CS 631 – Research Trends in Artificial Intelligence
Project Proposal
Project Title
Group
Number
Group
Members
Abstract
Role of Neural Networks in Stock market prediction
10
Student Number
2005-02-0062
2005-02-0113
Name
Hinna Aftab
Mustafa Mahmood
Stock price variation is an example of a multidimensional but
partially-deterministic phenomenon to an extent. Neural
networks are being used to predict stock prices based on a large
number of factors including historic prices of other stocks and
trends in various economic indicators. We would be analyzing
the crucial role of neural networks in stock market prediction.
Artificial Neural Networks (ANN) attempts to replicate the working of
biological neural systems in a less complex manner. The nodes in the network
behave like neurons in a biological system. All nodes in the network have
certain inputs and output lines and every output line is connected to the input
lines of another node. In this layered networks, the nodes are linked by a
weighted connection to every node in the preceding layer.
The nodes can be in one of the two states, active or inactive. The decision about
the state of the node is a function of the weights of the input connections to
adjacent nodes and the states of the adjacent nodes. Thus, the structure of the
ANN may be organized into an input layer, an output layer and numerous
intermediate layers.
The input to the ANN is in a certain format that activates certain nodes, and the
ANN produces an output pattern based on the weights of the connections. The
learning takes place by comparing the resulting output pattern to the required
output and then adjusting the weights of the connections accordingly. The
intermediate and output layers determine their state based on the weights of the
connections between nodes during their calculation iterations.
The output pattern which is a set of active and inactive nodes is externally
corrected for errors, while the error for intermediate nodes is determined by
internal calculations during the learning iterations. If the error is positive, the
weight is then redistributed to the active input lines strengthening the
connections to the active nodes below. But if the error is negative, the weight is
redistributed to the inactive input lines reducing the strength of the connections
to the active nodes below. In this process, the weights of the output and
intermediate layers are adjusted for error. The learning process comprises of the
adjustment of the weights.
As for stock market prediction, the ANN aims to assigns weights in a manner
where the output patterns being generated are an approximately accurate
measure of actual stock prices of the following days based on the stock price of
previous day.
Our project aims to cover the role of neural networks in stock market prediction
in depth. We intend to study the ability of the ANN to utilize the knowledge
stored in the connection weights to forecast stock prices, and its success to date.