The number of neurons in both of the first two fully connected layers is Then, because the process is a binary classification, the connection goes through an output layer that contains only one node. The last layer uses the sigmoid activation function. Adaptive Optimization Methods.
Despite its simplicity, SGD performs well empirically across a variety of applications but also has strong theoretical foundations [ 23 ]. Training neural networks is equivalent to solving the nonconvex optimization problem in where represents a loss function. The iterations of SGD can be described in where denotes the iteration, represents a tuned step size sequence also called the learning rate , and denotes the stochastic gradient computed at. The Adam optimization algorithm is an algorithm that can be used instead of the classical SGD procedure to update network weights iteratively based on training data.
The Adam algorithm is popular in the field of deep learning because it achieves good results quickly [ 24 ]. The updated Adam equation can be represented in where represents a momentum parameter, and is initialized to 0.
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The dropout method introduced by Hinton et al. In this paper, the dropout method was applied after the pooling operations. Loss Function. The ANN uses the loss function as an indicator to determine the optimal weight parameter through learning [ 26 ]. In this study, the mean square error MSE and cross entropy error CEE were adopted to comprise the objective function loss function. When calculating the MSE, the neurons in all output layers are entered.
This loss function is most commonly used because it is simple to calculate. Basically, the difference between the output of the model and the target distance is used as an error.
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The advantage of squaring the distance difference is that the difference between data with small distance differences and the large data error becomes larger, which has the advantage of being able to know exactly where the error is located. The CEE only counts the neuron corresponding to the target, which results in a larger penalty as it moves farther from the target. Epoch and Batch Sizes. An epoch consists of one full training cycle for the data. An epoch is an iteration over the entire training data and target data provided.
The epochs are equal to in this study.
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The batch size is a term used in machine learning and refers to the number of training examples utilized in one iteration. The batch size is 1 [ 27 ] in this study. Steps per epoch indicate the number of batch iterations before a training epoch is considered finished. These steps represent the total number of steps i. In this study, the empirical analysis covers a 1-month period. The entire dataset covers 41, minutes. When the time series data are converted into an image every 30 minutes, the training data consist of 1, input images, and the testing data consist of input images.
For experimenting with the CNN algorithm, the technical indicators used for forecasting the stock price in [ 21 ] are employed as input variables here.
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To evaluate the forecasting accuracy, the following three measurements are employed: hit ratio, sensitivity, and specificity see 12 — In 12 - 14 , and represent the number of predicted values of 0 and the number of predicted values of 1 when the actual value is 0, respectively. Additionally, and represent the number of predicted values of 0 and the number of predicted values of 1 when the actual value is 1, respectively. The hit ratio is a metric or measure of the prediction model performance when the target variable is binary.
While the hit ratio is simply a measure of discrepancies between the predicted value and actual observations, sensitivity and specificity measure the conditional discrepancies depending on actual observations. In this study, TensorFlow was used for the experiment. TensorFlow is a famous deep learning development framework in which grammar is developed in the form of a Python library.
To verify the usefulness of the technical indicators as an input variable, four CNN models are constructed with different technical indicators. The CNN models are created by applying 0, 2, 4, and 9 technical indicators. Table 2 presents the input variables applied to these four models. Table 3 shows the accuracies of the four models. To determine the adaptive optimization method, all CNN parameters except for the adaptive optimization method are applied equally to each model.
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Here, the dropout probability, batch size, and epoch are fixed at 0. Additionally, the steps per epoch in the training and testing data were set to and 50, respectively, and the loss function was the CEE. As shown in Table 3 , when the SGD optimizer is used for the adaptive optimization method, CNNs achieve a high level of predictive performance. CNN1, which is the prediction model without technical indicators, has the highest hit ratio among the four models. Therefore, technical indicators cannot affect the positive impact of the CNN on stock price forecasting. However, a large difference between the sensitivity and specificity of CNN1 indicates that an overfitting problem occurs due to considering only one input variable.
Table 4 shows the accuracies of the four models with SGD optimizers using different loss functions. The accuracies of the four models with the SGD optimizer and MSE loss function using different dropout probabilities are given in Table 5. CNN1 has the highest hit ratio 0. The results in Table 5 show that an increase in the dropout probability does not contribute to the predictive performance of the CNN, which is interesting because dropout options are widely known to play an important role in deep learning architecture construction.
In the case of this experiment, however, since the learning image of CNN models is simpler than the character recognition or text recognition generally applied to CNNs, it is considered that the dropout option has a negative effect. Based on the results of Table 6 , we can realize that an increase in steps per epoch causes an overfitting problem and results in a decrease in accuracy. As a result, it is not effective in increasing the number of steps for stock price prediction based on a CNN using technical indicators. As a result, the number of hidden layers, the number of hidden units, and the activation function of ANN are set to be 1, 3, and sigmoid, respectively.
And SVM uses polynomial kernel to make a nonlinear classification interface. Based on the results show in Table 7 , when the ANN and SVM are applied, technical indicators are shown to be input variables positively affecting the stock price prediction, as opposed to when the CNN is applied. Therefore, CNNs using input images can be a useful method for stock price prediction. In practice, CNN models are good at detecting patterns in images such as lines.
CNNs can detect relationships among images that humans cannot find easily; the structure of neural networks can help detect complicated relationships among features. For example, in CNN, color images are composed of RGB channels, and the features of input for each channel can be extracted. In this study, we attempted to check the applicability of the CNN for stock market prediction.
Previously, many researchers have suggested that ANNs offer a chance to achieve profits in financial markets. To design the CNN architecture, this study focused on two points. First, the CNN parameters were optimized. For this, the experiments were performed over the parameter range given in Table 8 , and the best experiments were obtained. Second, technical indicators, which are well known as efficient input variables in stock price forecasting, were verified to play a role as a suitable input image for CNNs when technical indicators are converted into images.
Our empirical experiments demonstrate the potential usefulness of the CNN by showing that it could improve the predictive performance more than the ANN. In this sense, the CNN appears to be a desirable choice for building stock prediction models. In addition, technical indicators were input variables that did not positively affect the stock price prediction when the CNN was implemented for the prediction model.
This result is because technical indicators cannot be good input variables, as they are similar to the moving pattern of the closing price. Therefore, building a stock price prediction model with better performance can be expected if other factors that move opposite the stock price, such as gold price and interest rate, are considered as input variables for the CNN. As a result of this study, it is difficult to predict technical indicators of stock market by general data mining classification technique. Therefore, CNN, which is a deep learning method that analyzes time series data into graphs, can be a useful for stock price prediction.
The authors declare that there are no conflicts of interest regarding the publication of this paper. This is an open access article distributed under the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Journal overview. Special Issues. Academic Editor: Thiago C. Received 06 Dec Accepted 10 Feb Published 19 Feb Abstract Stock market prediction is a challenging issue for investors.
Introduction Stock markets have random walk characteristics. Welcome to the Machine Learning for Forex and Stock analysis and algorithmic trading tutorial series. In this series, you will be taught how to apply machine learning and pattern recognition principles to the field of stocks and forex. This is especially useful for people interested in quantitative analysis and algo trading.
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Even if you are not, the series will still be of great use to anyone interested in learning about machine learning and automatic pattern recognition, through a hands-on tutorial series. You can also get my free 2 PDFs on in case you never got them :. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
Use of this web site signifies your agreement to the terms and conditions. Commodity and Forex trade automation using Deep Reinforcement Learning Abstract: Machine learning is an application of artificial intelligence based on the theory that machines can learn from data, discern patterns and make decisions with negligible human intervention.
With today's world being inundated by data, machine learning is very relevant due to the amount of learning potential. Machine learning caters to a variety of applications including image recognition, speech recognition, weather prediction, portfolio optimization and so on.