arXiv research

Clusters of highly correlated stocks are identified for better asset selection.

Clusters of crypto assets by path signature improve diversification and reduce fees.

Clusters asset classes to identify lead-lag relationships in market regimes.

The Split-Session Cluster GARCH model captures tail heterogeneity in overnight and intraday returns.

Develops a method for probabilistic simulation of renewable energy production at grid scale.

Quantum GBS boosts asset clustering for robust statistical arbitrage portfolios.

A new asset allocation model uses Markov states from clustered efficient frontier coefficients.

This paper proposes a new clustering method based on Stochastic Dominance for asset allocation.

Given a set of assets and an investment capital, the classical portfolio selection problem consists in determining the amount of capital to be invested in each asset in order to build the most profitable portfolio. The portfolio optimization problem is naturally modeled as a mean-risk bi-criteria optimization problem w…

The paper optimizes portfolios using clustering and Sharpe ratio-based optimization.

The collective phenomena of a liquid market is characterized in terms of a particle system scenario. This physical analogy enables us to disentangle intrinsic features from purely stochastic ones. The latter are the result of environmental changes due to a `heat bath' acting on the many-asset system, quantitatively des…

Unified approach for clustering financial multiplex networks.

Cluster GARCH model improves multivariate GARCH for high-dimensional asset returns.

Paper uses HPCA for better stock correlation modeling.

RPS uses graph-based representation learning for better portfolio optimization.

Maximum likelihood estimation applied to high-frequency data allows us to quantify intermittency in the fluctu- ations of asset prices. From time records as short as one month these methods permit extraction of a meaningful intermittency parameter λ characterising the degree of volatility clustering of asset prices. We…

Hybrid model improves synthetic equity data generation.

The paper revisits classical competition theory to explain speculative asset price dynamics.

An analysis of the stylized facts in financial time series is carried out. We find that, instead of the heavy tails in asset return distributions, the slow decay behaviour in autocorrelation functions of absolute returns is actually directly related to the degree of clustering of large fluctuations within the financial…

This paper examines cryptocurrency integration with traditional markets, showing how network structure and turbulence influence cross-asset spillovers.

The paper uses TDA to select stocks for a sparse portfolio, improving performance across market scenarios.

By analyzing a large data set of daily returns with data clustering technique, we identify economic sectors as clusters of assets with a similar economic dynamics. The sector size distribution follows Zipf's law. Secondly, we find that patterns of daily market-wide economic activity cluster into classes that can be ide…

The only input to attain the portfolio weights of global minimum variance portfolio (GMVP) is the covariance matrix of returns of assets being considered for investment. Since the population covariance matrix is not known, investors use historical data to estimate it. Even though sample covariance matrix is an unbiased…

A network-based approach identifies financial factors from asset interactions, explaining market dynamics.

We introduce an affine extension of the Heston model where the instantaneous variance process contains a jump part driven by $α$-stable processes with $α\in(1,2]$. In this framework, we examine the implied volatility and its asymptotic behaviors for both asset and variance options. Furthermore, we examine the jump clus…

Model predicts global financial market risks and asset allocation.

We build a simple model of leveraged asset purchases with margin calls. Investment funds use what is perhaps the most basic financial strategy, called "value investing", i.e. systematically attempting to buy underpriced assets. When funds do not borrow, the price fluctuations of the asset are normally distributed and u…

We present in this paper an empirical framework motivated by the practitioner point of view on stability. The goal is to both assess clustering validity and yield market insights by providing through the data perturbations we propose a multi-view of the assets' clustering behaviour. The perturbation framework is illust…

This paper analyzes the connection between innovation activities of companies -- implemented before crisis -- and their performance -- measured at time of crisis. The companies listed in the STAR Market Segment of the Italian Stock Exchange are analyzed. Innovation is measured through the level of investments in total …

Market dynamic is quantified in terms of the entropy $S(τ,n)$ of the clusters formed by the intersections between the series of the prices $p_t$ and the moving average $\widetilde{p}_{t,n}$. The entropy $S(τ,n)$ is defined according to Shannon as $\sum P(τ,n)\log P(τ,n),$ with $P(τ,n)$ the probability for the cluster t…

Crowded trades by similarly trading peers influence the dynamics of asset prices, possibly creating systemic risk. We propose a market clustering measure using granular trading data. For each stock the clustering measure captures the degree of trading overlap among any two investors in that stock. We investigate the ef…

Entropy measure quantifies volatility correlation and risk diversity in asset portfolios.

New model prices crypto options by clustering market regimes and using implied volatility.

We present a methodology for clustering N objects which are described by multivariate time series, i.e. several sequences of real-valued random variables. This clustering methodology leverages copulas which are distributions encoding the dependence structure between several random variables. To take fully into account …

Using data from 92 indices of stock exchanges worldwide, I analize the cluster formation and evolution from 2007 to 2010, which includes the Subprime Mortgage Crisis of 2008, using asset graphs based on distance thresholds. I also study the survivability of connections and of clusters through time and the influence of …

The paper analyzes how companies' investments before crises affect their performance after crises.

We investigate 17 digital currencies making an analogy with quantum systems and develop the concept of eigenportfolios. We show that the density of states of the correlation matrix of these assets shows a behavior between that of the Wishart ensemble and one whose elements are Cauchy distributed. A metric for the parti…

In an asset return series there is a conditional asymmetric dependence between current return and past volatility depending on the current return's sign. To take into account the conditional asymmetry, we introduce new models for asset return dynamics in which frequencies of the up and down movements of asset price hav…

In power systems, an asset class is a group of power equipment that has the same function and shares similar electrical or mechanical characteristics. Predicting failures for different asset classes is critical for electric utilities towards developing cost-effective asset management strategies. Previously, physical ag…

Study reveals jumps in crypto markets predict future prices.

This study diversifies stock and crypto portfolios using network analysis.

We extend existing models in the financial literature by introducing a cluster-derived canonical vine (CDCV) copula model for capturing high dimensional dependence between financial time series. This model utilises a simplified market-sector vine copula framework similar to those introduced by Heinen and Valdesogo (200…

This paper introduces an agent-based artificial financial market in which heterogeneous agents trade one single asset through a realistic trading mechanism for price formation. Agents are initially endowed with a finite amount of cash and a given finite portfolio of assets. There is no money-creation process; the total…

Unified Bayesian framework for CAT bond pricing.

A new deep learning model improves asset pricing predictions.

Optimizes portfolios using neural network approximations of asset sensitivities to common drivers.

New method detects and clusters market regimes in multidimensional data.

New method for portfolio management learns from past wealth evolution.