Optimal transport method rejects new classes and adjusts class ratios for open set domain adaptation.
problem Handling new classes in target domains with distribution shifts.
method Two-step optimal transport approach: reject new classes first, then adjust class ratios.
result Outperforms state-of-the-art methods in open set domain adaptation.
Investments with best performance are not associated with best Sharpe ratios.
problem The relationship between performance and risk-adjusted return (Sharpe ratio) is counterintuitive for heavy-tailed distributions.
method Synthetic and real data analysis of returns distributions.
result The best-performing investments are not the best in terms of Sharpe ratio, and vice versa.
Novel approach to compute hazard ratios from observational studies using SCMs and backdoor adjustment.
problem Identifying causal relationships from observational data using hazard ratios.
method Backdoor adjustment through structural causal models (SCMs) and do-calculus.
result Novel approach for computing hazard ratios from observational studies.
The study evaluates forecast risk-adjusted performance using various metrics.
problem Evaluating forecast reliability beyond accuracy.
method Risk-adjusted performance measures (Sharpe, Sortino, Omega ratios) and Edge Ratio.
result Machine learning models often offer attractive risk profiles but not necessarily higher reliability.
Paper introduces Market-adaptive Ratio for better portfolio management.
problem Traditional risk-adjusted ratios fail to account for bull and bear markets.
method Integrates ρ parameter and uses reinforcement learning to adjust portfolio allocations dynamically. result Market-adaptive Ratio outperforms traditional ratios in bull and bear markets.
When the in-sample Sharpe ratio is obtained by optimizing over a k-dimensional parameter space, it is a biased estimator for what can be expected on unseen data (out-of-sample). We derive (1) an unbiased estimator adjusting for both sources of bias: noise fit and estimation error. We then show (2) how to use the adjust…
Mitigates confirmation bias in SSL by adjusting pseudo labels dynamically.
problem Confirmation bias in semi-supervised learning leads to errors in pseudo labels.
method TaMatch framework adjusts scaling ratio to debias pseudo labels and dynamically adjusts target distribution.
result TaMatch significantly outperforms existing methods in SSL tasks.
New star-shaped acceptability indexes generalize existing methods.
problem Generalizing existing acceptability measures.
method Characterizing acceptability indexes through star-shaped risk measures and sets.
result Introducing concrete examples linked to various financial measures.
This paper improves bond market making by adjusting hit-ratios for client flow quality.
problem Economic misleading of raw hit-ratios in corporate bond market making.
method Stochastic-control framework with residual-quality-adjusted hit-ratio.
result Optimal quotes decompose into various components, improving service/economics frontier.
Proposes AAA for efficient association estimation with confounders.
problem Summarizing log odds ratio as a function of confounders.
method Develops efficient DML estimators for AAA.
result Demonstrates practicality and effectiveness of AAA estimators.
Prognostic scores improve logistic regression analysis in RCTs with binary outcomes.
problem Non-collapsibility in logistic regression analysis of RCTs with binary endpoints.
method Prognostic score adjustment using AI predictions to address non-collapsibility.
result Prognostic score adjustment increases power or reduces sample size for estimating conditional odds ratios.
Historical returns depend on historical closing prices and distributions. We describe how to compute adjusted closing prices from closing price/distribution data with an emphasis on spreadsheet implementation. Then the growth of a security from one date to another (1 + total return) is just the ratio of the correspondi…
Turnover-adjusted IR is always lower than classic IR, suggesting managers can improve performance by limiting turnover.
problem The classic relationship between IR and its determinants does not account for turnover costs.
method Mathematical derivations and simulations considering volatility of information coefficient and portfolio turnover.
result Turnover-adjusted IR is lower and managers can improve performance by limiting turnover.
Optimal reinsurance balances risk over surplus ratios for risk-adjusted surplus.
problem Balancing risk over surplus ratios in reinsurance contracts.
method Analyzes reinsurance contracts using Value at Risk and expected surplus ratio, derives simplifications for large portfolios, and considers approximations of the optimum portfolio.
result One or two-layer contracts are optimal for both risk-adjusted surplus and risk over expected surplus ratio, but no second layer for large portfolios or below certain reinsurance prices.
New method dynamically adjusts UTD ratio to balance under- and overfitting in RL.
problem Balancing under- and overfitting in world model learning for RL.
method Dynamic adjustment of UTD ratio based on validation performance on a small subset of experience data.
result Our method improves balance between under- and overfitting compared to default settings and competitive with extensive hyperparameter search.
Benchmarking deep learning models for financial time series, focusing on risk-adjusted performance.
problem Optimizing risk-adjusted performance in financial time series prediction.
method Evaluation of various deep learning architectures including linear models, RNNs, transformers, state space models, and sequence representation approaches.
result Hybrid models like VSN with LSTM and xLSTM achieve the highest overall Sharpe ratio and superior downside adjusted characteristics.
The major perspective of this paper is to provide more evidence into the empirical determinants of capital structure adjustment in different macroeconomics states by focusing and discussing the relative importance of firm-specific and macroeconomic characteristics from an alternative scope in U.S. This study extends th…
A new activation function improves credit scoring accuracy for imbalanced datasets.
problem Imbalanced datasets in credit scoring lead to underestimation of misclassification costs.
method Introduces ASIG, an asymmetric adjusted Sigmoid function.
result ASIG-embedded classifier outperforms traditional classifiers across various imbalance ratios.
Study post-hoc Learning to Defer using density-ratio losses.
problem Optimizing decision-making between models and experts.
method Density-ratio losses for post-hoc L2D scorers, derived from class-probability estimation.
result The approach recovers known results and introduces new connections to expert comparison and anomaly detection.
In this paper we investigate the relationship between Funding Value Adjustment (FVA) and Net Stable Funding Ratio (NSFR). FVA is defined in a consistent way with NSFR such that the new framework of FVA monitors the costs due to keeping NSFR at an acceptable level, as well. In addition, the problem of choosing the optim…
The paper optimizes portfolios using clustering and Sharpe ratio-based optimization.
problem Optimizing portfolio performance in financial modeling.
method Combines K-Means clustering for asset segmentation and Sharpe ratio-based optimization.
result Optimized portfolios outperform traditional equal-weighted benchmarks.
Introduces GA-P/E, a growth-adjusted stock valuation measure.
problem Evaluating stock value and predicting future returns.
method Computes a payback period adjusted for earnings growth, using a sorted portfolio methodology.
result Low GA-P/E stocks outperform high GA-P/E stocks in absolute and risk-adjusted returns.
Equity-Directed Bootstrapping improves model performance across groups in imbalanced datasets.
problem Improving model performance across different groups in imbalanced datasets.
method Equity-Directed Bootstrapping to balance training data with respect to both labels and group identity.
result The equity-directed bootstrap brings test set sensitivities and specificities closer to satisfying the equal odds criterion.
Paper extends ranking metrics theory for financial positions.
problem Developing a new class of functionals for evaluating financial positions.
method Axiomatic framework based on monotonicity and cash-quasiconcavity.
result Linking ranking metrics to families of acceptance sets and risk measures.
AI optimizing for risk-adjusted return may choose unethical strategies.
problem AI optimization for risk-adjusted return may lead to unethical outcomes.
method Defined Unethical Odds Ratio (Υ) to calculate probability of unethical strategies, derived formula for limit as strategy space grows, provided algorithm for estimation.
result Probability of picking an unethical strategy can become high even with small proportion of unethical strategies.
Paper extends ranking metrics theory for financial positions.
problem Developing a new class of performance evaluation methods.
method Axiomatic framework based on monotonicity and cash-quasiconcavity.
result Linking ranking metrics to families of acceptance sets and risk measures.
Smart beta, also known as strategic beta or factor investing, is the idea of selecting an investment portfolio in a simple rule-based manner that systematically captures market inefficiencies, thereby enhancing risk-adjusted returns above capitalization-weighted benchmarks. We explore the idea of applying a smart strat…
Modeling financial markets with sandpile model to understand price volatility and arbitrage constraints.
problem Understanding price volatility and arbitrage constraints in financial markets.
method Uses a sandpile model to represent information and price changes, linking size of price volatility to the scaling law of avalanches.
result Identifies a structural tension between non-arbitrage condition and price adjustments consistent with a constant Sharpe ratio.
Improved portfolio optimization method yields better risk-adjusted returns.
problem Optimizing global minimum variance portfolios with reduced risk.
method k-fold boosted k−BAHC covariance cleaning procedure for correlation matrices. result Our method outperforms other filtering methods in Sharpe ratios, despite higher turnover.
New method estimates hazard ratios without bias in observational studies.
problem Uninterpretable hazard ratios due to unspecified baseline hazard.
method Kernel-based machine learning to model risk set changes.
result Debiased maximum-likelihood estimators identify true hazard ratios.
We apply the procedure of Lee et al. to the problem of performing inference on the signal-noise ratio of the asset which displays maximum sample Sharpe ratio over a set of possibly correlated assets. We find a multivariate analogue of the commonly used approximate standard error of the Sharpe ratio to use in this condi…
We note a simple mechanism that may at least partially resolve several outstanding economic puzzles, including why the cyclically adjusted price to earnings ratio of the S&P 500 index has been oddly high for the past two decades, why gains to capital have outpaced gains to wages, and the persistence of the equity premi…
Algorithm improves SVM classification in non-Euclidean spaces.
problem Limitations of traditional SVM in non-Euclidean spaces.
method Covariance-adjusted SVM using Cholesky Decomposition.
result Cholesky-SVM outperforms traditional SVM in non-Euclidean spaces.
In this paper we investigate the expected terminal utility maximization approach for a dynamic stochastic portfolio optimization problem. We solve it numerically by solving an evolutionary Hamilton-Jacobi-Bellman equation which is transformed by means of the Riccati transformation. We examine the dependence of the resu…
The paper optimizes forecasting for risk-adjusted decisions under trading frictions.
problem Optimizing forecasting accuracy for investment decisions in the presence of transaction costs.
method Develops a utility-weighted calibration criterion to minimize decision loss net of costs.
result Utility-weighted calibration reduces decision loss by over 30% and improves Sharpe ratio.
Bayesian model explains and improves black-box estimators for class distribution.
problem Calibrating probabilistic classifiers and uncertainty quantification for unlabeled data.
method Introduced a Bayesian model approximating the ground-truth generative process, using efficient MCMC sampling.
result The Bayesian model is competitive and sometimes superior to established point estimators.
Importance weighting is a general way to adjust Monte Carlo integration to account for draws from the wrong distribution, but the resulting estimate can be highly variable when the importance ratios have a heavy right tail. This routinely occurs when there are aspects of the target distribution that are not well captur…
Metaheuristics optimize portfolios with pre-assignment and margin trading for better risk-adjusted returns.
problem Maximizing returns while minimizing risk in portfolio optimization.
method Incorporates pre-assignment constraints and margin trading strategies using Genetic Algorithms and Particle Swarm Optimization.
result Metaheuristic-based portfolio optimization yields superior risk-adjusted returns compared to traditional methods.
We develop a theory for pricing non-diversifiable mortality risk in an incomplete market. We do this by assuming that the company issuing a mortality-contingent claim requires compensation for this risk in the form of a pre-specified instantaneous Sharpe ratio. We prove that our ensuing valuation formula satisfies a nu…
Unified framework for OOD detection using class ratio estimation.
problem Density-based OOD detection is unreliable for OOD images.
method Unified framework that builds energy-based models and employs differing base distributions, directly estimating the density ratio through class ratio estimation.
result Competitive results on OOD image problems compared to recent work.
Paper develops a robust hedging framework to reduce market risk and uncertainty.
problem Managing uncertainty and risk exposure in portfolio management.
method Combines high-frequency realized variance, covariance measures, and autoregressive models for multi-step volatility forecasting. Uses a box-uncertainty robust optimization scheme to derive a closed-form solution for the robust hedge ratio.
result Robust hedge ratios are more stable and entail lower turnover than standard dynamic hedges, improving downside protection and risk-adjusted performance.
Exchange Traded Funds (ETFs) have been gaining increasing popularity in the investment community as is evidenced by the high growth both in the number of ETFs and their net assets since 2000. As ETFs are in nature similar to index mutual funds, in this paper we examined if this growing demand for ETFs can be explained …
PT network optimizes asset weights without forecasting returns.
problem Traditional asset allocation methods are error-prone and limit portfolio performance.
method PT network uses attention mechanisms to directly optimize Sharpe ratio.
result PT outperforms other algorithms in risk-adjusted performance.
Develops a method to estimate average hazard under non-proportional hazards without relying on proportional hazards assumption.
problem Estimation of treatment effects when hazards are non-proportional, leading to unstable hazard ratios.
method Semiparametric, doubly robust framework for covariate-adjusted average hazard estimation.
result Valid sqrt{n} inference with small bias and near-nominal confidence-interval coverage across proportional and non-proportional hazards settings.
Alpha-trimming prunes trees in random forests to improve predictive performance.
problem Improving predictive performance of random forests by locally adaptive tree pruning.
method Alpha-trimming is a fast pruning algorithm that prunes trees in a random forest based on signal-to-noise ratio, controlled by a tuning parameter.
result Alpha-trimming often lowers mean squared prediction error compared to fully grown random forests.
Paper introduces lexical ratio to measure portfolio diversification.
problem Traditional diversification metrics overlook non-numerical relationships.
method Uses textual data to capture diversification dimensions through entropy-based insights.
result Lexical ratio (LR) outperforms traditional metrics in optimizing portfolio returns.
Enhanced financial forecasting using supervised autoencoders with noise augmentation and triple labeling.
problem Improving investment strategy performance on noisy financial data.
method Supervised autoencoders with noise augmentation and triple barrier labeling.
result Supervised autoencoders with balanced noise augmentation and bottleneck size significantly boost strategy effectiveness.
Optimized Sharpe Ratio for better risk-adjusted decision-making in multi-armed bandits.
problem Challenging to optimize Sharpe Ratio (SR) in multi-armed bandits (MAB) due to constant regret.
method Proposed UCB-RSSR algorithm for RSSR maximization, derived path-dependent concentration bound and regret guarantees.
result UCB-RSSR outperforms existing algorithms and finds applications in risk-aware portfolio management.