Stock Trend Prediction Using Deep Learning Approach on Technical Indicator and Industrial Specific Information
A stock trend prediction has been in the spotlight from the past to the present. Fortunately, there is an enormous amount of information available nowadays. There were prior attempts that have tried to forecast the trend using textual information; however, it can be further improved since they relie...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-06-01
|
| Series: | Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2078-2489/12/6/250 |
| _version_ | 1797530037691351040 |
|---|---|
| author | Kittisak Prachyachuwong Peerapon Vateekul |
| author_facet | Kittisak Prachyachuwong Peerapon Vateekul |
| author_sort | Kittisak Prachyachuwong |
| collection | DOAJ |
| description | A stock trend prediction has been in the spotlight from the past to the present. Fortunately, there is an enormous amount of information available nowadays. There were prior attempts that have tried to forecast the trend using textual information; however, it can be further improved since they relied on fixed word embedding, and it depends on the sentiment of the whole market. In this paper, we propose a deep learning model to predict the Thailand Futures Exchange (TFEX) with the ability to analyze both numerical and textual information. We have used Thai economic news headlines from various online sources. To obtain better news sentiment, we have divided the headlines into industry-specific indexes (also called “sectors”) to reflect the movement of securities of the same fundamental. The proposed method consists of Long Short-Term Memory Network (LSTM) and Bidirectional Encoder Representations from Transformers (BERT) architectures to predict daily stock market activity. We have evaluated model performance by considering predictive accuracy and the returns obtained from the simulation of buying and selling. The experimental results demonstrate that enhancing both numerical and textual information of each sector can improve prediction performance and outperform all baselines. |
| first_indexed | 2024-03-10T10:22:16Z |
| format | Article |
| id | doaj.art-3674064f01e74b589f299a783f225850 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-10T10:22:16Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-3674064f01e74b589f299a783f2258502023-11-22T00:16:45ZengMDPI AGInformation2078-24892021-06-0112625010.3390/info12060250Stock Trend Prediction Using Deep Learning Approach on Technical Indicator and Industrial Specific InformationKittisak Prachyachuwong0Peerapon Vateekul1Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10300, ThailandDepartment of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10300, ThailandA stock trend prediction has been in the spotlight from the past to the present. Fortunately, there is an enormous amount of information available nowadays. There were prior attempts that have tried to forecast the trend using textual information; however, it can be further improved since they relied on fixed word embedding, and it depends on the sentiment of the whole market. In this paper, we propose a deep learning model to predict the Thailand Futures Exchange (TFEX) with the ability to analyze both numerical and textual information. We have used Thai economic news headlines from various online sources. To obtain better news sentiment, we have divided the headlines into industry-specific indexes (also called “sectors”) to reflect the movement of securities of the same fundamental. The proposed method consists of Long Short-Term Memory Network (LSTM) and Bidirectional Encoder Representations from Transformers (BERT) architectures to predict daily stock market activity. We have evaluated model performance by considering predictive accuracy and the returns obtained from the simulation of buying and selling. The experimental results demonstrate that enhancing both numerical and textual information of each sector can improve prediction performance and outperform all baselines.https://www.mdpi.com/2078-2489/12/6/250deep learningnatural language processingtime seriesstock market prediction |
| spellingShingle | Kittisak Prachyachuwong Peerapon Vateekul Stock Trend Prediction Using Deep Learning Approach on Technical Indicator and Industrial Specific Information Information deep learning natural language processing time series stock market prediction |
| title | Stock Trend Prediction Using Deep Learning Approach on Technical Indicator and Industrial Specific Information |
| title_full | Stock Trend Prediction Using Deep Learning Approach on Technical Indicator and Industrial Specific Information |
| title_fullStr | Stock Trend Prediction Using Deep Learning Approach on Technical Indicator and Industrial Specific Information |
| title_full_unstemmed | Stock Trend Prediction Using Deep Learning Approach on Technical Indicator and Industrial Specific Information |
| title_short | Stock Trend Prediction Using Deep Learning Approach on Technical Indicator and Industrial Specific Information |
| title_sort | stock trend prediction using deep learning approach on technical indicator and industrial specific information |
| topic | deep learning natural language processing time series stock market prediction |
| url | https://www.mdpi.com/2078-2489/12/6/250 |
| work_keys_str_mv | AT kittisakprachyachuwong stocktrendpredictionusingdeeplearningapproachontechnicalindicatorandindustrialspecificinformation AT peeraponvateekul stocktrendpredictionusingdeeplearningapproachontechnicalindicatorandindustrialspecificinformation |