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PROJECT TOPIC:  DESIGN AND IMPLEMENTATION OF A STOCK FORECASTING SYSTEM USING NEURAL NETWORK.
Department:  Computer Science
AMOUNT:  20000
FORMART:   MS WORD
PAGES:  95 pages, abstract, chapter 1-5 , APENDIX A source code and APENDIX B output, well reserached and supervised
  Algorithm

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CHAPTER ONE

INTRODUCTION

1.0   Introduction

Stock Market prediction/forecasting is the act of using a company’s historic data to try to determine the future value of a company’s stock and to determine the direction of future trends. Businesses utilize forecasting to determine how to allocate their budgets or plan for anticipated expenses for an upcoming period of time. This is typically based on the projected demand for the goods and services they offer.

The research topic, designing of stock forecasting system using neural networks thus sets out to use Artificial neural networks to simulate how the human brain functions/ human thinking by feeding computers with massive data that will mimic human thinking as regards stock market prediction.

The system will use low price and time lags to predict future lows, while another network would use lagged highs to predict future highs. The predicted future lows and highs are then used to form stop prices for buying or selling.

Outputs from the network would trigger buying, selling or market directional change.

1.1 Background of the Study              

An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application which in this case is stock market prediction through a learning process.


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