2019/04/17 by Hieu Quang Nguyen, Nguyen, Hieu Quang, Abdul Hasib Rahimyar +3
Computer Science · Decision Sciences · Economics, Econometrics and Finance · Engineering · Mathematics · #Artificial intelligence #Artificial neural network #Computational Finance (q-fin.CP) #Computer science #Data mining #Econometrics #Economics #Engineering #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine learning #Neural Networks and Applications #Recurrent neural network #Stock (firearms) #Stock Market Forecasting Methods #Support vector machine #Time Series Analysis and Forecasting #Upgrade #Wavelet #Wavelet transform #cs.LG #q-fin.CP #stat.ML
paper · pdf · doi:10.48550/arxiv.1904.08459
published in arXiv (Cornell University) (Cornell University)
arxiv created 2019/04/17 · openalex publication_date 2019/04/17 · arxiv updated 2019/04/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
The task of predicting future stock values has always been one that is heavily desired albeit very difficult. This difficulty arises from stocks with non-stationary behavior, and without any explicit form. Hence, predictions are best made through analysis of financial stock data. To handle big data sets, current convention involves the use of the Moving Average. However, by utilizing the Wavelet Transform in place of the Moving Average to denoise stock signals, financial data can be smoothened and more accurately broken down. This newly transformed, denoised, and more stable stock data can be followed up by non-parametric statistical methods, such as Support Vector Regression (SVR) and Recurrent Neural Network (RNN) based Long Short-Term Memory (LSTM) networks to predict future stock prices. Through the implementation of these methods, one is left with a more accurate stock forecast, and in turn, increased profits.