Financial planners helped preserve and increase household net financial assets during the Great Recession.
problem Impact of financial planners on household net financial assets during the Great Recession.
method Utilized 2007-2009 Survey of Consumer Finances (SCF) panel dataset, analyzed 3,862 respondents.
result Starting to use a financial planner during the Great Recession had a positive impact on preserving and increasing household net financial assets.
Predicts financial asset dependencies using spatiotemporal patterns.
problem Complex dependency structures in financial assets for risk mitigation.
method Proposes Asset Dependency Matrix (ADM) and Asset Dependency Neural Network (ADNN) with ConvLSTM for spatiotemporal asset dependency prediction.
result ADNN outperforms baselines in predicting asset dependencies and their applications.
A new contrastive learning method extracts asset embeddings from financial time series.
problem Extracting meaningful latent features from noisy financial data.
method Contrastive learning framework using hypothesis testing for positive and negative samples.
result Effective asset embeddings significantly outperform existing methods on financial tasks.
Proposes neural model for stock embeddings to capture nuanced asset correlations.
problem Lack of research on modelling financial asset correlations.
method Neural model using historical returns data to learn nuanced relationships.
result Outperforms benchmarks in two real-world financial analytics tasks.
Study introduces a new copula-based measure for financial asset cointegration.
problem Traditional correlation coefficient's limitations in measuring financial asset relationships.
method Utilizes copulas to measure dependence among financial asset returns.
result Enhanced stability and informativeness in measuring financial asset relationships.
I show the equivalence between a model of financial contagion and the threshold model of global cascades proposed by Watts (2002). The model financial network comprises banks that hold risky external assets as well as interbank assets. It is shown that a simple threshold model can replicate the size and the frequency o…
Digital transformation boosts corporate financial asset allocation, especially short-term.
problem Understanding how digital transformation affects corporate financial decisions.
method Fixed-effects models and staggered DID design using A-share listed companies data.
result Digital transformation significantly promotes corporate financial asset allocation, more pronounced in short-term.
Study examines financial contagion at community level, finding increased contagion density and widespread transmission.
problem Understanding and managing financial contagion in interconnected markets.
method High-frequency data, Louvain community detection, Vector Autoregression, Tracy-Widom random matrix theory.
result Contagion density increases over time, and there is no significant difference between intra- and inter-community contagion.
New model uses financial news to predict stock returns.
problem Predicting stock returns based on financial news.
method Derive company embedding vectors from news, select basis assets, and use statistical methods.
result NEUS model outperforms Fama-French 5-factor model.
Method detects and visualizes changes in financial markets' asset relationships.
problem Detecting and explaining changes in financial markets' asset relationships.
method Construct co-occurrence networks, calculate Graph-Based Entropy, apply Differential Network.
result Visualization of changes in financial markets with high interpretability.
A new model captures financial asset returns' tail behaviors and outperforms GARCH family.
problem Capturing the dynamic tail behaviors of financial asset returns.
method Combines LSTM with a novel parametric quantile function.
result Out-of-sample forecasts of conditional quantiles or VaR outperform GARCH family.
Paper proposes a CNN model for improved multi-asset portfolio risk prediction.
problem Challenges in risk management of multi-asset portfolios due to limited correlation capture.
method Uses CNN and image processing to convert financial data into images for enhanced feature extraction.
result CNN model significantly outperforms traditional methods in risk prediction accuracy.
New automated market makers for multi-asset trading.
problem Liquidity management in multi-asset trading.
method Derived from self-financing transactions and rebalancing principles.
result Constant product market maker as a special case.
We extend to the multi-asset case the framework of a discrete time model of a single asset financial market developed in Ghoulmie et al (2005). In particular, we focus on adaptive agents with threshold behavior allocating their resources among two assets. We explore numerically the effect of this diversification as an …
Review of financial dependencies using econophysics and financial economics.
problem Analyzing financial dependencies between markets.
method Combining econophysics and financial economics approaches to model financial markets.
result Information filtering networks effectively describe financial dependencies.
A new RL framework tackles asset allocation problems using Monte Carlo simulation.
problem Existing asset allocation methods fail to consider portfolio management and financial market characteristics.
method Proposes a new reinforcement learning framework that considers portfolio state and uses Monte Carlo simulation to prevent overfitting.
result The proposed method outperforms benchmarks in various test intervals.
In complete markets, there are risky assets and a riskless asset. It is assumed that the riskless asset and the risky asset are traded continuously in time and that the market is frictionless. In this paper, we propose a new method for hedging derivatives assuming that a hedger should not always rely on trading existin…
The paper shows how cross-ownership increases equity correlations during financial crises.
problem Understanding and explaining rising correlations in financial markets during crises.
method Examined interlinkages among firms through a financial network, mathematically relating equity correlations to asset correlations and network sensitivity.
result Equity correlations are higher than asset correlations, and this relationship is independent of the equities level.
Machine learning models outperform traditional CAPM in forecasting financial asset prices.
problem Predicting and forecasting financial asset prices and returns.
method Comparison of modern Machine Learning algorithms with the Capital Asset Pricing Model (CAPM) on U.S. equities data.
result Implemented Machine Learning models significantly outperform the CAPM on out-of-sample test data.
Deep learning enhances financial asset management through new models and data sources.
problem Improving portfolio performance and price forecasting accuracy in financial asset management.
method Systematic review using Scopus database, focusing on deep learning applications in financial asset management from 2018 to 2023.
result Deep learning models show promise in enhancing portfolio performance and price forecasting accuracy.
Study uses MTD model to optimize portfolios by capturing complex financial asset relationships.
problem Capturing nonlinear and directional relationships in financial markets.
method Directed and weighted financial networks using Mixture Transition Distribution (MTD) model.
result Portfolio optimization with network-based assortativity measures outperforms classical methods.
A network-based approach identifies financial factors from asset interactions, explaining market dynamics.
problem Characterizing joint financial asset behavior through underlying drivers.
method Modeling market as coupled iterated maps, where asset returns depend on past returns and interactions.
result Stable patterns of co-movement (financial factors) emerge from asset interactions, explaining asset variance.
Model predicts global financial market risks and asset allocation.
problem Predicting downside risk and market regime shifts.
method Dynamic regime switching model based on GARCH-DCC-Copula.
result Significantly improves risk and alpha-based asset allocation strategies.
The study identifies assets with local balance deviating from global balance to mitigate financial risk.
problem Selecting outperforming assets during financial crises.
method Investigates deviations of local balance from global balance as a criterion for asset selection.
result Assets with local balance deviating from global balance can mitigate financial risk.
Complex contagion model explains financial fire sales through continuous asset prices.
problem Modeling financial fire sales with a continuum of asset prices.
method Developed a threshold model of continuous-state cascades using real values for asset prices.
result Discretization approach accurately replicates the distribution of defaulted banks and asset prices.
New portfolio optimization method considers both asset-specific and systemic risks for financial networks.
problem Optimizing portfolios with both idiosyncratic and systemic risks in financial networks.
method Developed a multi-objective optimization model that incorporates idiosyncratic variance and network clustering coefficient.
result Optimal portfolios outperform in terms of return measures and have less drawdown compared to traditional strategies.
Model shows how relaxed leverage can lead to asset price bubbles.
problem Understanding how financial leverage affects asset prices and growth.
method Developed a macro-finance model with feedback loops between investment and land prices.
result Relaxed leverage can cause unbalanced growth and asset price bubbles.
Clusters of highly correlated stocks are identified for better asset selection.
problem Identifying a small set of stocks to approximate the diversification of the whole stock universe.
method Data-driven correlation blockmodel clustering approach.
result The algorithm effectively detects clusters of highly correlated stocks.
Optimal early liquidation strategy reduces financial losses during crises.
problem Substantial losses from simultaneous asset liquidation at depressed prices.
method Developed a worst-case approach for optimal early liquidation, considering uncertainty of other banks' decisions.
result Proposed robust optimal strategy maximizes liquid assets' value at clearing, even with uncertainty.
Quantum walks model financial returns with flexibility and asymmetry.
problem Accurate modeling of financial asset price dynamics.
method Discrete-time quantum walks to model asset price evolution.
result Quantum walk models can generate asymmetric return distributions and higher probabilities for extreme events.
FinTSBridge evaluates financial time series models for asset pricing.
problem Lack of effective evaluation methods for financial time series models.
method Developed FinTSBridge suite with new metrics and tasks.
result Showcased new metrics for financial time series models.
Model predicts asset prices from initial shocks using neural networks.
problem Missing data on actual asset liquidations limits model calibration.
method Dual neural network structure, first stage maps shocks to liquidations, second stage uses liquidations to predict prices.
result Model accurately predicts equilibrium prices from initial shocks without liquidation data.
Investigates cross-impact kernels for financial asset prices.
problem Understanding and parameterizing cross-impact kernels for financial asset prices.
method Examined martingale-admissible and no-statistical-arbitrage-admissible kernels, determined their overlap, and provided calibration formulas.
result Identified the overlap between martingale-admissible and no-statistical-arbitrage-admissible kernels and provided formulas for their calibration.
This paper provides a framework for modeling the financial system with multiple illiquid assets when liquidation of illiquid assets is caused by failure to meet a leverage requirement. This extends the network model of Cifuentes, Shin & Ferrucci (2005) which incorporates a single asset with fire sales and capital adequ…
In this paper, we present a novel approach to the generation of virtual scenarios of multivariate financial data of arbitrary length and composition of assets. With this approach, decades of realistic time-synchronized data can be simulated for a large number of assets, producing diverse scenarios to test and improve q…
Persistence norms explain financial uncertainty better than volatility.
problem Capturing financial instability and predictability.
method Applied topological data analysis to financial markets.
result Persistence norms are significant in explaining financial uncertainty, while volatility is less effective.
The article models financial asset returns using Gaussian mixtures and EVT-based copulas to price equity options.
problem Modeling financial asset returns and pricing equity options considering extreme values.
method Modeling marginal distributions with Gaussian mixtures and joint dependence structure with EVT-based copulas.
result The approach accurately prices various equity options on Atos and Dassault Systems actions.
FLARKO uses LLMs, KGs, and KTO to generate profitable, behaviorally aligned financial recommendations.
problem Financial recommendation systems often fail to account for behavioral and regulatory factors.
method FLARKO integrates LLMs, KGs, and KTO to generate profitable and behaviorally aligned recommendations.
result FLARKO consistently outperforms state-of-the-art recommendation baselines on behavioral alignment and joint profitability.
Unified approach for clustering financial multiplex networks.
problem Lack of methods to capture interconnections between assets over time.
method Tensor-based unified local and global clustering coefficients for multiplex networks.
result Unified clustering coefficients effectively describe dependencies between assets over time.
On 2 November 2009, the Financial Bubble Experiment was launched within the Financial Crisis Observatory (FCO) at ETH Zurich (\url{http://www.er.ethz.ch/fco/}). In that initial report, we diagnosed and announced three bubbles on three different assets. In this latest release of 23 December 2009 in this ongoing experime…
BreakGPT predicts asset price surges using LLMs.
problem Predicting sharp upward movements in volatile financial markets.
method Adapts LLMs for time series forecasting, combining LLM capabilities with Transformer models.
result BreakGPT effectively captures local and global temporal dependencies.
Measures risk contagion in financial networks using CoVaR.
problem Assessing stability of complex financial systems.
method Financial network model with bipartite graph of institutions and assets, heavy-tailed distributions, copula models, CoVaR and ECI.
result Proposes the Extreme CoVaR Index (ECI) for capturing risk contagion strength.
Financial models are studied where each asset may potentially lose value relative to any other. Conditioning on non-devaluation, each asset can serve as proper numéraire and classical valuation rules can be formulated. It is shown when and how these local valuation rules can be aggregated to obtain global arbitrage-fre…
Financial market created for wellbeing indices to mitigate socioeconomic risks.
problem Risk mitigation in financial indices of socioeconomic wellbeing.
method Developed new quantitative measure, created financial market, and implemented insurance instruments.
result Optimal portfolio weights and efficient frontiers for wellbeing indices.
The present paper provides the basis for a novel financial asset pricing model that could avoid the shortcomings of, or even completely replace the traditional DCF model. The model is based on Brownian motion logic and expected future cash flow values. It can be very useful for Islamic Finance.
NewsNet-SDF uses deep learning to integrate financial news with financial data for better asset pricing.
problem Combining unstructured text with structured financial data for accurate asset pricing.
method Adversarial networks and pretrained language model embeddings.
result Substantially outperforms alternatives with a Sharpe ratio of 2.80.
An asset network systemic risk (ANWSER) model is presented to investigate the impact of how shadow banks are intermingled in a financial system on the severity of financial contagion. Particularly, the focus of this study is the impact of the following three representative topologies of an interbank loan network betwee…
DeepPocket uses graph convolutional reinforcement learning for better financial portfolio management.
problem Maximizing return on investment while managing risk in correlated financial assets.
method Graph convolutional reinforcement learning framework with feature extraction, local information collection, and actor-critic reinforcement learning.
result DeepPocket outperformed market indexes on five real-life datasets over three investment periods, including during the Covid-19 crisis.