Stock market prediction is of immense interest to trading companies and buyers due to
high profit margins. The majority of successful buying or selling activities occur close
to stock price turning trends. This makes the prediction of stock indices and analysis a
crucial factor in the determination that whether the stocks will increase or decrease the
next day. Additionally, precise prediction of the measure of increase or decrease of
stock prices also plays an important role in buying/selling activities. This research
presents two core aspects of stock-market prediction. Firstly, it presents a Networkbased
Fuzzy Inference System (ANFIS) methodology to integrate the capabilities of
neural networks with that of fuzzy logic. A specialised extension to this technique is
known as the genetic programming (GP) and gene expression programming (GEP) to
explore and investigate the outcome of the GEP criteria on the stock market price
prediction.
The research presented in this thesis aims at the modelling and prediction of short-tomedium
term stock value fluctuations in the market via genetically tuned stock market
parameters. The technique uses hierarchically defined GP and gene-expressionprogramming
(GEP) techniques to tune algebraic functions representing the fittest
equation for stock market activities. The technology achieves novelty by proposing a
fractional adaptive mutation rate Elitism (GEP-FAMR) technique to initiate a balance
between varied mutation rates between varied-fitness chromosomes thereby improving
prediction accuracy and fitness improvement rate. The methodology is evaluated
against five stock market companies with each having its own trading circumstances
during the past 20+ years. The proposed GEP/GP methodologies were evaluated based
on variable window/population sizes, selection methods, and Elitism, Rank and Roulette
selection methods. The Elitism-based approach showed promising results with a low
error-rate in the resultant pattern matching with an overall accuracy of 95.96% for
short-term 5-day and 95.35% for medium-term 56-day trading periods. The
contribution of this research to theory is that it presented a novel evolutionary
methodology with modified selection operators for the prediction of stock exchange
data via Gene expression programming. The methodology dynamically adapts the
mutation rate of different fitness groups in each generation to ensure a diversification
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balance between high and low fitness solutions. The GEP-FAMR approach was
preferred to Neural and Fuzzy approaches because it can address well-reported
problems of over-fitting, algorithmic black-boxing, and data-snooping issues via GP
and GEP algorithmsSaudi Cultural Burea
