scholarly journals An Artificial Bee Colony Algorithm for Uncertain Portfolio Selection

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Wei Chen
Keyword(s):  
Portfolio Selection ◽  
Artificial Bee Colony ◽  
Mean Value ◽  
Investment Risk ◽  
Entropy Model ◽  
Uncertain Variables ◽  
Bee Colony ◽  
Proposed Model ◽  
Security Returns ◽  
Mean Variance

Portfolio selection is an important issue for researchers and practitioners. In this paper, under the assumption that security returns are given by experts’ evaluations rather than historical data, we discuss the portfolio adjusting problem which takes transaction costs and diversification degree of portfolio into consideration. Uncertain variables are employed to describe the security returns. In the proposed mean-variance-entropy model, the uncertain mean value of the return is used to measure investment return, the uncertain variance of the return is used to measure investment risk, and the entropy is used to measure diversification degree of portfolio. In order to solve the proposed model, a modified artificial bee colony (ABC) algorithm is designed. Finally, a numerical example is given to illustrate the modelling idea and the effectiveness of the proposed algorithm.

scholarly journals Application of Artificial Bee Colony Algorithm to Portfolio Adjustment Problem with Transaction Costs

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Wei Chen ◽  
Hui Ma ◽  
Yiping Yang ◽  
Mengrong Sun
Keyword(s):  
Transaction Costs ◽  
Artificial Bee Colony ◽  
Fuzzy Numbers ◽  
Mean Value ◽  
Investment Risk ◽  
Risky Assets ◽  
Bee Colony ◽  
Proposed Model ◽  
Mean Variance ◽  
Portfolio Adjustment

Compared with the conventional probabilistic mean-variance methodology, fuzzy number can better describe an uncertain environment with vagueness and ambiguity. In this paper, we discuss a portfolio adjusting problem under the assumption that the returns of risky assets are fuzzy numbers and there exist general transaction costs in portfolio adjusting process. In the proposed model, we take the first possibilistic moment about zero of a portfolio as the investment return and the second possibilistic moment about the possibilistic mean value of the portfolio as the investment risk. To solve the proposed model, a modified artificial bee colony (ABC) algorithm is developed for calculating the optimal portfolio adjusting strategy. Finally, a numerical example is given to illustrate the effectiveness of the proposed model and approach.

scholarly journals Uncertain portfolio optimization problem with liquidity and diversification

2020 ◽  
Author(s):  
Ranran Zhang ◽  
Bo Li
Keyword(s):  
Portfolio Selection ◽  
Selection Problem ◽  
Investment Risk ◽  
Deterministic Models ◽  
Entropy Model ◽  
Portfolio Selection Problem ◽  
Uncertain Variables ◽  
Proposed Model ◽  
Portfolio Optimization Problem ◽  
Mean Variance

This paper deals with a portfolio selection problem with uncertain returns. Here, the returns of the assets are regarded as uncertain variables which are estimated by experienced experts. First, an uncertain mean-variance-entropy model for portfolio selection problem is presented by taking into account four criteria viz., return, risk, liquidity and diversification degree of portfolio. In the proposed model, the investment return is quantified by uncertain expected value, the investment risk is characterized by uncertain variance and entropy is used to measure the diversification degree of portfolio. Moreover, different from the previous bi-objective optimization model, our model achieves both the maximum return and the minimum risk in a single objective form by introducing a risk aversion factor and the dimensional influence caused by different units is eliminated by normalization. Then, two auxiliary portfolio selection models are transformed into different equivalent deterministic models. Finally, a numerical simulation is given to verify the practicability of our model.

Artificial Bee Colony-Based Approach for Privacy Preservation of Medical Data

2021 ◽  
pp. 1203-1221
Author(s):  
Shivlal Mewada ◽  
Sita Sharan Gautam ◽  
Pradeep Sharma
Keyword(s):  
Privacy Preservation ◽  
Artificial Bee Colony ◽  
Model Simulation ◽  
Privacy Preserving ◽  
Global Network ◽  
Sensitive Information ◽  
Healthcare Applications ◽  
Efficient Manner ◽  
Bee Colony ◽  
Proposed Model

A large amount of data is generated through healthcare applications and medical equipment. This data is transferred from one piece of equipment to another and sometimes also communicated over a global network. Hence, security and privacy preserving are major concerns in the healthcare sector. It is seen that traditional anonymization algorithms are viable for sanitization process, but not for restoration task. In this work, artificial bee colony-based privacy preserving model is developed to address the aforementioned issues. In the proposed model, ABC-based algorithm is adopted to generate the optimal key for sanitization of sensitive information. The effectiveness of the proposed model is tested through restoration analysis. Furthermore, several popular attacks are also considered for evaluating the performance of the proposed privacy preserving model. Simulation results of the proposed model are compared with some popular existing privacy preserving models. It is observed that the proposed model is capable of preserving the sensitive information in an efficient manner.

scholarly journals School Timetabling Optimisation Using Artificial Bee Colony Algorithm Based on a Virtual Searching Space Method

Mathematics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 73
Author(s):  
Kaixiang Zhu ◽  
Lily D. Li ◽  
Michael Li
Keyword(s):  
Artificial Bee Colony ◽  
Search Space ◽  
Course Timetabling ◽  
Timetabling Problem ◽  
Bee Colony ◽  
Educational Timetabling ◽  
Proposed Model ◽  
School Timetabling ◽  
School Timetabling Problem ◽  
World School

Although educational timetabling problems have been studied for decades, one instance of this, the school timetabling problem (STP), has not developed as quickly as examination timetabling and course timetabling problems due to its diversity and complexity. In addition, most STP research has only focused on the educators’ availabilities when studying the educator aspect, and the educators’ preferences and expertise have not been taken into consideration. To fill in this gap, this paper proposes a conceptual model for the school timetabling problem considering educators’ availabilities, preferences and expertise as a whole. Based on a common real-world school timetabling scenario, the artificial bee colony (ABC) algorithm is adapted to this study, as research shows its applicability in solving examination and course timetabling problems. A virtual search space for dealing with the large search space is introduced to the proposed model. The proposed approach is simulated with a large, randomly generated dataset. The experimental results demonstrate that the proposed approach is able to solve the STP and handle a large dataset in an ordinary computing hardware environment, which significantly reduces computational costs. Compared to the traditional constraint programming method, the proposed approach is more effective and can provide more satisfactory solutions by considering educators’ availabilities, preferences, and expertise levels.

scholarly journals A New Adaptive Entropy Portfolio Selection Model

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 951
Author(s):  
Ruidi Song ◽  
Yue Chan
Keyword(s):  
Portfolio Selection ◽  
Stock Exchange ◽  
Selection Model ◽  
Portfolio Performance ◽  
Entropy Model ◽  
Data Set ◽  
Entropy Measurement ◽  
Self Adaptation ◽  
Portfolio Selection Model ◽  
Mean Variance

In this paper, we propose an adaptive entropy model (AEM), which incorporates the entropy measurement and the adaptability into the conventional Markowitz’s mean-variance model (MVM). We evaluate the performance of AEM, based on several portfolio performance indicators using the five-year Shanghai Stock Exchange 50 (SSE50) index constituent stocks data set. Our outcomes show, compared with the traditional portfolio selection model, that AEM tends to make our investments more decentralized and hence helps to neutralize unsystematic risks. Due to the existence of self-adaptation, AEM turns out to be more adaptable to market fluctuations and helps to maintain the balance between the decentralized and concentrated investments in order to meet investors’ expectations. Our model applies equally well to portfolio optimizations for other financial markets.

scholarly journals Fuzzy Investment Portfolio Selection Models Based on Interval Analysis Approach

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Haifeng Guo ◽  
BaiQing Sun ◽  
Hamid Reza Karimi ◽  
Yuanjing Ge ◽  
Weiquan Jin
Keyword(s):  
Portfolio Selection ◽  
Set Theory ◽  
Fuzzy Set Theory ◽  
Risk Preference ◽  
Expected Returns ◽  
Investment Risk ◽  
Investment Portfolio ◽  
Optimal Resolution ◽  
Portfolio Selection Model ◽  
Mean Variance

This paper employs fuzzy set theory to solve the unintuitive problem of the Markowitz mean-variance (MV) portfolio model and extend it to a fuzzy investment portfolio selection model. Our model establishes intervals for expected returns and risk preference, which can take into account investors' different investment appetite and thus can find the optimal resolution for each interval. In the empirical part, we test this model in Chinese stocks investment and find that this model can fulfill different kinds of investors’ objectives. Finally, investment risk can be decreased when we add investment limit to each stock in the portfolio, which indicates our model is useful in practice.

Artificial Bee Colony-Based Approach for Privacy Preservation of Medical Data

2020 ◽  
Vol 11 (3) ◽  
pp. 22-39
Author(s):  
Shivlal Mewada ◽  
Sita Sharan Gautam ◽  
Pradeep Sharma
Keyword(s):  
Artificial Bee Colony ◽  
Model Simulation ◽  
Privacy Preserving ◽  
Security And Privacy ◽  
Global Network ◽  
Healthcare Sector ◽  
Sensitive Information ◽  
Healthcare Applications ◽  
Bee Colony ◽  
Proposed Model

A large amount of data is generated through healthcare applications and medical equipment. This data is transferred from one piece of equipment to another and sometimes also communicated over a global network. Hence, security and privacy preserving are major concerns in the healthcare sector. It is seen that traditional anonymization algorithms are viable for sanitization process, but not for restoration task. In this work, artificial bee colony-based privacy preserving model is developed to address the aforementioned issues. In the proposed model, ABC-based algorithm is adopted to generate the optimal key for sanitization of sensitive information. The effectiveness of the proposed model is tested through restoration analysis. Furthermore, several popular attacks are also considered for evaluating the performance of the proposed privacy preserving model. Simulation results of the proposed model are compared with some popular existing privacy preserving models. It is observed that the proposed model is capable of preserving the sensitive information in an efficient manner.

scholarly journals An Incremental-Hybrid-Yager’s Entropy Model for Dynamic Portfolio Selection with Fuzzy Variable

2018 ◽  
Vol 2018 ◽  
pp. 1-15
Author(s):  
Yin Li ◽  
Jian Tao ◽  
Yazhi Song
Keyword(s):  
Markov Chain ◽  
Asset Price ◽  
Fuzzy Variable ◽  
Time Interval ◽  
Entropy Model ◽  
Fuzzy Variables ◽  
Proposed Model ◽  
Dynamic Portfolio ◽  
The Relationship ◽  
Mean Variance

To settle down the resolutional uncertainty in optimum portfolio strategy, this paper addresses an incremental-hybrid-Yager’s entropy model to newly describe the relationship between return and risk. Different from the traditional multiperiod portfolio, we design the ratio threshold to divide asset price into different time interval and use state instead of time point to model the dynamic portfolio process. In addition, fuzzy variables are utilized to represent prices of assets, while historical data based on Markov chain is exploited to estimate membership functions of fuzzy prices. At last, a compromised genetic algorithm is designed, and the numerical example shows that the proposed model achieves solid returns compared against the mean-variance model and Markov chain Monte Carlo method.

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