scholarly journals On Imposing ESG Constraints of Portfolio Selection for Sustainable Investment and Comparing the Efficient Frontiers in the Weight Space

SAGE Open ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 215824402097507
Author(s):  
Yue Qi ◽  
Xiaolin Li
Keyword(s):  
Portfolio Selection ◽  
Efficient Frontier ◽  
Weight Space ◽  
Efficient Frontiers ◽  
Risk Return ◽  
Screening Strategies ◽  
Dow Jones Industrial Average ◽  
Sustainable Investment ◽  
Selection For ◽  
Portfolio Selection Model

Sustainable investment is typically fulfilled by screening of environmental, social, and governance (ESG); the screening strategies are practical and expedite sustainable-investment development. However, the strategies typically build portfolios by a list of good stocks and ignore portfolio completeness. Moreover, there has been limited literature to study the portfolio weights of sustainable investment in the weight space. In such an area, this article contributes to the literature as follows: We extend a conventional portfolio-selection model and impose ESG constraints. We analytically solve our model by computing the efficient frontier and prove that the frontier’s portfolio weights all lie on a ray (half line). By the ray structure, we prove that portfolio selection for sustainable investment and conventional portfolio selection fundamentally possess highly different portfolio weights. Overall, our aim is comparing the portfolio weights of sustainable portfolio selection and of conventional portfolio selection; the comparison result has been unknown until now. The result is important for sustainable investment because portfolio weights are the foundation of portfolio selection and investments. We sample the component stocks of Dow Jones Industrial Average Index from 2004 to 2013 and find that our efficient frontier and the conventional efficient frontier are quite similar. Therefore, in plain financial language, investors can still obtain risk-return performance similar to conventional portfolio selection after imposing strong ESG requirements, although the portfolio weights can be totally different. The result is both an endorsement and a reminder for sustainable investment.

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.

Portfolio Selection Models and Their Discrimination

2011 ◽  
Vol 2 (2) ◽  
pp. 65-91 ◽  
Author(s):  
Satadal Ghosh ◽  
Sujit Kumar Majumdar
Keyword(s):  
Portfolio Selection ◽  
Portfolio Management ◽  
Stock Return ◽  
Signal To Noise Ratio ◽  
Selection Models ◽  
Markowitz Model ◽  
Risk Return ◽  
Future Return ◽  
Possibility Distributions ◽  
Portfolio Selection Model

The stochastic nature of financial markets is a barrier for successful portfolio management. Besides traditional Markowitz’s model, many other portfolio selection models in Bayesian and Non-Bayesian frameworks have been developed. Starting with the basic Markowitz model, several cardinal models are used to find optimum portfolios with select stock set. Having developed the regression model of the return of each stock with the market return, the unsystematic part of the uncertainty was used to find the optimum portfolio and efficient risk–return frontier within each portfolio selection model. The average stock return as estimated from its historical data and the forecasted stock return were used for maximizing return with quadratic programming formulation in Markowitz model. In the models involving Fuzzy probability and possibility distributions, the future return was estimated using the similarity grade of past returns. In the interval coefficient models, future return was estimated as interval variable. The optimum portfolios of different models were widely divergent and DEA was used to identify the model giving the best portfolio with higher appraisal, both overall and by peers, and least Maverick behavior. Use of Signal to Noise ratio proved equally efficient for model discrimination and yielded identical results.

scholarly journals Selecting Socially Responsible Portfolios: A Fuzzy Multicriteria Approach

2019 ◽  
Vol 11 (9) ◽  
pp. 2496 ◽  
Author(s):  
García ◽  
González-Bueno ◽  
Oliver ◽  
Riley
Keyword(s):  
Portfolio Selection ◽  
Investment Decision ◽  
Efficient Frontier ◽  
Cardinality Constraints ◽  
Socially Responsible ◽  
Data Set ◽  
Fuzzy Variables ◽  
Investment Decision Making ◽  
Proposed Model ◽  
Dow Jones Industrial Average

We propose a multi-objective approach for portfolio selection, which allows investors to consider not only return and downside risk criteria but also to include environmental, social and governance (ESG) scores in the investment decision-making process. Owing to the uncertain environment of portfolio selection, the return and ESG score of each asset are considered as independent L-R power fuzzy variables. To make the model more realistic, we take budget, floor ceiling and cardinality constraints into account. In order to select the optimal portfolio along the efficient frontier, we apply the Sortino ratio in a credibilistic environment. The subsequent empirical application uses a data set from Bloomberg's ESG Data in combination with US Dow Jones Industrial Average data. The experimental results show that the proposed model offers promising results for socially responsible investors seeking ethical and sustainability goals beyond the return-risk trade-off and its ability to beat the benchmark.

Provisions for bank deposit withdrawals and portfolio selection

2020 ◽  
Vol 07 (01) ◽  
pp. 1950037
Author(s):  
Ryle S. Perera
Keyword(s):  
Portfolio Selection ◽  
Closed Form Solution ◽  
Efficient Frontier ◽  
Optimal Investment ◽  
Form Solution ◽  
Investment Portfolio ◽  
Efficient Manner ◽  
Selection For ◽  
Mean Variance Portfolio ◽  
Mean Variance

The primary economic function of a bank is to redirect funds from savers to borrowers in an efficient manner, while increasing the value of the bank’s asset holdings in absolute terms. Within the regulatory framework of the Basel III accord, banks are required to maintain minimum liquidity to guard against withdrawals/liquidity risks. In this paper, we analyze a continuous-time mean-variance portfolio selection for a bank with stochastic withdrawal provisioning by relating the reserves as a proxy for the assets held by the bank. We then formulate an optimal investment portfolio selection for a banker by constructing a special Riccati equation as a continuous solution to the Hamilton–Jacobi–Bellman (HJB) equation under mean-variance paradigm. We obtain an explicit closed form solution for the optimal investment portfolio as well as the efficient frontier. The aforementioned modeling enables us to formulate a stochastic optimal control problem related to the minimization of the reserve, depository, and intrinsic risk that are associated with the reserve process.

Portfolio Selection Models and Their Discrimination

2013 ◽  
pp. 170-195
Author(s):  
Satadal Ghosh ◽  
Sujit Kumar Majumdar
Keyword(s):  
Portfolio Selection ◽  
Portfolio Management ◽  
Stock Return ◽  
Signal To Noise Ratio ◽  
Selection Models ◽  
Markowitz Model ◽  
Risk Return ◽  
Future Return ◽  
Possibility Distributions ◽  
Portfolio Selection Model

The stochastic nature of financial markets is a barrier for successful portfolio management. Besides traditional Markowitz’s model, many other portfolio selection models in Bayesian and Non-Bayesian frameworks have been developed. Starting with the basic Markowitz model, several cardinal models are used to find optimum portfolios with select stock set. Having developed the regression model of the return of each stock with the market return, the unsystematic part of the uncertainty was used to find the optimum portfolio and efficient risk–return frontier within each portfolio selection model. The average stock return as estimated from its historical data and the forecasted stock return were used for maximizing return with quadratic programming formulation in Markowitz model. In the models involving Fuzzy probability and possibility distributions, the future return was estimated using the similarity grade of past returns. In the interval coefficient models, future return was estimated as interval variable. The optimum portfolios of different models were widely divergent and DEA was used to identify the model giving the best portfolio with higher appraisal, both overall and by peers, and least Maverick behavior. Use of Signal to Noise ratio proved equally efficient for model discrimination and yielded identical results.

scholarly journals The Impact of COVID-19 Lockdowns on Sustainable Indexes

2021 ◽  
Vol 13 (4) ◽  
pp. 1846 ◽  
Author(s):  
Helen Chiappini ◽  
Gianfranco Vento ◽  
Leonardo De Palma
Keyword(s):  
Abnormal Returns ◽  
Nonparametric Tests ◽  
Market Growth ◽  
Bear Market ◽  
Global Pandemic ◽  
Screening Strategies ◽  
The Usa ◽  
Sustainable Investment ◽  
Sustainable Investments ◽  
The Impact

This paper analyzes the response of sustainable indexes to the pandemic lockdown orders in Europe and the USA, contributing to both the research on the effects of the global pandemic outbreak and the resiliency of sustainable investments under market distress. Our results demonstrate that sustainable indexes were negatively impacted by lockdown orders; however, they did not show statistically significant different abnormal returns compared to traditional indexes. Similarly, our empirical results confirm that sustainable screening strategies (negative, positive, best in class) did not have an influence during such announcements. These results are robust across several model specifications and robustness tests, including nonparametric tests, generalized autoregressive conditionally heteroskedastic (GARCH) estimation of abnormal returns, and alternative events. The findings suggest that investors do not have to pay the price for the investments in sustainable assets when a bear market occurs; consequently, ceteris paribus, these investments appear suitable for financial-first investors. Such results have relevant practical consequences in terms of sustainable investment attractiveness and market growth.

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