Portfolio Choice

In Search of the Origins of Financial Fluctuations: The Inelastic Markets Hypothesis ArXiv ID: ssrn-3875134 “View on arXiv” Authors: Unknown Abstract We develop a framework to theoretically and empirically analyze the fluctuations of the aggregate stock market. Households allocate capital to institutions, whi Keywords: Stock Market Fluctuations, Household Capital Allocation, Institutional Holdings, Financial Markets, Portfolio Choice, Equity Complexity vs Empirical Score Math Complexity: 8.0/10 Empirical Rigor: 7.0/10 Quadrant: Holy Grail Why: The paper introduces a novel theoretical framework with dynamic general equilibrium models and pricing kernels (high math complexity), while rigorously testing its core hypothesis using granular instrumental variables (GIV) on real financial data to estimate a precise price impact multiplier of ~5, including robustness checks (high empirical rigor). flowchart TD A["Research Goal<br>Understand aggregate stock market fluctuations"] --> B["Methodology<br>Develop theoretical & empirical framework"] B --> C["Input Data<br>Household & institutional capital allocation data"] C --> D["Computational Process<br>Estimate supply & demand elasticities"] D --> E["Key Finding<br>Markets are inelastic due to limited arbitrage"] E --> F["Outcome<br>Explains volatility puzzles & asset pricing"]

Breaking the Dimensional Barrier: Dynamic Portfolio Choice with Parameter Uncertainty via Pontryagin Projection ArXiv ID: 2601.03175 “View on arXiv” Authors: Jeonggyu Huh, Hyeng Keun Koo Abstract We study continuous-time portfolio choice in diffusion markets with parameter $θ\in Θ$ and uncertainty law $q(dθ)$. Nature draws latent $θ\sim q$ at time 0; the investor cannot observe it and must deploy a single $θ$-blind feedback policy maximizing an ex-ante CRRA objective averaged over diffusion noise and $θ$. Our methods access $q$ only by sampling and assume no parametric form. We extend Pontryagin-Guided Direct Policy Optimization (PG-DPO) by sampling $θ$ inside the simulator and computing discrete-time gradients via backpropagation through time (BPTT), and we propose projected PG-DPO (P-PGDPO) that projects costate estimates to satisfy the $q$-aggregated Pontryagin first-order condition, yielding a deployable rule. We prove a BPTT-PMP correspondence uniform on compacts and a residual-based $θ$-blind policy-gap bound under local stability with explicit discretization/Monte Carlo errors; experiments show projection-driven stability and accurate decision-time benchmark recovery in high dimensions. ...

Optimal Portfolio Choice with Cross-Impact Propagators ArXiv ID: 2403.10273 “View on arXiv” Authors: Unknown Abstract We consider a class of optimal portfolio choice problems in continuous time where the agent’s transactions create both transient cross-impact driven by a matrix-valued Volterra propagator, as well as temporary price impact. We formulate this problem as the maximization of a revenue-risk functional, where the agent also exploits available information on a progressively measurable price predicting signal. We solve the maximization problem explicitly in terms of operator resolvents, by reducing the corresponding first order condition to a coupled system of stochastic Fredholm equations of the second kind and deriving its solution. We then give sufficient conditions on the matrix-valued propagator so that the model does not permit price manipulation. We also provide an implementation of the solutions to the optimal portfolio choice problem and to the associated optimal execution problem. Our solutions yield financial insights on the influence of cross-impact on the optimal strategies and its interplay with alpha decays. ...