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A new investment method with AutoEncoder: Applications to crypto currencies
- Source :
- Expert Systems with Applications. 162:113730
- Publication Year :
- 2020
- Publisher :
- Elsevier BV, 2020.
-
Abstract
- This paper proposes a novel approach to the portfolio management using an AutoEncoder. In particular, features learned by an AutoEncoder with ReLU are directly exploited to portfolio constructions. Since the AutoEncoder extracts characteristics of data through a non-linear activation function ReLU, its realization is generally difficult due to the non-linear transformation procedure. In the current paper, we solve this problem by taking full advantage of the similarity of ReLU and an option payoff. Especially, this paper shows that the features are successfully replicated by applying so-called dynamic delta hedging strategy. An out of sample simulation with crypto currency dataset shows the effectiveness of our proposed strategy.
- Subjects :
- 0209 industrial biotechnology
Cryptocurrency
Similarity (geometry)
business.industry
Computer science
Activation function
General Engineering
02 engineering and technology
Machine learning
computer.software_genre
Autoencoder
Computer Science Applications
020901 industrial engineering & automation
Transformation (function)
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
Portfolio
020201 artificial intelligence & image processing
Artificial intelligence
Project portfolio management
business
computer
Realization (probability)
Subjects
Details
- ISSN :
- 09574174
- Volume :
- 162
- Database :
- OpenAIRE
- Journal :
- Expert Systems with Applications
- Accession number :
- edsair.doi...........1f5c24d273bb24c24ba2c6b83d4ba6d8