Study analyzes stock market correlations using multivariate distributions.
problem Capturing the correlation structure of complex, non-stationary systems.
method Applied Random Matrix Model to empirical data of 479 US stocks.
result Described and quantified changes in empirical distributions due to non-stationarity.
Investigates optimal trading strategies in multivariate stock returns with expert opinions and drift estimation.
problem Optimal trading strategies in a financial market with multidimensional stock returns and drift estimation.
method Uses expert opinions and logarithmic utility maximization to derive optimal trading strategies, considering the dynamics of conditional covariance matrices of the drift.
result Derives optimal expected logarithmic utility of terminal wealth and conditions for convergence of covariance matrices.
Study proposes a new portfolio selection method using non-Gaussian models and Esscher transform.
problem Portfolio selection with complex stock return structures and skewness, kurtosis.
method Multivariate non-Gaussian models (NTS and GH), Esscher transform for risk-neutral measure, simultaneous calibration of univariate log-returns and volatility.
result Demonstrated the effectiveness of the proposed models in fitting and selecting portfolios.
The paper analyzes how ESG investors can prioritize green stocks without sacrificing overall wealth.
problem Balancing sustainability goals with financial returns.
method Multivariate utility analysis with a one-factor CAPM structure.
result Investors can allocate more to high-rated ESG stocks without reducing overall wealth.
Matrix H-theory models stock market fluctuations using hierarchical multivariate distributions.
problem Understanding collective behavior in stock market fluctuations.
method Matrix H-theory framework for multivariate stochastic processes with hierarchical structure.
result Matrix H-theory effectively describes stock market fluctuations using Meijer G-functions.
Study forecasts stock returns on JSE using SGDLMs capturing cross-series dependencies.
problem Accurate forecasting of multivariate time series data.
method Simultaneous Graphical Dynamic Linear Models (SGDLMs) with customised DLMs and importance sampling/mean-field variational Bayes.
result SGDLMs accurately forecast stock data on JSE and respond to market changes.
Proves existence and uniqueness of optimal trading strategy for multivariate returns.
problem Finding optimal trading strategy for multiple asset returns.
method Proves existence and uniqueness of optimal solution using fractional trading ansatz.
result Optimal trading strategy can be numerically found using steepest ascent methods.
A new model captures multifractal volatility in stock returns.
problem Capturing multifractal volatility in stock returns.
method Introduced mLog S-fBM model, defined mS-fBM, and developed calibration procedure.
result Validated model on synthetic and real data, showing multifractal behavior.
In this paper we briefly review the recently inrtroduced Multifractal Random Walk (MRW) that is able to reproduce most of recent empirical findings concerning financial time-series : no correlation between price variations, long-range volatility correlations and multifractal statistics. We then focus on its extension t…
We develop a scalable model for large asset returns without volatility matrix restrictions.
problem Inference for large multivariate stochastic volatility models.
method Factor multivariate stochastic volatility model with scalable MCMC algorithm.
result Scalable inference achieved for 571 stock returns over 10 years.
New method quantifies market shocks and their effects.
problem Quantifying the impact and response to market shocks.
method Sparse probabilistic elliptical model with L0-norm regularization. result Shock patterns are related to network structure and sector diversification.
The paper examines non-Gaussian models for financial data.
problem Modeling financial data with non-Gaussian distributions.
method Analysis of multivariate non-Gaussian models focusing on parsimony, dependence structure, and computational aspects.
result Characterization and calibration of models for financial log-returns.
In the paper we compare the modelling ability of discrete-time multivariate Stochastic Volatility models to describe the conditional correlations between stock index returns. We consider four trivariate SV models, which differ in the structure of the conditional covariance matrix. Specifications with zero, constant and…
Dynamic portfolio strategy using generative model with attention mechanism.
problem Dynamic modeling of multivariate stock returns with tail-side properties.
method Dynamic generative factor model using Attention-GRU network for dynamic learning and forecasting.
result The proposed model leads to wiser investments with higher reward-risk ratios and lower tail risks.
Develops a new model to better estimate cryptocurrency and stock volatility.
problem Misrepresentation of volatility and co-movement in traditional models.
method Introduces liquidity-sensitive multivariate volatility framework with novel liquidity measures.
result Liquidity-adjusted models yield more stable and interpretable risk structures.
A new model captures multifractal volatility in stock returns.
problem Capturing multifractal volatility in stock returns.
method Introduced mLog S-fBM model, defined mS-fBM, and developed calibration procedure.
result Model captures multifractal behavior in stock returns, validating on real data.
Study uses VIX for zero-coupon Treasury rates, proving long-term stability and returns.
problem Modeling zero-coupon Treasury rates with VIX for volatility.
method Multivariate autoregressive stochastic volatility model, proving stability and Law of Large Numbers.
result VIX accurately models zero-coupon Treasury rates and returns.
A new model disentangles long-term and short-term sentiment components in stock returns.
problem Identifying distinct components of sentiment data in stock markets.
method Dynamic factor model with random walk and stationary VAR(1) components, estimated via Kalman filtering and EM.
result The long-term sentiment component co-integrates with market principal factor, while the short-term captures market swings.
The study uncovers the complex interactions between stock market instruments and their impact on trading costs.
problem Underestimation of trading costs and contagion effects due to ignoring interactions between different order flows.
method Introduces a multivariate linear propagator model to describe the joint dynamics of assets and accounts for significant covariance of stock returns.
result The model successfully describes the sectorial structure of market correlations and accounts for a significant fraction of the covariance of stock returns.
Optimizes U.S. stock portfolios with natural gas and crude oil to reduce risk and enhance returns.
problem Reduces portfolio risk and enhances returns by diversifying with natural gas and crude oil.
method Uses time-varying multivariate copula analysis and variance regimes to handle structural changes in asset prices.
result Minimizes portfolio variance, semi-variance, and tail risk with or without return constraints.
In this paper we propose a bivariate generalization of a weighted indexed semi-Markov chains to study the high frequency price dynamics of traded stocks. We assume that financial returns are described by a weighted indexed semi-Markov chain model. We show, through Monte Carlo simulations, that the model is able to repr…
Improved stock market valuation using modified dividend-price ratio.
problem Stationary assumption failure in classical dividend-price ratio.
method Multivariate regressions, dynamic econometric procedure, modified dividend-price ratio (mdp).
result Enhanced forecasting results over classical dividend-price ratio.
We consider the Fractionally Integrated Exponential Generalized Autoregressive Conditional Heteroskedasticity process, denoted by FIEGARCH(p,d,q), introduced by Bollerslev and Mikkelsen (1996). We present a simulated study regarding the estimation of the risk measure VaRp on FIEGARCH processes. We consider the distr…
Study analyzes how COVID-19 impacts crypto and stock market volatility.
problem Impact of COVID-19 on cryptocurrency and stock market volatility.
method Two-stage multivariate EGARCH model with DCC approach, VaR and CFVaR.
result Significant spillover effects and conditional volatility surges after shocks.
This paper examines volatility in REITs using a multivariate GARCH based model. The Multivariate VAR-GARCH technique documents the return and volatility linkages between REIT sub-sectors and also examines the influence of other US equity series. The motivation is for investors to incorporate time-varyng volatility and …
MACE optimizes stock portfolios for maximal predictability.
problem Maximizing risk-adjusted profitability through predictable stock returns.
method Developed a machine learning algorithm (MACE) using Random Forest and Ridge Regression.
result Significant increases in predictability and profitability with minimal conditioning information.
New model explains price, volume, and waiting times in financial markets.
problem Understanding price, volume, and waiting times in financial markets.
method Generalized semi-Markov chains with endogenous index process and copulae for dependence.
result Model accurately reproduces empirical evidence from Italian stock market data.
The study improves stock market valuation using volatility and earnings data.
problem Improving stock market valuation metrics.
method Time series model for asset returns, multivariate kernel density estimation, linear regression.
result The valuation measure is an improvement over Shiller's P/E ratio.
Study compares ERI to traditional portfolios, finds ERI outperforms on heavy-tailed assets.
problem Optimizing portfolios for assets with extreme tail risks.
method Uses multivariate extreme value theory to minimize large portfolio losses.
result ERI strategy significantly outperforms minimum variance and equally weighted portfolios on heavy-tailed assets.
Proposes a new model for learning multivariate tail dependence in financial assets.
problem Learning multivariate tail dependence among financial assets.
method Probabilistic model that allows separate modeling of pairwise tail dependence and correlation.
result Notable performance improvements in multi-dimensional coverage tests.
Model predicts stock correlations based on investors' expected returns.
problem Lack of microscopic explanation for stock correlations.
method Agent-based model derived from minority game.
result Stock returns are positively/negatively correlated when agents' expected returns for one stock are positively/negatively correlated with the historical return of the other.
Study uses Hawkes processes to analyze stock market contagion in China.
problem Understanding contagion in Chinese stock market.
method Fitting Hawkes processes to daily returns and sector indices.
result Identifies long-term dependencies and trending patterns in sector indices.
Regression Trees analyze stock returns, revealing market excess return as the most informative factor.
problem Understanding informational content of three factors in stock returns.
method Joint regression tree analysis of daily stock return data for 5 major US corporations.
result The market excess return factor is always the most informative in all cases (solo and joint).
The paper finds the normal distribution unsuitable for modeling daily stock returns and suggests using the Laplace distribution instead.
problem The difficulty in modeling the distribution of daily stock returns, especially for extreme outliers.
method Investigation of daily stock returns of major indices using both normal and Laplace distributions.
result The normal distribution is not a good model for stock returns, even over long periods of data.
Model uses LLM features to predict stock returns effectively.
problem Predicting stock returns from text data.
method Structured Event Representation (SER) model with attention mechanisms.
result SER-based model outperforms existing models in stock return prediction.
The paper uses PCA and HMM to forecast stock returns outperforming buy-and-hold.
problem Predicting stock returns accurately.
method Applied PCA to covariance matrix of S&P 500 stocks, used HMM on principal components, and forecasted stock returns.
result The model outperforms buy-and-hold strategy in terms of annualized Sharpe ratio.
Study shows lower market-cap and lower P/E penny stocks outperform higher market-cap and higher P/E stocks.
problem Determinants of expected returns on penny stocks in emerging markets.
method Cross-sectional analysis of 167 penny stocks listed in National Stock Exchange of India.
result Lower market-cap and lower P/E penny stocks outperform higher market-cap and higher P/E stocks.
Investigates a multivariate GARCH model with dynamic beta for large markets.
problem Application challenges in multivariate GARCH models for markets with many stocks.
method Factor model with six free GARCH parameters, dynamic beta coefficients.
result Competitive results compared to other GARCH models, confirms market transitions.
We report evidence of a deep interplay between cross-correlations hierarchical properties and multifractality of New York Stock Exchange daily stock returns. The degree of multifractality displayed by different stocks is found to be positively correlated to their depth in the hierarchy of cross-correlations. We propose…
This paper investigates the common intuition suggesting that during crises the shape of the financial market clearly differentiates from that of random walk processes. In this sense, it challenges the analysis of the nature of financial markets proposed by Fama and his associates. For this, a geometric approach is prop…
Stock markets show unusual overnight and intraday returns.
problem Unusual patterns of overnight and intraday returns in stock markets.
method Analyzed features of the returns to deduce the cause.
result The only plausible explanation for these returns is that they are due to market manipulation.
LLMs overestimate stock returns and are less accurate at predicting extreme outcomes.
problem Behavioral biases in LLMs' stock return forecasts.
method Comparison of LLM forecasts with crowd-sourced estimates and historical data.
result LLMs overestimate stock returns and are less accurate at predicting extreme outcomes.
Study news networks to predict stock returns.
problem Predicting cross-sectional stock returns using news networks.
method Constructed time-varying directed networks of S&P500 stocks from 1 million news articles, identified stock tickers using an algorithm, and tested for comovement and reversal effects.
result News network attention proxy, network degree, predicts monthly stock returns robustly.
EXAMM evolves RNNs for stock return prediction and portfolio trading.
problem Predicting stock returns for optimal portfolio trading.
method Evolutionary Neural Architecture Search (EXAMM) for evolving RNNs.
result Evolving RNNs outperform traditional benchmarks in stock trading.
Proposes a method to improve stock index prediction using cointegration and quantile loss.
problem Improving stock prediction accuracy by selecting informative factors and using quantile loss.
method Uses cointegration test to select factors and quantile loss for training models.
result Proposed method outperforms conventional approaches in terms of cumulative return and Sharpe ratio.
Paper quantifies how past stock returns inform about volatility and future returns.
problem Inferring volatility and future returns from past returns in stochastic volatility models.
method Quantifies mutual information between past and future stock returns and volatility.
result Past stock returns provide significant information about future volatility and returns.
Network analysis improves stock return forecasting.
problem Improving stock return forecasting using network properties.
method Network analysis of stock return correlations, using individual and global properties of stocks.
result 50% improvement in R2 score for long-term stock returns forecasting, 3% for short-term.
Artificial Neural Networks predict stock returns, finding larger stocks less predictable.
problem Evaluating the validity of the Efficient Market Hypothesis.
method Backpropagation Artificial Neural Network analysis of Brazilian stock market.
result Predictability of stock returns is related to market capitalization, with larger stocks less predictable.