Punctilious Training of Neural Networks for Efficacious Applications of Predictions in Android Phones

The use of trained Neural networks has found a variegated field of applications in the present world. In most of the developing countries, investing in stocks, albeit the risk factor is the most lucrative way of earning quick bucks. This has lead to the development of various models for financial markets and investment. Black-Scholes model opened a new domain for research in the field of stock markets. The model develops partial differential equations whose solution, the Black-Scholes formula, is widely used in the pricing of European-style options. The Aim of "Neural Network Based Stock Price Forecasting Model" is to develop a Model which will be used to Forecast Future Stock Prices using handheld Android Mobile phones. It will be developed by using one of the Concepts in Artificial Intelligence [8], "Artificial neural networks (ANNs) have rapidly grown in popularity. They are artificial intelligence adaptive software systems that have been inspired by how biological neural networks work. They are used because they can learn to detect complex patterns in data. In mathematical terms, they are universal function approximators, meaning that given the right data and configured correctly; they can capture and model any input-output relationships. This not only removes the need for human interpretation of charts or the series of rules for generating entry/exit signals, but also provides a bridge to fundamental analysis, as the variables used in fundamental analysis can be used as input. As ANNs are essentially non-linear statistical models, their accuracy and prediction capabilities can be both mathematically and empirically tested. In various studies, authors have claimed that neural networks used for generating trading signals given various technical and fundamental inputs have significantly outperformed buy-hold strategies as well as traditional linear technical analysis methods when combined with rule-based expert systems.