This Master Thesis introduces basic concepts and methods of Machine Learning as well as applying them on a virtual buy- or sell algorithm enabled by an accurately predicting classifier. In the first section, the historical development of Machine Learning is presented. Consequently, an array of classifiers is described in detail. The third section results in a python-based Machine Learning application of seven classifiers, a cross validation and in initializing the final valuation algorithm. The optimal classifier predicts following day Open Prizes for the S&P 500 with 72% accuracy outperforming market return by nearly 150% within the span of one month.
Table of Contents
1 Abstract
2 Artificial Intelligence and Machine Learning
3 Machine Learning Techniques
3.1 Cross Validation
3.2 Decision Trees
3.3 k Nearest Neighbors
3.4 Support Vector Machines
3.5 Ensemble Methods
4 S&P 500 Buy or Sell Valuation
4.1 Problem Introduction
4.2 Dataframe Features
4.3 Applicable Machine Learning Classifiers
4.4 Cross Validation and Model Performance
4.5 Virtual Portfolio
4.6 Caveats and Outlook
5 Appendix
5.1 Figures
5.2 Tables
5.3 Python Code
Research Objective and Topics
This master thesis aims to explore and apply machine learning algorithms to predict future return developments of the S&P 500 index. By constructing a virtual portfolio, the study investigates whether a model can generate actionable investment signals and outperform market returns through accurate classification.
- Theoretical foundations of machine learning and artificial intelligence
- Application of seven diverse machine learning classifiers
- Implementation of k-fold cross-validation for model optimization
- Construction of a virtual portfolio based on predictive valuation
- Comparative analysis of model performance in financial prediction
Excerpt from the Book
3.4 Support Vector Machines
Zhang (2011) summarizes Support Vector Machines as a collection of algorithms of value for classification, regression, density measurement, novelty identification as well as further applications. The most basic instance is a Support Vector Machine which divides data into two classes. The margin between the classes is maximized. Advantages of Support Vector Machines are their comparably high degree of generalization and their capability of complex optimization procedures enabling Support Vector Machines to learn from extensive bulks of data.
Support Vector Machine development took place over four decades in three major waves. The concept of a Decision Boundary dividing training instances of two classes with a maximized decision boundary was introduced by Vapnik and Lerner (1963). Then Wahba (1991) put forward the concept of Kernels. Boser, Guyon and Vapnik (1992) expanded the scope of Support Vector Machine application by suggesting to apply non-linear Kernel Support Vector Machines that can process real data and compose the best decision boundary in feature space. Lastly, Cortes and Vapnik (1995) published the framework of soft margins authorizing certain data points to breach the margin condition. This is necessary when instance class is not linearly distinguishable. For Support Vector Machines optimal decision boundary margins are vital for an exact classification. The data instances that are situated on the margin are called support vectors which explains the name Support Vector Machine. Support Vector Machines can be set to classify linearly as depicted by Figure 1 on page 16. They can, as already mentioned, be enhanced with Kernels which grants processing non-linear dependencies. Support Vector Machines can be optimized by quadratic programming increasing analysis convenience for extensive data sets. The extensive introduction by Ihler (2015) of the University of California Irvine serves as foundation for this section.
Summary of Chapters
Abstract: Provides a high-level overview of the thesis, detailing the introduction of machine learning concepts and their application to a virtual stock trading algorithm.
Artificial Intelligence and Machine Learning: Discusses the historical context, key figures, and the evolution of machine learning from early concepts of robotics to modern computational intelligence.
Machine Learning Techniques: Explains fundamental algorithms including Cross Validation, Decision Trees, k Nearest Neighbors, Support Vector Machines, and Ensemble Methods.
S&P 500 Buy or Sell Valuation: Describes the practical application of the discussed models to financial data, including data preprocessing, feature engineering, and the simulation of a virtual portfolio.
Appendix: Contains supporting documentation including heatmaps, comprehensive performance tables, and the full Python implementation code used in the research.
Keywords
Machine Learning, Artificial Intelligence, S&P 500, Asset Valuation, Support Vector Machines, Random Forests, Cross Validation, Algorithmic Trading, Virtual Portfolio, Financial Forecasting, Data Science, Python, Neural Networks, Decision Trees, Kernel Trick
Frequently Asked Questions
What is the primary focus of this thesis?
The work focuses on applying various machine learning classifiers to predict daily return directions for the S&P 500 index to derive buy or sell investment strategies.
Which specific machine learning techniques are analyzed?
The study evaluates Decision Trees, k Nearest Neighbors, several variants of Support Vector Machines (Gaussian RBF, Polynomial, Sigmoid), and ensemble methods like Random Forests and Boosting.
What is the core objective of the research?
The primary objective is to identify an optimal classifier and parameter configuration that maximizes prediction accuracy to build a profitable virtual investment portfolio.
How is the accuracy of the models assessed?
The models are evaluated using 10-fold cross-validation and assessed based on their accuracy in predicting future open prices.
What does the main body of the work address?
The main section moves from theoretical concepts to practical implementation, detailing data sourcing from global stock exchanges, feature engineering, and the iterative model tuning process.
What are the characterizing keywords of this study?
The research is characterized by terms such as Machine Learning, Asset Valuation, SVM, Algorithmic Trading, and Financial Forecasting.
What performance did the optimal classifier achieve?
The optimal classifier demonstrated a 72.4% accuracy on test data, significantly outperforming market returns over the simulation period.
How does the virtual portfolio model function?
The model simulates an investor who trades 200 ETF units daily based on the machine learning algorithm’s prediction of whether the next day's price will move up or down.
- Citation du texte
- Justus Wiedemann (Auteur), 2017, Machine Learning Asset Valuation, Munich, GRIN Verlag, https://www.grin.com/document/373193
