Intraday prediction of Borsa Istanbul using convolutional neural networks and feature correlations

Gündüz, Hakan; Yaslan, Yusuf; Çataltepe, Zehra

doi:10.1016/j.knosys.2017.09.023

Cited by 152 publications

(106 citation statements)

References 30 publications

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“…Technical (Kara et al, 2011) Technical indicators and ANN as classifier CNN-cor (Gunduz et al, 2017) A CNN with mentioned structure in the paper to make the situation equal for the other baseline algorithms, these algorithms are tested several times with the same condition. Then, average F-measure of the algorithms are compared.…”

Section: Resultsmentioning

confidence: 99%

“…However, in an imbalanced dataset, it may be biased toward the models that tend to predict the more frequent class. To address this issue, we report the Macro-Averaged-F-Measure that is the mean of F-measures calculated for each of the two classes (Gunduz et al, 2017;Özgür et al, 2005).…”

Section: Evaluation Methodologymentioning

confidence: 99%

“…Based on some reported experiments, the way the input data is designed to be fed and processed by CNN has an important role in the quality of the extracted feature set and the final prediction. For example, CNN was used in (Gunduz et al, 2017) in which data of 10 days of 100 companies in Borsa Istanbul were utilized to produce technical indicators and time-lagged features.…”

Section: Related Workmentioning

confidence: 99%

“…Since the behavior of stock markets is complex, nonlinear and noisy, it seems that extracting features that are informative enough for making predictions is a core challenge, and DL seems to be a promising approach to that. Algorithms like Deep Multilayer Perceptron (MLP) (Yong et al, 2017), Restricted Boltzmann Machine (RBM) (Cai et al, 2012;Zhu et al, 2014), Long Short-Term Memory (LSTM) (Chen et al, 2015;Fischer & Krauss, 2018), Auto-Encoder (AE) (Bao et al, 2017) and Convolutional Neural Network (CNN) (Gunduz et al, 2017;Di Persio & Honchar, 2016) are famous deep learning algorithms utilized to predict stock markets.…”

Section: Introductionmentioning

confidence: 99%

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CNNpred: CNN-based stock market prediction using a diverse set of variables

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Section: Resultsmentioning

confidence: 99%

Section: Evaluation Methodologymentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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CNNpred: CNN-based stock market prediction using a diverse set of variables

Hoseinzade

Haratizadeh

2019

Expert Systems with Applications

418

168

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“…Similarly, the authors of [199] also proposed a novel technique that converted financial time series data that consisted of technical analysis indicator outputs to 2-dimensional images and classified these images using CNN to determine the trading signals. The authors of [200] proposed a method that used CNN with correlated features combined together to predict the trend of the stocks prices.…”

Section: Trend Forecastingmentioning

confidence: 99%

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Sezer

Gudelek

Özbayoğlu

2020

Applied Soft Computing

795

294

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Financial time series forecasting is, without a doubt, the top choice of computational intelligence for finance researchers from both academia and financial industry due to its broad implementation areas and substantial impact. Machine Learning (ML) researchers came up with various models and a vast number of studies have been published accordingly. As such, a significant amount of surveys exist covering ML for financial time series forecasting studies. Lately, Deep Learning (DL) models started appearing within the field, with results that significantly outperform traditional ML counterparts. Even though there is a growing interest in developing models for financial time series forecasting research, there is a lack of review papers that were solely focused on DL for finance. Hence, our motivation in this paper is to provide a comprehensive literature review on DL studies for financial time series forecasting implementations. We not only categorized the studies according to their intended forecasting implementation areas, such as index, forex, commodity forecasting, but also grouped them based on their DL model choices, such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Long-Short Term Memory (LSTM). We also tried to envision the future for the field by highlighting the possible setbacks and opportunities, so the interested researchers can benefit.

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Unsupervised feature extraction with convolutional autoencoder with application to daily stock market prediction

Xie

2021

Concurrency and Computation

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Due to the volatility and noise of the stock market, accurately obtaining the trend of the stock market is a challenging problem, and gets the attention of many researchers and speculators. Recently, convolutional neural network (CNN) has been used to automatically learn effective features and predict stock market trends. In CNN-based methods reported so far, less focus has been paid to time series information of the stock, but is very crucial for stock forecasting. In this study, an unsupervised feature extraction method with convolutional autoencoder (CAE) with application to daily stock market prediction is proposed, which has a higher prediction than traditional models.The proposed method mainly consists of the data processing part, unsupervised feature learning part, and the support vector machine model part. Data processing part includes time series data transform into two-dimensional data and data normalization. CAE network-based unsupervised feature learning is designed by fusing convolution and autoencoder. In order to verify the performance of the model, various initial financial and economic variables of stock indices are chosen for prediction experiments. The experimental results on different stock indices demonstrate a significant improvement in prediction's performance compared with the baseline methods. K E Y W O R D S convolutional autoencoder network, convolutional neural network, stock market prediction, support vector machine INTRODUCTIONThe stock market has become an important component for listed companies to raise funds from investors. However, the law of stock price change is difficult to accurately grasp, and most investors usually depend on subjective judgment to conduct stock trading. 1,2 The stock market has always been affected by political factors, the industry specific factors, the world economic situation, and the price trends are highly nonlinear and nonstationary. [3][4][5] Therefore, effective and efficient predicting the stock is an extremely challenging task for both investors and researchers. And thus, it is significant to research how to build a universal and effective model to forecast future movements of the stock market.There are a number of advanced methods that are applied for prediction of the stock market. Depending on the relationship between the historical behavior and future trend of the stock price is processed, forecasting methods can be divided into three categories. [6][7][8] Among statistical methodologies, due to the interpretability, linear regression, autoregression, and moving average are helpful in financial time series forecasting. Assume that the stock price trend is a result of historical behaviors, extracting effective features from the original stock data is the key of the prediction process. However, features are subjectively designed, and the accurate prediction of models mostly depended on the A preliminary version of this article has been published by the seventh International Conference on Information Science and Control Engineering.

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Intraday prediction of Borsa Istanbul using convolutional neural networks and feature correlations

Cited by 152 publications

References 30 publications

CNNpred: CNN-based stock market prediction using a diverse set of variables

CNNpred: CNN-based stock market prediction using a diverse set of variables

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Unsupervised feature extraction with convolutional autoencoder with application to daily stock market prediction

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