Predicting Short Trend of Stocks by Using Convolutional Neural Network and Candlestick Patterns

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dc.contributor.authorJearanaitanakij, Kietikul
dc.contributor.authorPassaya, Bundit
dc.date.accessioned2026-08-06T10:25:55Z
dc.date.available2026-08-06T10:25:55Z
dc.date.issued2019-10-01
dc.description.abstractCandlestick chart pattern is a technical tool that encapsulates the price of the asset for multiple time frames into a single price bar. The expertized trader can predict the price trend of the asset by looking at the pattern of some adjacent candlesticks. This paper proposes the architecture for predicting the short trend of the stocks by using the convolutional neural network and the candlestick patterns. The experiments are conducted with a set of candlestick pattern images collected from various stocks in the stock exchange of Thailand (SET). Each image captures six to twelve adjacent candlesticks. The experimental results indicate that the proposed method can correctly predict the short trend for most stocks with acceptable accuracy. In addition, the proposed architecture achieves better accuracy and training time than that of the well-known architecture, ResNet-18.
dc.identifier.citationProceedings of 2019 4th International Conference on Information Technology Encompassing Intelligent Technology and Innovation Towards the New Era of Human Life Incit 2019, 159-162, 2019
dc.identifier.doi10.1109/INCIT.2019.8912115
dc.identifier.other2-s2.0-85076736583
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/10238
dc.sourceProceedings of 2019 4th International Conference on Information Technology Encompassing Intelligent Technology and Innovation Towards the New Era of Human Life Incit 2019
dc.subjectCandlestick
dc.subjectconvolutional neural network
dc.subjectdeep learning
dc.subjectprediction
dc.subjectstock exchange of Thailand
dc.titlePredicting Short Trend of Stocks by Using Convolutional Neural Network and Candlestick Patterns
dc.typeConference Paper

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