Binary Beetle Antennae Search Algorithm for Tangency Portfolio Diversification

Katsikis, Vasilios N.; Mourtas, Spyridon D.

doi:10.32732/jmo.2021.13.1.44

Cited by 16 publications

(4 citation statements)

References 20 publications

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“…4. Because of its simplicity, BAS permits novel optimisation algorithms to be developed (see [31][32][33][34][35]). As a result, the BIWASD algorithm uses the beetle searching behavior for finding the global minimum to specify the optimal weights of the NN.…”

Section: Biwasd Algorithmmentioning

confidence: 99%

Feedback Control Systems Stabilization Using a Bio-inspired Neural Network

Mourtas

Katsikis

Kasimis

2022

EAI Endorsed Trans AI Robotics

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The proportional–integral–derivative (PID) control systems, which have become a standard for technical and industrial applications, are the fundamental building blocks of classical and modern control systems. In this paper, a three-layer feed-forward neural network (NN) model trained to replicate the behavior of a PID controller is employed to stabilize control systems through a NN feedback controller. A novel bio-inspired weights-and-structure-determination (BIWASD) algorithm, which incorporates a metaheuristic optimization algorithm dubbed beetle antennae search (BAS), is used to train the NN model. More presicely, the BIWASD algorithm identifies the ideal weights and structure of the BIWASD-based NN (BIWASDNN) model utilizing a power sigmoid activation function while handling model fitting and validation. The results of three simulated trials on stabilizing feedback control systems validate and demonstrate the BIWASDNN model’s exceptional learning and prediction capabilities, while achieving similar or better performance than the corresponding PID controller. The BIWASDNN model is compared to three other high-performing NN models, and a MATLAB repository is accessible in public through GitHub to encourage and enhance this work.

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Section: Biwasd Algorithmmentioning

confidence: 99%

Feedback Control Systems Stabilization Using a Bio-inspired Neural Network

Mourtas

Katsikis

Kasimis

2022

EAI Endorsed Trans AI Robotics

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“…The tradeoff between risk and return is most efficient at tangency portfolio, where the curve's tangent is located. The most effective portfolio, known as the tangency portfolio, is the intersection point of the Capital Market Line (CML) and the efficient frontier [15]. Investors will try to manage their portfolio to fit the asset weight at this point, to get the best relatively investment option.…”

Section: Modelmentioning

confidence: 99%

Impact of Covid-19 on Portfolio Selection Based on Case Study of Pharmaceutical, High Technology and Expression Industry

Chen¹

2022

Proceedings of the International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2022, 17-19 June 2

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The spread of Covid-19 has an impact on humans in both physical and economic ways. The pandemic resulting from coronavirus changes the financial world a lot. Numerous sectors experienced dramatic changes in its market environment. The stock market is also largely affected by pandemic. It is worth to explore the impact of Covid-19 on portfolio selections. Three different sectors (pharmaceutical, high technology and expression) were chosen in this paper for building portfolios. The data includes the performance of the stock of three firms ((

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“…Thus, this algorithm has the advantage of quick convergence properties and simple implementation compared to other swarm optimization algorithms [13]. In recent years, in the financial field, the BAS algorithm has been applied to the portfolio insurance problem [14][15][16][17][18][19]. However, the research on public trade audit companies is limited and few studies use the BAS algorithm for fraud detection [20].…”

Section: Introductionmentioning

confidence: 99%

Adopting Nonlinear Activated Beetle Antennae Search Algorithm for Fraud Detection of Public Trading Companies: A Computational Finance Approach

et al. 2022

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With the emergence of various online trading technologies, fraudulent cases begin to occur frequently. The problem of fraud in public trading companies is a hot topic in financial field. This paper proposes a fraud detection model for public trading companies using datasets collected from SEC’s Accounting and Auditing Enforcement Releases (AAERs). At the same time, this computational finance model is solved with a nonlinear activated Beetle Antennae Search (NABAS) algorithm, which is a variant of the meta-heuristic optimization algorithm named Beetle Antennae Search (BAS) algorithm. Firstly, the fraud detection model is transformed into an optimization problem of minimizing loss function and using the NABAS algorithm to find the optimal solution. NABAS has only one search particle and explores the space under a given gradient estimation until it is less than an “Activated Threshold” and the algorithm is efficient in computation. Then, the random under-sampling with AdaBoost (RUSBoost) algorithm is employed to comprehensively evaluate the performance of NABAS. In addition, to reflect the superiority of NABAS in the fraud detection problem, it is compared with some popular methods in recent years, such as the logistic regression model and Support Vector Machine with Financial Kernel (SVM-FK) algorithm. Finally, the experimental results show that the NABAS algorithm has higher accuracy and efficiency than other methods in the fraud detection of public datasets.

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Binary Beetle Antennae Search Algorithm for Tangency Portfolio Diversification

Cited by 16 publications

References 20 publications

Feedback Control Systems Stabilization Using a Bio-inspired Neural Network

Feedback Control Systems Stabilization Using a Bio-inspired Neural Network

Impact of Covid-19 on Portfolio Selection Based on Case Study of Pharmaceutical, High Technology and Expression Industry

Adopting Nonlinear Activated Beetle Antennae Search Algorithm for Fraud Detection of Public Trading Companies: A Computational Finance Approach

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