Content Details | NUML Online Research Repository

Content Details

Back to Department Listing

Title PAKISTAN STOCK MARKET PRICE PREDICTION USING MACHINE LEARNING

Author(s) Kainat Mumtaz

Abstract The stock market is a regulated marketplace where companies raise capital by selling shares of stock, or equity to investors. The stock market is the backbone of a country because it is essential for country’s development, corporate governance, capital formation, investment, and economic growth. However, due to various factors such as company performance, financial crises, political instability, and pandemic outbreaks, the stock market is very challenging to predict. This study uses a dataset from different sectors of Pakistan Stock market, carefully processed by adjusting sizes, normalizing, and fixing errors. Initially, Moving Average (MA) and Exponential Moving Average (EMA) are used to identify crisis points in stock market. Afterward, the Stochastic Relative Strength Index (Stoch RSI) is applied to predict the stock market. The novel part comes in the third step, where an advanced transformer model is used for better predictions of stock market prices. The model's performance is thoroughly assessed using standard measures like Root Mean Square Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE). The Average evaluation scores for all indices of Pakistan stock Market Sectors are RMSE=0.052865, MSE=0.002866, and MAE=0.071720. The results now improve understanding of the Pakistan stock market and also highlight the effectiveness of transformer models in predicting stock prices by tunning different parameters and hyperparameters. The transformer layers used in the proposed studies for extracting the most effective features which outperforms as compared to the techniques used in previous studies for Pakistan stock market price predictions.

Type Thesis/Dissertation MS

Faculty Engineering and Computer Science

Department Engineering

Language English

Publication Date 2024-07-02

Subject Software Engineering

Publisher

Contributor(s)

Format

Identifier

Source

Relation

Coverage

Rights