Temporal-difference comparison of learning methods for stock market prediction
Date
2022
Authors
Maina, Stephen Gakuo
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Publisher
Strathmore University
Abstract
Background: a stock/securities exchange is considered to be among the primary indicators’ of a country’s economic strength and development. Stock market prices are volatile in nature and are affected by factors like inflation, economic growth, etc. Prices depend heavily on demand and supply dynamics. Stock market price determination using ANNs has gained a lot of traction lately due to the obvious advantages this would represent to traders. Most methods in use today have largely been based on the feed forward algorithms, however, evolutionary techniques remain largely unexplored for this process despite their obvious robustness. Method: Using data from the Nairobi Securities Exchange, and specifically the NSE20 share index, the project will seek to apply and compare traditional ANN techniques for stock market prediction against the relatively new evolution algorithms. The Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and a confusion matrix will be calculated for performance evaluation. Results: the empirical results showed that the proposed evolutionary techniques out performed classic artificial neural networks methods-feed forward backpropagation.
Description
Thesis presented in fulfillment of the academic requirement for the degree of Masters in Statistical Science of Strathmore University