Abstract
MPT or the modern portfolio theory, also known as mean–variance analysis, is a mathematical modeling technique that is deployed in constructing portfolios that can maximize the portfolio return for a given amount of risk. In this paper we optimize portfolios in accordance with the modern portfolio theory for US-based equity instruments using Monte-Carlo simulations. For a given Portfolio ‘P’ having ‘n’ number of stocks, with each stock ‘i’ having a weight of ‘wi’ we compute the mean and risk (standard deviation) and optimize our portfolio by optimizing the weights ‘wi’ for the equity instruments using Monte-Carlo simulation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bertoluzzo F, Corazza M (2012) Testing different reinforcement learning configurations for financial trading: Introduction and applications. Procedia Econ Finance 3:68–77
Markowitz HM (1952) Portfolio selection. J Financ 7(1):77–91. https://doi.org/10.2307/297597
Markowitz H (February 2000) Mean-variance analysis in portfolio choice and capital markets. Wiley. ISBN 978-1-883-24975-5
Sharpe WF (1964) Capital asset prices: a theory of market equilibrium under conditions of risk. J Financ 19(3):425–442 (Posted: 1964)
Firman S, & Hidayat Y, Lesmana E, Sukma Putra A, Napitupulu H, Supian S (2018) Portfolio optimization by using linear programing models based on genetic algorithm. In: IOP conference series: materials science and engineering, vol 300, p 012001. https://doi.org/10.1088/1757-899X/300/1/012001
Liu C, Gan W, Chen Y (2017) Research on portfolio optimization based on affinity propagation and genetic algorithm. In: 2017 14th web information systems and applications conference (WISA), pp 122–126. https://doi.org/10.1109/WISA.2017.9
Mohapatra SK, Nayak P, Mishra S, Bisoy SK (2019) Green computing: a step towards eco-friendly computing. In: Emerging trends and applications in cognitive computing. IGI Global, pp 124–149
Chekhlov A, Uryasev S, Zabarankin M (2005) Drawdown measure in portfolio optimization. Int J Theor Appl Financ 8(01):13–58
Choueifaty Y, Coignard Y (2008) Toward maximum diversification. J Portfolio Mgmt 35(1):40–51
Mishra S, Mishra BK, Tripathy HK, Dutta A (2020) Analysis of the role and scope of big data analytics with IoT in health care domain. In: Handbook of data science approaches for biomedical engineering. Academic Press, pp 1–23
Rath M, Mishra S (2020) Security approaches in machine learning for satellite communication. In: Machine learning and data mining in aerospace technology. Springer, Cham, pp 189–204
Mishra S, Tripathy HK, Kishore B (2017) Filter based attribute optimization: a performance enhancement technique for healthcare experts
Mishra S, Tripathy HK, Panda AR (2018) An IMPRoved and adaptive attribute selection technique to optimize dengue fever prediction. Int J Eng Technol 7:480–486
Jena L, Patra B, Nayak S, Mishra S, Tripathy S (2019) Risk prediction of kidney disease using machine learning strategies. In: Intelligent and cloud computing. Springer, Singapore, pp 485–494
Mishra S, Tadesse Y, Dash A, Jena L, Ranjan P (2019) Thyroid disorder analysis using random forest classifier. In: Intelligent and cloud computing. Springer, Singapore, pp 385–390
Sahoo S, Das M, Mishra S, Suman S (2021) A hybrid DTNB model for heart disorders prediction. In: Advances in electronics, communication and computing. Springer, Singapore, pp 155–163
Mishra S, Mallick PK, Tripathy HK, Jena L, Chae G-S (2021) Stacked KNN with hard voting predictive approach to assist hiring process in IT organizations. Int J Electr Eng Edu. https://doi.org/10.1177/0020720921989015
Jena KC, Mishra S, Sahoo S, Mishra BK (2017, January) Principles, techniques and evaluation of recommendation systems. In: 2017 international conference on inventive systems and control (ICISC). IEEE, pp 1–6
Mishra S, Thakkar H, Mallick PK, Tiwari P, Alamri A (2021) A sustainable IoHT based computationally intelligent healthcare monitoring system for lung cancer risk detection. Sustain Cities Soc 103079
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Mukherjee, A., Singh, A.K., Mallick, P.K., Samanta, S.R. (2022). Portfolio Optimization for US-Based Equity Instruments Using Monte-Carlo Simulation. In: Mallick, P.K., Bhoi, A.K., Barsocchi, P., de Albuquerque, V.H.C. (eds) Cognitive Informatics and Soft Computing. Lecture Notes in Networks and Systems, vol 375. Springer, Singapore. https://doi.org/10.1007/978-981-16-8763-1_57
Download citation
DOI: https://doi.org/10.1007/978-981-16-8763-1_57
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-8762-4
Online ISBN: 978-981-16-8763-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)