Sarat Chandra Nayak

Index prediction with neuro-genetic hybrid network: A comparative analysis of performance

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 analyzed are artificial neural network (ANN) trained with gradient descent (GD) technique, ANN trained with genetic algorithm (GA) and functional link neural network (FLANN) trained with GA. The stock price index of Bombay stock exchange data has been considered to train these models and to compare their relative performance. Experimental results and analysis has been presented to show the performance of different models.

Evaluation of normalization methods on neuro-genetic models for stock index forecasting

With the rise of artificial intelligence technology and the growing interrelated markets of the last two decades offering unprecedented trading opportunities, technical analysis simply based on forecasting models is no longer enough. To meet the trading challenge in todays global market, technical analysis must be redefined. Before using the neural network models some issues such as data preprocessing, network architecture and learning parameters are to be considered. Data normalization is a fundamental data preprocessing step for learning from data before feeding to the Artificial Neural Network (ANN). Finding an appropriate method to normalize time series data is not a simple task. This work evaluates various normalization methods used in ANN model trained with gradient descent (ANN-GD), genetic algorithm (ANN-GA), and functional link artificial neural network model trained with GD (FLANN-GD) and genetic algorithm (FLANN-GA). The study is applied on daily closing price of Bombay stock exchange (BSE) and experimental result.

Hybridzing chemical reaction optimization and Artificial Neural Network for stock future index forecasting

Stock index forecasting has been a cornerstone and challenging task in computational statistics and financial mathematics since last few decades. Several machine learning methods have been proposed in order to forecast the future value of stocks effectively as well as efficiently. In this paper we considered an Artificial Neural Network (ANN) combined with a Chemical Reaction Optimization (CRO) algorithm forming a hybridized model (ANN-CRO) to forecast the Bombay Stock Exchange (BSE) future indices. Uniform population method (UP) has been used as initial population for CRO. The preprocessed data which includes the daily closing prices of BS E have been used for training and testing purpose. The predictability performance of the model is evaluated in terms of Average Percentage of Errors (APE), and compared with the result obtained by using a multilayer perceptron (MLP) model. It may be concluded that the ANN-CRO model can be a promising tool for the purpose of stock index prediction.

An Adaptive Second Order Neural Network with Genetic-Algorithm-based Training (ASONN-GA) to Forecast the Closing Prices of the Stock Market

Successful prediction of stock indices could yield significant profit and hence require an efficient prediction system. Higher order neural networks (HONN) have several advantages over traditional neural networks such as stronger approximation, higher fault tolerance capacity and faster convergence characteristics. This paper proposes an adaptive single layer second order neural network with genetic algorithm based training (ASONN-GA) applied to forecast daily closing prices of the stock market. For comparative study of performance, two conventional neural based models such as a recurrent neural network (RNN) and a multilayer perceptron (MLP) have been developed. The optimal network parameters for all the three models are tuned by genetic algorithm (GA). The efficiencies of the models have been evaluated by forecasting the one-day-ahead closing prices of real stock markets. From simulation studies, it is revealed that the ASONN-GA model achieve better forecasting accuracy over other two models.

Efficient financial time series prediction with evolutionary virtual data position exploration

Prediction of stock index remains a challenging task of the financial time series prediction process. Random fluctuations in the stock index make it difficult to predict. Usually the time series prediction is based on the observations of past trend over a period of time. In general, the curve the time series data follows has a linear part and a non-linear part. Prediction of the linear part with past history is not a difficult task, but the prediction of non linear segments is difficult. Though different non-linear prediction models are in use, but their prediction accuracy does not improve beyond a certain level. It is observed that close enough data positions are more informative where as far away data positions mislead prediction of such non linear segments. Apart from the existing data positions, exploration of few more close enough data positions enhance the prediction accuracy of the non-linear segments significantly. In this study, an evolutionary virtual data position (EVDP) exploration method for financial time series is proposed. It uses multilayer perceptron and genetic algorithm to build this model. Performance of the proposed model is compared with three deterministic methods such as linear, Lagrange and Taylor interpolation as well as two stochastic methods such as Uniform and Gaussian method. Ten different stock indices from across the globe are used for this experiment and it is observed that in majority of the cases performance of the proposed EVDP exploration method is better. Some stylized facts exhibited by the financial time series are also documented.

Evolving Low Complex Higher Order Neural Network Based Classifiers for Medical Data Classification

Multilayer neural network based classifiers have been proven their better approximation and generalization ability in medical data classification. However they are characterize with both computational and structural complexities. This article proposes an Evolving Functional Link Network (EFLN) for medical data classification. First, the input signals are mapped from lower to higher dimensional feature space applying some trigonometric expansion functions. Then the optimal number of expanded input signals, weight vectors and network parameters are obtained by an evolutionary search technique. Therefore the optimal network structure can be achieved on fly by evolving a set of FLNs during training rather fixing it earlier. The proposed EFLN classifiers are validated with some benchmark data sets from UCI machine learning repository. The performances are compared with that of a gradient descent based FLN (GDFLN), multiple linear regressions (MLR) and a multilayer perceptron (MLP) and found to be superior.

Fluctuation prediction of stock market index by adaptive evolutionary higher order neural networks

The stock market is complex and dynamic in nature, and has been a subject of research for modelling its random fluctuations. Higher order neural network (HONN) has the ability to expand the input representation space, perform high learning capabilities and have been utilised to solve many complex data mining problems. To capture the extreme volatility, nonlinearity and uncertainty associated with stock data, this paper compares two adaptive evolutionary optimisation-based Pi-Sigma neural networks (AE-PSNN), for prediction of closing prices of five real stock markets. For this experimental study, BSE, DJIA, NASDAQ, FTSE and TAIEX stock indices are employed for short, medium and long term predictions. The performance of the AE-PSNN models has been compared with that of a gradient descent-based PSNN (GD-PSNN) model and found to be superior in terms of prediction accuracy and prediction of change in direction.

Comparison of Performance of Different Functions in Functional Link Artificial Neural Network: A Case Study on Stock Index Forecasting

The rapid growth of world economy and globalization has been attracting researchers to develop intelligent forecasting models for stock market prediction. In order to forecasting the stock market trend efficiently, this paper developed four single layer low complex forecasting models known as functional link artificial neural network (FLANN). Different basis functions such as Trigonometric, Chebysheb, Legendre and Lagurre polynomials are used for functional expansion of input signals to achieve higher dimensionality. The models are termed as TFLANN, CFLANN, LeFLANN and LFLANN respectively. The weight and bias vectors are optimized by genetic algorithm (GA). The number of functional expansion for each models are optimized by GA during the training process instead of fixing it earlier, which is the novelty of this research work. The models are employed to forecast the one-day-ahead prediction of three fast growing global stock markets. Different types of FLANN are considered and their comparative performance is investigated.

A Pi-Sigma Higher Order Neural Network for Stock Index Forecasting

Multilayer perceptron (MLP) has been found to be most frequently used model for stock market forecasting. MLP is characterized with black-box in nature and lack of providing a formal method of deriving ultimate structure of the model. Higher order neural network (HONN) has the ability to expand the input representation space, perform high learning capabilities that require less memory in terms of weights and nodes and have been utilized in many complex data mining problems. To capture the extreme volatility, nonlinearity and uncertainty associated with stock data, this paper considered a HONN, called Pi-Sigma Neural Network (PSNN), for prediction of closing prices of five real stock markets. The tunable weights are optimized by Gradient Descent (GD) and a global search technique, Genetic Algorithm (GA). The model proves its superiority when trained with GA in terms of Average Percentage of Errors (APE).

Forecasting foreign exchange rates using CRO based different variants of FLANN and performance analysis

Globalisation has been the most influential shift of human civilisation since its inception. Foreign exchange (FOREX) market rates played a pivotal role in this extravagant change. FOREX rates have been a vital factor while deciding any international deals among the countries. Though FOREX exhibits a nonlinear trend, evolutionary techniques like artificial neural networks (ANNs) make it possible to predict. This paper deliberates the prediction using different variants of FLANN, i.e., CFLANN, LFLANN, PFLANN and TFLANN with chemical reaction optimisation (CRO) technique by using the real-time series of rupees, euro, yen and pound. Experimental analysis indicates that PFLANN and LFLANN perform better in most of the cases.