Prediction on tribological properties of carbon fiber and TiO2 synergistic reinforced polytetrafluoroethylene composites with artificial neural networks

The stock market is characterized by extreme fluctuations, non-linearity, and shifts in internal and external environmental variables. Artificial intelligence (AI) techniques can detect such non-linearity, resulting in much-improved forecast results. This paper reviews 148 studies utilizing neural and hybrid-neuro techniques to predict stock markets, categorized based on 43 auto-coded themes obtained using NVivo 12 software. We group the surveyed articles based on two major categories, namely, study characteristics and model characteristics, where ‘study characteristics’ are further categorized as the stock market covered, input data, and nature of the study; and ‘model characteristics’ are classified as data pre-processing, artificial intelligence technique, training algorithm, and performance measure. Our findings highlight that AI techniques can be used successfully to study and analyze stock market activity. We conclude by establishing a research agenda for potential financial market analysts, artificial intelligence, and soft computing scholarship.

Application of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda

The research described in this chapter is concerned with the development of a novel artificial higherorder neural networks architecture called the recurrent Pi-sigma neural network. The proposed artificial neural network combines the advantages of both higher-order architectures in terms of the multi-linear interactions between inputs, as well as the temporal dynamics of recurrent neural networks, and produces highly accurate one-step ahead predictions of the foreign currency exchange rates, as compared to other feedforward and recurrent structures.

A Novel Recurrent Polynomial Neural Network for Financial Time Series Prediction

Forecasting is very important in the present world and has vast applications in various domains like weather forecasting, stock market trading, energy forecasting, etc. One of the ways of doing for...

Forecasting Through Motifs Discovered by Genetic Algorithms

Forecasting banking sectors in Indian stock markets using machine intelligence

Predicting stock data with traditional time series analysis has proved to be difficult. An artificial neural network may be more suitable for the task. The reason is that no assumption about a suitable mathematical model has to be made prior to forecasting. Furthermore, a neural network has the ability to extract useful information from large sets of data, which often is required for a satisfying description of a financial time series. This paper discusses the various conventional analysis methods and neural network methodology to forecast a .stock market series. This paper also provides the effect of various topological parameters on the accuracy and training time of neural networks. A topology of neural network is proposed for the prediction of Indian stock market index S&P CNX NIFTY.

Neural Networks for the Prediction of Stock Market

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Neural Networks for the Prediction of Stock Market