Paper: arXiv 2304.11856

Authors: Jiwook Kim, Minhyeok Lee

Abstract

In financial engineering, portfolio optimization has been of consistent interest. Portfolio optimization is a process of modulating asset distributions to maximize expected returns and minimize risks. To obtain the expected returns, deep learning models have been explored in recent years. However, due to the deterministic nature of the models, it is difficult to consider the risk of portfolios because conventional deep learning models do not know how reliable their predictions can be. To address this limitation, this paper proposes a probabilistic model, namely predictive auxiliary classifier generative adversarial networks (PredACGAN). The proposed PredACGAN utilizes the characteristic of the ACGAN framework in which the output of the generator forms a distribution. While ACGAN has not been employed for predictive models and is generally utilized for image sample generation, this paper proposes a method to use the ACGAN structure for a probabilistic and predictive model. Additionally, an algorithm to use the risk measurement obtained by PredACGAN is proposed. In the algorithm, the assets that are predicted to be at high risk are eliminated from the investment universe at the rebalancing moment. Therefore, PredACGAN considers both return and risk to optimize portfolios. The proposed algorithm and PredACGAN have been evaluated with daily close price data of S&P 500 from 1990 to 2020. Experimental scenarios are assumed to rebalance the portfolios monthly according to predictions and risk measures with PredACGAN. As a result, a portfolio using PredACGAN exhibits 9.123% yearly returns and a Sharpe ratio of 1.054, while a portfolio without considering risk measures shows 1.024% yearly returns and a Sharpe ratio of 0.236 in the same scenario. Also, the maximum drawdown of the proposed portfolio is lower than the portfolio without PredACGAN.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 6.0/10 (capped from 7.0: transaction costs not modelled; see penalty flags)
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents an innovative adaptation of ACGANs for probabilistic financial forecasting and uncertainty measurement in portfolio optimization. The empirical validation spans 30 years of S&P 500 data, showing significant risk-adjusted performance improvements, though reproducibility is slightly hampered by a lack of explicit code availability links in the summary.

Research Flowchart

  flowchart TD
    A[Goal: Optimize Portfolio Returns & Minimize Risks via Probabilistic Deep Learning] --> B[Input Data: S&P 500 Daily Close Prices 1990–2020]
    B --> C[PredACGAN Model: Generate Return Predictions & Output Distribution]
    C --> D[Quantify Asset Risk & Uncertainty from Generated Distribution]
    D --> E[Rebalancing Algorithm: Eliminate High-Risk Assets Monthly]
    E --> F[Outcomes: 9.123% Yearly Return, 1.054 Sharpe Ratio, Lower Max Drawdown]