Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data

Taewook Kim, Ha Young Kim

Research output: Contribution to journalArticlepeer-review

159 Citations (Scopus)


Forecasting stock prices plays an important role in setting a trading strategy or determining the appropriate timing for buying or selling a stock. We propose a model, called the feature fusion long short-term memory-convolutional neural network (LSTM-CNN) model, that combines features learned from different representations of the same data, namely, stock time series and stock chart images, to predict stock prices. The proposed model is composed of LSTM and a CNN, which are utilized for extracting temporal features and image features. We measure the performance of the proposed model relative to those of single models (CNN and LSTM) using SPDR S&P 500 ETF data. Our feature fusion LSTM-CNN model outperforms the single models in predicting stock prices. In addition, we discover that a candlestick chart is the most appropriate stock chart image to use to forecast stock prices. Thus, this study shows that prediction error can be efficiently reduced by using a combination of temporal and image features from the same data rather than using these features separately.

Original languageEnglish
Article numbere0212320
JournalPloS one
Issue number2
Publication statusPublished - 2019 Feb

Bibliographical note

Funding Information:
Financial support for this research was supported by a grant (17CTAP-C129782-01) from the Technology Advancement Research Program funded by the Korean Ministry of Land, Infrastructure and Transport ( kr). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Publisher Copyright:
© 2019 Kim, Kim. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

All Science Journal Classification (ASJC) codes

  • General


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