Generative Meta-Learning Robust Quality-Diversity Portfolio

ArXiv ID: 2307.07811 “View on arXiv”

Authors: Unknown

Abstract

This paper proposes a novel meta-learning approach to optimize a robust portfolio ensemble. The method uses a deep generative model to generate diverse and high-quality sub-portfolios combined to form the ensemble portfolio. The generative model consists of a convolutional layer, a stateful LSTM module, and a dense network. During training, the model takes a randomly sampled batch of Gaussian noise and outputs a population of solutions, which are then evaluated using the objective function of the problem. The weights of the model are updated using a gradient-based optimizer. The convolutional layer transforms the noise into a desired distribution in latent space, while the LSTM module adds dependence between generations. The dense network decodes the population of solutions. The proposed method balances maximizing the performance of the sub-portfolios with minimizing their maximum correlation, resulting in a robust ensemble portfolio against systematic shocks. The approach was effective in experiments where stochastic rewards were present. Moreover, the results (Fig. 1) demonstrated that the ensemble portfolio obtained by taking the average of the generated sub-portfolio weights was robust and generalized well. The proposed method can be applied to problems where diversity is desired among co-optimized solutions for a robust ensemble. The source-codes and the dataset are in the supplementary material.

Keywords: Meta-learning, Deep generative models, Portfolio ensemble, LSTM, Convolutional layers, Equities

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 5.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematics such as deep generative models, convolutional layers, LSTMs, and multi-objective optimization, which is mathematically dense. It also demonstrates empirical rigor with backtesting, results in Figure 1, and availability of source code and dataset in supplementary material.

  flowchart TD
    A["Research Goal<br>Optimize robust portfolio ensemble via meta-learning"] --> B["Data Input<br>Stochastic financial rewards & Gaussian noise"]
    B --> C["Generative Model Architecture<br>Convolutional + Stateful LSTM + Dense"]
    C --> D["Generative Process<br>Sample noise → Generate population of sub-portfolios"]
    D --> E["Objective Function<br>Maximize performance & minimize max correlation"]
    E --> F["Gradient-based Optimization<br>Update model weights"]
    F --> D
    F --> G["Key Findings<br>Robust ensemble portfolio via averaging sub-portfolios<br>Generalizes well under systematic shocks"]