A MULTIOBJECTIVE MODEL FOR PASSIVE PORTFOLIO MANAGEMENT: AN APPLICATION ON THE S&amp;P 100 INDEX

Technological and Economic Development of Economy

González-Bueno

Guijarro

et al. 2020

Self Cite

The present research proposes a novel methodology to solve the problems faced by investors who take into consideration different investment criteria in a fuzzy context. The approach extends the stochastic mean-variance model to a fuzzy multiobjective model where liquidity is considered to quantify portfolio’s performance, apart from the usual metrics like return and risk. The uncertainty of the future returns and the future liquidity of the potential assets are modelled employing trapezoidal fuzzy numbers. The decision process of the proposed approach considers that portfolio selection is a multidimensional issue and also some realistic constraints applied by investors. Particularly, this approach optimizes the expected return, the risk and the expected liquidity of the portfolio, considering bound constraints and cardinality restrictions. As a result, an optimization problem for the constraint portfolio appears, which is solved by means of the NSGA-II algorithm. This study defines the credibilistic Sortino ratio and the credibilistic STARR ratio for selecting the optimal portfolio. An empirical study on the S&P100 index is included to show the performance of the model in practical applications. The results obtained demonstrate that the novel approach can beat the index in terms of return and risk in the analyzed period, from 2008 until 2018.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiobjective Approach to Portfolio Optimization in the Light of the Credibility Theory

Technological and Economic Development of Economy

González-Bueno

Guijarro

et al. 2020

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“…Shortterm investors mainly employ technical analysis (Sobreiro et al, 2016;Zhu, Atri, & Yegen, 2016) and chartist analysis (Gerritsen, 2016;Schmitt & Westerhoff, 2017). Long-term inves-tors widely use fundamental analysis (De Oliveira, Nobre, & Zárate, 2013;Shen, Yan, & Tzeng, 2014) or passive investment strategies (García, Guijarro, & Moya, 2013;García, Guijarro, & Oliver, 2018). Basically, all these strategies concentrate on the expected return of the assets, which are analyzed individually, not as a portfolio.…”

Section: Introductionmentioning

confidence: 99%

“…Following Omidi, Abbasi, and Nazemi (2017), when other criteria are considered, it may be possible to obtain portfolios in which lower return or higher risk are compensated by other criteria, which may produce more satisfaction to investors seeking, not just to maximize return and minimize risk, but to consider other variables. A review of the literature shows that numerous papers have been devoted to develop the original mean-variance model by Markowitz into a new multicriteria framework in order to account for additional decision criteria (Fang, Chen, & Fukushima, 2008;García et al, 2013;Li, Zhu, Sun, Aw, & Teo, 2018;Xia, Liu, Wang, & Lai, 2000). Following this trend, the price-to-earnings ratio (P/E ratio) is one important criterion applied by practitioners to select stocks, due to its ability to capture the current expectations of the market about the companies (Pouya, Solimanpur, & Rezaee, 2016).…”

Section: Introductionmentioning

confidence: 99%

A Credibilistic Mean-Semivariance-Per Portfolio Selection Model for Latin America

Journal of Business Economics and Management

González-Bueno

Oliver

et al. 2019

Self Cite

Many real-world problems in the financial sector have to consider different objectives which are conflicting, for example portfolio selection. Markowitz proposed an approach to determine the optimal composition of a portfolio analysing the trade-off between return and risk. Nevertheless, this approach has been criticized for unrealistic assumptions and several changes have been proposed to incorporate investors’ constraints and more realistic risk measures. In this line of research, our proposal extends the mean-semivariance portfolio selection model to a multiobjective credibilistic model that besides risk and return, also considers the price-to-earnings ratio to measure portfolio performance. Uncertain future returns and PER ratio of each asset are approximated using L-R power fuzzy numbers. Furthermore, we consider budget, bound and cardinality constraints. To solve the constrained portfolio optimization problem, we use the algorithm NSGA-II. We assess the proposed approach generating a portfolio with shares included in the Latin American Integrated Market. Results show that this new approach is a good alternative to solve the portfolio selection problem when multiple objectives are considered.

“…The success of speculative investment strategies in the financial markets depends on market efficiency. If investors believe that the market is efficient, then they will choose passive investment strategies applying index tracking techniques (García, Guijarro, & Moya, 2013). These strategies imply following the evolution of a market index such as DJIA, etc.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Fuzzy Neural Network to Predict Price Direction in the German Dax-30 Index

Technological and Economic Development of Economy

Guijarro

Oliver

et al. 2018

Self Cite

Intraday trading rules require accurate information about the future short term market evolution. For that reason, next-day market trend prediction has attracted the attention of both academics and practitioners. This interest has increased in recent years, as different methodologies have been applied to this end. Usually, machine learning techniques are used such as artificial neural networks, support vector machines and decision trees. The input variables of most of the studies are traditional technical indicators which are used by professional traders to implement investment strategies. We analyse if these indicators have predictive power on the German DAX-30 stock index by applying a hybrid fuzzy neural network to predict the one-day ahead direction of index. We implement different models depending on whether all the indicators and oscillators are used as inputs, or if a linear combination of them obtained through a factor analysis is used instead. In order to guarantee for the robustness of the results, we train and apply the HyFIS models on randomly selected subsamples 10,000 times. The results show that the reduction of the dimension through the factorial analysis generates more profitable and less risky strategies.