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.
New methods improve uncertainty in machine learning predictions for asset returns.
Study improves stock return uncertainty prediction using Gaussian mixture distributions.
Paper predicts high-frequency futures return directions using mean-uncertainty methods.
Study finds monetary policy uncertainty negatively impacts Bitcoin returns.
This paper studies directed exploration for reinforcement learning agents by tracking uncertainty about the value of each available action. We identify two sources of uncertainty that are relevant for exploration. The first originates from limited data (parametric uncertainty), while the second originates from the dist…
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 …
Improved Markowitz method handles uncertainty in return forecasts.
Cryptocurrencies have heavy-tailed return distributions, requiring diversification.
The main objective is to present a some variant of the Black - Litterman model. We consider the canonical case when priori return is determined by means such excess return from the CAPM market portfolio which is derived using reverse optimization method. Then the a priori return is at risk quantified uncertainty. On th…
This paper provides an innovative perspective on the role of gold as a hedge and safe haven. We use a quantile-on-quantile regression approach to capture the dependence structure between gold returns and changes in uncertainty under different gold market conditions, while considering the nuances of uncertainty levels. …
This paper considers mean-variance optimization under uncertainty, specifically when one desires a sparsified set of optimal portfolio weights. From the standpoint of a Bayesian investor, our approach produces a small portfolio from many potential assets while acknowledging uncertainty in asset returns and parameter es…
Paper studies portfolio investment under volatility uncertainty and short-sale constraints, improving risk-adjusted returns.
Unified framework for reliable uncertainty quantification in RL.
This study shows how monetary uncertainty affects stock market reactions to macroeconomic news.
Proposes a simpler method for quantifying uncertainty in time-series with volatility clustering.
There is bountiful evidence that political uncertainty stemming from presidential elections or doubt about the direction of future policy make financial markets significantly volatile, especially in proximity to close elections or elections that may prompt radical policy changes. Although several studies have examined …
Develops methods to estimate and quantify uncertainty in off-policy evaluation.
In this paper, we present a two-stage stochastic international portfolio optimisation model to find an optimal allocation for the combination of both assets and currency hedging positions. Our optimisation model allows a "currency overlay", or a deviation of currency exposure from asset exposure, to provide flexibility…
The paper introduces new portfolio rules beyond mean-variance, addressing asymmetry and uncertainty.
This paper investigates how realized and option implied volatilities are related to the future quantiles of commodity returns. Whereas realized volatility measures ex-post uncertainty, volatility implied by option prices reveals the market's expectation and is often used as an ex-ante measure of the investor sentiment.…
GPR ensemble method predicts stock returns efficiently.
Trading off exploration and exploitation in an unknown environment is key to maximising expected return during learning. A Bayes-optimal policy, which does so optimally, conditions its actions not only on the environment state but on the agent's uncertainty about the environment. Computing a Bayes-optimal policy is how…
The investor is interested in the expected return and he is also concerned about the risk and the uncertainty assumed by the investment. One of the most popular concepts used to measure the risk and the uncertainty is the variance and/or the standard-deviation. In this paper we explore the following issues: Is the stan…
The paper sets limits on the accuracy of macroeconomic forecasts based on statistical moments and trade volumes.
Robust optimization improves portfolio selection by accounting for deep uncertainties.
We propose a probabilistic framework for pricing derivatives, which acknowledges that information and beliefs are subjective. Market prices can be translated into implied probabilities. In particular, futures imply returns for these implied probability distributions. We argue that volatility is not risk, but uncertaint…
Improved stock selection through predictive fundamentals and uncertainty estimates.
The paper links labor income risk to stock returns using industry portfolio returns.
Bayesian method predicts asset returns for better portfolio optimization.
The accurate estimation of predictive uncertainty carries importance in medical scenarios such as lung node segmentation. Unfortunately, most existing works on predictive uncertainty do not return calibrated uncertainty estimates, which could be used in practice. In this work we exploit multi-grader annotation variabil…
The paper clarifies long-horizon investment and DCA, showing no risk reduction but different exposure profiles.
Accounting for the non-normality of asset returns remains challenging in robust portfolio optimization. In this article, we tackle this problem by assessing the risk of the portfolio through the "amount of randomness" conveyed by its returns. We achieve this by using an objective function that relies on the exponential…
We study super-replication of contingent claims in an illiquid market with model uncertainty. Illiquidity is captured by nonlinear transaction costs in discrete time and model uncertainty arises as our only assumption on stock price returns is that they are in a range specified by fixed volatility bounds. We provide a …
Analyzing a comprehensive news dataset, we document that joint news coverage triggers attention contagion, causing temporarily inflated valuations for affected stocks. Tracing SEC EDGAR visits from unique IPs, we provide direct evidence of attention spillovers between stocks. Stocks with greater joint news coverage exh…
A new method for measuring prediction uncertainty in classifiers.
This paper presents several models addressing optimal portfolio choice, optimal portfolio liquidation, and optimal portfolio transition issues, in which the expected returns of risky assets are unknown. Our approach is based on a coupling between Bayesian learning and dynamic programming techniques that leads to partia…
δ-CLUE generates diverse explanations for model uncertainty.
NBE method speeds up Lévy process parameter estimation.
Robust MCVaR portfolio optimization using RKHS for risk management.
The future value of a security is described as a random variable. Distribution of this random variable is the formal image of risk uncertainty. On the other side, any present value is defined as a value equivalent to the given future value. This equivalence relationship is a subjective. Thus follows, that present value…
A new portfolio model considers investor aversion to loss and risk.
We report on a study of the Tehran Price Index (TEPIX) from 2001 to 2006 as an emerging market that has been affected by several political crises during the recent years, and analyze the non-Gaussian probability density function (PDF) of the log returns of the stocks' prices. We show that while the average of the index…
Study measures uncertainty in MST identification across different correlation networks.
Normalizing flows improve ptychography reconstruction quality and uncertainty quantification.
Models necessarily capture only parts of a reality. Prediction models aim at capturing a future reality. In this paper we address the question of how the future is constructed (or: imagined) in an investment context where market participants form expectations on the returns of a risky investment. We observe that the pa…
The paper proposes a new risk model for foundation models in finance.
New bidirectional model predicts magnetohydrodynamics fields and estimates uncertainty.
Bayesian approach for constructing and rebalancing sparse index-tracking portfolios.