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RETRACTED PAPER: Energy conversion from ocean waves has become the need of the hour in view of the renewable energy awakening occurring all over the world. Energy conversion by Oscillating Water Column (OWC) concept has become an established technology in converting mechanical energy of ocean waves to electrical energy. But the limitations of OWC concept calls for further research and developments to make the technology commercially an attractive one. In this context Boccotti, the Italian scientist advanced the double chamber concept and the implications of the concept still remains to be investigated through model studies. This paper presents the details of a generic study carried out in a physical model device under regular waves.

Financial forecasting is an area of research which has been attracting a lot of attention recently from practitioners in the field of artificial intelligence. Apart from the economic benefits of accurate financial prediction, the inherent nonlinearities in financial data make the task of analyzing and forecasting an extremely challenging task. This paper presents a survey of more than 100 articles published over two centuries from 1933 up to 2013 in an attempt to identify the developments and trends in the field of financial forecasting with focus on application of artificial intelligence for the purpose. The findings from the survey indicate that artificial intelligence and signal processing based techniques are more efficient when compared to traditional financial forecasting techniques and these techniques appear well suited for the task of financial forecasting. Some of the issues that need addressing are discussed in brief. A novel technique for selection of the input dataset size for ensuring best possible forecast accuracy is also presented. The results confirm the effectiveness of the proposed technique in improving the accuracy of forecasts.

Returns on stocks have traditionally been modelled by fitting a suitable statistical process to empirical returns. Studies on agent based models of stock market have been carried out by researchers, primarily on US markets. This paper analyzes the empirical features generated using historical data from the Bombay Stock Exchange (BSE), employing the concept of agent based model proposed by LeBaron[2,3,8]. Agent-based approach to stock market considers stock prices as arising from the interaction of a number of individual investors. These investors are modeled as intelligent agents, using differing lengths of past information, each trading with its own rules adapting and evolving over time, and this in turn determines the market prices. It is seen that the model generates some features that are similar to those from actual data of the BSE.

Recommender systems capable of discovering patterns in stock price movements and generating stock recommendations based on the patterns thus discovered can significantly supplement the decision-making process of a stock trader. Such recommender systems are of great significance to a layperson who wishes to profit by stock trading even while not possessing the skill or expertise of a seasoned trader. A genetic algorithm optimized Symbolic Aggregate approXimation (SAX)–Apriori based stock trading recommender system, which can mine temporal association rules from the stock price data set to generate stock trading recommendations, is presented in this article. The proposed system is validated on 12 different data sets. The results indicate that the proposed system significantly outperforms the passive buy-and-hold strategy, offering scope for a layperson to successfully invest in capital markets.

Accurate prediction of financial time series (which can be considered as nonlinear systems) especially in relation to emerging markets like India assumes prominence in that, these markets offer significantly higher opportunities for wealth creation for the investor. This paper compares the effectiveness of different types of Adaptive network architectures in one-step ahead prediction of the daily returns of Bombay Stock Exchange Sensitive Index (SENSEX). The performance of each network is evaluated using 17 different performance measures to find the best network architecture. Also, an empirical evaluation of the weak form of Efficient Market Hypothesis (EMH) for the data in reference is carried out here.

This paper outlines a methodology for aiding the decision making process for investment between two financial market assets (eg a risky asset versus a risk-free asset or between two risky assets itself), using neural network architecture. A Feed Forward Neural Network (FFNN) and a Radial Basis Function (RBF) Network has been evaluated. The model is employed for arriving at a decision as to where to invest in the next time step, given data from the current time step. The time step could be chosen on daily/weekly/monthly basis, based on the investment requirement. In this study, the FFNN has yielded good results over RBF. Consequently two such FFNN have been developed to enable us make a decision on investment in the next time step to decide between two risky assets. The prediction made by the two FFNN models has been validated from the actual market data.

Accurate prediction of financial time series, such as those generated by stock markets, is a highly challenging task due to the highly nonlinear nature of such series. A novel method of predicting the next day’s closing value of a stock market is proposed and empirically validated in the present study. The system uses an adaptive artificial neural network based system to predict the next day’s closing value of a stock market index. The proposed system adapts itself to the changing market dynamics with the help of genetic algorithm which tunes the parameters of the neural network at the end of each trading session so that best possible accuracy is obtained. The effectiveness of the proposed system is established by testing on five international stock indices using ten different performance measures.

Stock market prediction is of great interest to stock traders and applied researchers. Main issues in developing a fully automated stock market prediction system are: feature extraction from the stock market data, feature selection for highest prediction accuracy, the dimensionality reduction of the selected feature set and the accuracy and robustness of the prediction system. In this paper, an automated decision tree-adaptive neuro-fuzzy hybrid automated stock market trend prediction system is proposed. The proposed system uses technical analysis (traditionally used by stock traders) for feature extraction and decision tree for feature selection. Selected features are then subjected to dimensionality reduction and the reduced dataset is then applied to the adaptive neuro-fuzzy system for the next-day stock market trend prediction. The proposed system is tested on four major international stock markets. The results show that the proposed hybrid system produces much higher accuracy when compared to stand-alone decision tree based system and ANFIS based system without feature selection and dimensionality reduction.

Stock market prediction is an important area of financial forecasting, which is of great interest to stock investors, stock traders and applied researchers. Main issues in developing a fully automated stock market prediction system are: feature extraction from the stock market data, feature selection for highest prediction accuracy, the dimensionality reduction of the selected feature set and the accuracy and robustness of the prediction system. In this paper, an automated decision tree-adaptive neuro-fuzzy hybrid automated stock market prediction system is proposed. The proposed system uses technical analysis (traditionally used by stock traders) for feature extraction and decision tree for feature selection. Dimensionality reduction is carried out using fifteen different dimensionality reduction techniques. The dimensionality reduction technique producing the best prediction accuracy is selected to produce the reduced dataset. The reduced dataset is then applied to the adaptive neuro-fuzzy system for the next-day stock market prediction. The neuro-fuzzy system forms the stock market model adaptively, based on the features present in the reduced dataset. The proposed system is tested on the Bombay Stock Exchange sensitive index (BSE-SENSEX). The results show that the proposed hybrid system produces much higher accuracy when compared to stand-alone decision tree based system and ANFIS based system without feature selection and dimensionality reduction.

Ant Colony Optimisation (ACO) algorithms use simple mutually cooperating agents (ants) to produce a robust and adaptive search system, which can be used for knowledge discovery. In this paper, a Support Vector Machine (SVM)-cAnt-Miner-based system for predicting the next-day's trend in stock markets is proposed. The trend predicted by the proposed system is then used to identify the appropriate time to buy and sell securities. Performance of the proposed system is evaluated against SVM-Ant-Miner, SVM-Ant-Miner2, Naive-Bayes and an Artificial Neural Network (ANN)-based trend prediction system. The results indicate that the proposed system outperforms all the other techniques considered.