Financial Time Series Simulation with GANs and Attention Mechanisms

Financial time series simulation using deep learning and autoencoder-based pair trading strategies.

February 02, 2024

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Hi! Here's Iván with this week's exciting newsletter, brimming with insights and discoveries on building robust investment strategies and risk models using Machine Learning.

In this edition, I am presenting the following sections:

  • 🕹️ AI-Finance Insights: I summarize two must-read academic papers that mix cutting-edge ML/DL with Quant Finance:

    • “Financial Time Series Simulation with GANs and Attention Mechanisms”

    • “Pair Trading with Convolutional Autoencoders”

  • 💊 AI Essentials: The section on top free AI learning resources. Today, I present a complete list of 12 free books to start learning Deep Learning or to advance your knowledge. From beginners to advanced readers.

  • 🥐 Quant Finance Insights: In this edition, I summarize a paper on the predictive ability of the Option Volume Imbalance (OVI).

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AI-Finance Insights

“Financial Time Series Simulation with GANs and Attention Mechanisms”

Simply put, when historical data is scarce (as in many real investment situations), simulating financial time series is the way to go.

The paper introduces a new methodology, leveraging Deep Learning to simulate financial time series.

Let me summarize the paper in 6 main takeaways: 👇

➡Financial time series simulation is crucial for extending limited real data used in trading strategy training and evaluation.

➡Simulating financial time series is challenging due to the complex nature of real financial data.

➡The paper introduces two generative adversarial networks (GANs) that leverage convolutional networks with attention mechanisms and transformers for financial time series simulation.

➡These GANs learn statistical properties in a data-driven manner, with attention mechanisms enhancing the replication of long-range dependencies.

➡The proposed GANs were tested on S&P 500 index and option data, evaluated using scores based on stylized facts, and compared to QuantGAN, a pure convolutional GAN.

➡ Results show that the attention-based GANs not only reproduce stylized facts effectively but also smooth the autocorrelation of returns

“Pair Trading with Convolutional Autoencoders” 

Summary of the paper's main results in under 3 minutes. 👇

➡️ The paper compares classical factor model-based pair trading, utilizing PCA (Principal Component Analysis) and K-means, with its non-linear version based on Convolutional Autoencoders (CAE).

➡️ Specifically, for the factor model-based standard approach, PCA and K-means clustering are employed, representing two of the most well-known methods in pair trading.

➡️ For the non-linear empirical tests, the authors propose the use of CAE (refer to the post figure). In this method, time series data are first converted into images using Gramian Angular Field, enabling the feeding of these images into the CAE architecture.

➡️ For the empirical test, price data from 500 companies listed in the S&P 500 index during the years 2010 and 2011 were used. Daily price data were converted into daily return data, including only the stocks with no missing values in both years (467 stocks).

➡️ The data were divided into two datasets: one for 2010 and another for 2011. The 2010 dataset was used to train the model, and the 2011 dataset to test it.

➡️ The paper shows good results for both the Factor Model with PCA and the CAE methods. These methods did better than just picking stocks randomly from the same industry.

➡️ The settings chosen for the methods (5 factors and about 20 clusters) match well with what's found in other finance studies, like the Fama-French 5 model.

➡️ The CAE method is more complex but slightly outperforms the Factor Model in both training and testing scenarios (see the post figure).

AI-Essentials

📌 From Beginners to Advanced Readers.

Here is the list ✍

Deep Learning: Technical Introduction by Thomas Epelbaum - Covers neural network architectures including Feedforward, Convolutional, and Recurrent.

Neural Networks and Deep Learning by Charu C. Aggarwal - A comprehensive guide on neural networks and deep learning.

Neural Networks and Deep Learning by Michael Nielsen - Focuses on neural nets for recognizing handwritten digits and backpropagation.

Dive into Deep Learning by Aston Zhang et al. - A detailed journey into deep learning, starting from machine learning basics.

Neural Network Design by Martin T. Hagan et al. - Explains Neuron Model and Network Architectures and perceptron learning rule.

Deep Learning Methods and Applications by Li Deng and Dong Yu - Overview of deep learning methodologies and applications.

Applied Deep Learning by Umberto Michelucci - A case-based approach to Deep Neural Networks.

Advanced Applications for Artificial Neural Networks by Adel El-Shahat - Discusses advanced applications of neural networks.

Deep Learning Interviews: Problems and Solutions by Shlomo Kashani - Offers interview preparation for deep learning roles.

Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville - Two sections covering basics of machine learning and deep neural networks.

Deep Learning on Graphs by Yao Ma and Jiliang Tang - Emphasizes the role of graphs in deep learning.

Deep Learning for Coders with Fastai and Pytorch by Howard, J. and Gugger, S. - Practical guide on using the fastai library.

Quant Finance Insights

“Option Volume Imbalance for Stock Market Predictions” 

Strong empirical evidence supports the forecasting power of Option Volume Imbalance (OVI) in predicting stock and ETF returns.

We should consider incorporating OVI into any set of predictive inputs for ML/DL or other algorithmic approaches.

The paper, in a nutshell. 👇

➡ The research explores how imbalances in option trading volumes can predict future price movements in financial markets.

➡ By analyzing these imbalances through a nonlinear approach and categorizing market participants, the study uncovers strong indicators of future market returns happening overnight.

➡ The analysis identifies Market-Maker volumes as a crucial source of predictive signals, particularly emphasizing that options with high implied volatility, especially put options, have a greater predictive value than call options. The core of the paper is the examination of Option Volume Imbalance (OVI) and its connection to the future prices of stocks or ETFs.

➡ The findings demonstrate that OVI is a reliable predictor of the direction of asset prices over the next day.

➡ The study innovatively demonstrates how Option Volume Imbalances (OVIs) from various market participants contribute unique insights to the market, and further investigates how different characteristics of options influence the predictive strength of OVI for future stock returns.

➡ Finally, the paper also presents evidence of interconnected impacts across various stocks and ETFs in the market.

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