state-of-the-art methods in profitability and sentiment signal quality.
LLMs struggle with financial reasoning but can outperform the market with human oversight.
problem Financial reasoning failures in LLM-generated stock market predictions.
method Evaluated four LLMs using three prompting strategies and compared to human oversight.
result LLMs require human oversight to fully realize their potential in financial markets.
FNSPID dataset integrates financial news and stock prices for improved market predictions.
problem Lack of comprehensive datasets combining quantitative and qualitative financial data.
method Developed a large-scale dataset (FNSPID) with 29.7M stock prices and 15.7M financial news records.
result FNSPID significantly boosts market prediction accuracy and sentiment analysis.
Study optimizes financial investments in large markets.
problem Maximizing expected utility in large financial markets with factor structures.
method Establishes optimizers under weaker assumptions, studies convergence of optimal investments.
result Continuity rules hold for optimal investments in small and large markets.
This study uses AI to analyze financial market coverage from YouTube videos.
problem Challenges in analyzing a large number of financial market videos.
method Used Whisper model to generate text from videos, applied natural language processing.
result Highlights dynamics of financial market coverage and identifies trending topics.
Fine-tuning a time series model improves financial price prediction accuracy.
problem Improving accuracy in predicting financial market prices using large models.
method Continual pre-training of a time series foundation model on financial data to fine-tune its performance for price prediction.
result The fine-tuned model outperforms the baseline in various financial metrics.
Enhances anomaly detection in financial markets using AI agents.
problem Manual verification of financial market anomalies is time-consuming and error-prone.
method A multi-agent LLM framework for automated anomaly detection.
result Framework reduces human intervention and improves efficiency and accuracy.
We investigate the large-volatility dynamics in financial markets, based on the minute-to-minute and daily data of the Chinese Indices and German DAX. The dynamic relaxation both before and after large volatilities is characterized by a power law, and the exponents p± usually vary with the strength of the large vo…
Closed-form optimal portfolios for exponential utility in small/large markets.
problem Optimal portfolios maximizing exponential utility in small/large financial markets.
method Closed-form expressions for optimal portfolios in small markets, convergence to large market optimal utility, numerical procedure for general utility functions.
result Optimal utility in large markets converges to optimal utility in small markets, requiring infinite diversification.
Motivated by recent financial crises significant research efforts have been put into studying contagion effects and herding behaviour in financial markets. Much less has been said about influence of financial news on financial markets. We propose a novel measure of collective behaviour in financial news on the Web, New…
Cryptocurrencies show mature market characteristics but vary by size.
problem Understanding maturity in cryptocurrency markets.
method Quantitative analysis of return distributions, volatility, and correlations.
result Smaller cryptocurrencies lack mature market characteristics.
Agent-based model helps design financial markets.
problem Designing financial markets that work well is complex and risky.
method Agent-based model to simulate and analyze financial markets.
result Tick size reduction impacts market efficiency.
Simulation of financial markets with 300 assets shows volatility clustering and unstable periods.
problem Understanding volatility clustering and unstable periods in multi-asset financial markets.
method Large-scale simulation of an Ising-based financial market model with 300 assets.
result Volatility clustering and unstable periods identified in the simulated financial market.
MarS simulates financial markets using generative models.
problem Simulating realistic financial market effects.
method Order-level generative foundation model (LMM) for realistic, interactive, and controllable order generation.
result Strong scalability and robust realism in MarS.
Extends FTAP to large financial markets with two filtrations.
problem Modeling asset pricing in complex financial markets.
method Generalizes FTAP to continuous time, large markets with two filtrations, without assuming specific properties of price processes.
result A simplified version of FTAP for large financial markets with two filtrations.
Study shows publicly available news impacts financial markets.
problem Impact of publicly available news on financial markets.
method Extracted news from Common Crawl, identified relevant companies, used sentiment analysis and information theory.
result Publicly available news has significant impact on financial markets.
This paper analyzes financial sentiment using LLMs and FinBERT, improving accuracy with few-shot examples.
problem Financial sentiment analysis for market evaluation.
method Application of large language models and FinBERT, with focus on prompt engineering and few-shot learning.
result GPT-4o achieves similar sentiment classification accuracy to FinBERT with fewer examples.
Model predicts trade volume changes from financial filings.
problem Improving financial market understanding through machine learning.
method Hierarchical Reformer model trained on SEDAR filings.
result Model can predict trade volume changes without explicit training.
A new method uses diffusion models to simulate financial markets accurately.
problem Generating realistic synthetic financial market data.
method Diffusion model approach with efficient numerical integration.
result Synthetic data closely matches observed market data in key aspects.
This paper deals with the notion of a large financial market and the concepts of asymptotic arbitrage and strong asymptotic arbitrage (both of the first kind), introduced by Yu.M. Kabanov and D.O. Kramkov. We show that the arbitrage properties of a large market are completely determined by the asymptotic behavior of th…
Paper presents a hybrid framework combining sentiment analysis and market indicators for financial portfolio optimization.
problem Improving financial portfolio optimization through better integration of sentiment and market data.
method A three-tier hierarchical RL framework integrating LLMs, DRL, and market data.
result Achieved a 26% annualized return and Sharpe ratio of 1.2, outperforming benchmarks.
New method identifies precursors of financial crises in market correlation structures.
problem Predicting long-term financial crises in non-Markovian, non-stationary markets.
method Identifying quasi-stationary market states and their precursor properties.
result Certain features of market states show potential as indicators of financial crises.
We contrast Arbitrage Pricing Theory (APT), the theoretical basis for the development of financial instruments, with a dynamical picture of an interacting market, in a simple setting. The proliferation of financial instruments apparently provides more means for risk diversification, making the market more efficient and…
TSFMs improve financial forecasting from diverse datasets.
problem Challenges in forecasting financial time series due to noisy, non-stationary, and heterogeneous data.
method Empirical study of TSFMs in global financial markets, evaluating zero-shot inference, fine-tuning, and pre-training from scratch.
result Pre-trained TSFMs on financial data achieve substantial forecasting and economic improvements, highlighting the value of domain-specific adaptation.
We study the emergence of instabilities in a stylized model of a financial market, when different market actors calculate prices according to different (local) market measures. We derive typical properties for ensembles of large random markets using techniques borrowed from statistical mechanics of disordered systems. …
New analysis shows low volatility can be unstable in financial markets.
problem Understanding the relationship between volatility and market stability.
method Using mean first hitting time as a stability indicator and comparing to standard volatility measures.
result Low volatility can be associated with higher instability in financial markets.
ChatGPT predicts stock market reactions from news headlines without financial training.
problem Predicting stock price movements using non-financial data.
method Used post-knowledge-cutoff headlines to train ChatGPT-4, which forecasts stock market reactions.
result ChatGPT-4 can predict stock market reactions with high accuracy, especially for small stocks and negative news.
Develops a stochastic approach to financial market delays.
problem Modeling delays in financial markets with multiple assets.
method Introduces a general stochastic framework for information and order execution delays.
result Delayed markets maintain fundamental asset pricing theorems and no asymptotic free lunch condition.
Paper optimizes a big data and ML risk monitoring system for financial markets.
problem Traditional risk monitoring methods are inadequate for modern financial markets due to data complexity and volume.
method Four-layer architecture integrating big data and advanced ML algorithms (LSTM, RF, GB).
result Significantly enhances efficiency and accuracy in risk management, especially in market crash risk detection.
Study compares LLMs vs classical models for financial sentiment analysis.
problem Improving sentiment analysis in financial market news.
method Comparative analysis of LLMs and classical models.
result LLMs outperform classical models in sentiment analysis of financial news.
Using virtual stock markets with artificial interacting software investors, aka agent-based models (ABMs), we present a method to reverse engineer real-world financial time series. We model financial markets as made of a large number of interacting boundedly rational agents. By optimizing the similarity between the act…
FinRL-Meta creates diverse market environments for DRL in finance.
problem Inaccurate financial data and diverse market environments challenge DRL in finance.
method Open-source data processing tools, hundreds of market environments, and multiprocessing.
result FinRL-Meta improves DRL accuracy and speed in financial simulations.
Contextualizing financial news improves stock price predictions.
problem Predicting stock prices from financial news requires understanding historical context.
method Proposed a method using a large language model for main articles and a small model for historical context.
result Historical context significantly improves model performance across methods and time horizons.
The principal aim of this work is the evidence on empirical way that catastrophic bifurcation breakdowns or transitions, proceeded by flickering phenomenon, are present on notoriously significant and unpredictable financial markets. Overall, in this work we developed various metrics associated with catastrophic bifurca…
We investigate the large-fluctuation dynamics in financial markets, based on the minute-to-minute and daily data of the Chinese Indices and German DAX. The dynamic relaxation both before and after the large fluctuations is characterized by a power law, and the exponents p± usually vary with the strength of the lar…
I study the limit of a large random economy, where a set of consumers invests in financial instruments engineered by banks, in order to optimize their future consumption. This exercise shows that, even in the ideal case of perfect competition, where full information is available to all market participants, the equilibr…
We describe dynamics of financial market observables and apply to portfolio performance.
problem Lack of mathematical description of financial market observables.
method Rank-based models and functionally generated portfolios.
result Performance of functionally generated portfolios studied over short and medium-term horizons.
LLMs in financial markets show diverse behaviors, from stable to speculative, challenging rational expectations.
problem Understanding the economic behaviors of LLMs in financial markets.
method Simulated financial market with 15 LLMs of varying sizes and capabilities.
result LLMs exhibit a spectrum of behaviors, including speculative bubbles, inconsistent with rational expectations.
Market opening affects bid-ask spread stability.
problem Understanding how market opening affects financial stability.
method Analyzing bid-ask spread data for NASDAQ stocks.
result Market opening affects the stability of bid-ask spread.
Developed scalable ABM for complex financial markets.
problem Simulating large-scale agent-based financial markets.
method Agent-based modeling, distributed computing, continuous double auction.
result Captures statistical properties of real financial markets.
MarketGPT models financial time series with realistic order flow data.
problem Creating accurate financial market simulations.
method Generative pre-trained transformer (GPT) for long sequence generation.
result Model reproduces key features of real financial markets and stylized facts.
We compare correlations and coherent structures in nuclei and financial markets. In the nuclear physics part we review giant resonances which can be interpreted as a coherent structure embedded in chaos. With similar methods we investigate the financial empirical correlation matrix of the DAX and Dow Jones. We will sho…