Finding socially responsible portfolios close to conventional ones

Calvo, Clara; Ivorra, Carlos; Liern, Vicente

doi:10.1016/j.irfa.2015.03.014

Cited by 28 publications

(15 citation statements)

References 47 publications

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“…Accordingly, the superiority of the GA-ELM is further confirmed. When the number of high-return stocks N equals other values, i.e., 16,17,18,19, or 20, we obtain similar conclusions, which indicate that the GA-ELM model has good generalizability. erefore, the GA-ELM is a promising tool for use in the practice of stock screening.…”

Section: Stock Double-screeningsupporting

confidence: 60%

“…e conventional GMV and MSPR models cannot trade off the financial and ESG performance of SRI investors since they do not incorporate the ESG factor. erefore, this paper includes the ESG score within the objective functions of the GMV and MSPR models to form extended portfolio optimization models, i.e., the GMV-ESG and MSPR-ESG models, to balance the ESG Input: initialize the chromosome populations Output: the optimal input weights and biases of the hidden layer of the ELM (1) Begin (2) Set the parameters of the ELM (3) Define the fitness function (4) Evaluate the fitness of all chromosomes in population (5) C � the best chromosome (6) g � 1 (7) while g < maximum generation do (8) for i � 0 ⟶ P do (9) Generate rd1 randomly in the interval [0, 1] (10) if rd1< crossover probability then (11) Update the chromosome according to equation ( 7) (12) end if (13) Generate rd2 randomly in the interval [0, 1] ( 14) if rd2< mutation probability then (15) Update the chromosome according to equation ( 8) (16) end if (17) Evaluate the chromosome fitness (18) end for (19) Update the chromosome populations according to equation (6) (20) Rank the chromosome populations according to fitness (21) C � the best current chromosome (22) g � g + 1 (23) end while (24) Return the optimal input weights and biases of the hidden layer of the ELM according to C (25) End ALGORITHM 1: GA-ELM algorithm. and financial objectives of SRI investors.…”

Section: E Proposed Modelsmentioning

confidence: 99%

“…However, as discussed by Diouf et al [15], MacKenzie and Lewis [16], and Rosen et al [17], SRI investors are concerned with both ESG and financial performance at the same time. ere is no conclusive evidence that stocks screened based on ESG criteria can deliver high returns [2,[18][19][20]. erefore, it is necessary to consider the return potential of candidate stocks in the screening process to ensure that the screened stocks meet both the ESG criteria and the return demand of SRI investors.…”

Section: Introductionmentioning

confidence: 99%

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Socially Responsible Investment Portfolio Construction with a Double-Screening Mechanism considering Machine Learning Prediction

Jun

Chen

2021

Discrete Dynamics in Nature and Society

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Although socially responsible investment (SRI) has developed into an important investment style, only a small number of studies discuss SRI portfolio construction. In view of the overwhelming breakthrough of machine learning in prediction, this paper proposes SRI portfolio construction models by combining a double-screening mechanism considering machine learning prediction and an extended global minimum variance (GMV) model (or extended maximum Sharpe ratio (MSPR) model), which are, respectively, named double-screening socially responsible investment (DSSRI) portfolio models I and II. The proposed models consist of two stages, i.e., stock screening and asset allocation. First, this paper develops a novel double-screening mechanism incorporating environmental, social, and corporate governance (ESG) and return potential criteria to ensure that high-quality stocks with good ESG performance and high-return potential are input into the optimal portfolio. Specifically, to obtain accurate stock return predictions, an extreme learning machine model optimized by the genetic algorithm is employed to predict stock prices. Next, to trade off the financial and ESG objectives of SRI investors, an extended GMV model (or extended MSPR model) considering the ESG factor is introduced to determine the capital allocation proportion of the stocks. We take the A-share market of China as the sample to verify the effectiveness of the proposed models. The empirical results demonstrate that compared with alternative models, the proposed models can yield better annualized return and ESG score performance as well as competitive Sharpe ratio performance.

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Section: Stock Double-screeningsupporting

confidence: 60%

Section: E Proposed Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Socially Responsible Investment Portfolio Construction with a Double-Screening Mechanism considering Machine Learning Prediction

Jun

Chen

2021

Discrete Dynamics in Nature and Society

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“…This found that in terms of risk-adjusted returns, ethical pension plans, which invest in companies with SRI, achieve the same financial gains with traditional pension plans [5]. The authors suggested that the reasons might be that companies in stock markets have made green investments, which allows for improvements in their SRI, resulting in more cost-effective and technically feasible production methods, and consequently a performance level with similar financial gains [27,28].…”

Section: Sustainable Investmentmentioning

confidence: 99%

Sustainable Investment: Interrelated among Corporate Governance, Economic Performance and Market Risks Using Investor Preference Approach

et al. 2019

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Prior studies are lacking on the drivers of sustainable investment. Hence, this study examines the relationship between the social aspects, environmental aspects, economic benefits, market conditions, and corporate governance issues on sustainable investment. Sustainable investment has been rising since the last decade. However, sustainable investment is preceded by ethical investment, green investment, and socially responsible investment. In order to understand the sustainability of an investment before decision-making, it proposed a set of attributes to measure its sustainability using investor’s linguistics preferences. The proposed attributes are interrelated and based on investor’s linguistic preferences. The study employs the fuzzy set theory to handle the uncertainty resulting from the vagueness of linguistic terms and applies decision making trial and evaluation laboratory (DEMATEL) to determine the nature of interrelationships among sustainable investment attributes. The result indicates that corporate governance, economic performance, and market risks are the causal aspects of sustainable investment. In addition, this study found that transparency, anti-corruption, and board diversity were the two most important criteria of corporate governance. Furthermore, the three most important criteria of economic performance presented the model were excess return, market value, and shareholder loyalty. The theoretical and practical implications of sustainable investment are discussed.

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“…Obtaining the efficient frontier is a mathematical problem, which does not require a subjective choice. The selection of a particular portfolio on the efficient frontier, by contrast, is highly subjective, as it involves satisfying the investor's personal preferences (Calvo et al, 2014).…”

Section: Literature Reviewmentioning

confidence: 99%

The risk in socially responsible investing: the other side of the coin

Burchi

2019

JRF

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Purpose The field of socially responsible investment (SRI) has become a central theme in the mutual funds industry. The risk implications associated with this investment approach are less explored. This study further investigates the real contribution to the investor offered by the SRI alternative.The aim of this paper is to throw more light on this debate. Design/methodology/approach Analyzing a large sample of US companies, this study investigates the tendency to generate risk when the portfolio is built, taking into account SRI. The research is based on the backtest of the real performance obtainable by adopting different investment strategies in which the red line is the selection method based on the principles of corporate social responsibility. Findings The investor must pay a cost that depends on the degree of rigor in the selection criteria. The risk associated with SRI is influenced by the measure adopted. SRI has a better asymmetric risk behavior than other securities. The results suggest using different selection models according to the investor’s objectives. When the objective is to maximize the average return and the remuneration risk, the SRI selection model should be negative or at least as inclusive as possible. In the event that the investor’s objective is to contain risk indices, a restrictive approach to the selection of investments is advisable. Originality/value Academic research has long been investigating the ability to generate profits but often neglects the levels of risk implicit in such investment approaches. The originality of this research consists in the adoption of a model based on the continuous optimization of the portfolio. This approach allows the results to be assessed by the returns actually obtained.

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Finding socially responsible portfolios close to conventional ones

Cited by 28 publications

References 47 publications

Socially Responsible Investment Portfolio Construction with a Double-Screening Mechanism considering Machine Learning Prediction

Socially Responsible Investment Portfolio Construction with a Double-Screening Mechanism considering Machine Learning Prediction

Sustainable Investment: Interrelated among Corporate Governance, Economic Performance and Market Risks Using Investor Preference Approach

The risk in socially responsible investing: the other side of the coin

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