Predicts asset return distributions using LSTM and quantile regression.
problem Predicting complex asset return distributions.
method Two-stage approach: quantile prediction using asset-specific features, market data adjustment.
result Significantly outperforms existing models (98% improvement over baseline).
The CAPM's market returns are endogenously determined, affecting all assets' expected returns.
problem The standard CAPM's market return assumption is not endogenously consistent.
method Demonstrates the impact of endogenously determined market returns on asset returns and the range of feasible market returns.
result Expected returns are influenced by all assets' risks, and market returns are limited by asset distribution.
Decomposes portfolio returns into drift and asset price distribution changes.
problem Understanding efficient markets through portfolio returns and asset price distributions.
method Continuous semimartingale price representations and accounting identity.
result Existence of an asset pricing factor emerges from an accounting identity across various economic and financial environments.
This paper applies quantum probability theory to model asset returns, avoiding assumptions about quantum effects.
problem Modeling asset returns with classical probability theory.
method Derives a Schrödinger-like trading equation using quantum probability, linking it to traders' decisions and market behaviors.
result Quantum probability can describe multimodal distributions of asset returns without assuming quantum effects.
Simple model forecasts asset return distributions precisely.
problem Forecasting dynamic return distributions of assets.
method Ordered binary choice regression with sign predictability constraints.
result Precise forecasts of asset return distributions using past indicators and volatility proxies.
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.
Distributions of assets returns exhibit a slight skewness. In this note we show that our model of endogenous price formation \cite{Reimann2006} creates an asymmetric return distribution if the price dynamics are a process in which consecutive trading periods are dependent from each other in the sense that opening price…
The paper uses FRFT to fit GTS distribution to asset returns.
problem Modeling asset returns with GTS distribution.
method Fractional Fourier Transform (FRFT) for fitting.
result GTS distribution fits SPY ETF and Bitcoin BTC returns.
Paper uses news data to model asset correlations without market data.
problem Traditional risk models rely on market data; this paper offers an alternative.
method Uses encoder-only language models to embed news data, then calculates asset return distributions and covariance through Energy Distance.
result Established connections between distributional differences and excess returns co-movements using Energy Distance.
Using a family of modified Weibull distributions, encompassing both sub-exponentials and super-exponentials, to parameterize the marginal distributions of asset returns and their multivariate generalizations with Gaussian copulas, we offer exact formulas for the tails of the distribution P(S) of returns S of a port…
In stochastic finance, one traditionally considers the return as a competitive measure of an asset, {\it i.e.}, the profit generated by that asset after some fixed time span Δt, say one week or one year. This measures how well (or how bad) the asset performs over that given period of time. It has been established tha…
This paper revisits mean-variance portfolio theory, addressing limitations in diversification models.
problem Current diversification models assume exchangeable asset returns and ignore risk-free assets.
method Analyzes diversification under full information about asset returns and risk, considering both risky and risk-free assets.
result The conventional wisdom of mean-variance portfolio theory is not universally valid, especially when asset returns are not exchangeable.
Based on a faithful representation of the heavy tail multivariate distribution of asset returns introduced previously (Sornette et al., 1998, 1999) that we extend to the case of asymmetric return distributions, we generalize the return-risk efficient frontier concept to incorporate the dimensions of large risks embedde…
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.
We show how to reduce the problem of computing VaR and CVaR with Student T return distributions to evaluation of analytical functions of the moments. This allows an analysis of the risk properties of systems to be carefully attributed between choices of risk function (e.g. VaR vs CVaR); choice of return distribution (p…
In this paper we derive the exact solution of the multi-period portfolio choice problem for an exponential utility function under return predictability. It is assumed that the asset returns depend on predictable variables and that the joint random process of the asset returns and the predictable variables follow a vect…
New methods improve uncertainty in machine learning predictions for asset returns.
problem Uncertainty in machine learning predictions for asset returns.
method Developed new methods to construct forecast confidence intervals for expected returns from neural networks.
result Neural network forecasts of expected returns have the same asymptotic distribution as classic nonparametric methods, enabling standard error calculation.
Decomposes returns of bottom-ranked assets, finds excess returns.
problem Excess returns of bottom-ranked assets not explained by existing models.
method Decomposes returns into rank crossovers and relative price changes.
result Excess returns of bottom-ranked assets are driven by relative price changes, not rank.
An analytic solution for asset allocation with Laplace distribution.
problem Asset allocation with multivariate Laplace distribution.
method Specialization of elliptically symmetric distribution theory to Laplace distribution, accounting for dimensionality and variance rescaling.
result A result consistent with conjecture but with differences due to omitted term and rescaling.
Study improves stock return uncertainty prediction using Gaussian mixture distributions.
problem Improving prediction of stock market return uncertainty.
method Gaussian mixture distribution-based deep learning model.
result Superior performance in volatility estimation, especially during market volatility.
Market timing yields below median returns, even with perfect hindsight.
problem The likelihood of successful market timing strategies.
method Modeling and analysis of index mutual fund data over 1993-2017.
result The probability distribution of market timing returns is asymmetrical, with a higher probability of below median returns.
Using a family of modified Weibull distributions, encompassing both sub-exponentials and super-exponentials, to parameterize the marginal distributions of asset returns and their natural multivariate generalizations, we give exact formulas for the tails and for the moments and cumulants of the distribution of returns o…
We introduce a new set of consistent measures of risks, in terms of the semi-invariants of pdf's, such that the centered moments and the cumulants of the portfolio distribution of returns that put more emphasis on the tail the distributions. We derive generalized efficient frontiers, based on these novel measures of ri…
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…
Method recovers asset return distributions from option prices.
problem Recovering implied physical densities from option prices.
method Non-parametric method based on Distribution Matching.
result Complete recovery of physical probability distributions.
We derive asset pricing formula for markets with incomplete information and subjective views.
problem Asset pricing in markets with informational imperfections and subjective investor beliefs.
method Closed-form market equilibrium formula based on Merton's model, non-linear system of equations, conditional posterior distribution.
result Derivation of market reference model for excess returns under random shadow-costs.
The Split-Session Cluster GARCH model captures tail heterogeneity in overnight and intraday returns.
problem Capturing tail behavior and dependence in multivariate asset returns.
method Convolution-t distributions, session and sector clustering, block-structured correlation matrices. result Session-specific and sector-level tail parameters improve model fit and out-of-sample performance.
A new portfolio model considers investor aversion to loss and risk.
problem Constructing a robust portfolio under uncertain asset returns and investor aversion.
method Distributional robust optimization (DRP) with a Wasserstein ball centered on empirical distribution, mixed-integer quadratic programming, and hybrid algorithm.
result Empirical testing shows superior performance in asset allocation compared to common strategies.
The paper improves asset allocation using a skew-normal distribution in the Black-Litterman model.
problem Improving asset allocation under skewed return distributions.
method Using the Black-Litterman model with hidden truncation skew-normal distribution and Simaan's three-moment risk model.
result Optimal portfolios have less risk and higher skewness compared to classical BL model.
Classical mean-variance portfolio theory tells us how to construct a portfolio of assets which has the greatest expected return for a given level of return volatility. Utility theory then allows an investor to choose the point along this efficient frontier which optimally balances her desire for excess expected return …
Study finds significant premium for low-beta stocks in firm-level idiosyncratic return distributions.
problem Understanding the role of common idiosyncratic quantile factors in asset pricing.
method Quantile factor analysis to extract common idiosyncratic quantile factors with asymmetric pricing effects.
result Significant premium for innovations to the lower-tail factor: high-beta stocks outperform low-beta stocks by around 7-8% per year.
Bayesian method improves portfolio selection under uncertain parameters.
problem Optimal portfolio choice with unknown asset return parameters.
method Bayesian posterior predictive distribution for optimization.
result Bayesian approach yields better portfolio predictions and returns.
The study revisits portfolio diversification by relaxing assumptions for skewed, multi-regime, and leptokurtic asset returns.
problem Underestimation of risk in portfolio diversification due to assumptions that are inconsistent with real-world asset returns.
method Calibrated a Markov-modulated Levy process model to equity market data to demonstrate the merits of the approach.
result The calibrated models effectively match empirical moments and show the importance of relaxing assumptions in portfolio diversification.
Study shows risk-averse investors have consistent ranking of risky assets.
problem Ranking of risky assets in short-term investments.
method Analyzes various decision problems regarding risky assets with continuous returns.
result Risk-averse decision makers have the same ranking over risky assets.
Bayesian method predicts asset returns for better portfolio optimization.
problem Uncertainty in financial markets makes traditional portfolio optimization methods unreliable.
method Bayesian predictive synthesis (BPS) combined with dynamic linear models.
result Predicted distribution information improves portfolio performance.
We find a rank effect in commodity prices that yields higher returns.
problem Understanding the pricing dynamics of commodities over time.
method Nonparametric econometric methods to demonstrate the rank effect as a consequence of stationary relative asset price distribution.
result A portfolio of lower-ranked, lower-priced commodities yields 23% higher annual returns than a portfolio of higher-ranked, higher-priced commodities.
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.
The estimation of asset return distributions is crucial for determining optimal trading strategies. In this paper we describe the constrained mixture model, based on a mixture of Gamma and Gaussian distributions, to provide an accurate description of price trends as being clearly positive, negative or ranging while acc…
New model analyzes dynamic correlations in stock returns.
problem Analyzing time-varying correlations in high-dimensional data.
method Dynamic factor correlation model with novel parametrization.
result Model accurately captures heterogeneous heavy-tailed distributions and dependent shocks.
Study compares Bitcoin and Ethereum tail behavior using Q-Q plots.
problem Examining tail risk in cryptocurrency returns.
method Used Q-Q plots and Generalized Tempered Stable (GTS) distribution.
result Ethereum shows more extreme values than Bitcoin, indicating greater tail risk.
A new model optimizes portfolios by learning stock return distributions conditioned on factors.
problem Optimizing portfolios with high-dimensional asset-specific factors.
method Conditional Diffusion Transformer architecture linking each asset's return to its factor vector.
result The model outperforms benchmarks in mean-variance and mean-CVaR optimization.
The study assesses music as an investment asset class using discounted cashflow models.
problem Quantifying the risk and return characteristics of music royalty assets.
method Fitting three discounted cashflow models to Royalty Exchange platform transactions and backtesting performance.
result Life of Rights music assets had risk and return characteristics comparable to stocks in the S\&P500 over 5 years.
We consider a popular model of microeconomics with countably many assets: the Arbitrage Pricing Model. We study the problem of optimal investment under an expected utility criterion and look for conditions ensuring the existence of optimal strategies. Previous results required a certain restrictive hypothesis on the ta…
Investor optimizes portfolio to manage risk with heavy-tailed stock returns.
problem Managing risk in portfolios with heavy-tailed stock returns.
method Markov Decision Process and dynamic programming for optimal strategies and value function.
result Optimal strategies and value function maximizing expected utility for both parametric and non-parametric distributions.
We investigate the use of Kelly's strategy in the construction of an optimal portfolio of assets. For lognormally distributed asset returns, we derive approximate analytical results for the optimal investment fractions in various settings. We show that when mean returns and volatilities of the assets are small and ther…
The time development of the price of a financial asset is considered by constructing and solving Langevin equations for a homogeneously saturated model, and for comparison, for a standard model and for a logistic model. The homogeneously saturated model uses coupled rate equations for the money supply and for the price…
Quantum walk model captures asymmetry and bimodality in long-term financial returns.
problem Inadequate classical models for long-term financial return distributions.
method Discrete-time quantum walk model.
result Captures bimodal and asymmetric probability distributions.
Price and return predictions are limited by economic complexity, not just volatility.
problem Limited accuracy of price and return probability forecasts by Gaussian distributions.
method Analyzes economic reasons behind limitations in predicting price and return statistical moments.
result Predictions of price and return probabilities by Gaussian distributions are inaccurate due to economic complexity.