Machine Learning Asset Valuation

Inhaltsverzeichnis (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

Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)

This Master Thesis explores the application of Machine Learning techniques to develop a virtual buy or sell algorithm for the S&P 500 index. It aims to demonstrate the potential of Machine Learning in financial prediction and analyze the accuracy and effectiveness of various classification algorithms.

• Historical development of Machine Learning
• Overview of different machine learning classifiers
• Implementation of machine learning algorithms for financial prediction
• Evaluation of classifier performance through cross-validation
• Creation and analysis of a virtual portfolio based on the prediction model

Zusammenfassung der Kapitel (Chapter Summaries)

• Chapter 1: Abstract: This chapter provides a brief overview of the thesis, outlining its objectives, methodology, and key findings. It introduces the concept of using Machine Learning for financial prediction and highlights the application of a virtual buy or sell algorithm.
• Chapter 2: Artificial Intelligence and Machine Learning: This chapter delves into the historical development and fundamental concepts of Artificial Intelligence and Machine Learning, providing a theoretical foundation for the subsequent analysis.
• Chapter 3: Machine Learning Techniques: This chapter presents a detailed description of various machine learning techniques, including cross validation, decision trees, k nearest neighbors, support vector machines, and ensemble methods. It explores the strengths and weaknesses of each technique and their relevance to financial prediction.
• Chapter 4: S&P 500 Buy or Sell Valuation: This chapter focuses on the practical application of Machine Learning to predict the future price movements of the S&P 500 index. It introduces the problem, data features, applicable classifiers, cross-validation results, and the performance of the virtual portfolio. The chapter concludes with a discussion of limitations and potential future directions.

Schlüsselwörter (Keywords)

Machine Learning, Artificial Intelligence, Financial Prediction, S&P 500, Classification Algorithms, Cross Validation, Virtual Portfolio, Buy or Sell Algorithm, Data Analysis, Python.