Stock Price Forecasting for Netflix with Machine Learning Models

Abstract

Stock price prediction is a challenging yet valuable task in financial forecasting. This paper explores the application of machine learning for predicting Netflix’s stock price, comparing Linear Regression and Random Forest models. Using a five-year dataset, we evaluated model performance based on R² Score and Mean Absolute Error (MAE). The Random Forest model outperformed Linear Regression, achieving a higher R² Score (0.7202) and lower MAE (0.4092), indicating better predictive accuracy. These results suggest that ensemble methods like Random Forest offer more reliable stock price forecasts, particularly in complex, volatile financial datasets.

Referencces

[1] Shah D, Isah H, Zulkernine F. Stock market analysis: A review and taxonomy of prediction techniques. International Journal of Financial Studies. 2019 May 27;7(2):26.

[2] Kumar D, Sarangi PK, Verma R. A systematic review of stock market prediction using machine learning and statistical techniques. Materials Today: Proceedings. 2022 Jan 1;49:3187-91.

[3] Strader TJ, Rozycki JJ, Root TH, Huang YH. Machine learning stock market prediction studies: review and research directions. Journal of International Technology and Information Management. 2020;28(4):63-83.

[4] Kumar G, Jain S, Singh UP. Stock market forecasting using computational intelligence: A survey. Archives of computational methods in engineering. 2021 May;28(3):1069-101.

[5] Tashakkori A, Talebzadeh M, Salboukh F, Deshmukh L. Forecasting Gold Prices with MLP Neural Networks: A Machine Learning Approach. International Journal of Science and Engineering Applications (IJSEA). 2024;13:13-20.

[6] Rath S, Das NR, Pattanayak BK. An Analytic Review on Stock Market Price Prediction using Machine Learning and Deep Learning Techniques. Recent Patents on Engineering. 2024 Feb 1;18(2):88-104.

[7] Ayyildiz N, Iskenderoglu O. How effective is machine learning in stock market predictions?. Heliyon. 2024 Jan 30;10(2).

[8] Sui M, Zhang C, Zhou L, Liao S, Wei C. An ensemble approach to stock price prediction using deep learning and time series models.

[9] Tashakkori A, Erfanibehrouz N, Mirshekari S, Sodagartojgi A, Gupta V. Enhancing stock market prediction accuracy with recurrent deep learning models: A case study on the CAC40 index. World Journal of Advanced Research and Reviews. 2024;23(1):2309-1.

[10] https://www.kaggle.com/datasets/jainilcoder/netflix-stock-price-prediction