Deep Generative Models

Neuro-Symbolic Traders: Assessing the Wisdom of AI Crowds in Markets ArXiv ID: 2410.14587 “View on arXiv” Authors: Unknown Abstract Deep generative models are becoming increasingly used as tools for financial analysis. However, it is unclear how these models will influence financial markets, especially when they infer financial value in a semi-autonomous way. In this work, we explore the interplay between deep generative models and market dynamics. We develop a form of virtual traders that use deep generative models to make buy/sell decisions, which we term neuro-symbolic traders, and expose them to a virtual market. Under our framework, neuro-symbolic traders are agents that use vision-language models to discover a model of the fundamental value of an asset. Agents develop this model as a stochastic differential equation, calibrated to market data using gradient descent. We test our neuro-symbolic traders on both synthetic data and real financial time series, including an equity stock, commodity, and a foreign exchange pair. We then expose several groups of neuro-symbolic traders to a virtual market environment. This market environment allows for feedback between the traders belief of the underlying value to the observed price dynamics. We find that this leads to price suppression compared to the historical data, highlighting a future risk to market stability. Our work is a first step towards quantifying the effect of deep generative agents on markets dynamics and sets out some of the potential risks and benefits of this approach in the future. ...

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. ...