| dc.creator |
Sanga, Bahati A. |
|
| dc.date |
2019-01-10T12:49:18Z |
|
| dc.date |
2019-01-10T12:49:18Z |
|
| dc.date |
2015 |
|
| dc.date.accessioned |
2022-10-20T13:46:54Z |
|
| dc.date.available |
2022-10-20T13:46:54Z |
|
| dc.identifier |
Sanga, B. A. (2015). Forecasting of stock market trends using a Decision Tree and Naïve Bayes hybrid model.Dodoma: The University of Dodoma. |
|
| dc.identifier |
http://hdl.handle.net/20.500.12661/533 |
|
| dc.identifier.uri |
http://hdl.handle.net/20.500.12661/533 |
|
| dc.description |
Dissertation (MSc. Computer Science) |
|
| dc.description |
Forecasting of stock market trends has been an area of great interest to researchers
who are attempting to uncover the information hidden in the stock market data and
to traders who wish to profit by trading stocks. An accurate forecasting of stock
market trends may yield profits for investors. Forecasting of stock price trend is
regarded as a challenging task. Due to the complexity of stock market data,
development of efficient models for forecasting stock market trends is highly
challenging. Applications of data mining techniques for stock market forecasting are
an area of research which has been receiving a lot of attention recently.
This study presents the development and evaluation of a decision tree and naïve
Bayes hybrid model for stock market next day’s trend forecast in Dar-Es-Salaam
Stock Exchange (DSE). Historical DSE data is used in the present study to extract
features that can cause change in stocks price trends. In the developed hybrid model,
decision tree is used to select the subsets of relevant features and naïve Bayes is used
to produce a stable model for forecasting stock market trends.
This study found that, the proposed hybrid model outperforms both the baseline
decision tree and naïve Bayes models. It is found that features selection using
decision tree employed in this study significantly improved the trend forecasting
performance in stock market. It can be concluded from this study that, the decision
tree and naïve Bayes hybrid model performs well and is reliable in stock market
trend forecasting. |
|
| dc.publisher |
The University of Dodoma |
|
| dc.subject |
Stock market |
|
| dc.subject |
Stock market data |
|
| dc.subject |
Stock market trend |
|
| dc.subject |
Naïve Bayes hybrid model |
|
| dc.subject |
Decision tree hybrid model |
|
| dc.subject |
Dar-Es-Salaam stock exchange |
|
| dc.subject |
Stock investors |
|
| dc.subject |
Market trends |
|
| dc.subject |
Stock market forecasting |
|
| dc.subject |
Stock trading |
|
| dc.subject |
Decision tree |
|
| dc.title |
Forecasting of stock market trends using a Decision Tree and Naïve Bayes hybrid model |
|
| dc.type |
Dissertation |
|