Forecasting stock exchange rates is an important financial problem that is receiving increasing attention. During the last few years, a number of neural network models and hybrid models have been proposed for obtaining accurate prediction results, in an attempt to outperform the traditional linear and nonlinear approaches. This paper evaluates the effectiveness of neural network models which are known to be dynamic and effective in stock-market predictions. The models analysed are multi-layer perceptron (MLP), dynamic artificial neural network (DAN2) and the hybrid neural networks which use generalized autoregressive conditional heteroscedasticity (GARCH) to extract new input variables. The comparison for each model is done in two view points: Mean Square Error (MSE) and Mean Absolute Deviate (MAD) using real exchange daily rate values of NASDAQ Stock Exchange index.

Using artificial neural network models in stock market index prediction

Nowadays, many current real financial applications have nonlinear and uncertain behaviors which change across the time. Therefore, the need to solve highly nonlinear, time variant problems has been growing rapidly. These problems along with other problems of traditional models caused growing interest in artificial intelligent techniques. In this paper, comparative research review of three famous artificial intelligence techniques, i.e., artificial neural networks, expert systems and hybrid intelligence systems, in financial market has been done. A financial market also has been categorized on three domains: credit evaluation, portfolio management and financial prediction and planning. For each technique, most famous and especially recent researches have been discussed in comparative aspect. Results show that accuracy of these artificial intelligent methods is superior to that of traditional statistical methods in dealing with financial problems, especially regarding nonlinear patterns. However, this outperformance is not absolute.

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

A novel hybrid model is proposed for prediction of stocks returns.The proposed model is hybrid of two linear models and a non-linear model.An optimization model is introduced which generates weights for proposed model.Proposed model is able to capture non-linear patterns of stock data very well. In this paper, we propose a robust and novel hybrid model for prediction of stock returns. The proposed model is constituted of two linear models: autoregressive moving average model, exponential smoothing model and a non-linear model: recurrent neural network. Training data for recurrent neural network is generated by a new regression model. Recurrent neural network produces satisfactory predictions as compared to linear models. With the goal to further improve the accuracy of predictions, the proposed hybrid prediction model merges predictions obtained from these three prediction based models. An optimization model is introduced which generates optimal weights for proposed model; the model is solved using genetic algorithms. The results confirm about the accuracy of the prediction performance of recurrent neural network. As expected, an outstanding prediction performance has been obtained from proposed hybrid prediction model as it outperforms recurrent neural network. The proposed model is certainly expected to be a promising approach in the field of prediction based models where data is non-linear, whose patterns are difficult to be captured by traditional models.

Recurrent neural network and a hybrid model for prediction of stock returns

We introduce a literature review considering articles on artificial neural networks in business published in last two decades.412 suitable articles are identified and classified according to defined methodology.We focus on date, area of interest, type of neural network, benchmark method, journal and citations.The most applied are multilayer feedforward networks with backpropagation learning performed by gradient descent algorithm.Majority (25.73%) of the examined articles were published in Expert Systems with Applications. In recent two decades, artificial neural networks have been extensively used in many business applications. Despite the growing number of research papers, only few studies have been presented focusing on the overview of published findings in this important and popular area. Moreover, the majority of these reviews were introduced more than 15 years ago. The aim of this work is to expand the range of earlier surveys and provide a systematic overview of neural network applications in business between 1994 and 2015. We have covered a total of 412 articles and classified them according to the year of publication, application area, type of neural network, learning algorithm, benchmark method, citations and journal. Our investigation revealed that most of the research has aimed at financial distress and bankruptcy problems, stock price forecasting, and decision support, with special attention to classification tasks. Besides conventional multilayer feedforward network with gradient descent backpropagation, various hybrid networks have been developed in order to improve the performance of standard models. Even though neural networks have been established as well-known method in business, there is enormous space for additional research in order to improve their functioning and increase our understanding of this influential area.

Artificial neural networks in business

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

In this paper, we investigate the statistical properties of fluctuations of Chinese stock index. According to the theory of artificial neural network, a stochastic time effective function is introduced in the forecasting model of the index in the present paper, which gives an improved neural network - the stochastic time effective neural network model. In this model, a promising data mining technique in machine learning has been proposed to uncover the predictive relationships of numerous financial and economic variables. We suppose that the investors decide their investment positions by analyzing the historical data on the stock market, and the historical data is given a weight depending on it's time, in details that, the nearer the time of the historical data is to the present, the stronger impact the data has on the predictive model, and we also use the probability density functions to classifying the various variables from training samples. In the last part of the paper, the data of 16 years' period of Shenzhen stock index is analyzed in the stochastic time effective neural network model, and we show some analysis results for the fluctuations of the index by the model.

Forecasting Model of Chinese Stock Index by Stochastic Time Effective Neural Network

A new neural network (EBPNN) is developed.An approach to cross-correlations prediction between financial time series.Empirical research is performed in testing the forecasting effect of EBPNN.Forecasting long-term cross-correlations by training short-term cross-correlations.The proposed model is advantageous in increasing the forecasting precision. An improved neural network is developed to predict the cross-correlations between two financial time series. In order to capture the large fluctuations of data set, an exponent back propagation neural network (EBPNN) is introduced in the present work, which information is not only processed locally in each neural unit by computing the dot product between its input vector and its weight vector, but also processed by adding the dot product between its exponential type function of the input vector and its corresponding new weight vector. The proposed prediction model improves the activation function of the neural network, and makes an approach on cross-correlations forecasting with the particular input and output variables. The empirical research is performed in testing the forecasting effect of the EBPNN model and a comparison to back propagation neural network (BPNN). The empirical results show that the EBPNN is advantageous in increasing the predicting precision.

Exponent back propagation neural network forecasting for financial cross-correlation relationship

Machine learning algorithms provide feasibility for crude oil price prediction. In this paper, a novel multi-hybrid predictive neural network model is proposed based on complex deep learning algorithm, which integrates empirical wavelet transform, random inheritance formula error correction algorithm, deep bidirectional LSTM neural network and Elman recurrent neural network with variational learning rate. The prediction model is selected according to the sequence frequency after EWT feature extraction, and the prediction results are obtained by separately predicting and reintegrating. On the basis of individual model, the structure of deep bidirectional training, random inheritance formula and variational learning rate are proposed, which further ameliorate the performance of the model and achieve more effective data information capture. Simultaneously, the examination of variational learning rate provides us with a feasible parameter selection. The proposed model achieves high-precision prediction of crude oil futures price, and stands out in the multi-model comparison analysis and q-DSCID synchronous evaluation, with superior prediction accuracy.

Deep multi-hybrid forecasting system with random EWT extraction and variational learning rate algorithm for crude oil futures

By considering the properties of nonlinear data and the impact of historical data, this paper combines ensemble empirical mode decomposition (EEMD) into wavelet neural network with random time effective (WNNRT) to establish a hybrid neural network prediction model to improve the prediction accuracy of energy prices The EEMD is a noise-aided data analyze method, since it can effectively suppress pattern confusion and restore signal essence. Different from traditional models, the random time effective function that considers the timeliness of historical data and the random change of market environment is applied to the wavelet neural network to establish the WNNRT model. Moreover, multiscale complexity invariant distance (MCID) is utilized to evaluate the predicting performance of EEMD-WNNRT model. Further, the proposed model which is tested in predicting the impact on the global energy prices has carried on the empirical research, and it has also proved the corresponding superiority.

Jun Wang

Papers

Forecasting Model of Chinese Stock Index by Stochastic Time Effective Neural Network

Exponent back propagation neural network forecasting for financial cross-correlation relationship

Deep multi-hybrid forecasting system with random EWT extraction and variational learning rate algorithm for crude oil futures

Forecasting wavelet neural hybrid network with financial ensemble empirical mode decomposition and MCID evaluation