Details
| Original language | English |
|---|---|
| Pages (from-to) | 100-108 |
| Number of pages | 9 |
| Journal | Decis. Support Syst. |
| Volume | 64 |
| Publication status | Published - Aug 2014 |
Abstract
Seasonalities and empirical regularities on financial markets have been well documented in the literature for three decades. While one should suppose that documenting an arbitrage opportunity makes it vanish there are several regularities that have persisted over the years. These include, for example, upward biases at the turn-of-the-month, during exchange holidays and the pre-FOMC announcement drift. Trading regularities is already in and of itself an interesting strategy. However, unfiltered trading leads to potential large drawdowns. In the paper we present a decision support algorithm which uses the powerful ideas of reinforcement learning in order to improve the economic benefits of the basic seasonality strategy. We document the performance on two major stock indices.
Keywords
- Neural networks, Reinforcement learning, Seasonalities, Trading system
ASJC Scopus subject areas
- Business, Management and Accounting(all)
- Management Information Systems
- Computer Science(all)
- Information Systems
- Psychology(all)
- Developmental and Educational Psychology
- Arts and Humanities(all)
- Arts and Humanities (miscellaneous)
- Decision Sciences(all)
- Information Systems and Management
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: Decis. Support Syst., Vol. 64, 08.2014, p. 100-108.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Intelligent trading of seasonal effects - A decision support algorithm based on reinforcement learning.
AU - Eilers, Dennis
AU - Dunis, Christian L.
AU - Mettenheim, Hans-Jörg von
AU - Breitner, Michael H.
N1 - Copyright: Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2014/8
Y1 - 2014/8
N2 - Seasonalities and empirical regularities on financial markets have been well documented in the literature for three decades. While one should suppose that documenting an arbitrage opportunity makes it vanish there are several regularities that have persisted over the years. These include, for example, upward biases at the turn-of-the-month, during exchange holidays and the pre-FOMC announcement drift. Trading regularities is already in and of itself an interesting strategy. However, unfiltered trading leads to potential large drawdowns. In the paper we present a decision support algorithm which uses the powerful ideas of reinforcement learning in order to improve the economic benefits of the basic seasonality strategy. We document the performance on two major stock indices.
AB - Seasonalities and empirical regularities on financial markets have been well documented in the literature for three decades. While one should suppose that documenting an arbitrage opportunity makes it vanish there are several regularities that have persisted over the years. These include, for example, upward biases at the turn-of-the-month, during exchange holidays and the pre-FOMC announcement drift. Trading regularities is already in and of itself an interesting strategy. However, unfiltered trading leads to potential large drawdowns. In the paper we present a decision support algorithm which uses the powerful ideas of reinforcement learning in order to improve the economic benefits of the basic seasonality strategy. We document the performance on two major stock indices.
KW - Neural networks
KW - Reinforcement learning
KW - Seasonalities
KW - Trading system
UR - http://www.scopus.com/inward/record.url?scp=84906784113&partnerID=8YFLogxK
U2 - 10.1016/j.dss.2014.04.011
DO - 10.1016/j.dss.2014.04.011
M3 - Article
VL - 64
SP - 100
EP - 108
JO - Decis. Support Syst.
JF - Decis. Support Syst.
ER -