Deep Stock Ranker: A LSTM Neural Network Model for Stock Selection
By Xiaolin Zhang, Ying Tan. 2018
34 citations recorded by the source corpus.
Subfields: Finance, Management Science and Operations Research, Time-Series & Forecasting
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Finance
- M. Ugur Gudelek
- A. Ozbayoglu
- Omer Berat Sezer
- Ze Hu
- Matloob Khushi
- Yiqi Zhao
- Weiwei Jiang
- Longbing Cao
In Management Science and Operations Research
- M. Ugur Gudelek
- Omer Berat Sezer
- A. Ozbayoglu
- Ze Hu
- Matloob Khushi
- Yiqi Zhao
- Weiwei Jiang
- Longbing Cao
In Time-Series & Forecasting
- M. Ugur Gudelek
- Omer Berat Sezer
- A. Ozbayoglu
- Ze Hu
- Matloob Khushi
- Yiqi Zhao
- Weiwei Jiang
- Longbing Cao
Citations in the corpus
- Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “[101] Stocks in China’s Ashare 2006-2007 11 technical indicators 1d LSTM AR, IR, IC -”
- A Survey of Forex and Stock Price Prediction Using Deep Learning (2021, methodology): “proposed a simple but efficient method to predict future stock return ranking without handcrafted features [48].”
- A graph-based CNN-LSTM stock price prediction algorithm with leading indicators (2021, background): “In [37], it proposed a model to predict the stock rankings future returns.”