Technical indicator empowered intelligent strategies to predict stock trading signals
Technical analysis is widely employed in stock trading, relying on popular indicators such as MACD, DMI, KST etc. to predict stock trends. Despite their common use, these lagging indicators can occasionally generate misleading signals. In the literature, machine learning researchers developed many i...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Journal of Open Innovation: Technology, Market and Complexity |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2199853124001926 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846140697442254848 |
|---|---|
| author | Arjun Singh Saud Subarna Shakya |
| author_facet | Arjun Singh Saud Subarna Shakya |
| author_sort | Arjun Singh Saud |
| collection | DOAJ |
| description | Technical analysis is widely employed in stock trading, relying on popular indicators such as MACD, DMI, KST etc. to predict stock trends. Despite their common use, these lagging indicators can occasionally generate misleading signals. In the literature, machine learning researchers developed many intelligent strategies for predicting stock trading signals using these indicators as inputs. However, significant differences exist in how these indicators are applied by technical analysts and machine learning experts. Building on this knowledge, this study developed intelligent stock trading signal prediction strategies using MACD, DMI, and KST indicators, and implemented these strategies with LSTM and GRU networks due to their ability to manage long-term dependencies and maintain context. The proposed intelligent trading strategies were assessed using ARR, SR, and win rate metrics, based on historical trading data from 18 stocks—six each from NEPSE, BSE, and NYSE—leading to four key insights. (1) For predicting stock trading signals, a 5-day lookback period is optimal for intelligent strategies based on MACD and DMI, while a 10-day period is best for the KST-based strategy. (2) Intelligent trading strategies implemented with GRU networks demonstrated superior performance compared to those implemented with LSTM. (3) The intelligent trading strategies based on MACD, DMI, and KST indicators outperform their peer classical stock trading methods. (4) Among the three proposed intelligent strategies, the MACD-based approach is found to be the safest and most effective. |
| format | Article |
| id | doaj-art-5f6ba65ba4be40d1a34dfd1c8585352b |
| institution | Kabale University |
| issn | 2199-8531 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Open Innovation: Technology, Market and Complexity |
| spelling | doaj-art-5f6ba65ba4be40d1a34dfd1c8585352b2024-12-05T05:20:08ZengElsevierJournal of Open Innovation: Technology, Market and Complexity2199-85312024-12-01104100398Technical indicator empowered intelligent strategies to predict stock trading signalsArjun Singh Saud0Subarna Shakya1Central Department of Computer Science and Information Technology, IOST, Tribhuvan University, Nepal; Corresponding author.Department of Electronics and Computer Engineering, IOE, Tribhuvan University, NepalTechnical analysis is widely employed in stock trading, relying on popular indicators such as MACD, DMI, KST etc. to predict stock trends. Despite their common use, these lagging indicators can occasionally generate misleading signals. In the literature, machine learning researchers developed many intelligent strategies for predicting stock trading signals using these indicators as inputs. However, significant differences exist in how these indicators are applied by technical analysts and machine learning experts. Building on this knowledge, this study developed intelligent stock trading signal prediction strategies using MACD, DMI, and KST indicators, and implemented these strategies with LSTM and GRU networks due to their ability to manage long-term dependencies and maintain context. The proposed intelligent trading strategies were assessed using ARR, SR, and win rate metrics, based on historical trading data from 18 stocks—six each from NEPSE, BSE, and NYSE—leading to four key insights. (1) For predicting stock trading signals, a 5-day lookback period is optimal for intelligent strategies based on MACD and DMI, while a 10-day period is best for the KST-based strategy. (2) Intelligent trading strategies implemented with GRU networks demonstrated superior performance compared to those implemented with LSTM. (3) The intelligent trading strategies based on MACD, DMI, and KST indicators outperform their peer classical stock trading methods. (4) Among the three proposed intelligent strategies, the MACD-based approach is found to be the safest and most effective.http://www.sciencedirect.com/science/article/pii/S2199853124001926MACDDMIKSTLSTMGRUIntelligent stock trading strategies |
| spellingShingle | Arjun Singh Saud Subarna Shakya Technical indicator empowered intelligent strategies to predict stock trading signals Journal of Open Innovation: Technology, Market and Complexity MACD DMI KST LSTM GRU Intelligent stock trading strategies |
| title | Technical indicator empowered intelligent strategies to predict stock trading signals |
| title_full | Technical indicator empowered intelligent strategies to predict stock trading signals |
| title_fullStr | Technical indicator empowered intelligent strategies to predict stock trading signals |
| title_full_unstemmed | Technical indicator empowered intelligent strategies to predict stock trading signals |
| title_short | Technical indicator empowered intelligent strategies to predict stock trading signals |
| title_sort | technical indicator empowered intelligent strategies to predict stock trading signals |
| topic | MACD DMI KST LSTM GRU Intelligent stock trading strategies |
| url | http://www.sciencedirect.com/science/article/pii/S2199853124001926 |
| work_keys_str_mv | AT arjunsinghsaud technicalindicatorempoweredintelligentstrategiestopredictstocktradingsignals AT subarnashakya technicalindicatorempoweredintelligentstrategiestopredictstocktradingsignals |