{'fraction_non_alphanumeric': 0.013295346628679962, 'fraction_numerical': 0.0, 'mean_word_length': 5.387878787878788, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Voting shrinkage algorithm for Covariance Matrix Estimation and its application to portfolio selection

Reducing errors of covariance matrix estimation plays a very important role in many optimization problems, e.g., portfolio optimization. In this paper we propose a data-driven approach which basically combines the original framework of a popular approach named shrinkage estimation and cross-validation technique to adapt the shrinkage intensity with different levels of uncertainty of real data. Particularly, this approach can be applied well to asset management to enhance the quality of portfolio selection which is known as a huge area of research in modern portfolio theory. Experimental results carried out using the prices and volumes data of the Vietnamese stock market show that our proposed method can practically improve the quality and robustness of portfolio selection. Last but not least, we introduce an automatic backtesting system that can help us evaluate the portfolio based on various financial indicators in a real-time manner.