The Application of the Main Components in Investment Basket Management: A Case Study of Fifty Stock Exchange Companies

Document Type : Original Article

Authors

1 Corresponding Author, Ph.D. in Financial Engineering, Accounting, and Financial Department, Faculty of Economics, Management and Accounting, Yazd University, Yazd.Yazd.Iran

2 Associate Professor, Department of Accounting and Financial Department, Faculty of Economics, Management, and Accounting, Yazd University, Yazd.Yazd.Iran

Abstract

Abstract
Establishing an investment portfolio is one of the main concerns of managers and investors who are always looking for an effort to form the best investment basket so that they can achieve the most returns. So far, there have been many ways to form an investment basket, the most famous of which is Maritz's approach. The average theory of variance has many practical drawbacks due to the difficulty in estimating the expected returns and covariance for different asset classes. The purpose of this study is to maximize risk -adjusted return on the portfolio using PCA method in a data base of stock returns. The data base used for this case study is the daily data modified of 50 top stock and relevant stock index companies for the period 25/4/2016 to 7/2/2021 for 1027 trading days. We use a dimensional reduction algorithm (PCA) to allocate capital to different asset classes to maximize risk -adjusted returns and the results are compared with the equal weight allocation approach (1/N). There is also a post -test framework for evaluating the performance of the investment baskets provided. According to the results, the variance explained by the three main components can be an indicator for identifying the most important business risks.

Keywords


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راعی، رضا؛ باجلان، سعید و عجم، علیرضا (1397). بررسی کارآیی بهینه‏سازی سبد سرمایه‏گذاری با استفاده از الگوی ترکیبی حداقل واریانس و 1/N. . مدیریت دارایی و تأمین مالی، 6(4)، 155–166.
کریمی، آرزو (1400). بهینه‌سازی سبد سهام با استفاده از الگوریتم ژنتیک چند هدفه (NSGA II) و ماکزیمم نسبت شارپ. مهندسی مالی و مدیریت اوراق بهادار، 12(46), 410-389.
نبی‌زاده، احمد؛ قره‌باغی، هادی و بهزادی، عادل (1396). بهینه‌سازی پرتفوی ردیابی شاخص بر اساس بتای نامطلوب مبتنی بر الگوریتم‌های تکاملی. تحقیقات مالی، 19(2)، 340-319.
Agarwal, T., Quelle, H., & Ryan, C. (2021). Principal Component Analysis for Clustering Stock Portfolios. Arizona Journal of Interdisciplinary Studies, 7,
Bechis, L., Cerri, F., & Vulpiani, M. (2020). Machine Learning Portfolio Optimization: Hierarchical Risk Parity and Modern Portfolio Theory.
Billio, M., Getmansky, M., Lo, A. W., & Pelizzon, L. (2012). Econometric measures of connectedness and systemic risk in the finance and insurance sectors. Journal of Financial Economics, 104(3), 535–559.
Brinson, G. P., Hood, L. R., & Beebower, G. L. (1986). Determinants of portfolio performance. Financial Analysts Journal, 42(4), 39–44.
Cochrane, J. H. (1999). Portfolio advice for a multifactor world. National Bureau of Economic Research,
Conlon, T., Cotter, J., & Kynigakis, I. (2021). Machine Learning and Factor-Based Portfolio Optimization. Available at SSRN 3889459.
Gabriel, C. (2014). Common factors in international bond returns and a joint ATSM to match them. Theoretical Economics Letters,
Gorakala, S. K., & Usuelli, M. (2015). Building a recommendation system with R. Packt Publishing Ltd.
Gu, S., Kelly, B., & Xiu, D. (2021). Autoencoder asset pricing models. Journal of Econometrics, 222(1), 429–450.
Jolliffe, I. T. (1986). Principal components in regression analysis. In Principal component analysis (pp. 129–155), Springer.
Kelly, B. T., Pruitt, S., & Su, Y. (2019). Characteristics are covariances: A unified model of risk and return. Journal of Financial Economics, 134(3), 501–524.
Kim, D.-H., & Jeong, H. (2005). Systematic analysis of group identification in stock markets. Physical Review E, 72(4), 46133.
Kritzman, M., Li, Y., Page, S., & Rigobon, R. (2011). Principal components as a measure of systemic risk. The Journal of Portfolio Management, 37(4), 112–126.
Kumar, S. (2022). Effective hedging strategy for us treasury bond portfolio using principal component analysis. Academy of Accounting and Financial Studies Journal, 26(2), 1–17.
Nourahmadi, M., & Sadeqi, H. (2021). Hierarchical Risk Parity as an Alternative to Conventional Methods of Portfolio Optimization: (A Study of Tehran Stock Exchange). Iranian Journal of Finance, 5(4), 1–24.
Partovi, M. H., & Caputo, M. (2004). Principal portfolios: Recasting the efficient frontier. Economics Bulletin, 7(3), 1–10.
Pérignon, C., Smith, D. R., & Villa, C. (2007). Why common factors in international bond returns are not so common. Journal of International Money and Finance, 26(2), 284–304.
Tatsat, H., Puri, S., & Lookabaugh, B. (2020). Machine Learning and Data Science Blueprints for Finance From Building Trading Strategies to Robo-Advisors Using Python. O’Reilly Media, Inc.
Tobin, J., & Hester, D. D. (1967). Risk aversion and portfolio choice. Wiley.
Yang, L. (2015). An application of principal component analysis to stock portfolio management.
Zheng, Z., Podobnik, B., Feng, L., & Li, B. (2012). Changes in cross-correlations as an indicator for systemic risk. Scientific Reports, 2(1), 1–8.