Optimal Investment with Quadratic Transaction Costs in a Multi-factor and Stochastic Interest Rate Environment
Researchers present a new framework for optimal investment in markets with realistic frictions, including quadratic transaction costs and changing interest rates. Their semi-analytical solution to nonlinear partial differential equations lets investors adjust portfolios gradually, balancing costs and future opportunities. A deep learning algorithm efficiently solves high-dimensional problems, surpassing traditional methods. The study uses geometric Brownian motion for asset prices and Picard fixed-point iterations, but calls for more real-world data and practical implementation discussion.
What it examines
This paper develops a new method for choosing the best investment strategy when trading costs and market conditions change over time. It uses advanced math and deep learning to find solutions in complex financial markets with unpredictable interest rates, asset prices, and transaction costs.
What it concludes
The results show that the proposed strategy helps investors balance profits and costs, especially in markets with changing conditions. The deep learning approach makes it practical for real-world use, such as automated trading and risk management. Future research may extend these methods to more assets and market scenarios.
Evidence objects
Researchers unveil a groundbreaking framework for optimal investment in markets with real-world frictions, including quadratic transaction costs, stochastic interest rates, and multi-factor asset models, offering unprecedented realism in financial mathematics.
key_findings bullet 1 · key_findings · validation V0
A standout innovation is the semi-analytical solution to nonlinear PDEs, enabling investors to trade toward a dynamic 'aim portfolio' that adapts to interest rate risk and market liquidity, using deep learning for high-dimensional problems.
key_findings bullet 2 · key_findings · validation V0
The study employs geometric Brownian motion for asset prices, mean-quadratic variation as the objective, and Picard fixed-point iterationsnovel in this contextbut calls for more real-world data validation and practical implementation discussion.
key_findings bullet 3 · key_findings · validation V0
This paper advances portfolio optimization by modeling quadratic transaction costs within a realistic, multi-factor, stochastic interest rate framework. Its originality lies in integrating GBM-type asset dynamics and state-dependent costs, and its novelty is the deep learning-based Picard iteration scheme, offering impactful, practical solutions for high-dimensional, data-driven quantitative finance.
key_findings bullet 4 · key_findings · validation V0
Raw abstract and provenance
- … In this paper, we study an optimal investment problem featuring stochastic asset returns and volatility factors, stochastic interest rates, and stochastic state-dependent …
Source row: 1498 · abstract type: snippet