Sage Journals: Discover world-class research

Abstract

The prediction and recommendation of financial stocks are of great values. This study mainly analyzed the application of K-means clustering algorithm in stock forecasting and recommendation. Firstly, it introduced the k-means algorithm briefly and analyzed its advantages and disadvantages. Then, the k-means algorithm was optimized by introducing artificial fish swarm algorithm (AFSA) to obtain KAFSA. Then 100 stocks of listed companies were taken as the research subject and predicted by KAFSA designed in this study. The prediction results were verified through closing price, price earning ratio, earnings per share and return on net assets. The results showed that there were obvious differences between A and B stocks divided by KAFSA, and the differences of B stocks were significantly larger than those of A stocks. It shows that 100 stocks are well divided into high performance stocks and poor performance stocks through clustering, which provides a good reference for investors to invest in stocks and is worth of further application.

Keywords

K-means clustering algorithm data mining artificial fish swarm algorithm Stock forecast

Get full access to this article

View all access options for this article.

References

Gupta

Chaudhary

D.K.

and Choudhury

, Stock prediction using functional link artificial neural network (FLANN), International Conference on Computational Intelligence and Networks. IEEE (2018), 10–16.

Izzah

Sari

Y.A.

Widyastuti

and Cinderatama

T.A.

, Mobile app for stock prediction using improved multiple linear regression, International Conference on Sustainable Information Engineering and Technology. IEEE (2018), 150–154.

Sharma

Bhuriya

and Singh

, Survey of stock market prediction using machine learning approach, Electronics, Communication and Aerospace Technology. IEEE (2017), 506–509.

Bini

B.S.

and Mathew

, Clustering and regression techniques for stock prediction? Procedia Technology 24 (2016), 1248–1255.

Tsai

C.F.

and Quan

Z.Y.

, Stock prediction by searching for similarities in candlestick charts, Acm Transactions on Management Information Systems 5(2) (2014), 1–21.

Das

Safa Sadiq

Mirjalili

et al., Hybrid clustering-GWO-NARX neural network technique in predicting stock price, Journal of Physics Conference Series, (2017), 012018.

Wang

Zhang

T.Q.

Rao

K.S.

and Zhang

, Exploring mutual information-based sentimental analysis with kernel-based extreme learning machine for stock prediction, Soft Computing – A Fusion of Foundations, Methodologies and Applications 21(12) (2017), 3193–3205.

Kaur

Dhar

and Guha

R.K.

, A hybrid approach to forecast stock market index, International Journal of Artificial Intelligence and Soft Computing 5(2) (2015), 165-176.

Huang

Zhang

Deng

and Chen

, Predicting stock trend using fourier transform and support vector regression, International Conference on Computational Science and Engineering. IEEE, (2015), 213–216.

10.

Sadaei

H.J.

Enayatifar

Lee

M.H.

and Mahmud

, A hybrid model based on differential fuzzy logic relationships and imperialist competitive algorithm for stock market forecasting, Applied Soft Computing 40(C) (2016), 132–149.

11.

Chen

J.C.

Chen

H.Z.

and Huo

Y.J.

, Application of SVR models in stock index forecast based on different parameter search methods, Open Journal of Stats 7(2) (2017), 194–202.

12.

Chou

J.S.

and Nguyen

T.K.

, Forward forecast of stock price using sliding-window metaheuristic-optimized machine learning regression, IEEE Transactions on Industrial Informatics, (2018), 1.

13.

Wang

J.Z.

Hou

Wang

and Shen

, Improved v, Support vector regression model based on variable selection and brain storm optimization for stock price forecasting, Applied Soft Computing 49 (2016), 164–178.

14.

Dudhwala

N.D.

Jadhav

Gabda

and Kishor

, Prediction of stock market using data mining and artificial intelligence, International Journal of Computer Applications 134(12) (2016), 9–11.

15.

Kumar

and Bala

, Intelligent stock data prediction using predictive data mining techniques, International Conference on Inventive Computation Technologies. IEEE, (2017), 1–5.

16.

Kapil

Chawla

and Ansari

M.D.

, On K-means data clustering algorithm with genetic algorithm, International Conference on Parallel. IEEE, (2017), 202–206.

17.

Xia

Liu

and Chen

, Support vector regression for prediction of stock trend, International Conference on Information Management, Innovation Management and Industrial Engineering. IEEE, (2014), 123–126.

18.

Berradi

and Lazaar

, Integration of principal component analysis and recurrent neural network to forecast the stock price of casablanca stock exchange, Procedia Computer Ence 148 (2019), 55–61.

19.

Jiang

and Chen

, BDI based stock prediction, Online Analysis and Computing Science. IEEE, (2016), 119–122.

Research on prediction and recommendation of financial stocks based on K-means clustering algorithm optimization

Abstract

Keywords

Get full access to this article

References