A new behavioral finance mean variance framework

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Todd Feldman ◽  
Shuming Liu
Keyword(s):  
Risk Aversion ◽  
Behavioral Finance ◽  
Superior Performance ◽  
Risky Assets ◽  
Content Type ◽  
Market Sentiment ◽  
Dynamic Risk ◽  
The Mean ◽  
Sharpe Ratios ◽  
Mean Variance

PurposeThe author proposes an update to the mean variance (MV) framework that replaces a constant risk aversion parameter using a dynamic risk aversion indicator. The contribution to the literature is made through making the static risk aversion parameter operational using an indicator of market sentiment. Results suggest that Sharpe ratios improve when the author replaces the traditional risk aversion parameter with a dynamic sentiment indicator from the behavioral finance literature when allocating between a risky portfolio and a risk-free asset. However, results are mixed when using the behavioral framework to allocate between two risky assets.Design/methodology/approachThe author includes a dynamic risk aversion parameter in the mean variance framework and back test using the traditional and updated behavioral mean variance (BMV) framework to see which framework leads to better performance.FindingsThe author finds that the behavioral framework provides superior performance when allocating between a risky and risk-free asset; however, it under performs when allocating between risky assets.Research limitations/implicationsThe research is based on back testing; therefore, it cannot be concluded that this strategy will perform well in real-time circumstances.Practical implicationsPortfolio managers may use this strategy to optimize the allocation between a risky portfolio and a risk-free asset.Social implicationsAn improved allocation between risk-free and risky assets that could lead to less leverage in the market.Originality/valueThe study is the first to use such a sentiment indicator in the traditional MV framework and show the math.

Portfolio optimization using the generalized reduced gradient nonlinear algorithm

2016 ◽  
Vol 9 (4) ◽  
pp. 570-582 ◽  
Author(s):  
Dima Waleed Hanna Alrabadi
Keyword(s):  
Portfolio Optimization ◽  
Stock Exchange ◽  
Arab World ◽  
Content Type ◽  
Optimization Framework ◽  
Monthly Return ◽  
Reduced Gradient ◽  
The Mean ◽  
Generalized Reduced Gradient ◽  
Mean Variance

Purpose This study aims to utilize the mean–variance optimization framework of Markowitz (1952) and the generalized reduced gradient (GRG) nonlinear algorithm to find the optimal portfolio that maximizes return while keeping risk at minimum. Design/methodology/approach This study applies the portfolio optimization concept of Markowitz (1952) and the GRG nonlinear algorithm to a portfolio consisting of the 30 leading stocks from the three different sectors in Amman Stock Exchange over the period from 2009 to 2013. Findings The selected portfolios achieve a monthly return of 5 per cent whilst keeping risk at minimum. However, if the short-selling constraint is relaxed, the monthly return will be 9 per cent. Moreover, the GRG nonlinear algorithm enables to construct a portfolio with a Sharpe ratio of 7.4. Practical implications The results of this study are vital to both academics and practitioners, specifically the Arab and Jordanian investors. Originality/value To the best of the author’s knowledge, this is the first study in Jordan and in the Arab world that constructs optimum portfolios based on the mean–variance optimization framework of Markowitz (1952) and the GRG nonlinear algorithm.

Prescriptive portfolio selection: a compromise between fast and slow thinking

2017 ◽  
Vol 9 (2) ◽  
pp. 98-116 ◽  
Author(s):  
Omid Momen ◽  
Akbar Esfahanipour ◽  
Abbas Seifi
Keyword(s):  
Portfolio Selection ◽  
Stock Exchange ◽  
Efficient Frontier ◽  
Fast System ◽  
Content Type ◽  
The Mean ◽  
Selection For ◽  
Mean Variance ◽  
Prescriptive Analysis

PurposeThe purpose of this paper is to develop a prescriptive portfolio selection (PPS) model based on a compromise between the idea of “fast” and “slow” thinking proposed by Kahneman. Design/methodology/approach“Fast” thinking is effortless and comfortable for investors, while “slow” thinking may result in better performance. These two systems are related to the first two types of analysis in the decision theory: descriptive, normative and prescriptive analysis. However, to compromise between “fast” and “slow” thinking, “overconfidence” is used as a weighting parameter. A case study including a sample of 161 active investors in Tehran Stock Exchange (TSE) is provided. Moreover, the feasibility and optimality of the model are discussed. FindingsResults show that the PPS recommendations are efficient with a shift from the mean-variance efficient frontier; investors prefer PPS portfolios over the advisor recommendations; and investors have no significant preference between PPS and their own expectations. Research limitations/implicationsTwo assumptions of this study include: first, investors follow their “fast” system of thinking by themselves. Second, the investors’ “slow” system of thinking is represented by advisor recommendations which are simple expected value of risk and return. Therefore, considering these two assumptions for any application is the main limitation of this study. Moreover, the authors did not have access to more investors in TSE or other financial markets. Originality/valueThis is the first study that includes overconfidence in modeling portfolio selection for the purpose of achieving a portfolio that has a reasonable performance and one that investors are comfortable with.

An empirical test of the perceived relationship between risk and the constituents severity and probability

2016 ◽  
Vol 24 (2) ◽  
pp. 194-204 ◽  
Author(s):  
Teodor Sommestad ◽  
Henrik Karlzén ◽  
Peter Nilsson ◽  
Jonas Hallberg
Keyword(s):  
Decision Making ◽  
Information Security ◽  
Empirical Test ◽  
Security Risk ◽  
Content Type ◽  
Procedural Support ◽  
The Mean ◽  
Information Security Risk ◽  
The Relationship ◽  
Mean Variance

Purpose In methods and manuals, the product of an information security incident’s probability and severity is seen as a risk to manage. The purpose of the test described in this paper is to investigate if information security risk is perceived in this way, if decision-making style influences the perceived relationship between the three variables and if the level of information security expertise influences the relationship between the three variables. Design/methodology/approach Ten respondents assessed 105 potential information security incidents. Ratings of the associated risks were obtained independently from ratings of the probability and severity of the incidents. Decision-making style was measured using a scale inspired from the Cognitive Style Index; information security expertise was self-reported. Regression analysis was used to test the relationship between variables. Findings The ten respondents did not assess risk as the product of probability and severity, regardless of experience, expertise and decision-making style. The mean variance explained in risk ratings using an additive term is 54.0 or 38.4 per cent, depending on how risk is measured. When a multiplicative term was added, the mean variance only increased by 1.5 or 2.4 per cent. For most of the respondents, the contribution of the multiplicative term is statistically insignificant. Practical Implications The inability or unwillingness to see risk as a product of probability and severity suggests that procedural support (e.g. risk matrices) has a role to play in the risk assessment processes. Originality/value This study is the first to test if information security risk is assessed as an interaction between probability and severity using suitable scales and a within-subject design.

scholarly journals Ambiguity Aversion and the Variance Premium

2019 ◽  
Vol 09 (02) ◽  
pp. 1950003 ◽  
Author(s):  
Jianjun Miao ◽  
Bin Wei ◽  
Hao Zhou
Keyword(s):  
Risk Aversion ◽  
Ambiguity Aversion ◽  
Equity Premium ◽  
Return Variance ◽  
Quantitative Performance ◽  
The Mean ◽  
The Difference ◽  
Risk Free Rate ◽  
Mean Variance ◽  
Free Rate

This paper offers an ambiguity-based interpretation of the variance premium — the difference between risk-neutral and objective expectations of market return variance — as a compounding effect of both belief distortion and variance differential regarding the uncertain economic regimes. Our calibrated model can match the variance premium, the equity premium, and the risk-free rate in the data. We find that about 97% of the mean–variance premium can be attributed to ambiguity aversion. A three-way separation among ambiguity aversion, risk aversion, and intertemporal substitution, permitted by the smooth ambiguity preferences, plays a key role in our model’s quantitative performance.

scholarly journals The gompertz exponential pareto distribution with the properties and applications to bladder cancer and hydrological datasets

2021 ◽  
Vol 6 (2) ◽  
pp. 107-116
Author(s):  
Adewunmi Olaniran Adeyemi ◽  
Eno Emmanuella Akarawak ◽  
Ismail Adedeji Adeleke
Keyword(s):  
Real Life ◽  
Likelihood Estimation ◽  
Moment Generating Function ◽  
Superior Performance ◽  
Lifetime Distributions ◽  
Mathematical Properties ◽  
Special Cases ◽  
The Mean ◽  
Life Phenomena ◽  
Mean Variance

Many existing distributions in literatures does not have the modeling fits capacity to adequately describe the real-life phenomena. The Exponential Pareto (EP) distribution has further gained some generalizations among several authors using different generator techniques with an aim to obtain a new distribution with greater flexibility. This article proposes Gompertz Exponential Pareto (GEP) distribution using the Gompertz generator. Findings from the study revealed some lifetime distributions as special cases and mathematical properties of the distribution investigated including the mean, variance, coefficient of variation, quantile, moment, moment generating function and, order statistics. The distribution can be positively or negatively skewed. It is unimodal with failure rates whose shapes could be reversed J bathtub, constant, decreasing and, increasing and the parameters were estimated using maximum likelihood estimation approach. Applications to two real-life datasets revealed the ability of GEP distribution to provide more flexibilities and better fit to the dataset compared to some previously proposed distributions for the data. The results also revealed that GEP had the superior performance over other generalizations of EP distribution existing in literatures and the performance has further strengthened the usefulness of the Gompertz-generator technique.

Portfolio forming decisions: the role of intellectual capital

2016 ◽  
Vol 17 (3) ◽  
pp. 439-456 ◽  
Author(s):  
Marina Zavertiaeva
Keyword(s):  
Intellectual Capital ◽  
Design Methodology ◽  
Growth Potential ◽  
Innovation Activity ◽  
Investment Portfolio ◽  
Content Type ◽  
The Mean ◽  
Sharpe Ratios ◽  
Practical Implications

Purpose – The purpose of this paper is to present a tool to categorize companies as potentially profitable on the basis of an intellectual capital (IC) analysis. Design/methodology/approach – The paper distinguishes two crucial attributions for picking shares: IC and capitalization of IC-based growth potential. Using these two attributions, the author creates a portfolio from a sample of European companies and annually rebalances it. To test its attractiveness, the author then compares the portfolio with benchmarks and random portfolios during the period from 2006 to 2013 using a Sharpe coefficient. Findings – The comparison of the constructed portfolio with the benchmarks demonstrates the importance of IC for market investors and the validity of the proposed tool. The Sharpe ratio of the portfolio is significantly higher than the mean and median Sharpe ratios of random portfolios. In addition, the importance of IC for choosing proper investment goal increases in crisis. Research limitations/implications – This investigation can be improved by analysing other IC such as the qualification of CEOs, participation of the company in business alliances, and a company’s innovation activity. In addition, the paper considers only European companies. Practical implications – The proposed tool provides a method to construct investment-attractive portfolios on the basis of IC. Originality/value – The paper contributes to the literature by identifying the underestimated shares on the basis of a company’s IC and by developing an algorithm to create an IC-based investment portfolio.

Does skewness help in better investment decision making?

2018 ◽  
Vol 13 (5) ◽  
pp. 824-836
Author(s):  
Satish Kumar
Keyword(s):  
Utility Function ◽  
Stock Returns ◽  
Stock Exchange ◽  
Investment Decision ◽  
Generalized Least Squares ◽  
Financial Ratios ◽  
Content Type ◽  
The Mean ◽  
Pe Ratio ◽  
Mean Variance

Purpose The purpose of this paper is to examine the significance of skewness in maximizing the investor utility using the daily data for eight sectors listed on the National Stock Exchange of India. Design/methodology/approach The analysis is carried out in three different steps. In the first part, the author analyzes the monthly stock returns and the important financial ratios – price-to-book (PB) ratio, price-earnings (PE) ratio and dividend yield (DY). Second, the author tests the sector-wise return predictability using Westerlund and Narayan (2012) flexible generalized least squares estimator. Third, the author compares the mean–variance–skewness (MVS) utility function with the mean–variance (MV) utility function. Findings The author forecasts the sectoral stock returns using three financial ratios – PB ratio, PE ratio and DY – as predictors. The results indicate that sectoral stock returns are significantly predicted by these financial ratios. The author then formulates trading strategies by including skewness in the utility function and finds that the investor utility is high when the utility function includes skewness as opposed to when the skewness is excluded. Originality/value The author extends the MV utility function to the MVS utility function and shows that the Indian stock market is more profitable when the investor uses a MVS utility function which highlights the main contribution to the literature.

The Impact of Financial Crises on the Asset Allocation: Classical Theory Versus Behavioral Theory

2019 ◽  
Vol 32 (2) ◽  
pp. 218-236
Author(s):  
Amen Aissi Harzallah ◽  
Mouna Boujelbene Abbes
Keyword(s):  
Financial Crisis ◽  
Risk Aversion ◽  
Asset Allocation ◽  
Global Financial Crisis ◽  
Optimal Portfolio ◽  
Optimal Portfolios ◽  
The Mean ◽  
Degree Of Risk ◽  
The Impact ◽  
Mean Variance

The aim of this article is to compare the portfolio optimization generated by the behavioral portfolio theory (BPT) and the mean variance theory (MVT) by investigating the impact of the global financial crisis on the asset allocation. We use data from the Canadian Stock Exchange over the 2002–2015 period. By comparing both approaches, we show that for any level of aspiration and admissible failure, the BPT optimal portfolio will always contain a part of the mean–variance frontier. Thus, in the case of higher degree of risk aversion induced by typical BPT investors, the security set is located on the upper right of the Markowitz frontier. However, even if the optimal portfolios of MVT and BPT may coincide, MVT investors associated with an extremely low degree of risk aversion will not systematically choose BPT optimal portfolios. Our results also indicate the period of financial crisis generate huge losses in MVT portfolio values that implies a lower expected return and a higher level of risk. Furthermore, we point out the absence of the BPT optimal portfolio when potential losses are higher during the 2008 global financial crisis. JEL: G11, G17, G40

Portfolio optimization based on modified expected shortfall

2019 ◽  
Vol 36 (3) ◽  
pp. 440-463
Author(s):  
Deepak Jadhav ◽  
T.V. Ramanathan
Keyword(s):  
New York ◽  
Portfolio Optimization ◽  
Stock Exchange ◽  
Risk Measure ◽  
Natural Extension ◽  
Efficient Frontier ◽  
Expected Shortfall ◽  
Content Type ◽  
The Mean ◽  
Mean Variance

Purpose An investor is expected to analyze the market risk while investing in equity stocks. This is because the investor has to choose a portfolio which maximizes the return with a minimum risk. The mean-variance approach by Markowitz (1952) is a dominant method of portfolio optimization, which uses variance as a risk measure. The purpose of this paper is to replace this risk measure with modified expected shortfall, defined by Jadhav et al. (2013). Design/methodology/approach Modified expected shortfall introduced by Jadhav et al. (2013) is found to be a coherent risk measure under univariate and multivariate elliptical distributions. This paper presents an approach of portfolio optimization based on mean-modified expected shortfall for the elliptical family of distributions. Findings It is proved that the modified expected shortfall of a portfolio can be represented in the form of expected return and standard deviation of the portfolio return and modified expected shortfall of standard elliptical distribution. The authors also establish that the optimum portfolio through mean-modified expected shortfall approach exists and is located within the efficient frontier of the mean-variance portfolio. The results have been empirically illustrated using returns from stocks listed in National Stock Exchange of India, Shanghai Stock Exchange of China, London Stock Exchange of the UK and New York Stock Exchange of the USA for the period February 2005-June 2018. The results are found to be consistent across all the four stock markets. Originality/value The mean-modified expected shortfall portfolio approach presented in this paper is new and is a natural extension of the Markowitz’s mean-variance and mean-expected shortfall portfolio optimization discussed by Deng et al. (2009).

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