Academic Journal of Computing & Information Science, 2021, 4(8); doi: 10.25236/AJCIS.2021.040808.
Yang Wang1, Linjie Huang2, Yidan Zheng3, Siuwa Lee4, Yuanming Fu5
1China University of Petroleum, Beijing, China
2Nanjing Agricultural University, Nanjing, Jiangsu, China
3University of Waterloo, ON, Canada
4Jinan University, Guangzhou, Guangdong, China
5Tianjin No.1 High School, Tianjin, China
These authors contributed equally to this work
With the continuous development of financial informatization and the continuous improvement of China's economic system, analyzing and mining financial data has become an important means to study financial problems. Compared with other industries in the financial field, stock data is easier to collect and store, and its application is more convenient. Analyzing and predicting the changing trend of the future stock market through the historical data of stocks is helpful to reduce the risk of investors and increase income. It has become a research hotspot in the financial field. Using the historical price data of domestic stocks, a method based on a clustering algorithm is proposed to analyze the periodic characteristics of stock price changes. This method can provide a basis for the prediction of stock price and the detection of trading behaviour in the stock market.
Moving average; K-means clustering; Stocks price prediction
Yang Wang, Linjie Huang, Yidan Zheng, Siuwa Lee, Yuanming Fu. Stock Price Trend Analysis Method Based on Change Cycle Clustering. Academic Journal of Computing & Information Science (2021), Vol. 4, Issue 8: 42-47. https://doi.org/10.25236/AJCIS.2021.040808.
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