The profitability of CPMMs is significantly impacted by mint and burn fees.
problem Understanding the profitability of decentralized exchanges.
method Formalized liquidity providers' profitability conditions, studied the effect of mint and burn fees, and compiled a large data set from Uniswap V2 transactions.
result The profitability of liquidity provision is severely affected by mint and burn costs.
The paper explores fair machine learning policies for balancing competing objectives in noisy data.
problem Balancing competing objectives in noisy data.
method Analyzes a class of policies that trace an empirical Pareto frontier based on learned scores.
result Characterizes optimal strategies and bounds Pareto errors due to score inaccuracies.
We analyze correlations among stock returns via a series of widely adopted parameters which we refer to as explanatory variables. We subsequently exploit the results to propose a long only quantitative adaptive technique to construct a profitable portfolio of assets which exhibits minor drawdowns and higher recoveries …
LemonadeBench evaluates LLMs' economic intuition through a simulated lemonade stand.
problem Evaluating LLMs' economic understanding and decision-making in simple markets.
method Simulated lemonade stand business to test LLMs' long-term planning and profit maximization.
result Models achieve profitability but exhibit local rather than global optimization.
We investigate the performance of dynamic portfolios constructed using more than 21,000 technical trading rules on 12 categorical and country-specific markets over the 2004-2015 study period, on rolling forward structures of different lengths. We also introduce a discrete false discovery rate (DFRD+/-) method for contr…
Derives equations for capital deepening in a competitive economy without assuming a production function.
problem Understanding capital deepening and firm survival in a competitive economy.
method Derives equations of motion from accounting identities, without assuming a production function. Uses four coupled relaxation equations to govern capital productivity, labor share, and new investment productivity.
result A 1% improvement in new-capital productivity nearly doubles the aggregate growth rate within one capital lifetime.
New tools quantify deep generative models' performance.
problem Measuring the quality-diversity trade-off in deep generative models.
method Established non-asymptotic bounds on sample complexity and introduced frontier integrals.
result Smoothed estimators improve convergence rates of divergence frontiers.
Neural network approximates weakly efficient frontier of convex vector optimization problems.
problem Approximating the weakly efficient frontier of convex vector optimization problems.
method Designing a neural network architecture to approximate the weakly efficient frontier of convex vector optimization problems (CVOP) satisfying Slater's condition.
result The proposed algorithm effectively approximates the true weakly efficient frontier of CVOPs, even for large problems.
This paper analyzes and compares different Automated Market Maker mechanisms.
problem Impermanent loss in Constant Function Market Makers.
method Mean-Variance analysis of liquidity providers' profit and loss, comparison of different mechanisms.
result Optimized oracle-based mechanisms outperform Constant Function Market Makers.
A new method for multi-objective Bayesian optimization using entropy search and variational lower bound maximization.
problem Efficiently optimizing multiple objectives in continuous domains.
method Approximates the Pareto-frontier using a mixture distribution and optimizes the balance through variational lower bound maximization.
result Demonstrated effectiveness especially with many objective functions.
Algorithm maps trade-off between clustering fidelity and representation size.
problem Optimizing trade-off between clustering fidelity and representation size.
method Introduces primal Deterministic Information Bottleneck (DIB) problem for discrete search spaces.
result Shows richer Pareto frontier over Lagrangian relaxation.
A power-law fit to the empirical inference-compute frontier in LOB prediction suggests a scaling-law-style frontier.
problem Limit order book prediction
method Using a suite of models ranging from small decision trees to neural LOB architectures
result A power-law fit to the low- and mid-compute non-MLPLOB frontier extrapolates across multiple orders of magnitude and attains R2=0.941 on the excluded high-compute MLPLOB target frontier. The paper develops methods to estimate the high-dimensional efficient frontier without distributional assumptions.
problem Estimating the mean-variance efficient frontier in high-dimensional settings.
method Random matrix theory and asymptotic analysis for high-dimensional data.
result Developed consistent estimators for the mean, variance, and covariance of the efficient frontier.
GeMA learns latent manifolds to benchmark complex systems.
problem Benchmarking complex systems like rail networks and economies with classical methods.
method Geometric Manifold Analysis (GeMA) using a productivity-manifold variational autoencoder (ProMan-VAE).
result GeMA provides more nuanced efficiency evaluations in complex systems.
New research shows shrinkage methods re-scale portfolio efficient frontiers under distributional misspecification.
problem Poor performance of mean-variance portfolio decisions under distributional assumptions.
method Investigation of shrinkage methods under different distributional assumptions (auto-correlation, skewness, excess kurtosis).
result Shrinkage methods re-scale the sample efficient frontier, implying standard comparison methods are flawed.
Enhances hedging strategies using deep neural networks.
problem Optimizing risks and returns in financial hedging.
method Integrates deep neural networks and random forest classifiers to find optimal hedging strategies.
result Improved hedging strategies with lower costs and risks.
Optimizes bank capital structure under Basel III constraints, simplifying complex dynamics.
problem Optimizing risky investments, dividends, and capital structure under Basel III constraints.
method Formulated as a stochastic control problem, reducing dynamics to a one-dimensional process in leverage ratio.
result Simple policy: pay dividends at an upper barrier and recapitalize at the distress boundary.
Researchers study fairness-accuracy tradeoffs in predictive models for multiple groups.
problem Understanding the tradeoff between fairness and accuracy in models serving multiple demographic groups.
method Characterizing the fairness-accuracy (FA) Pareto frontier, approximating it from limited data, and bounding the worst-case gap.
result Derivation of worst-case-optimal estimators and uniform finite-sample bounds for the entire FA frontier.
A new asset allocation model uses Markov states from clustered efficient frontier coefficients.
problem Characterizing market regimes using efficient frontiers for better asset allocation.
method Hierarchical clustering of monthly efficient frontier coefficients to define states, then a Markov process on these states for portfolio optimization.
result The model significantly outperforms benchmark portfolios empirically.
The p-index improves investment performance for NYSE stocks but not for SSE stocks.
problem Improving investment performance for stocks using the p-index.
method Comparing different p-ratio strategies and empirical efficient frontiers for SSE and NYSE stocks.
result The p-index enhances investment performance for NYSE stocks but not for SSE stocks.
We identify and optimize the fairness-accuracy tradeoff through TAF Curves and FAUC metrics.
problem Balancing fairness and accuracy in machine learning models for high-stakes decisions.
method Developed TAF Curves and FAUC metric to quantify the tradeoff, and introduced FairStacks framework to expand the Pareto frontier.
result FairStacks framework expands the empirical Pareto frontier and improves the FAUC for model ensembles.
P-Trees improve investment performance by optimizing the efficient frontier.
problem Optimizing investment performance in complex financial markets.
method Introducing P-Trees, a new tree-based model for analyzing panel data.
result P-Trees significantly advance the efficient frontier and outperform existing models.
A simplified model for fixed income portfolio optimisation.
problem Modeling interest rates and credit risk in fixed income portfolios.
method Proposes a two-factor model for the time evolution of the efficient frontier.
result The efficient frontier is mainly controlled by linear constraints, with standard deviation less important.
This paper presents an analysis of the study variables such as gdp, employment levels, the level of R & D and technology that will serve as the basis for stochastic modeling of production possibilities frontier in the goodness of fractal dimensions Ex Ante and Ex Post a priori to determine the levels of causality immed…
Risk hedging can reduce operational costs by adjusting prices and production levels in response to asset price movements.
problem How risk hedging impacts operational decisions in response to asset price movements.
method Developed and solved a risk-management model integrating risk hedging into a price-setting newsvendor problem.
result Hedging generally reduces optimal price and VPQ, but may increase VPQ under certain conditions.
The paper introduces a machine learning method to forecast market direction using efficient frontier coefficients.
problem Improving asset return estimation for portfolio optimization.
method Monthly directional market forecast using an online decision tree trained on efficient frontier coefficients.
result The method outperforms baseline portfolios and other feature sets.
Benchmark evaluates LLM trading agents by masking identifiers to prevent memory leaks.
problem Evaluate LLM trading agents without relying on market memory or noise.
method Data-side masking protocol, Barra-style performance attribution framework.
result LLM agents' returns are largely explained by market and style exposure, not stock selection.
It is well established that in a market with inclusion of a risk-free asset the single-period mean-variance efficient frontier is a straight line tangent to the risky region, a fact that is the very foundation of the classical CAPM. In this paper, it is shown that in a continuous-time market where the risky prices are …
Differentiable relaxation for inferring partial orders from noisy linear data.
problem Inference of partial orders from linear data with noisy observations.
method Introducing a differentiable relaxation to model noisy linear extensions, replacing discontinuous precedence and feasibility with smooth surrogates.
result Smooth posterior that preserves partial-order semantics, supports gradient-based inference, and converges to hard likelihood.
Deep learning improves portfolio optimization efficiency.
problem Efficient frontier calculation in high-dimensional finance problems.
method Deep neural networks for portfolio optimization with added constraints.
result A new projected feedforward network outperforms classical methods.
Blockchain funds balance risk and return for various investors.
problem Creating diversified portfolios with risk parity for different risk appetites.
method Developed three funds (Alpha, Beta, Gamma) with distinct risk and return profiles, setting weights inversely proportional to risk.
result Blockchain enables investors to select their preferred risk-return combination and allocate wealth accordingly.
We consider the problem of finding the efficient frontier associated with the risk-return portfolio optimization model. We derive the analytical expression of the efficient frontier for a portfolio of N risky assets, and for the case when a risk-free asset is added to the model. Also, we provide an R implementation, an…
This note finds closed-form solutions for mean-risk portfolios using a specific type of mixture distribution.
problem Finding optimal portfolios under mean-risk criteria for general distributions.
method Using normal mean-variance mixture (NMVM) distributions, the paper derives closed-form expressions for mean-risk frontiers by optimizing a Markowitz model with adjusted return vectors.
result Closed-form solutions for mean-risk portfolios are found for return vectors following NMVM distributions.
The study compares profitability of conventional and Islamic banks in Bangladesh.
problem Evaluating profitability of commercial banks in Bangladesh.
method Examined bank-specific, industry-specific, and banking system factors on profitability.
result Islamic banks consistently outperform conventional banks in profitability.
New algorithm reduces regret in online portfolio and quantum state learning.
problem Efficiently learning portfolios and quantum states online with minimal regret.
method BISONS algorithm for online portfolio selection, SCHRODINGER'S BISONS for quantum states, with polylogarithmic regret.
result First efficient algorithm with polylogarithmic regret for online portfolio selection and quantum states.
Study optimizes fairness in predictive models by balancing utility and separation.
problem Balancing fairness and utility in predictive models.
method Information-theoretic approach using conditional mutual information (CMI).
result Reduces separation violations while maintaining or improving utility.
This paper studies an entropy-based multi-objective Bayesian optimization (MBO). The entropy search is successful approach to Bayesian optimization. However, for MBO, existing entropy-based methods ignore trade-off among objectives or introduce unreliable approximations. We propose a novel entropy-based MBO called Pare…
The paper tackles fair policy targeting by optimizing allocation rules to minimize unfairness.
problem Discrimination in individualized treatments of social welfare programs.
method Formulated as a mixed-integer linear program, solved using off-the-shelf algorithms, derived regret bounds and small sample guarantees.
result Designs fair and efficient treatment allocation rules within the Pareto frontier.
Fossil power firms have recently profited more than renewables, but this may be a temporary phenomenon.
problem The profitability gap between renewable and fossil power firms in Europe.
method Machine-learning clustering and Bayesian model averaging.
result Renewable power firms are becoming more profitable, while fossil power firms are becoming less so.
Many domains of science have developed complex simulations to describe phenomena of interest. While these simulations provide high-fidelity models, they are poorly suited for inference and lead to challenging inverse problems. We review the rapidly developing field of simulation-based inference and identify the forces …
We propose a stochastic approximation method for approximating the efficient frontier of chance-constrained nonlinear programs. Our approach is based on a bi-objective viewpoint of chance-constrained programs that seeks solutions on the efficient frontier of optimal objective value versus risk of constraint violation. …
This paper introduces strategies to maximize arbitrage profits in decentralized exchanges.
problem Maximizing profits from arbitrage loops in decentralized exchanges.
method Three strategies: MaxPrice, MaxMax, and Convex Optimization.
result The Convex Optimization strategy yields the highest monetized arbitrage profit in theory and practice.
The paper proposes a method to ensure fairness in machine learning models.
problem Ensuring fairness in machine learning models powered by supervised learning.
method Optimal affine transport and Wasserstein-2 barycenter to characterize the Pareto frontier between prediction error and statistical disparity.
result The proposed method effectively balances prediction accuracy and fairness, as demonstrated by numerical simulations.
Marxism's rate of profit thesis rejected as illogical.
problem Marxism's rate of profit thesis is challenged as illogical.
method Rejects Marx's thesis on the long-term tendency of the rate of profit to fall.
result Marxism's rate of profit thesis is rejected as illogical.
ML Compass helps organizations choose AI models that balance utility, cost, and compliance.
problem Selecting AI models that meet user utility, deployment costs, and compliance requirements.
method Develops ML Compass, a framework for constrained optimization over a capability-cost frontier, using internal measures and empirical data.
result ML Compass produces deployment-aware recommendations that differ from capability-only rankings, clarifying trade-offs between capability, cost, and safety.
Equations track profits and losses in trading algorithms.
problem Evaluating the performance of trading algorithms.
method Formal equations incorporating spread.
result Evaluate trading model algorithms' performance.
We analyze annual revenues and earnings data for the 500 largest-revenue U.S. companies during the period 1954-2007. We find that mean year profits are proportional to mean year revenues, exception made for few anomalous years, from which we postulate a linear relation between company expected mean profit and revenue. …
The paper compares various portfolio construction methods and their impacts on allocation, performance, and stability.
problem Investment portfolio optimization and allocation under different constraints and models.
method Comparison of mean-variance optimization, constrained optimization, Fama French five factor regression, Monte Carlo simulation, and Black-Litterman model.
result Black-Litterman model produces more stable and economically intuitive allocations compared to standard mean-variance optimization.