Diversification Measures and the Optimal Number of Stocks in a Portfolio: An Information Theoretic Explanation

Oyenubi, Adeola

doi:10.1007/s10614-016-9600-5

Cited by 8 publications

(7 citation statements)

References 21 publications

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“…Based on modern portfolio theory by Harry Markowitz (1952), investors are advised not to invest only in one security, but to spread their investment in several securities, thus it is necessary for investors to form a portfolio through a combination of a number of stocks to minimize risk without reducing the expected return. This is confirmed by the statement of Oyenubi (2019) that increasing the number of shares in the portfolio can increase the level of diversification and have an impact on reducing portfolio risk.…”

Section: Introductionsupporting

confidence: 52%

Investor Competence and Decision Familiarity Bias Analysis for Portfolio Diversification

Nurcahya¹,

Maharani²

2021

Proceedings of the First International Conference on Economics, Business and Social Humanities, ICONEBS 2020, November 4-5, 202

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Investor's decision to maximize returns and reduce investment risk is to diversify their portfolios. According to the Behavioral Finance study, the investment decision is influenced by emotional psychology. Therefore, this study aims to examine psychological factors, that is investor competence and familiarity bias towards the decision of individual investor portfolio diversification. Data were collected using a questionnaire for active investors in Malang, Surabaya and Jakarta. Research questionnaire were distributed in person or online. The results showed that the competence investors has a positive and significant effect on the decision of investor portfolio diversification. Meanwhile, familiarity bias has a significant negative effect on investor portfolio diversification decision. This reflects that investment decisions in trading are influenced by limited rationality, emotional factors, and individual personality traits. Whereas in conditions of economic and financial uncertainty, its is believed that humans cannot escape the aspects of psychological bias in the decision-making process.

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Section: Introductionsupporting

confidence: 52%

Investor Competence and Decision Familiarity Bias Analysis for Portfolio Diversification

Nurcahya¹,

Maharani²

2021

Proceedings of the First International Conference on Economics, Business and Social Humanities, ICONEBS 2020, November 4-5, 202

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“…Most of the studies dealing with the necessary number of stocks to diversify the unsystematic risk were mainly conducted in the U.S. market (Evans and Archer 1968;Fielitz 1974;Statman 1987;Beck et al 1996;O'Neal 1997;Barber and Odean 2000;Statman 2002;Domian et al 2007;Benjelloun 2010;Diyarbakırlıo glu and Satman 2013;Alexeev and Tapon 2014;Zhou 2014;Oyenubi 2019;Kurtti 2020), while few have attempted to investigate this issue in other (underdeveloped) markets (Gupta and Khoon 2001;Brands and Gallagher 2005;Irala and Patil 2007;Alekneviciene et al 2012;Stotz and Lu 2014;Ahuja 2015;Bradfield and Munro 2017;Murthy 2018;Raju and Agarwalla 2021). Based on the results, most studies in the past have used the variance or standard deviation of returns as a metric to assess risk reduction (Evans and Archer 1968;Solnik 1974;Statman 1987;Beck et al 1996), and this has continued to be the authors' first choice in recent years (Brands and Gallagher 2005;Benjelloun 2010).…”

Section: Number Of Stocks Required For Risk Diversificationmentioning

confidence: 99%

“…Just for comparison, early studies based on (semi-)annual and quarterly data have shown that 8 to 16 stocks are sufficient for optimal diversification (Evans and Archer 1968;Fielitz 1974;Zhou 2014). Some have used monthly data (Statman 1987;Beck et al 1996;Gupta and Khoon 2001;Statman 2002;Tang 2004;Brands and Gallagher 2005;Irala and Patil 2007;Dbouk and Kryzanowski 2009;Benjelloun 2010;Kryzanowski and Singh 2010;Stotz and Lu 2014;Kisaka et al 2015;Haensly 2020;Raju and Agarwalla 2021), but also weak data (Solnik 1974;Bradfield and Munro 2017;Oyenubi 2019;Lee et al 2020) or even daily data (Domian et al 2007;Alekneviciene et al 2012;Alexeev and Tapon 2012;Chong and Phillips 2013;Ahuja 2015). As Alexeev and Dungey (2015) further point out, high-frequency data undeniably improves risk assessment and brings significant benefits to decisionmaking.…”

Section: -50 Stocksmentioning

confidence: 99%

“…Numerous studies have shown that the traditional rule of thumb of 8-10 stocks established by the pioneering study of Evans and Archer (1968) is indeed sufficient to achieve optimal diversification effects, but on the other hand numerous works, especially recently, have challenged this fact by showing that 30-50 stocks are required for maximum diversification effect (Benjelloun 2010;Chong and Phillips 2013;Alexeev and Tapon 2014;Bradfield and Munro 2017;Oyenubi 2019;Kurtti 2020;Raju and Agarwalla 2021) or even 100 stocks or more (Statman 2002;Domian et al 2007;Diyarbakırlıo glu and Satman 2013).…”

Section: Introductionmentioning

confidence: 99%

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How Many Stocks Are Sufficient for Equity Portfolio Diversification? A Review of the Literature

Zaimović

Omanovic

Arnaut-Berilo

2021

JRFM

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Using extensive and comprehensive databases to select a subset of research papers, we aim to critically analyze previous empirical studies to identify certain patterns in determining the optimal number of stocks in well-diversified portfolios in different markets, and to compare how the optimal number of stocks has changed over different periods and how it has been affected by market turmoil such as the Global Financial Crisis (GFC) and the current COVID-19 pandemic. The main methods used are bibliometric analysis and systematic literature review. Evaluating the number of assets which lead to optimal diversification is not an easy task as it is impacted by a huge number of different factors: the way systematic risk is measured, the investment universe (size, asset classes and features of the asset classes), the investor’s characteristics, the change over time of the asset features, the model adopted to measure diversification (i.e., equally weighted versus optimal allocation), the frequency of the data that is being used, together with the time horizon, conditions in the market that the study refers to, etc. Our paper provides additional support for the fact that (1) a generalized optimal number of stocks that constitute a well-diversified portfolio does not exist for whichever market, period or investor. Recent studies further suggest that (2) the size of a well-diversified portfolio is larger today than in the past, (3) this number is lower in emerging markets compared to developed financial markets, (4) the higher the stock correlations with the market, the lower the number of stocks required for a well-diversified portfolio for individual investors, and (5) machine learning methods could potentially improve the investment decision process. Our results could be helpful to private and institutional investors in constructing and managing their portfolios and provide a framework for future research.

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“…Their problem is verified by existed stock returns data, and results show the MDI can be powerfully applied to define a large set of investable assets. Oyenubi (2016) reported an acceptable description for the elusiveness of the optimum amount of stocks in a portfolio. He used the Portfolio Diversification Index (PDI) to quantify diversification.…”

Section: Research Backgroundmentioning

confidence: 99%

The Quantitative Diversity Index in Multi-Objective Portfolio Model

Ebrahimi

Moghadam

Safaie

2021

IJFIFSA

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The primary purpose of investors is maximizing the utility that is characterized by two essential criteria include risk and return. Regarding investors' uncertainty about the future, one of the main ways to reduce risk is to diversify the investment portfolio. In this research, we proposed an index conducted by Euclidean distance for assessing portfolio diversity. Besides, we designed a multi-objective model to select optimal stock portfolios with considering value at risk (VaR), which is one of the critical indicators of unacceptable risk, portfolio Beta as systematic risk, and portfolio variance as unsystematic risk simultaneously. The model presented in this paper aims to maximize diversification while minimizing value at risk and stock risks. Furthermore, maximizing returns are considered as a limitation of this model. Since the proposed model is nonlinear and concerning computational complexity, it is NPhard; therefore, we utilized the PSO and the GE metaheuristic algorithms that are improved for solving multi-objective problems to solve the model. The results of the model implementation in multiple iterations showed that the average yield of selected portfolios by the model is higher than the desirable condition. The evaluation of stock performance indicators also shows the satisfactory performance of the multi-objective model.

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Diversification Measures and the Optimal Number of Stocks in a Portfolio: An Information Theoretic Explanation

Abstract: The views expressed are those of the author(s) and do not necessarily represent those of the funder, ERSA or the author's affiliated institution(s). ERSA shall not be liable to any person for inaccurate information or opinions contained herein.

Cited by 8 publications

References 21 publications

Investor Competence and Decision Familiarity Bias Analysis for Portfolio Diversification

Investor Competence and Decision Familiarity Bias Analysis for Portfolio Diversification

How Many Stocks Are Sufficient for Equity Portfolio Diversification? A Review of the Literature

The Quantitative Diversity Index in Multi-Objective Portfolio Model

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