Penalized logistic regressions predict stock price trends

Junwen Yang; Jing Wu; Xiang Li

doi:10.1117/12.2646650

23 August 2022 Penalized logistic regressions predict stock price trends

Junwen Yang, Jing Wu, Xiang Li

Proceedings Volume 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022); 123301C (2022) https://doi.org/10.1117/12.2646650
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 2022, Huzhou, China

Abstract

In recent years, machine learning and data mining methods have been applied to stock price forecasting problems. However, due to the time series characteristics, data noise and multicollinearity of stock price data, many traditional machine learning methods cannot accurately predict the trend of stock price movement. This paper applies the Least Absolute Shrinkage and Selection Operator (LASSO) penalized logistic regression model and the ridge penalized regression model to the stock price prediction problem and evaluates their performance. First, we select the stock data of CMA, EQR and IRM from 2016-07-21 to 2020-07-13. Then we apply the TTR package to calculate 17 technical indicators. After that, we divide the data set into the training set and test set, train the model using the training set, and evaluate the performance of models on the test set. Finally, we introduce Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN) as comparative models and evaluate the predictive performance of models by evaluating indicators such as sensitivity, specificity, and ROC curve. After the above work, we find that LASSO penalized logistic regression has the best prediction performance, reaching an accuracy of 0.81 on the EQR data set, and the ridge has a prediction performance of 0.79, which is much higher than RF, SVM and ANN.

Citation Download Citation

Junwen Yang, Jing Wu, and Xiang Li "Penalized logistic regressions predict stock price trends", Proc. SPIE 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 123301C (23 August 2022); https://doi.org/10.1117/12.2646650

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available