Application of artificial neural networks with backpropagation technique in the financial data
01 Nov 2017-Vol. 263, Iss: 4, pp 042139
TL;DR: This paper has applied neural network with backpropagation technique to find the data pattern from finance section and prediction for stock values as well.
Abstract: The propensity of applying neural networks has been proliferated in multiple disciplines for research activities since the past recent decades because of its powerful control with regulatory parameters for pattern recognition and classification. It is also being widely applied for forecasting in the numerous divisions. Since financial data have been readily available due to the involvement of computers and computing systems in the stock market premises throughout the world, researchers have also developed numerous techniques and algorithms to analyze the data from this sector. In this paper we have applied neural network with backpropagation technique to find the data pattern from finance section and prediction for stock values as well.
Citations
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TL;DR: A hybrid model with ANN and data envelopment analysis (DEA) techniques for stock prices forecasting in share market and a closed performance of the hybrid model has been achieved by carrying out experimentation with different number of nodes in the hidden layer of ANN model.
Abstract: The implementation of artificial neural network techniques has become quite prevalent in the field of nonlinear data modelling and forecasting in this era. The only application of ANN models for model fitting may not be sufficient for close and satisfactory performances; hence the researchers are adopting hybrid models of ANN with different statistical and machine learning approaches such as support vector machines, particle sworm optimization, principal component analysis, etc. We have also developed a hybrid model in this paper with ANN and data envelopment analysis (DEA) techniques for stock prices forecasting in share market. The efficient decision making units have been selected with help of DEA approach and provided it as input to the Lavenberg-Marquardt technique based ANN model in sliding window manner. Further a closed performance of our hybrid model has been achieved by carrying out our experimentation with different number of nodes in the hidden layer of ANN model. Since the prices of stocks follow numerous factors such as demand and supply, political environments, economy and finance, buy and sell, etc., the historical prices for stocks may be convenient for the further forecasting.
1 citations
13 Mar 2020
TL;DR: A Hybrid model using ANN with GA to carry out predictive modelling of rice in order to generate superior prediction and it is recognized that the error of prediction is minimized using the proposed hybrid model in comparison to the existing model where Artificial Neural Network is used exclusively.
Abstract: Both Genetic Algorithms (GA) and Artificial Neural Networks (ANN) are extensively employed for predictive analysis in the research study. Agriculture is the foundation of our country and Rice production is one of the major crops in India. Thus, predictive modelling of rice production becomes extremely important task. This paper aims to develop a Hybrid model using ANN with GA to carry out predictive modelling of rice in order to generate superior prediction. The proposed model is tested on Rice production in India from 1981 to 2003. The prediction performance is evaluated using error calculation techniques indices like ‘Mean Squared Error’ (MSE), ‘Root Mean Square Error’ (RMSE) and ‘Mean Absolute Percentage Error’ (MAPE). Which is recognized that the error of prediction is minimized using our proposed hybrid model in comparison to the existing model where Artificial Neural Network is used exclusively.
01 Jan 2018
TL;DR: Two ANN techniques are considered, viz., backpropagation-based neural network (BPNN) and radial basis function network (RBFN), first, without principal component analysis (PCA), and further modified the model with PCA, to execute financial time series forecasting for the next 5 days.
Abstract: The application of artificial neural network (ANN) has become quite ubiquitous in numerous disciplines with different motivations and approaches. One of the most contemporary implementations accounts it for stock price behavior analysis and forecasting. The stochastic behavior of stock market follows numerous factors to determine the price vicissitudes such as GDP, supply and demand, political influences, finance, and many more. In this paper, we have considered two ANN techniques, viz., backpropagation-based neural network (BPNN) and radial basis function network (RBFN), first, without principal component analysis (PCA), and further modified the model with PCA, to execute financial time series forecasting for the next 5 days (which can also be extended for some other number of days) by accepting the input as historical data on the sliding window basis. Moreover, the empirical research is conducted to verify the forecasting impact on the stock prices for oil and natural gas sector in India with the developed model, and subsequently a comparison study has also been performed for the effectiveness of the two models without and with PCA, on the basis of mean square percentage error.
References
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TL;DR: Inspired by one of the traditional credit risk models developed by Merton (1974), it is shown that the use of novel indicators for the NN system provides a significant improvement in the (out-of-sample) prediction accuracy.
Abstract: The prediction of corporate bankruptcies is an important and widely studied topic since it can have significant impact on bank lending decisions and profitability. This work presents two contributions. First we review the topic of bankruptcy prediction, with emphasis on neural-network (NN) models. Second, we develop an NN bankruptcy prediction model. Inspired by one of the traditional credit risk models developed by Merton (1974), we propose novel indicators for the NN system. We show that the use of these indicators in addition to traditional financial ratio indicators provides a significant improvement in the (out-of-sample) prediction accuracy (from 81.46% to 85.5% for a three-year-ahead forecast).
667 citations
TL;DR: Just four years ago, the only widely reported commercial application of neural network technology outside the financial industry was the airport baggage explosive detection system developed at Science Applications International Corporation (SAIC).
Abstract: Just four years ago, the only widely reported commercial application of neural network technology outside the financial industry was the airport baggage explosive detection system [27] developed at Science Applications International Corporation (SAIC). Since that time scores of industrial and commercial applications have come into use, but the details of most of these systems are considered corporate secrets and are shrouded in secrecy. This hastening trend is due in part to the availability of an increasingly wide array of dedicated neural network hardware. This hardware is either in the form of accelerator cards for PCs and workstations or a large number of integrated circuits implementing digital and analog neural networks either currently available or in the final stages of design
572 citations
TL;DR: Results suggest that RF may be a promising pattern recognition method for E-tongue data processing, because it can deal with classification problems of unbalanced, multiclass and small sample data without data preprocessing procedures.
Abstract: Random forest (RF) has been proposed on the basis of classification and regression trees (CART) with “ensemble learning” strategy by Breiman in 2001. In this paper, RF is introduced and investigated for electronic tongue (E-tongue) data processing. The experiments were designed for type and brand recognition of orange beverage and Chinese vinegar by an E-tongue with seven potentiometric sensors and an Ag/AgCl reference electrode. Principal component analysis (PCA) was used to visualize the distribution of total samples of each data set. Back propagation neural network (BPNN) and support vector machine (SVM), as comparative methods, were also employed to deal with four data sets. Five-fold cross-validation (CV) with twenty replications was applied during modeling and an external testing set was employed to validate the prediction performance of models. The average correct rates (CR) on CV sets of the four data sets performed by BPNN, SVM and RF were 86.68%, 66.45% and 99.07%, respectively. RF has been proved to outperform BPNN and SVM, and has some advantages in such cases, because it can deal with classification problems of unbalanced, multiclass and small sample data without data preprocessing procedures. These results suggest that RF may be a promising pattern recognition method for E-tongues.
265 citations
TL;DR: This study analyzes the beneficial aspects of using both neurofuzzy systems as well as neural networks for credit-risk evaluation decisions.
242 citations
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TL;DR: This paper investigates the possible application of an artificial neural network model and its cross-application of weights at three study areas in Malaysia, Penang Island, Cameron Highland and Selangor, and verification results showed satisfactory agreement between the susceptibility map and the existing data on the landslide location.
219 citations