TITLE:
Stock Price Prediction Research—Machine Learning Model Evaluation
AUTHORS:
Navye Vedant
KEYWORDS:
Machine Learning, Artificial Intelligence, Random Sample Consensus, Line of Best Fit, Support Vector Regression, Mean Squared Error
JOURNAL NAME:
Open Journal of Business and Management,
Vol.12 No.2,
March
29,
2024
ABSTRACT: Stock investment
prices are never still; they are always changing. It is important to stay
informed on the upward or downward trends of the market to make future
investments. This paper aims to examine the question: which of the python
models used in this study are the most accurate at predicting the price of the
stock market, x days into the future?
To accustom the machine learning (ML) predictor to the multitude of
possibilities that could categorize stock patterns, 7 different ML models were
trained on 1250 pieces of open stock market data dating to the last 5 years by
assigning weight values to all the models based on their accuracy. Results
showed that two of the ML models, specifically the Linear Regression and the
Random Sample Consensus (RANSAC) Regressor models consistently outperformed the
other 5 models, both ending up with the highest weight values of around 0.5
when predicting for Amazon, Apple, and
Tesla. Therefore, the RANSAC and Linear Regression models are the best
models to rely on when predicting open stock market prices using ML.