Abstract
With the development of cloud computing and ICT at present, financial technology has undergone major changes in recent years. The big data technology has been used to combine internal and external information, called weighted forecasting. The fundamentals of internal information include company financial statements, after-hours transaction information and other information; the technical facet analysis aspects include moving average and a variety of indicators. Also, external information includes Taiwan stock market related articles called chip facet analysis.In this paper, three facets were systematically analyzed to find the weights and calculate the fluctuation trend of individual stock, thereby more accurately predicting fluctuations in the following month. Also, this paper took Taiwan’s financial stock as an example. The simulation-based period was from January to September 2017. The external information sources include: CNYES.com. and MoneyDJ. First, the initial weighted ratio of 8:2 for the internal and external information was found, and rolling learning was done to make adjustments and predict the monthly fluctuation from October to December 2017. Empirical results show that the accuracy of the traditional forecasting in predicting actual fluctuation trend is 62.25%. The internal information is the data reference of the fundamental and technical facets analysis. After coupled with the chip facet analysis was added and the three facets analyzed, the accuracy of the actual individual stock fluctuations obtained using weighted forecasting is 73.55%. Overall, the weighted forecasting can be used to calculate fluctuations of individual stock, also enhancing the accuracy of predicting stock fluctuations the following month, also serve as a reference for experts and investors.
Published Version
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