Abstract:
The study will consider how machine learning models can be used to
predict Sri Lankan stock market prices. Artificial intelligence is an
emerging trend for most of the things. So why not for stock market
prediction. Researchers point out that these models can provide more than
70% accuracy rate. When it comes to Sri Lankan context we need to specify
most appropriate data set that we can use with machine learning models.
Then feed these data to machine learning models such as artificial neural
network (ANN), Support vector machine (SVM) and Decision trees (DT).
ANN is one of the main tools used in machine learning, which is a braininspired
system that intended to replicate the way that humans learn. Since
there are several types of ANNs, Multilayer Perceptron (MLP) model is
used in this research. Decision tree algorithm can be used as a tool for data
mining and trading. It performs a set of recursive actions before it output
the result. SVM is a relatively new learning algorithm that can used to
calculate price volatility and momentum for individual stocks. This study
uses daily open, high, low, close prices, trade volume, share volume,
turnover and beta value as input variables for all the models. Prediction
results are compared with the actual values. To evaluate the performance of
three models three commonly used evaluation criteria are applied in this
study. Evaluation criteria consists of root mean square error (RMSE), mean
absolute error (MAE), and mean absolute percentage error (MAPE).