Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach

Sezer, Ömer Berat; Özbayoğlu, A. Murat

doi:10.1016/j.asoc.2018.04.024

Cited by 278 publications

(151 citation statements)

References 38 publications

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“…The 2D CNN model has been especially popular in stock forecasting. In [19], the said techniques have been used on stock prices for forecasting. A slightly different input is used in [20]: instead of using the standard stock variables (open, close, high, low, and NAV), it uses high frequency data for forecasting major points of inflection in the financial market.…”

Section: Cnn For Time Series Analysismentioning

confidence: 99%

“…We will use the five raw inputs for both the tasks, namely open price, close price, high, low, and net asset value (NAV). One could compute technical indicators based on the raw inputs [19], but in keeping with the essence of true representation learning, we chose to stay with those raw values. Each of the five inputs is processed by a separate 1D processing pipeline.…”

Section: Experimental Evaluationmentioning

confidence: 99%

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DeConFuse: a deep convolutional transform-based unsupervised fusion framework

Gupta

Maggu

Majumdar

et al. 2020

EURASIP J. Adv. Signal Process.

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This work proposes an unsupervised fusion framework based on deep convolutional transform learning. The great learning ability of convolutional filters for data analysis is well acknowledged. The success of convolutive features owes to the convolutional neural network (CNN). However, CNN cannot perform learning tasks in an unsupervised fashion. In a recent work, we show that such shortcoming can be addressed by adopting a convolutional transform learning (CTL) approach, where convolutional filters are learnt in an unsupervised fashion. The present paper aims at (i) proposing a deep version of CTL , (ii) proposing an unsupervised fusion formulation taking advantage of the proposed deep CTL representation , and (iii) developing a mathematically sounded optimization strategy for performing the learning task. We apply the proposed technique, named DeConFuse, on the problem of stock forecasting and trading. A comparison with state-of-the-art methods (based on CNN and long short-term memory network) shows the superiority of our method for performing a reliable feature extraction.

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Section: Cnn For Time Series Analysismentioning

confidence: 99%

Section: Experimental Evaluationmentioning

confidence: 99%

DeConFuse: a deep convolutional transform-based unsupervised fusion framework

Gupta

Maggu

Majumdar

et al. 2020

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

show abstract

“…The idea of using ML in banking was first given to make financial decisions using neural networks by Hawaly et al [2], which laid the foundation for the automation of the banking sector. Nowadays, machine learning is not only used for automation, but also for predicting the future, finding customer loyalty, information on standard recovery rates [3], learning optimal coverage rates [4], modeling investor sentiment [5], fraud detection [6], detecting the stock price [7,8], client maintenance, programmed credit endorsement, extortion discovery, showcasing and hazard the executives in the financial part [9]. Banks keep up a lot of data about their clients.…”

Section: Related Workmentioning

confidence: 99%

“…Whereas, in the second stage DT (cart & chaid), BBN, SVM, and ANN are used to detect fraudulent transactions. Sezer et al Proposed an image recognition technology to predict technical stock patterns [7]. The proposed model is a CNN model that performs five functions, dataset pre-processing (extract/transform), data labeling, image formation, and CNN performance.…”

Section: Related Workmentioning

confidence: 99%

Predicting the Future Transaction from Large and Imbalanced Banking Dataset

Ilyas¹,

Zia²,

Butt³

et al. 2020

IJACSA

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Machine learning (ML) algorithms are being adopted rapidly for a range of applications in the finance industry. In this paper, we used a structured dataset of Santander bank, which is published on a data science and machine learning competition site (kaggle.com) to predict whether a customer would make a transaction or not? The dataset consists of two classes, and it is imbalanced. To handle imbalance as well as to achieve the goal of prediction with the least log loss, we used a variety of methods and algorithms. The provided dataset is partitioned into two sets of 200,000 entries each for training and testing. 50% of data is kept hidden on their server for evaluation of the submission. A detailed exploratory data analysis (EDA) of datasets is performed to check the distributions of values. Correlation between features and importance of characteristics is calculated. To calculate the feature importance, random forest and decision trees are used. Furthermore, principal component analysis and linear discriminant analysis are used for dimensionality reduction. We have used 9 different algorithms including logistic regression (LR), Random forests (RF), Decision tree (DT), Multilayer perceptron (MLP), Gradient boosting method (GBM), Category boost (CatBoost), Extreme gradient boosting (XGBoost), Adaptive boosting (Adaboost) and Light gradient boosting (LigtGBM) method on the dataset. We proposed LighGBM as a regression problem on the dataset and it outperforms the stateof-the-art algorithms with 85% accuracy. Later, we have used fine-tune hyperparameters for our dataset and implemented them in combination with the LighGBM. This tuning improves performance, and we have achieved 89% accuracy.

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“…In this paper, we propose a Deep Learning model based on an image processing approach for predicting the daily price movement of the Forex market. The accuracy of the prediction is compared with the accuracy of the model-based Machine Learning algorithms including C5.0 [22], Logistic Regression [23], Discriminant [24], ANN [25], CHAID [26] and C&R Tree [27]. For Deep Learning model, the experimental process is divided into six steps including data collection, data preparation, data transformation, data labeling, modeling, performance evaluation and comparison, and financial evaluation.…”

Section: A Research Frameworkmentioning

confidence: 99%