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 finds whitepaper narratives do not predict market factor structure.
We investigate the emergence of a structure in the correlation matrix of assets' returns as the time-horizon over which returns are computed increases from the minutes to the daily scale. We analyze data from different stock markets (New York, Paris, London, Milano) and with different methods. Result crucially depends …
HapNet predicts marketing campaign effects using a hierarchical structure.
We demonstrate that future market correlation structure can be predicted with high out-of-sample accuracy using a multiplex network approach that combines information from social media and financial data. Market structure is measured by quantifying the co-movement of asset prices returns, while social structure is meas…
Combining neural networks and multiscale decomposition for financial market analysis.
The self-organizing methods were used for the investigation of financial market. As an example we consider data time-series of Dow Jones index for the years 2002-2003 (R. Mantegna, cond-mat/9802256). In order to reveal new structures in stock market behavior of the companies drawing up Dow Jones index we apply SOM (Sel…
We study the effect of globalization on the Korean market, one of the emerging markets. Some characteristics of the Korean market are different from those of the mature market according to the latest market data, and this is due to the influence of foreign markets or investors. We concentrate on the market network stru…
Over-the-counter markets are at the center of the postcrisis global reform of the financial system. We show how the size and structure of such markets can undergo rapid and extensive changes when participants engage in portfolio compression, a post-trade netting technology. Tightly-knit and concentrated trading structu…
Paper proposes a GAN-based approach for RTLMP prediction.
Develops a statistical model for SOFR term structure in incomplete markets.
Study models opaque financial markets using multi-agent simulation.
Interbank markets are often characterised in terms of a core-periphery network structure, with a highly interconnected core of banks holding the market together, and a periphery of banks connected mostly to the core but not internally. This paradigm has recently been challenged for short time scales, where interbank ma…
Algorithm classifies market regimes using time series signatures.
New method detects and clusters market regimes in multidimensional data.
Simple linear models reveal complex cryptocurrency networks.
DeepCausalMMM models marketing impacts using deep learning and causal inference.
The paper tackles decision making problems with funnel structure in email marketing campaigns.
Study financial market graphs with Laplacian constraints.
New model predicts energy prices under different scenarios.
The paper uses deep learning to detect financial market regimes from correlation matrices.
Copulas model cross-product effects in intraday power markets.
Research optimizes a small RES utility's portfolio by dynamically trading in German electricity markets.
Financial markets are highly correlated systems that reveal both the inter-market dependencies and the correlations among their different components. Standard analyzing techniques include correlation coefficients for pairs of signals and correlation matrices for rich multivariate data. In the latter case one constructs…
We combine geometric data analysis and stochastic modeling to describe the collective dynamics of complex systems. As an example we apply this approach to financial data and focus on the non-stationarity of the market correlation structure. We identify the dominating variable and extract its explicit stochastic model. …
Clusters of financial market states identified over 2006-2019.
A new RL framework tackles asset allocation problems using Monte Carlo simulation.
Generates realistic stock market order streams using GANs.
With the random matrix theory, we study the spatial structure of the Chinese stock market, American stock market and global market indices. After taking into account the signs of the components in the eigenvectors of the cross-correlation matrix, we detect the subsector structure of the financial systems. The positive …
Market structure changed dramatically in US during COVID-19, mirroring 2008 crisis.
Model forecasts market structure from financial networks using machine learning.
A non-trivial probability structure is evident in the binary data extracted from the up/down price movements of very high frequency data such as tick-by-tick data for USD/JPY. In this paper, we analyze the Sony bank USD/JPY rates, ignoring the small deviations from the market price. We then show there is a similar non-…
Improved eigenvalue distribution method for financial data.
This paper analyzes the process of long-run co-movements and stock market globalization on the basis of cointegration tests and vector error correction (VEC) models. The cointegration tests used here allow for structural breaks to be explicitly modeled and breakpoints to be computed on a relative-time basis. The data u…
The paper analyzes Nordic stock markets' correlation structures and regime shifts.
Study simulates liquidity in fractional ownership markets using ABM.
Using a metric related to the returns correlation, a method is proposed to reconstruct an economic space from the market data. A reduced subspace, associated to the systematic structure of the market, is identified and its dimension related to the number of terms in factor models. Example were worked out involving sets…
We present a simple model of a stock market where a random communication structure between agents gives rise to a heavy tails in the distribution of stock price variations in the form of an exponentially truncated power-law, similar to distributions observed in recent empirical studies of high frequency market data. Ou…
Paper proposes a hybrid MTL framework for improved stock market prediction accuracy.
Study cryptocurrency market complexity using multifractal and cross-correlation analyses.
Study applies market microstructure to Cuban informal currency market, finding market makers improve liquidity.
Study identifies a Strategic Gap in market efficiency due to AI-driven timing and complexity in disclosure.
LLMs detect market patterns through causal reasoning, not just temporal association.
The structure of return spillovers is examined by constructing Granger causality networks using daily closing prices of 20 developed markets from 2nd January 2006 to 31st December 2013. The data is properly aligned to take into account non-synchronous trading effects. The study of the resulting networks of over 94 sub-…
This paper investigates the dynamics of in the S&P500 index from daily returns for the last 30 years. Using a stochastic geometry technique, each S&P500 yearly batch of data is embedded in a subspace that can be accurately described by a reduced number of dimensions. Such feature is understood as empirical evidence for…
Financial markets for Liquified Natural Gas (LNG) are an important and rapidly-growing segment of commodities markets. Like other commodities markets, there is an inherent spatial structure to LNG markets, with different price dynamics for different points of delivery hubs. Certain hubs support highly liquid markets, a…
We analyze the daily stock data of the Nasdaq Composite index in the 22-year period 1992-2013 and identify market states as clusters of correlation matrices with similar correlation structures. We investigate the stability of the correlation structure of each state by estimating the statistical fluctuations of correlat…
LLMs improve financial analysis by processing large data sets.
Improved ABFMs capture market complexities, aiding policy decisions.