Text Mining, Google Trends Keywords and Least Squares Support Vector Regression in Forecasting Stock Prices
碩士 === 國立暨南國際大學 === 資訊管理學系 === 104 === In this study, values of three stock markets, Dow Jones Industrial Average, Nasdaq Composite and Russell 2000, are predicted. Traditionally, time series models were applied in forecasting stock markets without considering external factors. This study uses Least...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2016
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| Online Access: | http://ndltd.ncl.edu.tw/handle/41015037127421933376 |
| Summary: | 碩士 === 國立暨南國際大學 === 資訊管理學系 === 104 === In this study, values of three stock markets, Dow Jones Industrial Average,
Nasdaq Composite and Russell 2000, are predicted. Traditionally, time series models
were applied in forecasting stock markets without considering external factors. This
study uses Least Squares Support Vector Regression (LSSVR) model with hybrid
data containing historical data and Google Trends keywords to forecast stock markets.
This study proposes two ways to select keywords for Google Trends. The first one is
the selection of popular keywords on the Google Trends homepage, and the second
one is based on the text of Twitter. In this study, a three-stage experiment architecture
was proposed to forecast stock markets and the Auto Regressive Integrated Moving
Average (ARIMA) model is used predict time series data of stock markets. Numerical
results show that the proposed model is a feasible way in predicting stock markets.
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