A new investment method with AutoEncoder: Applications to crypto currencies(Forthcoming in "Expert Systems with Applications")
Masafumi Nakano and
Additional contact information
Masafumi Nakano: GCI Asset Management
Akihiko Takahashi: Graduate School of Economics and CARF, University of Tokyo
No CARF-F-489, CARF F-Series from Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo
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.
References: Add references at CitEc
Citations: Track citations by RSS feed
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:cfi:fseres:cf489
Access Statistics for this paper
More papers in CARF F-Series from Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo Contact information at EDIRC.
Bibliographic data for series maintained by ().