Abstract:
Stock market prediction is considered as a challenging problem because of the non-linear
and dynamic price changes in stock markets. And need to deal with high volume and high
frequency data. Despite the fact that a variety of machine learning and deep learning
approaches can be applied to construct prediction algorithms, stock value prediction is
difficult due to the high frequency data. Economic factors such as change in corporate
policy, economic shifts, expectations of investors, other stock markets’ movements and
government change influence the stock market movements. When developing a
prediction model, these influenced factors need to be considered to get highly accurate
results. The successful stock market prediction results in better decisions and high profits.
Minute-wise stock market prices provide better understanding about stock price behavior
within a particular day. Since it is very important to thoroughly analyze stock price
behavior to make trading decisions, analyzing and predicting trading trends within a day
is very crucial. Rather than predicting daily close price, open price and highest price, if
we can predict the next upcoming couple of minutes or hours stock price with highest
accuracy, then it is a great improvement in stock market prediction. Stakeholders
including buyers and sellers can get good predictions and they can make proficient
decisions on time.
This paper considers implementing a hybrid CNN-LSTM model to predict minute wise
stock market prices by using minute-wise stock market data which provides a best
performance. Stock market data of different companies including Apple, Google and
Amazon were collected from Yahoo Finance API. As for the evaluation, several
benchmark models were created and compared their performance with the proposed
model. Furthermore, proposed model was evaluated using various datasets and
timeframes. The next 5 minutes forecasted stock prices were compared with the actual
prices and measured the performance of model. In this research, as for the evaluation
metrics, Mean Absolute Percentage Error and Root Mean Square Error were used and the
best model was selected considering the validation results. Models were fine-tuned using
different time windows, model parameters and selected the best parameters for the
forecasting model. Finally, the proposed model outperformed the state-of-art models for
predicting short-term stock market values.
Citation:
Vijithasena, M.R.G. (2022). Hybrid CNN-LSTM model for minute - wise stock market price prediction [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/21655