VGU RESEARCH REPOSITORY
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Title: | Research and applying data mining techniques in Vietnam stock market - case study of hose analysis | Authors: | Truong Tan Duc | Keywords: | Data mining;ARIMA;Apriori | Issue Date: | 2018 | Publisher: | Vietnamese-German University | Abstract: | Data is growing big and fast everyday at every moment, especially real time data such as stock prices. In Vietnam, the stock market is approximately twenty years of age. It more and more becomes popular to everybody nowadays. Not only analysts, investors, but also office staffs or my friends in other communities those asked me and would like to invest and gain benefits from stock market regardless they are lack of knowledge of it. Some people consider the prices are unpredictable as it depends on many factors of the size of company issuing stock, its financial conditions, industrial news, and even trading groups... However, is there anything valuable that we can achieve, to learn from experience and know some interesting facts from the past data that left traces? This addresses data mining field. In the context of this thesis, two research questions are specified: 1. What are the possible relationships between elements displayed on stock board, i.e. ticker's attributes such as open price, close price, total volume... which indicate one can affect another's value or vice versa so that it helps investor to facilitate his/her decision when manage stock? 2. Is it possible to predict an exact price (or with a defined deviation) of particular ticker in a future time? The research is by first step studying a trustful IT science based, data mining approaching techniques for stock data in both descriptive and predictive tasks, and figuring out a picture of recent techniques. Then it specifically focuses on two principal approaches, namely Association rule, using Apriori method and Time series forecasting, using ARIMA method. The next step is to use common open source tool Knime in order to apply mining process on real data of HOSE stock exchange and output good association rules. For ARIMA, R is used in parallel with Knime to make better analysis. An evaluation based on actual data of next periods also is analyzed. As a result, it helps user to make decision more easily and precisely base on the accuracy of knowledge output. |
URI(1): | http://epub.vgu.edu.vn/handle/dlibvgu/734 |
Appears in Collections: | Business Information Systems (BIS) |
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