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

Jearanaitanakij, Kietikul; Passaya, Bundit

doi:10.1109/incit.2019.8912115

Cited by 6 publications

(4 citation statements)

References 2 publications

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“…Kietikul Jearanaitanakij and Bundit Passaya (2019) The brain mind association and flame plans are utilized in this makers article to recommend a designing for determining the short example of stocks. The investigations are directed by an assortment of candle configuration pictures obtained from different stocks in Thailand's financial exchange (SET).…”

Section: Marcmentioning

confidence: 99%

ML based stock prediction method for accurate future prediction of stock market

Hiray

Patankar

Doke

2022

ijhs

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A CNN methodology can yield pretty accurate results on stock prices if we look at day-to-day fluctuation in stock prices, but where this method fails is in anticipating big changes in prices that are not based on trends. In technical analysis, price patterns are used to identify transitions between rising and falling trends. A price movement pattern that may be calculated using a series of trend lines and/or curves is known as a cost sequence. Finding patterns in high-dimensional data might be difficult since it is difficult to visualize. Many different machine learning algorithms can match this high-dimensional data to predict and classify future events, but having the computer learn the match for a specific area of the dataset might be expensive. Using deep learning, this study proposes a way for identifying various stock market pricing styles. A CNN is used to find the pattern in stock market data, and projections are made based on it. The stock pattern is divided into five pieces. Price stability, stock value fall (quick decline, moderate decline), and stock value increase (rapid increase, gradual increase). The accuracy of our system is 98.17 %.

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Section: Marcmentioning

confidence: 99%

ML based stock prediction method for accurate future prediction of stock market

Hiray

Patankar

Doke

2022

ijhs

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“…To compensate for the lack of sufficient information in one-dimensional input, researchers attempted to provide more sufficient financial variables for CNN to extract market features. In fact, some researchers directly used the candlestick chart as the input of CNN [23,24]. Furthermore, instead of directly taking the image as the input of CNN, Sim et al [25] employed high-frequency data of close price to construct the input image as the input [6] recently proposed an approach to build a three-dimensional input tensor for CNN to extract market features.…”

Section: Feature Extractionmentioning

confidence: 99%

Deep Learning for Price Movement Prediction Using Convolutional Neural Network and Long Short-Term Memory

Yang

Zhai

Tao

2020

Mathematical Problems in Engineering

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The prediction of stock price movement direction is significant in financial studies. In recent years, a number of deep learning models have gradually been applied for stock predictions. This paper presents a deep learning framework to predict price movement direction based on historical information in financial time series. The framework combines a convolutional neural network (CNN) for feature extraction and a long short-term memory (LSTM) network for prediction. We specifically use a three-dimensional CNN for data input in the framework, including the information on time series, technical indicators, and the correlation between stock indices. And in the three-dimensional input tensor, the technical indicators are converted into deterministic trend signals and the stock indices are ranked by Pearson product-moment correlation coefficient (PPMCC). When training, a fully connected network is used to drive the CNN to learn a feature vector, which acts as the input of concatenated LSTM. After both the CNN and the LSTM are trained well, they are finally used for prediction in the testing set. The experimental results demonstrate that the framework outperforms state-of-the-art models in predicting stock price movement direction.

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“…From the small number of studies, it appears that the use of visual inputs (e.g. price or candlestick charts) is able to produce significant results [28][29][30]. In addition, research is ongoing to improve our ability to understand what features of the input data (or features extracted within hidden layers) are having the most important effect on the learning and prediction process.…”

Section: Introductionmentioning

confidence: 99%

Adapting deep learning models between regional markets

Tonkin

Gepp

Harris

et al. 2022

Neural Comput & Applic

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This paper extends a series of deep learning models developed on US equity data to the Australian market. The model architectures are retrained, without structural modification, and tested on Australian data comparable with the original US data. Relative to the original US-based results, the retrained models are statistically less accurate at predicting next day returns. The models were also modified in the standard train/validate manner on the Australian data, and these models yielded significantly better predictive results on the holdout data. It was determined that the best-performing models were a CNN and LSTM, attaining highly significant Z-scores of 6.154 and 8.789, respectively. Due to the relative structural similarity across all models, the improvement is ascribed to regional influences within the respective training data sets. Such unique regional differences are consistent with views in the literature stating that deep learning models in computational finance that are developed and trained on a single market will always contain market-specific bias. Given this finding, future research into the development of deep learning models trained on global markets is recommended.

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

Cited by 6 publications

References 2 publications

ML based stock prediction method for accurate future prediction of stock market

ML based stock prediction method for accurate future prediction of stock market

Deep Learning for Price Movement Prediction Using Convolutional Neural Network and Long Short-Term Memory

Adapting deep learning models between regional markets

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