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 …
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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.
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48 results for “centrality indicators”
Investment patterns in Eurozone linked to economic indicators, revealing network interconnectedness.
problem Understanding the relationship between foreign portfolio investment and economic indicators in the Eurozone network.
method Analysis of strongly connected investment network of Eurozone and its major trading partners, using centrality measures and network visualization techniques.
result Strong correlation between investment patterns and economic indicators within the Eurozone network, with varying centrality measures among network members.
Graph-based method ranks features using Eigenvector Centrality for feature selection.
problem Feature selection in high-dimensional data.
method Mapping features onto an affinity graph and ranking nodes based on Eigenvector Centrality.
result The method identifies effective features for classification, outperforming other methods in accuracy, stability, and speed.
Clearing model connects to centrality measures for financial contagion.
problem Understanding systemic risk indicators in financial systems.
method Expressed clearing model as a specific form of Katz centrality measure.
result Clearing models provide a better theoretical basis for systemic risk analyses.
We briefly indicate some implications of [1] for the second Lie algebra cohomology of equivariant map algebras and (twisted multi) loop algebras.
This study aims to identify the leading of inflation indicators of monetary policy in DRC. The results reveal that the most relevant inflation indicators usually come from the monetary origin than the real sector. Variance decomposition analyzes place in the foreground the rate of exchange, the money supply and the pub…
Network theory assesses systemic risk in the insurance sector.
problem Detecting critical insurance companies in systemic risk.
method Complex network approach with weighted effective resistance centrality.
result Identifies companies with significant influence on network robustness.
Scaling Properites of Price Changes for Korean Stock Indicescond-mat.stat-mech
We consider returns of two Korean stock market indices, KOSPI and KOSDAQ index. Central parts of the probability distribution function of returns are well fitted by the Lorentzian distribution function. However, tail parts of the probability distribution function follow a power law behavior well. We found that the prob…
Novel risk matrix for optimal portfolio choice with tail risk considerations.
problem Optimal portfolio choice with tail risk events.
method Risk matrix with Value-at-Risk and Delta-CoVaR measures, derived conditions for closed-form solution, examination of portfolio risk and centrality, demonstration of asset centrality's impact on optimal weight allocation.
result Portfolio risk is not necessarily increasing with stock centrality and can be improved by high connectivity.
Decentralization illusion in Decentralized Finance: Evidence from tokenized voting in MakerDAO pollscs.CR
MakerDAO's governance is centralized despite its decentralized claim.
problem Decentralization illusion in Decentralized Finance (DeFi) governance.
method Empirical analysis using financial, transaction, network, and sentiment indicators.
result Centralized governance impacts Maker protocol and voting power distribution.
Multi-centrality Graph Spectral Decompositions and their Application to Cyber Intrusion Detectioncs.SI
Many modern datasets can be represented as graphs and hence spectral decompositions such as graph principal component analysis (PCA) can be useful. Distinct from previous graph decomposition approaches based on subspace projection of a single topological feature, e.g., the Fiedler vector of centered graph adjacency mat…
Decentralized algorithms can potentially outperform centralized ones in certain scenarios.
problem High communication cost in centralized algorithms.
method Study of a decentralized parallel stochastic gradient descent (D-PSGD) algorithm and theoretical analysis.
result Decentralized algorithms can outperform centralized ones in specific network configurations.
Complex products trade through fewer countries, making them more fragile.
problem Fragility in the global economy due to centralized trade networks for complex products.
method Used network science and product complexity theory indicators to analyze trade networks.
result Products with higher complexity trade through fewer countries, making them more fragile.
Long-Time Fluctuations in a Dynamical Model of Stock Market Indicescond-mat.stat-mech
Financial time series typically exhibit strong fluctuations that cannot be described by a Gaussian distribution. In recent empirical studies of stock market indices it was examined whether the distribution P(r) of returns r(tau) after some time tau can be described by a (truncated) Levy-stable distribution L_{alpha}(r)…
New algorithm reduces privacy cost of LDP to central privacy model.
problem Collecting sensitive statistics from multiple users over time.
method Privacy amplification technique using permutation-invariant algorithms.
result Privacy cost of LDP can be much lower in central model.
Using particle system methodologies we study the propagation of financial distress in a network of firms facing credit risk. We investigate the phenomenon of a credit crisis and quantify the losses that a bank may suffer in a large credit portfolio. Applying a large deviation principle we compute the limiting distribut…
Model predicts Mozambique bank failures, aiding risk management.
problem Lack of bankruptcy prediction model in Mozambique banking sector.
method Linear Discriminant Analysis method, using financial indicators.
result Model accurately predicted 84% of bank failures 1 year before Central Bank intervention.
Paper introduces efficient methods for estimating cross-partial derivatives and sensitivity indices.
problem Efficiently estimating cross-partial derivatives and sensitivity indices in complex models.
method Using randomized points and constraints, the paper develops estimators with optimal convergence rates and low bias.
result The estimators achieve optimal rates of convergence and do not suffer from the curse of dimensionality.
Transformer models improve financial sentiment measurement.
problem Capturing nuanced sentiment from financial news articles.
method Transformer-based language models for sentiment classification and aggregation.
result Transformer models outperform traditional dictionary-based methods in sentiment classification.
Paper tackles structure learning of sparse GGMs over multiple access networks.
problem Estimating sparse Gaussian Graphical Model structure from multiple local machines with limited communication.
method Proposes Signs and Uncoded methods for reliable structure learning under power and bandwidth limitations.
result Both methods can recover the structure with high probability for large enough sample size.
Scalable web crawling using noisy change-indicating signals.
problem Optimizing web page freshness with limited bandwidth and noisy side information.
method Proposes a scalable crawling algorithm that uses noisy side information optimally.
result Achieves constant total rate of crawling without spikes in bandwidth usage.
We consider forecasting a single time series when there is a large number of predictors and a possible nonlinear effect. The dimensionality was first reduced via a high-dimensional (approximate) factor model implemented by the principal component analysis. Using the extracted factors, we develop a novel forecasting met…
Study fixed point indices and words at infinity for graph selfmaps.
problem Estimate indices of fixed point classes for graph selfmaps.
method Extend attracting fixed words at infinity, use relative train track technique, algebraic approach.
result Upper bound for attracting fixed words of injective endomorphisms of free groups.
Following the financial crisis of the late 2000s, policy makers have shown considerable interest in monitoring financial stability. Several central banks now publish indices of financial stress, which are essentially based upon market related data. In this paper, we examine the potential for improving the indices by de…
We show, using a theorem of Milnor and Margulis, that string theory on compact negatively curved spaces grows new effective dimensions as the space shrinks, generalizing and contextualizing the results in hep-th/0510044. Milnor's theorem relates negative sectional curvature on a compact Riemannian manifold to exponenti…
Develops a framework for synthetic banking microdata evaluation.
problem Lack of evaluation frameworks for synthetic banking microdata.
method Develops a framework considering utility and privacy requirements.
result Synthetic datasets are particularly suited for frequency tables.
FedForest adapts RF for federated learning, improving performance and efficiency.
problem Adapting RF for federated learning with heterogeneous data.
method FedForest uses a novel splitting procedure to aggregate client statistics, allowing non-parametric personalization.
result FedForest's federated RF achieves performance close to centralized models while being communication-efficient.
This paper contributes to the literature on international stock market comovements and contagion. The novelty of our approach lies in application of wavelet tools to high-frequency financial market data, which allows us to understand the relationship between stock markets in a time-frequency domain. While major part of…
A new ODE model explains gradient descent dynamics near edge of stability.
problem Understanding gradient-based training over non-convex landscapes.
method Rod Flow, a new ODE approximation of GD dynamics.
result Rod Flow accurately predicts critical sharpness threshold and self-stabilization in quartic potentials.
This paper introduces online algorithms to estimate robust geometric median in large data streams.
problem Detecting outliers in large data sets using robust statistical measures.
method Online stochastic Newton methods for estimating the geometric median.
result Rates of convergence for online estimation of the geometric median.
This paper reviews incompatibilities of comonotonic risk measures.
problem Incompatibilities of comonotonic risk measures with central properties.
method Literature review and Choquet representation of comonotonic additive risk measures.
result Comonotonic additive risk measures cannot be surplus invariant.
Analyzes systemic risk in European insurance sector using MST and deltaCoVaR.
problem Assessing systemic risk in European insurance sector over 2005-2019.
method Minimum spanning trees (MST) and deltaCoVaR measure.
result The contribution to systemic risk varies by company's centrality in MST.
Better to stay apart: asset commonality, bipartite network centrality, and investment strategiesq-fin.PM
Better investment strategies identified through a network metric of asset commonality.
problem Identifying investment strategies based on fund portfolio asset popularity.
method Bipartite network analysis of mutual funds and their holdings, calculating the Average Commonality Coefficient (ACC).
result Funds investing in less popular assets outperform those in more popular ones, even after adjusting for standard factors.
Study improves U.S. monetary policy forecasting by integrating text and data.
problem Forecasting central bank policy decisions, especially the Fed's rate changes.
method Multi-modal approach combining structured data and unstructured text from Fed communications.
result Hybrid models outperform unimodal baselines, achieving a test AUC of 0.83.
Motivated by considerations of euclidean quantum gravity, we investigate a central question of spectral geometry, namely the question of reconstructability of compact Riemannian manifolds from the spectra of their Laplace operators. To this end, we study analytic paths of metrics that induce isospectral Laplace-Beltram…
Bayesian VFLMSP improves multimodal survival prediction with privacy.
problem Privacy and reliability in multimodal time-to-event prediction.
method Bayesian Vertical Federated Learning (VFL) with differential privacy.
result Consistent improvements in C-index compared to existing methods.
Proposes a new model to simulate interbank market liquidity risk.
problem Lack of complete interbank network data for systemic liquidity risk analysis.
method Epidemic model using funding liquidity shortage mechanism, enriched with country and bank risk features.
result Model successfully reproduces systemic liquidity risk across different years and countries.
The study shows portfolios based on core-periphery stock structure outperform traditional strategies.
problem Optimizing stock portfolios using mesoscale structures.
method Constructing portfolios based on the core-periphery profile of stocks from Pearson correlations.
result Portfolios based on the core-periphery profile of stocks outperform traditional strategies.
Stock market price fluctuations follow Lévy's stable distribution over long term.
problem Understanding the stability of stock market price fluctuations over different time scales.
method Estimated Lévy's stable parameters from four stock markets over long and short term.
result Stable parameters from different stock markets showed a unique value over long term, but fluctuated with correlation in short term.
Study assesses impact of CBDC on financial stability in dual-currency economy.
problem Impact of CBDC on financial stability in dual-currency economy (Romania).
method Integrated analytical framework combining econometrics, machine learning, and behavioural modelling. CBDC adoption probabilities estimated using XGBoost and logistic regression models. Liquidity stress simulations and VAR, MSVAR, SVAR models capture macro-financial transmission.
result CBDC uptake would be moderate, primarily driven by digital readiness and trust in the central bank.
Paper explores differential privacy in high-dimensional federated learning, tackling server trustworthiness and estimation.
problem Maintaining privacy in distributed environments with high-dimensional data.
method Investigates scenarios with untrusted and trusted central servers, introduces novel federated estimation algorithms for linear regression models.
result Tight minimax rates depend on high-dimensionality even with sparsity assumptions, and novel algorithms handle slight variations among distributed models.
Quantifying dynamics of the financial correlationscond-mat.soft
A novel application of the correlation matrix formalism to study dynamics of the financial evolution is presented. This formalism allows to quantify the memory effects as well as some potential repeatable intradaily structures in the financial time-series. The present study is based on the high-frequency Deutsche Aktie…
Network analysis detects insider trading by flagging coordinated trades.
problem Detecting insider trading due to limited labelled data.
method Data-driven network approach using SEC trade data.
result Algorithm identifies insider trading clusters with high accuracy.
Game theory shows miners' hardware improvements don't centralize mining.
problem Decentralization of cryptocurrency mining.
method Game-theoretical model of mining efficiency and competition.
result Advancements in mining hardware efficiency do not lead to centralization.
Federated learning can propagate bias from a few parties to all participants.
problem Bias from a few parties in federated learning can spread to all participants.
method Analysis of naturally partitioned real-world datasets.
result Bias in federated learning is higher than in centralized training.
The generalization of Bertrand's theorem to abstract surfaces of revolution without "equators" is proved. We prove a criterion for the existence on such a surface of exactly two central potentials (up to an additive and a multiplicative constants) all of whose bounded nonsingular orbits are closed and which admit a bou…
A new measure DCSI quantifies separability for density-based clustering.
problem Quantifying meaningful clusters in data sets.
method Developed a new separability measure DCSI based on separation and connectedness.
result Correctly identifies touching or overlapping classes that do not correspond to meaningful density-based clusters.
Antimonopoly regulation method in energy markets based on the Vickrey-Clarke-Groves mechanismmath.OC
We evaluate the applicability of the generic Vickrey-Clarke-Groves (VCG) mechanism as an antimonopoly measure against a profit-maximizing producer with market power operating a portfolio of generating units at the centralized two-settlement energy market. The producer may indicate in its bid not only the altered cost f…