Fetching the paper…
Reading the bibliography…
Genetic programming (GP) is the state-of-the-art in financial automated feature construction task.
A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing
Sidra Mehtab and Jaydip Sen. 2019 · 1912
Earlier work this paper cites.
Common risk factors in the returns on stocks and bonds
Eugene F. Fama and Kenneth R. French. 1993 · 1993
Earlier work this paper cites.
On portfolio optimization under" drawdown" constraints
Jaksa Cvitanic and Ioannis Karatzas. 1994 · 1994
Earlier work this paper cites.
The sharpe ratio
William F Sharpe. 1994 · 1994
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner, et al · 1998
Earlier work this paper cites.
Feature extraction, construction and selection: A data mining perspective . Vol. 453
Huan Liu and Hiroshi Motoda. 1998 · 1998
Earlier work this paper cites.
Using genetic algorithms to find technical trading rules
Franklin Allen and Risto Karjalainen. 1999 · 1999
Earlier work this paper cites.
The importance of simplicity and validation in genetic programming for data mining in financial data. In Proceedings of the joint AAAI-1999 and GECCO-1999 Workshop on Data Mining with Evolutionary Algorithms
James D Thomas and Katia Sycara. 1999 · 1999
Earlier work this paper cites.
Genetic programming-based construction of features for machine learning and knowledge discovery tasks
Krzysztof Krawiec. 2002 · 2002
Earlier work this paper cites.
Feature selection, extraction and construction
Hiroshi Motoda and Huan Liu. 2002 · 2002
Cited alongside, same era.
Knowledge discovery with genetic programming for providing feedback to courseware authors
Cristóbal Romero, Sebastián Ventura, and Paul De Bra. 2004 · 2004
Cited alongside, same era.
Mining conference proceedings for corporate technology knowledge management
Robert J Watts and Alan L Porter. 2007 · 2007
Cited alongside, same era.
Generation of stable monoclonal antibody–producing B cell receptor–positive human memory B cells by genetic programming
Mark J Kwakkenbos, Sean A Diehl, Etsuko Yasuda, Arjen Q Bakker, Caroline MM Van Geelen, Michaël V Lukens, Grada M Van Bleek, Myra N Widjojoatmodjo, Willy MJM Bogers, Henrik Mei, et al · 2010
Cited alongside, same era.
Text mining for the Vaccine Adverse Event Reporting System: medical text classification using informative feature selection
Taxiarchis Botsis, Michael D Nguyen, Emily Jane Woo, Marianthi Markatou, and Robert Ball. 2011 · 2011
Cited alongside, same era.
Parallel recurrent neural network architectures for feature-rich session-based recommendations. In Proceedings of the 10th ACM conference on recommender systems . ACM, 241–248
Balázs Hidasi, Massimo Quadrana, Alexandros Karatzoglou, and Domonkos Tikk. 2016 · 2016
Later among the works it cites.
101 Formulaic Alphas
Zura Kakushadze. 2016 · 2016
Later among the works it cites.
Face attribute prediction using off-the-shelf cnn features. In 2016 International Conference on Biometrics (ICB) . IEEE, 1–7
Yang Zhong, Josephine Sullivan, and Haibo Li. 2016 · 2016
Later among the works it cites.
Temporal convolutional networks for action segmentation and detection. In proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 156–165
Colin Lea, Michael D Flynn, Rene Vidal, Austin Reiter, and Gregory D Hager. 2017 · 2017
Later among the works it cites.
Automatic facial expression recognition based on a deep convolutional-neural-network structure. In 2017 IEEE 15th International Conference on Software Engineering Research, Management and Applications (SERA) . IEEE, 123–128
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A survey on feature selection methods
Girish Chandrashekar and Ferat Sahin. 2014 · 2014
Cited alongside, same era.
Xgboost: extreme gradient boosting
Tianqi Chen, Tong He, Michael Benesty, Vadim Khotilovich, and Yuan Tang. 2015 · 2015
Cited alongside, same era.
A five-factor asset pricing model
Eugene F. Fama and Kenneth R. French. 2015 · 2015
Cited alongside, same era.
Recurrent convolutional neural networks for text classification. In Twenty-ninth AAAI conference on artificial intelligence
Siwei Lai, Liheng Xu, Kang Liu, and Jun Zhao. 2015 · 2015
Cited alongside, same era.
Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition . 770–778
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
Cited alongside, same era.
Ke Shan, Junqi Guo, Wenwan You, Di Lu, and Rongfang Bie. 2017 · 2017
Later among the works it cites.
Attention is all you need. In Advances in neural information processing systems . 5998–6008
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Later among the works it cites.
The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin. 2018 · 2018
Later among the works it cites.
Enhancing Stock Movement Prediction with Adversarial Training
Fuli Feng, Huimin Chen, Xiangnan He, Ji Ding, Maosong Sun, and Tat-Seng Chua. 2019 · 2019
Closest in time.
Crowdsourced employer reviews and stock returns
T. Clifton Green, Ruoyan Huang, Quan Wen, and Dexin Zhou. 2019 · 2019
Closest in time.
PRADA: protecting against DNN model stealing attacks. In 2019 IEEE European Symposium on Security and Privacy (EuroS&P) . IEEE, 512–527
Mika Juuti, Sebastian Szyller, Samuel Marchal, and N Asokan. 2019 · 2019
Closest in time.