A novel model by evolving partially connected neural network for stock price trend forecasting
Highlights
► A novel model by evolving partially connected neural networks is developed to predict the stock price trend using technical indicators as inputs. ► EPCNN utilizes random connection between neurons and more hidden layers to learn the knowledge stored within the historic time series data. ► Experimental results show that EPCNN is a very promising tool in forecasting of the financial time series data.
Introduction
Recently, stock price prediction has been a subject of interest for most investors and professional analysts. Nevertheless, mining stock market trend is a challenging task due to its high volatility and noisy environment. Many factors influence the performance of a stock market including political events, general economic conditions, and traders’ expectations. Although, a number of papers are describing the predictability of share prices, the performances of these models developed are still quite limited owing to noise and nonlinearity in stock price prediction.
During the past decades, many researchers have predicted stock market returns using artificial intelligence (AI) approaches. Recently, artificial neural networks (ANNs) combined with other approaches have been applied to this area. Chang, Liu, Lin, Fan, and Ng (2009) had applied neural network, dynamic time windows, and Case Based Reasoning (CBR) to predict stock trading. They concluded that percentage prediction accuracy for stock buying or selling decisions was promising. Two neural network architectures, namely multi-layer perception (MLP) neural networks and generalized regression neural networks have been used to forecast closing price movements of Kuwait stock exchange (Mostafa, 2010). Chang, Liu, Fan, Lin, and Lai (2009) also used ensemble of neural networks and intelligent piecewise linear representation to determine the stock turning points. The results demonstrated that the hybrid system can make a significant and constant amount of profit compared to other approaches. Levenberg–Marquardt BP algorithm (Li & Liu, 2009) has been adopted for stock market prediction in order to avoid local extremities and promote convergence speed. The prediction accuracy of stock rates was different for back-propagation neural network and genetic algorithms based on neural network (Khan et al., 2008, Kim and Han, 2000, Mandziuk and Jaruszewicz, 2007). The experimental results showed that genetically evolving weights mitigated the well-known limitations of the gradient descent algorithm. Kim and Chun (1998) had applied a graded forecasting of multiple discrete values to circumvent yielding only to achieve a bipolar output of probabilistic neural networks (PNNs) (Montana, 1992). For the prediction of stock turning points, a piecewise linear representation method and dynamic time warping system was integrated and back-propagation neural network (BPN) was further used to learn the connection weights (Chang et al., 2008, Chang et al., 2009). The BPN integrated with an improved bacterial chemotaxis optimization (IBCO) was applied to predict various stock indices of S&P 500 (Zhang & Wu, 2009). Trippi and DeSieno (1992) had integrated neural network with conventional rule-based expert system techniques to outperform passive investment in the stock index. GGAP-RBF network was also applied to predict stock prices and achieve good prediction accuracy (Wang, Huang, Saratchandran, & Sundararajan, 2005).
Many researchers have used time series to study stock market (Amihud et al., 2010, Gong and Sun, 2009, Jiang et al., 2009, Wang et al., 2010). A new approach based on logistic regression model predicted the stock price trend of next month by using the stock prices of current month (Gong & Sun, 2009). This approach achieved a minimum prediction accuracy of 83% which is unsatisfactory. Markov chain concept was also incorporated into fuzzy stochastic prediction of stock indexes to attain better performances and confidence (Wang et al., 2010). Predictive regressions with order-p autoregressive predictors were used to predict quarterly stock returns by dividend yield which is apparently AR(2) (Amihud et al., 2010).
Other approaches such as hybrid method, rough set; Fuzzy Rule Based System, etc. are used to study the stock index. Hybrid classification which includes probabilistic neural network, rough set and C4.5 was applied by Cheng, Chen, and Lin (2010) to create a model with better predictive power in terms of stock market timing analysis. Cheng, Chen, and Wei (2010) applied a hybrid model based on rough sets theory, genetic algorithms and multi-technical indicators to forecast stock price trends. Zhang, Sai, and Yuan (2008) combined rough set with support vector machine (a hybrid prediction model) to explore the future tendencies of stock index. A Takagi–Sugeno–Kang (TSK) type Fuzzy Rule Based System (Chang & Liu, 2008) was developed to predict the stock prices. A random level shift model (Lu & Perron, 2010) was applied to the logarithm of daily absolute returns for stock market return indices to forecast the stock return volatility. For the same time series stock, multivariate adaptive regression splines (MARS) was used to predict stock price and the performance of MARS, BPN and SVR was compared (Lu, Chang, Chen, Chiu, & Lee, 2009).
From the above mentioned papers, many kinds of neural networks have been applied to predict stock market tendencies but they have many flaws. When there are many numbers of neurons and many numbers of layers, the neural networks are over-fitting. The purpose of this paper is to propose a novel approach of partially connected neural model (Canning and Gardner, 1988, Garis, 2008, Hubert, 1993) to improve expressive ability and predictive percentage. This research will apply EPCNN for training historic stock data and test data. There can be more than one hidden layers, so the additional hidden knowledge stored within the historic time series data are utilized to improve the expressive ability of network. As gradient descents, the algorithm searches the local solution. The genetically evolved weights mitigate limitations of gradient descent algorithm and search the global solution (Kim & Han, 2000). EPCNN consists of six steps: (1) provide number of neurons and layers; (2) normalize input variables; (3) initialize weights and make sure that the neurons are connected; (4) calculate the output value by inputting variables into the network; (5) search optimal or near-optimal connection weights by GA; (6) store optimized weights. The input and output of neural network is technical indexes and stock price index respectively. Finally, financial time series data from S&P 500 is applied to demonstrate the effectiveness of the system.
In addition, the activation function is defined by sin(x) because the range of sin(x) is [−1, 1] and sin(x) is monotonous in the range. Sin(x) and sigmoid function have similar effect on the neural network. The experiment in this paper shows the performance of sin(x) is better than sigmoid function.
The remainder of the paper is organized as follows. Section 2 introduces EPCNN. In Section 3, input variables for the forecasting model are discussed and the implementation of EPCNN is described. In Section 4, the empirical results are summarized and discussed. In Section 5, conclusions are presented.
Section snippets
Evolving partially connected neural networks (EPCNNs)
This section firstly introduces partially connected neural networks and then applies genetic algorithms to further fine-tune the connection weights of the neural network architecture.
Research method
This section firstly states input variables for EPCNN. Traditionally, financial experts have been proposing a set of prediction based on moving average crosses, head and shoulders, range breakout, triangle breakout, etc. In this paper, technical indexes are applied as inputs to EPCNN. Technical indexes are calculated with the help of set of formulas, variation in stock price, and trading volumes. Technical indexes reflect the current tendency of the stock price fluctuations. These indexes can
Research data and experiments
In this section, two different stocks are selected. The selected stocks are Citigroup and Motors Liquidation Company. The historic data is collected form ⧹S&P 500 and covers the financial time-series data from 2008/01/01 to 2009/6/31. The historic data from 2008/01/01 to 2008/12/31 is used for training and the historic data from 2009/01/01 to 2009/06/31 is used for testing. For Citigroup, the size of the training and test data is 254 and 124, respectively. For Motors Liquidation Company the
Conclusions
As mentioned earlier, previous studies tried to optimize the controlling parameters of ANN using local search algorithms whereas this paper combines global search algorithms with a partially connected neural network to forecast stock price index trend. The expressive ability of EPCNN is stronger than back propagation neural network since connection between neurons is probabilistic. Normalization is made to dispose the historical data which includes the technical indexes and stock price. The
References (31)
- et al.
Predictive regression with order-p autoregressive predictors
Journal of Empirical Finance
(2010) - et al.
A TSK type fuzzy rule based system for stock price prediction
Expert Systems with Applications
(2008) - et al.
A neural network with a case based dynamic window for stock trading prediction
Expert Systems with Applications
(2009) - et al.
A hybrid forecast marketing timing model based on probabilistic neural network, rough set and C4.5
Expert Systems with Applications
(2010) - et al.
A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting
Information Sciences
(2010) - et al.
Graded forecasting using an array of bipolar predictions: Application of probabilistic neural networks to a stock market index
International Journal of Forecasting
(1998) - et al.
Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index
Expert Systems with Applications
(2000) - et al.
Modeling and forecasting stock return volatility using a random level shift model
Journal of Empirical Finance
(2010) Forecasting stock exchange movements using neural networks: Empirical evidence from Kuwait
Expert Systems with Applications
(2010)- et al.
Incorporating the Markov chain concept into fuzzy stochastic prediction of stock indexes
Applied Soft Computing
(2010)
Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network
Expert Systems with Applications
Machine learning: Neural networks, genetic algorithms, and fuzzy systems
Partially connected models of neural networks
Journal of Physics A
Integrating a piecewise linear representation method and a neural network model for stock trading points prediction
IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
Cited by (87)
Hybrid ARMA-GARCH-Neural Networks for intraday strategy exploration in high-frequency trading
2024, Pattern RecognitionCo-evolution of neural architectures and features for stock market forecasting: A multi-objective decision perspective
2023, Decision Support SystemsA feature-enhanced long short-term memory network combined with residual-driven ν support vector regression for financial market prediction
2023, Engineering Applications of Artificial IntelligenceMachine learning models predicting returns: Why most popular performance metrics are misleading and proposal for an efficient metric
2022, Expert Systems with ApplicationsRapid assessments of light-duty gasoline vehicle emissions using on-road remote sensing and machine learning
2022, Science of the Total EnvironmentMachine learning approaches in stock market prediction: A systematic literature review
2022, Procedia Computer Science