Abstract
The goal of this paper is to compare and analyze the forecasting performance of two artificial neural network models (i.e., MLP (multi-layer perceptron) and DNN (deep neural network)), and to conduct an experimental investigation by data flow, not economic flow. In this paper, we investigate beyond the scope of simple predictions, and conduct research based on the merits and data of each model, so that we can predict and forecast the most efficient outcomes based on analytical methodology with fewer errors. In particular, we focus on identifying two models of neural networks (NN), a multi-layer perceptron (i.e., MLP) model and an excellent model between the neural network (i.e., DNN) model. At this time, predictability and accuracy were found to be superior in the DNN model, and in the MLP model, it was found to be highly correlated and accessible. The major purpose of this study is to analyze the performance of MLP and DNN through a practical approach based on an artificial neural network stock forecasting method. Although we do not limit S&P (i.e., Standard&Poor’s 500 index) to escape other regional exits in order to see the proper flow of capital, we first measured S&P data for 100 months (i.e., 407 weeks) and found out the following facts: First, the traditional artificial neural network (ANN) model, according to the specificity of each model and depending on the depth of the layer, shows the model of the prediction well and is sensitive to the index data; Second, comparing the two models, the DNN model showed better accuracy in terms of data accessibility and prediction accuracy than MLP, and the error rate was also shown in the weekly and monthly data; Third, the difference in the prediction accuracy of each model is not statistically significant. However, these results are correlated with each other, and are considered robust because there are few error rates, thanks to the accessibility to various other prediction accuracy measurement methodologies.
Highlights
The stock market index, which is shaking many capital markets around the world, can be used as an inflection point in the economy for a measure of important changes [1,2,3,4]
(i.e., Standard&Poor’s 500 index) to escape other regional exits in order to see the proper flow of capital, we first measured S&P data for 100 months (i.e., 407 weeks) and found out the following facts: First, the traditional artificial neural network (ANN) model, according to the specificity of each model and depending on the depth of the layer, shows the model of the prediction well and is sensitive to the index data; Second, comparing the two models, the deep neural network (DNN) model showed better accuracy in terms of data accessibility and prediction accuracy than multi-layer perceptron (MLP), and the error rate was shown in the weekly and monthly data; Third, the difference in the prediction accuracy of each model is not statistically significant
What is unusual is that the DNN model provides a smaller MAE and Mean Square Error (MSE) than the MLP model does for all forecast horizons, with the exception of the short forecast horizon (23 weeks ahead)
Summary
The stock market index, which is shaking many capital markets around the world, can be used as an inflection point in the economy for a measure of important changes [1,2,3,4]. As a part of traditional supervised learning, we used an artificial neural network (i.e., ANN) model that uses labels as data. There are many examples [5,6,7], based on forecasting purpose data research, which are sometimes correct. This approach is worth investigating even the results are not correct. Numerous model design and performance analysis studies for stock forecasting have been carried out with various economic and statistical approaches [8,9,10,11,12]. From the viewpoint of prediction and forecasting, it is most important to establish each model by identifying the neural networks (NN) model and data learning method
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