Forecasting the direction of China''s stock index by machine learning
碩士 === 國立中山大學 === 金融創新產業碩士專班 === 107 === Stock forecasting has become a very popular issue since time immemorial. This paper examines the forecasting ability between the chosen stock market index(SHA: 000300) and macroeconomic variables. The period cover in this study is between January 2009 to Dece...
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| Other Authors: | |
| Format: | Others |
| Language: | zh-TW |
| Published: |
2019
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| Online Access: | http://ndltd.ncl.edu.tw/handle/tz586d |
| Summary: | 碩士 === 國立中山大學 === 金融創新產業碩士專班 === 107 === Stock forecasting has become a very popular issue since time immemorial.
This paper examines the forecasting ability between the chosen stock market
index(SHA: 000300) and macroeconomic variables. The period cover in this study is
between January 2009 to December 2017. The macroeconomics variables will first
transform into specific momentum indicator. Then we will use the Classification
Decision Tree Model for further variable selection. The variable will be selected as
“capable indicator” only if the forecasting accuracy is greater than 50%. The capable
indicators will later be added in Artificial Neural Network(ANN) Model to forecast
the monthly volatility direction of the chosen stock market index simultaneously.
After optimized the parameters setting and reinforced the model, the results shows the
back-testing forecasting accuracy is up to 60%. This empirical study indicated that it
is possible to establish meaningful insight about the relationship between
macroeconomic variables and stock market index.
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