Stock Portfolio Construction Using Evolved Bat Algorithm

Chang, Jui-Fang; Yang, Tsai-Wei; Tsai, Pei‐Wei

doi:10.1007/978-3-319-07455-9_35

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…Further, in stock index prediction, the bat algorithm will play a pivotal role in combination with other population intelligence algorithms or machine learning algorithms as well. In 2014, Chang et al [14] used the investment satisfaction ability index to screen potential candidates and used the evolutionary bat algorithm to construct stock portfolios. Hafezi [15] designed a multi-agent framework for predicting stock prices with a bat neural network multi-agent system (BNNMAS), which tested a model for predicting stock prices in a global facing financial crisis for predicting DAX stock prices over a period of time.…”

Section: Literature Reviewmentioning

confidence: 99%

The Stock Index Prediction Based on SVR Model with Bat Optimization Algorithm

Zheng

Wang

et al. 2021

Algorithms

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Accurate stock market prediction models can provide investors with convenient tools to make better data-based decisions and judgments. Moreover, retail investors and institutional investors could reduce their investment risk by selecting the optimal stock index with the help of these models. Predicting stock index price is one of the most effective tools for risk management and portfolio diversification. The continuous improvement of the accuracy of stock index price forecasts can promote the improvement and maturity of China’s capital market supervision and investment. It is also an important guarantee for China to further accelerate structural reforms and manufacturing transformation and upgrading. In response to this problem, this paper introduces the bat algorithm to optimize the three free parameters of the SVR machine learning model, constructs the BA-SVR hybrid model, and forecasts the closing prices of 18 stock indexes in Chinese stock market. The total sample comes from 15 January 2016 (the 10th trading day in 2016) to 31 December 2020. We select the last 20, 60, and 250 days of whole sample data as test sets for short-term, mid-term, and long-term forecast, respectively. The empirical results show that the BA-SVR model outperforms the polynomial kernel SVR model and sigmoid kernel SVR model without optimized initial parameters. In the robustness test part, we use the stationary time series data after the first-order difference of six selected characteristics to re-predict. Compared with the random forest model and ANN model, the prediction performance of the BA-SVR model is still significant. This paper also provides a new perspective on the methods of stock index forecasting and the application of bat algorithms in the financial field.

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Section: Literature Reviewmentioning

confidence: 99%

The Stock Index Prediction Based on SVR Model with Bat Optimization Algorithm

Zheng

Wang

et al. 2021

Algorithms

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“…In addition, Wang et al (2012) use CSO to optimize the information hiding results. Interactive artificial bee colony (IABC), which is proposed by Tsai et al (2009), is successfully used to improve the recognition rate of the continuous authentication system (Tsai et al , 2012) and to forecast the trends of the foreign exchange rate (Chang et al , 2014a); and evolved bat algorithm (EBA) has been used to illustrate its usability in providing the optimal recommended stock portfolio (Chang et al , 2014b).…”

Section: Literature Reviewmentioning

confidence: 99%

A relaxed structural mechanics and fuzzy control for fluid–structure dynamic analysis

Chen

Khurram

Cheng

2019

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Purpose This paper aims to deal with the problem of the global stabilization for a class of tension leg platform (TLP) nonlinear control systems. Design/methodology/approach It is well-known that, in general, the global asymptotic stability of the TLP subsystems does not imply the global asymptotic stability of the composite closed-loop system. Findings An effective approach is proposed to control chaos via the combination of fuzzy controllers, fuzzy observers and dithers. Research limitations/implications If a fuzzy controller and a fuzzy observer cannot stabilize the chaotic system, a dither, as an auxiliary of the controller and the observer, is simultaneously introduced to asymptotically stabilize the chaotic system. Originality/value Thus, the behavior of the closed-loop dithered chaotic system can be rigorously predicted by establishing that of the closed-loop fuzzy relaxed system.

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Stock Portfolio Construction Using Evolved Bat Algorithm

Cited by 2 publications

References 7 publications

The Stock Index Prediction Based on SVR Model with Bat Optimization Algorithm

The Stock Index Prediction Based on SVR Model with Bat Optimization Algorithm

A relaxed structural mechanics and fuzzy control for fluid–structure dynamic analysis

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