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- Machine Learning for Short Straddles on the S&P500
Machine Learning for Short Straddles on the S&P500
Supervised Machine Learning Classification for Short Straddles on the S&P50
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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 three must-read academic papers that mix cutting-edge ML/DL with Quant Finance:
Supervised Machine Learning Classification for Short Straddles on the S&P500
The Virtue of Complexity in Return Prediction.
Conditional GAN for Portfolio-Based Investment Strategies.
💊 AI Essentials: The section on top AI & Quant Finance learning resources: Today, I introduce a GitHub repository with 150 scripts designed for quantitative finance, algorithmic trading, and market data analysis.
🥐 Quant Finance Insights: In this edition, I provide a brief overview of how to improve your Alpha by combining factors (anomalies) using ML/DL.
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AI-Finance Insights
“Supervised Machine Learning Classification for Short Straddles on the S&P500”
This is a simple application of meta-labeling in options strategies that can serve as a source of new ideas; discussing the potential application of meta-labeling to options strategies is worthwhile.
In my opinion, one of the most effective uses of ML/DL is to provide additional "incremental" improvement on top of existing algorithmic strategies.
This paper serves as an example. Briefly, the authors employ a supervised approach to assign a probability to the success of well-known straddle strategies on a daily basis.
Here, I summarize the work in a nutshell: 👇
➡ The study introduces machine learning models to implement specific short-option strategies on the S&P 500, focusing on a supervised classification task to assess the daily viability of executing short straddles.
➡ The methodology and evaluation criteria for different classification models are detailed, showcasing a systematic approach to hyperparameter optimization.
➡ The research identifies a statistically significant benefit when employing the gradient tree boosting algorithm over a simplistic "trade always" strategy, indicating a strategic edge in certain conditions.
➡ This research represents a first step for applying supervised classification methods to broader derivative trading strategies, marking a significant step forward in refining machine learning applications for financial markets.
“The Virtue of Complexity in Return Prediction”
The paper provides a theoretical analysis rationalizing why ML/DL models (or "complex" models) outperform simple models in the prediction of stock returns.
Let me summarize the paper in just 4 key takeaways: 👇
➡ The study challenges the effectiveness of "simple" models, which use only a few parameters for predicting market returns, demonstrating theoretically that these models significantly understate return predictability compared to "complex" models, where parameters outnumber observations.
➡ Through empirical evidence, the virtue of complexity in predicting U.S. equity market returns is documented, underscoring the superiority of complex models over traditional simple ones.
➡ The findings advocate for the use of machine learning in modeling expected returns, highlighting its potential in capturing nuances that simple models overlook.
➡ This research sets a precedent for integrating machine learning into financial market predictions, emphasizing the shift towards more sophisticated, data-driven approaches for enhanced accuracy.
“Conditional GAN for Portfolio-Based Investment Strategies”
A new approach to portfolio allocation utilizes GANs, introducing a framework based on conditional GAN (CGAN) that incorporates a mechanism for normalizing data using projected mean values of future series, leading to stable strategies. This is called "HybridCGAN".
Here, I summarize the paper in a nutshell: 👇
➡ The paper introduces a novel hybrid conditional Generative Adversarial Network (GAN) strategy for portfolio analysis, enhancing traditional methods by predicting market trends alongside managing uncertainties.
➡ It critiques the Markowitz framework and existing GAN approaches for their focus on series generation and trend identification rather than future forecasting.
➡ By integrating deep generative models, the proposed HybridCGAN and HybridACGAN demonstrate superior portfolio allocation against conventional Markowitz, CGAN, and ACGAN methods, validated through datasets from US and European markets.
➡ This advancement highlights the potential of advanced machine learning techniques in improving market trend prediction and portfolio optimization, marking a significant development in financial market analysis and derivative trading strategy innovation.
AI-Essentials
Discover a comprehensive GitHub repository featuring over 150 scripts tailored for quantitative finance, algorithmic trading, and market data analysis. This repository serves as a prime resource for collecting, processing, and examining stock market data. 👇
Quant Finance Insights
“The Expected Returns on Machine-Learning Strategies”
How to improve your Alpha by combining factors (anomalies) using ML/DL?
I summarize a nice piece of research with many interesting takeaways for ML/DL-based investors in less than 2 min 👇
➡ The study evaluates machine learning-based trading strategies with anomalies (factors) as inputs, considering transaction costs and liquidity changes.
➡ The paper utilizes a comprehensive dataset that includes signals based on anomalies, macro-indicators, and industry dummies, resulting in 376 features per observation for their models.
➡ Sophisticated machine learning strategies can profit, with monthly returns up to 1.42%, despite high turnover.
➡ A strategy using a long-short-term memory model shows a net alpha of 1.20%.
➡ Cost reduction techniques don't enhance performance.
➡ Deep-learning models predict returns beyond common risk factors, showing unexplained profitability.
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