New method decomposes portfolio returns into generating and trading processes.
problem Decompose portfolio returns for arbitrary stock portfolios.
method Fisk-Stratonovich integration to extend decomposition to arbitrary portfolios.
result Portfolio returns can be represented by a structural process and trading process.
Develops portfolio theory without probabilistic analysis, focusing on pathwise decomposition.
problem Ensuring market viability without probabilistic assumptions.
method Uses pathwise decomposition and trend extractors to replace semimartingale decomposition.
result Growth-numéraire and viability equivalences are similar but not identical in pathwise setting.
This paper derives a portfolio decomposition formula when the agent maximizes utility of her wealth at some finite planning horizon. The financial market is complete and consists of multiple risky assets (stocks) plus a risk free asset. The stocks are modelled as exponential Brownian motions with drift and volatility b…
New heuristic selects fewer assets for efficient portfolios, reducing costs.
problem High transaction costs and fees from including many assets in portfolios.
method Surrogate formulation to select assets, re-optimizes portfolio with fewer assets.
result Effective in constructing portfolios with fewer assets, reducing costs.
Paper breaks down risk contribution into inherent and correlation risk components.
problem Understanding the sources of risk in portfolio contributions.
method Leave-one-out decomposition approach to separate inherent and correlation risk contributions.
result The decomposition reveals distinct contributions of position volatility and correlation to portfolio risk.
Agent maximizes utility with pathwise constraint on portfolio value.
problem Maximizing utility with a pathwise constraint on portfolio value.
method Max-plus decomposition for supermartingales, Black-Scholes-Merton model.
result Explicit form of optimal terminal wealth and process involved.
Pipeline decomposes portfolio optimization problems into smaller, solvable subproblems.
problem Large-scale portfolio optimization with constraints.
method Decomposition pipeline with preprocessing, clustering, and risk rebalancing.
result Pipeline reduces problem size by 80% and computation time.
The paper analyzes risk spillovers between AI ETFs, AI tokens, and green markets.
problem Risk spillovers among AI ETFs, AI tokens, and green markets.
method R2 decomposition method
result AI ETFs and clean energy act as risk transmitters, while AI tokens and green assets act as receivers.
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.
New approach avoids restrictive assumptions for optimal portfolio in default risk scenarios.
problem Optimal portfolio optimization under default risk when traditional techniques are not applicable.
method Alternative approach using forward integration to avoid Jacod density hypothesis.
result Weaker intensity hypothesis is the appropriate condition for optimality in logarithmic utility.
Market-based portfolio variance measures risks using trade data.
problem Measuring portfolio risks using traditional methods ignores trade volume randomness.
method Uses time series of trades with securities and portfolio to assess variance.
result Portfolio variance can be decomposed into securities' contributions, accounting for trade volume randomness.
We consider the fundamental theorem of asset pricing (FTAP) and hedging prices of options under non-dominated model uncertainty and portfolio constrains in discrete time. We first show that no arbitrage holds if and only if there exists some family of probability measures such that any admissible portfolio value proces…
We are concerned with a new type of supermartingale decomposition in the Max-Plus algebra, which essentially consists in expressing any supermartingale of class (D) as a conditional expectation of some running supremum process. As an application, we show how the Max-Plus supermartingale decomposition allows…
D-Wave hybrid quantum-classical portfolio optimization shows classical decomposition is key, not quantum sampling.
problem Optimizing portfolios with constraints using hybrid quantum-classical methods.
method Operational decomposition audit of D-Wave's hybrid quantum-classical service on mean-variance-turnover instances.
result Classical decomposition and feasibility-aware reassembly are key to hybrid quantum-classical performance.
Paper introduces a new method for efficient portfolio risk quantification.
problem Efficiently quantify risk in large portfolios with many trades and few dominant risk factors.
method Combines Fourier-cosine series with tensor decomposition techniques for dimension reduction.
result Achieves relative errors below 0.1% with significant runtime improvement.
Analyst reports contain valuable information for investment decisions.
problem Investment value in analyst reports is not fully understood or utilized.
method Embedded analyst reports with LLMs and ML forecasts of future returns.
result Portfolios formed on analyst report narratives outperform numerical forecasts and established factors.
New method to decompose portfolio performance ratios.
problem Understanding the drivers of portfolio performance ratios.
method Using Euler's theorem, decomposes performance ratios into modified ratios.
result Derives condition for new asset to improve portfolio performance.
The paper analyzes optimal portfolio allocation under a fast mean-reverting fractional stochastic environment.
problem Optimal portfolio allocation under a fractional stochastic environment with long-range dependence.
method Analyzes the nonlinear optimal portfolio allocation problem using a stationary fractional Ornstein-Uhlenbeck process with fast mean-reverting.
result Establishes asymptotic optimality of zeroth order trading strategies and general utility functions within specific families of admissible strategies.
A new portfolio optimization method using the Sherman-Morrison identity.
problem Portfolio optimization with covariance and variance.
method Sherman-Morrison identity applied to replace covariance with second moment matrix.
result Sherman-Morrison-Markowitz portfolio solves standard portfolio optimization problems.
We present an algorithm for the decomposition of periodic financial return data into orthogonal factors of expected return and "systemic", "productive", and "nonproductive" risk. Generally, when the number of funds does not exceed the number of periods, the expected return of a portfolio is an affine function of its pr…
New method connects portfolio generation to optimal transport.
problem Investment performance attributed to observable market quantities.
method Characterizes functional portfolio constructions involving divergences.
result Additively generated portfolios can be interpreted in terms of dually flat information geometry.
Paper proposes a new method to efficiently compute counterparty credit risk exposure.
problem High dimensionality and large variances in estimating counterparty credit risk exposure.
method Novel approach based on Kolmogorov forward and backward PDEs, using anchored-ANOVA decompositions to reduce dimensionality.
result Significant computational speed-up and variance reduction achieved through truncated decomposition and control variates.
Neural FGP learns portfolio generating functions from data.
problem Portfolio optimisation challenges in estimating drifts and covariances.
method Neural network approach to learn G(⋅) from market data. result Neural FGP outperforms classical benchmarks.
The paper analyzes how wealth affects investment strategies in incomplete markets.
problem Investment strategies in markets with incomplete information.
method Developed a five-component decomposition for optimal portfolio choice, solved explicitly for HARA utility and nonrandom interest rate, and used a stochastic volatility model for US equity data.
result Demonstrated the impacts of wealth-dependent utilities on optimal portfolio allocation, including cycle-dependence and hysteresis effect.
Reverse-weighted portfolios outperform in commodity futures markets.
problem Efficiency of commodity futures markets.
method Permutation-weighted portfolios, rank-based methods.
result Reverse-weighted portfolio outperforms price-weighted portfolio.
Study dynamic hedging of credit risk using a new model.
problem Dynamic hedging of counterparty risk for credit derivatives.
method Empirically driven credit model with interacting default intensities; Galtchouk-Kunita-Watanabe decomposition; closed-form risk minimizing strategy.
result Closed-form representation for risk minimizing strategy in nonlinear recursive systems.
Study shows OAT decomposition generates unexplained profit and loss, while SU decompositions depend on risk factor order.
problem Understanding profit and loss attribution in financial markets.
method Used financial market data from 2003 to 2022 to compare OAT, SU, and ASU decompositions.
result SU decompositions are sensitive to risk factor order and cannot identify all relevant risk factors.
This paper analyzes portfolio optimization with multi-scale volatility.
problem Optimizing portfolio under multi-scale volatility in a stochastic environment.
method Zeroth-order strategy followed by first-order approximation via PDE analysis.
result Asymptotic optimality of the proposed strategy in specific families of controls.
We demonstrate the application of an algorithmic trading strategy based upon the recently developed dynamic mode decomposition (DMD) on portfolios of financial data. The method is capable of characterizing complex dynamical systems, in this case financial market dynamics, in an equation-free manner by decomposing the s…
Paper introduces a new principle for fair redistribution of insurance surplus.
problem Fair redistribution of surplus in life insurance policies.
method Introduces ISU decomposition principle based on infinitesimal sequential updates.
result Existing heuristic formulas can be replicated as ISU decompositions.
New tensor-based method for estimating stock correlation matrices.
problem Choosing a proper sample period for estimating correlation matrices.
method Slice-Diagonal Tensor (SDT) factorization technique.
result The new method produces a stable correlation matrix unaffected by the sample period.
We formalize causal separation in portfolio theory, deriving a closed-form projected Markowitz solution.
problem Portfolio optimization under causal separation conditions.
method Derive a closed-form solution for portfolio optimization using causal separation conditions.
result A closed-form projected Markowitz solution is derived under causal separation conditions.
Spectral portfolio theory links neural networks to wealth dynamics via SGD weight matrices.
problem Understanding wealth dynamics from neural network training.
method Direct identification of weight matrices as portfolio allocation matrices, linking SGD forces to portfolio dynamics.
result Spectral properties of SGD weight matrices transition between additive and multiplicative regimes, influencing wealth dynamics.
The paper analyzes arbitrage theory in a fluctuating market of stochastic dimension.
problem Arbitrage opportunities in a market with time-varying asset numbers.
method Develops the fundamental theorem of asset pricing and optional decomposition theorem in a stochastic dimension market.
result Equivalence of conditions for no arbitrage and viability in a stochastic dimension market.
Study on risk contributions of portfolios using lambda quantile risk measures.
problem No known allocation rule for non-positively homogeneous risk measures.
method Defined lambda quantiles on portfolio compositions, derived derivatives, and introduced generalized Euler contributions.
result Explicit formulae for the derivatives of lambda quantiles, showing their homogeneity properties.
Develops Heuristic Portfolio Optimization (HPO) as an information-restricted projection of Markowitz/tangency solution
problem Practitioners allocate capital with forecast-light rules like equal weight, inverse volatility, risk parity, HRP, and RA-HRP
method Implies-return principle and fixed-tree cluster-Sharpe recursion
result Formalizes HPO maps, proves defect equals squared inefficiency, and identifies nodewise alphas as policy-gradient coordinates
Solves multi-objective risk-averse portfolio optimization with convex risk measures.
problem Portfolio optimization under risk and uncertainty.
method Convex vector optimization, Benson's algorithm, Lagrangian duality, scenario-wise decomposition.
result Developed methods to solve complex portfolio optimization problems.
New method optimizes portfolios by dynamically integrating ESG constraints.
problem Static ESG scores mismatch sequential portfolio decisions.
method MACF-X, a family of adapters that learns ESG costs from multimodal evidence.
result Reduces tail ESG budget pressure while maintaining financial performance.
EMD reveals dynamic cross-correlations across financial indices at various time-scales.
problem Characterizing time-varying multidimensional cross-correlations in financial indices.
method Empirical Mode Decomposition applied to intraday time series of financial indices.
result Uncovered rich heterogeneity of interactions dependent on time-scale and led-lag relations.
Study decomposes market portfolio into body and tail legs, revealing systematic differences.
problem Understanding the relationship between body and tail components in market portfolios.
method Decomposes CRSP market portfolio into body and tail legs, analyzes their recombination identity.
result Recombination identity holds for all models but not for all, indicating systematic differences.
Unified framework for portfolio optimization using multiple hypotheses.
problem Risk diversification in portfolio allocation.
method Structured ensemble learning approach with diversity control.
result Structured ensembles link predictor diversity to risk diversification.
LLM generates coherent macroeconomic stress scenarios for portfolio risk assessment.
problem Macro-financial stress testing and portfolio risk assessment using traditional methods.
method Hybrid prompt-RAG pipeline combining structured prompting and retrieval of country fundamentals and news.
result LLM-generated scenarios yield stable tail-risk amplification with limited sensitivity to retrieval choices.
We decompose the squared price-of-risk premium into three components: intervention-stable premium, confounding wedge, and information loss.
problem Decomposing the squared price-of-risk premium into its components
method Identifying an order-three obstruction to aggregation across portfolios
result The decomposition is estimable and detectable with a permutation-calibrated screen
New method solves nonseparable stochastic control problems.
problem Nonseparable and non-monotonic stochastic control problems.
method Scenario-decomposition solution framework using progressive hedging algorithm.
result Extends reach of stochastic optimal control.
WaveLSFormer learns profitable trading policies from financial time series data.
problem Challenges in learning profitable intraday trading policies from financial time series data.
method WaveLSFormer uses a learnable wavelet-based long-short Transformer to jointly perform multi-scale decomposition and return-oriented decision learning.
result WaveLSFormer consistently outperforms MLP, LSTM, and Transformer backbones in trading performance.
Anticipatory portfolios use richer models to optimize investments.
problem Optimizing investments with richer models than used for calibration.
method Decision-theoretic definition of anticipation, quadratic geometry, and LQG decomposition.
result Correct anticipation creates value, vacuous anticipation has zero value, and misspecified anticipation is harmful.
No arbitrage in financial markets with special semimartingales.
problem Proving the absence of arbitrage in non-numéraire financial markets.
method Proving the absence of arbitrage using a multiplicative special semimartingale deflator.
result The market is free of arbitrage if and only if there exists a multiplicative special semimartingale deflator.
It is well known that mean-variance portfolio selection is a time-inconsistent optimal control problem in the sense that it does not satisfy Bellman's optimality principle and therefore the usual dynamic programming approach fails. We develop a time- consistent formulation of this problem, which is based on a local not…