Financial time series forecasting (Stock prediction)
Accurate prediction of stock price trend greatly helps stock investor to react correctly in the stock market. The unsteadiness of the stock market has caused serious profit loss to many people. Stock markets are easily affected by many factors. It includes financial, political and unknown com...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2016
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| Online Access: | http://hdl.handle.net/10356/69322 |
| _version_ | 1811695365885263872 |
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| author | Chen, Hai Hui |
| author2 | Ponnuthurai Nagaratnam Suganthan |
| author_facet | Ponnuthurai Nagaratnam Suganthan Chen, Hai Hui |
| author_sort | Chen, Hai Hui |
| collection | NTU |
| description | Accurate prediction of stock price trend greatly helps stock investor to react correctly in
the stock market. The unsteadiness of the stock market has caused serious profit loss to
many people. Stock markets are easily affected by many factors. It includes financial,
political and unknown company development. In order for one to make profit from the
stock market, it needs adequate forecast to plan the future. Hence, effective, stable and
accurate methods which able to build a model to have the ability to predict the stock
market trend are needed.
The dissertation aims to provide an analysis of Neural Network (NN) and Support
Vector Machine (SVM) method to build a prediction model by using Matlab software
with the input data of Singapore Technology (ST) engineering company stock price. By
using the two methods mentioned to determine the Absolute Error (AE) between
predicted stock price value and the actual stock price value and hence to find the Mean
Square Error (MSE), the results suggest that SVM method has outperformed NN method
on the ST stock price trend prediction. |
| first_indexed | 2024-10-01T07:22:19Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/69322 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:22:19Z |
| publishDate | 2016 |
| record_format | dspace |
| spelling | ntu-10356/693222023-07-07T17:03:52Z Financial time series forecasting (Stock prediction) Chen, Hai Hui Ponnuthurai Nagaratnam Suganthan School of Electrical and Electronic Engineering DRNTU::Engineering Accurate prediction of stock price trend greatly helps stock investor to react correctly in the stock market. The unsteadiness of the stock market has caused serious profit loss to many people. Stock markets are easily affected by many factors. It includes financial, political and unknown company development. In order for one to make profit from the stock market, it needs adequate forecast to plan the future. Hence, effective, stable and accurate methods which able to build a model to have the ability to predict the stock market trend are needed. The dissertation aims to provide an analysis of Neural Network (NN) and Support Vector Machine (SVM) method to build a prediction model by using Matlab software with the input data of Singapore Technology (ST) engineering company stock price. By using the two methods mentioned to determine the Absolute Error (AE) between predicted stock price value and the actual stock price value and hence to find the Mean Square Error (MSE), the results suggest that SVM method has outperformed NN method on the ST stock price trend prediction. Bachelor of Engineering 2016-12-13T07:58:13Z 2016-12-13T07:58:13Z 2016 Final Year Project (FYP) http://hdl.handle.net/10356/69322 en Nanyang Technological University 49 p. application/pdf |
| spellingShingle | DRNTU::Engineering Chen, Hai Hui Financial time series forecasting (Stock prediction) |
| title | Financial time series forecasting (Stock prediction) |
| title_full | Financial time series forecasting (Stock prediction) |
| title_fullStr | Financial time series forecasting (Stock prediction) |
| title_full_unstemmed | Financial time series forecasting (Stock prediction) |
| title_short | Financial time series forecasting (Stock prediction) |
| title_sort | financial time series forecasting stock prediction |
| topic | DRNTU::Engineering |
| url | http://hdl.handle.net/10356/69322 |
| work_keys_str_mv | AT chenhaihui financialtimeseriesforecastingstockprediction |