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.
Characterizes super-replication prices in a financial market model.
PRISM-VQ combines financial priors with vector quantization for better stock prediction.
In this paper we develop a novel neural network model for predicting implied volatility surface. Prior financial domain knowledge is taken into account. A new activation function that incorporates volatility smile is proposed, which is used for the hidden nodes that process the underlying asset price. In addition, fina…
This paper distills financial indicators into neural networks to reduce noise and improve accuracy.
We consider the scenario where the parameters of a probabilistic model are expected to vary over time. We construct a novel prior distribution that promotes sparsity and adapts the strength of correlation between parameters at successive timesteps, based on the data. We derive approximate variational inference procedur…
Dynamic skewness models improve financial time series analysis.
Enhanced financial reward with shuffled feature CNN-DRL.
Federated learning predicts financial distress across U.S. states without centralizing data.
Quality-designed consumer products are easy to recognize. Wouldn't it be great if the quality of financial products became just as apparent? This paper is addressed to financial practitioners. It provides an informal introduction to Quantitative Structuring -- a technology of manufacturing quality financial products (i…
Using data from world stock exchange indices prior to and during periods of global financial crises, clusters and networks of indices are built for different thresholds and diverse periods of time, so that it is then possible to analyze how clusters are formed according to correlations among indices and how they evolve…
This paper formulates an utility indifference pricing model for investors trading in a discrete time financial market under non-dominated model uncertainty. The investors preferences are described by strictly increasing concave random functions defined on the positive axis. We prove that under suitable conditions the m…
We develop a topology data analysis-based method to detect early signs for critical transitions in financial data. From the time-series of multiple stock prices, we build time-dependent correlation networks, which exhibit topological structures. We compute the persistent homology associated to these structures in order…
A new explainable CBR system predicts financial risks with interpretability and good performance.
We study the concept of financial bubble in a market model endowed with a set of probability measures, typically mutually singular to each other. In this setting we introduce the notions of robust bubble and robust fundamental value in a consistent way with the existing literature in the case a unique prior exists. The…
ST-GAN predicts stock trends using financial news and data.
New model predicts financial connectedness via COVID-19 spread.
Bayesian GPR model predicts extreme stock market losses.
Develops information geometry for Lévy processes in finance.
Study tests financial market efficiency using random number generator tests.
Pretrained time-series models outperform train-from-scratch baselines in financial return forecasting.
Paper proposes DigMA to generate controllable financial market orders.
Enhanced CNN for financial data improves predictive accuracy and stability.
Bayesian neural SDEs calibrate financial models robustly.
Genetic programming (GP) is the state-of-the-art in financial automated feature construction task. It employs reverse polish expression to represent features and then conducts the evolution process. However, with the development of deep learning, more powerful feature extraction tools are available. This paper proposes…
A major impact of globalization has been the information flow across the financial markets rendering them vulnerable to financial contagion. Research has focused on network analysis techniques to understand the extent and nature of such information flow. It is now an established fact that a stock market crash in one co…
The paper argues for using more degrees of freedom in empirical financial analysis to improve conclusions.
Different optimizer choices lead to different financial model predictions.
We explore the evolution of daily returns of four major US stock market indices during the technology crash of 2000, and the financial crisis of 2007-2009. Our methodology is based on topological data analysis (TDA). We use persistence homology to detect and quantify topological patterns that appear in multidimensional…
FinAI-BERT classifies AI disclosures in financial reports with high accuracy.
Estimates financial networks using high-frequency trade data.
Study shows group structures are crucial for financial model explanations.
TC-VAE generates robust financial time series data with causal constraints.
It is suggested to consider long term trends of financial markets as a growth phenomenon. The question that is asked is what conditions are needed for a long term sustainable growth or contraction in a financial market? The paper discuss the role of traditional market players of long only mutual funds versus hedge fund…
Within the setup of continuous-time semimartingale financial markets, we show that a multiprior Gilboa-Schmeidler minimax expected utility maximizer forms a portfolio consisting only of the riskless asset if and only if among the investor's priors there exists a probability measure under which all admissible wealth pro…
SAGE-FIN detects financial fraud using GNNs and Granger causality.
We investigate the tendency for financial instruments to form clusters when there are multiple factors influencing the correlation structure. Specifically, we consider a stock portfolio which contains companies from different industrial sectors, located in several different countries. Both sector membership and geograp…
Estimating covariances between financial assets plays an important role in risk management. In practice, when the sample size is small compared to the number of variables, the empirical estimate is known to be very unstable. Here, we propose a novel covariance estimator based on the Gaussian Process Latent Variable Mod…
This study examines representation bias in open-source Qwen models for investment decisions.
This paper models financial contagion with endogenously determined market liquidity.
Global neural networks improve financial forecasting accuracy with larger, diverse datasets.
Generalized autoregressive conditional heteroscedasticity (GARCH) models have long been considered as one of the most successful families of approaches for volatility modeling in financial return series. In this paper, we propose an alternative approach based on methodologies widely used in the field of statistical mac…
Optimal early liquidation strategy reduces financial losses during crises.
FinReflectKG - EvalBench benchmarks financial KG extraction from SEC 10-K filings.
Study uses neural networks to filter financial spillovers from noise.
We apply two non-parametric methods to test further the hypothesis that log-periodicity characterizes the detrended price trajectory of large financial indices prior to financial crashes or strong corrections. The analysis using the so-called (H,q)-derivative is applied to seven time series ending with the October 1987…
Unified framework for generating synthetic financial time series that accurately capture both marginal distributions and temporal dynamics.
The 2008 financial crisis has been attributed to "excessive complexity" of the financial system due to financial innovation. We employ computational complexity theory to make this notion precise. Specifically, we consider the problem of clearing a financial network after a shock. Prior work has shown that when banks ca…
New method clusters financial time series into volatility regimes.