This episode explores unsupervised learning techniques in finance, focusing on clustering algorithms and their applications. The lecture begins by introducing unsupervised learning as a method for grouping observations without pre-defined targets, contrasting it with supervised learning. More significantly, the instructor delves into k-means clustering, explaining the algorithm's steps and illustrating its application with examples involving customer segmentation and stock grouping for pair trading strategies. For instance, the instructor demonstrates how to cluster S&P 500 stocks based on historical price data to identify potential pairs for pair trading. The challenges of high dimensionality are discussed, along with techniques like feature scaling and the use of cosine similarity for measuring distances in high-dimensional spaces. The episode concludes with a practical demonstration using Python code and the scikit-learn library, showcasing how to perform k-means clustering on real-world financial data, highlighting the importance of data preparation and hyperparameter selection. This detailed walkthrough provides a valuable resource for students and practitioners seeking to apply unsupervised learning methods to financial data analysis.
