Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
An error correction neural network for stock market prediction
Thesis (MSc)--Stellenbosch University, 2019.
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | en_ZA |
| Published: |
Stellenbosch : Stellenbosch University
2019
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867613879105748992 |
|---|---|
| access_status_str | Open Access |
| author | Mvubu, Mhlasakululeka |
| author2 | Sanders, J. W. |
| author_browse | Mvubu, Mhlasakululeka Sanders, J. W. |
| author_facet | Sanders, J. W. Mvubu, Mhlasakululeka |
| author_sort | Mvubu, Mhlasakululeka |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MSc)--Stellenbosch University, 2019. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/105798 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:43:09.148Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/105798 An error correction neural network for stock market prediction Mvubu, Mhlasakululeka Sanders, J. W. Becker, Ronald I. Bah, Bubacarr Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. Division Mathematics. UCTD Stock price forecasting -- Mathematical models Stock exchanges -- Computer networks Neural network Economic forecasting -- Mathematical models Thesis (MSc)--Stellenbosch University, 2019. ENGLISH ABSTRACT : Predicting stock market has long been an intriguing topic for research in different fields. Numerous techniques have been conducted to forecast stock market movement. This study begins with a review of the theoretical background of neural networks. Subsequently an Error Correction Neural Network (ECNN), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) are defined and implemented for an empirical study. This research offers evidence on the predictive accuracy and profitability performance of returns of the proposed forecasting models on futures contracts of Hong Kong’s Hang Seng futures, Japan’s NIKKEI 225 futures, and the United State of America S&P 500 and DJIA futures from 2010 to 2016. Technical as well as fundamental data are used as input to the network. Results show that the ECNN model outperforms other proposed models in both predictive accuracy and profitability performance. These results indicate that ECNN shows promise as a reliable deep learning method to predict stock price. AFRIKAANSE OPSOMMING : Die voorspelling van die aandele mark was al lank ´n interge onderwerp in verskillende navorsingsvelde. Verskeie tegnieke was al so ver toegepas om aandelemark beweging te voorspel. Hierdie studie begin met ´n oorsig van die teoretiese agtergrond van neutrale netwerke. Daarna is ´n Fout Neurale Netwerk (FNN), Herhalende Neurale Netwerk (HNN); en Lank- en - Kort Termyn Gehee (LKTG) word gedefinieer en geïmplenteer vir ´n empiriese studie. Hierdie navorsing bied bewyse oor die voorspellende akkuraatheid en winsgewendheid van die opbrengste van die voorgestelde vooruitskatting modelle op termynkontrakte van; Hongkong se Hang Seng-toekoms, Japan se NIKKEI 225 termyne, en die Verenigde State van Amerika S&P 500 en DJIA termynkontrakte vanaf 2010 tot en met 2016. Resultate toon dat die FNNmodel beter presteer as ander voorgestelde modelle in beide voorspellings akkuraatheid en winsgewendheid prestasie. Hierdie resultate dui daarop dat FNN belofte toon as ´n betroubaar masjienleermetode om die aandeelprys te voorspel. 2019-02-13T12:39:54Z 2019-04-17T08:13:21Z 2019-02-13T12:39:54Z 2019-04-17T08:13:21Z 2019-04 Thesis http://hdl.handle.net/10019.1/105798 en_ZA Stellenbosch University ix, 75 pages : illustrations (some colour) application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | UCTD Stock price forecasting -- Mathematical models Stock exchanges -- Computer networks Neural network Economic forecasting -- Mathematical models Mvubu, Mhlasakululeka An error correction neural network for stock market prediction |
| title | An error correction neural network for stock market
prediction |
| title_full | An error correction neural network for stock market
prediction |
| title_fullStr | An error correction neural network for stock market
prediction |
| title_full_unstemmed | An error correction neural network for stock market
prediction |
| title_short | An error correction neural network for stock market
prediction |
| title_sort | error correction neural network for stock market prediction |
| topic | UCTD Stock price forecasting -- Mathematical models Stock exchanges -- Computer networks Neural network Economic forecasting -- Mathematical models |
| url | http://hdl.handle.net/10019.1/105798 |
| work_keys_str_mv | AT mvubumhlasakululeka anerrorcorrectionneuralnetworkforstockmarketprediction AT mvubumhlasakululeka errorcorrectionneuralnetworkforstockmarketprediction |