Details
| Original language | English |
|---|---|
| Title of host publication | Engineering Applications of Neural Networks - 13th International Conference, EANN 2012, Proceedings |
| Editors | Shigang Yue, Lazaros Iliadis |
| Pages | 423-432 |
| Number of pages | 10 |
| Publication status | Published - 2012 |
| Event | 2012 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2012 - Chengdu, China Duration: 26 Oct 2012 → 28 Oct 2012 |
Publication series
| Name | Communications in Computer and Information Science |
|---|---|
| Volume | 311 |
| ISSN (Print) | 1865-0929 |
Abstract
Intraday trading has some appealing characteristics. For example, overnight gap risks are greatly reduced. Intraday trading strategies tend to achieve better risk adjusted returns. However, academic literature on intraday trading strategies is relatively scarce compared to a significant amount of literature based on daily closing data. This may be partly related to the increased difficulty of dealing with intraday data. In the present paper we expand on a novel approach that builds an intraday trading strategy on open-high-low-close (OHLC) data. OHLC data is easily available from most database vendors. We use OHLC data to train neural networks that forecast the day's high and low of liquid US stocks and ETFs. The resulting long-short strategy tries to take advantage of the daily trading range of a security and exits all positions at the close. A volatility filter further improves risk-adjusted returns.
Keywords
- intraday trading, Neural networks, open-high-low-close data
ASJC Scopus subject areas
- Computer Science(all)
- General Computer Science
- Mathematics(all)
- General Mathematics
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Engineering Applications of Neural Networks - 13th International Conference, EANN 2012, Proceedings. ed. / Shigang Yue; Lazaros Iliadis. 2012. p. 423-432 (Communications in Computer and Information Science; Vol. 311).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Forecasting and Trading the High-Low Range of Stocks and ETFs with Neural Networks
AU - von Mettenheim, Hans Jörg
AU - Breitner, Michael H.
N1 - Copyright: Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2012
Y1 - 2012
N2 - Intraday trading has some appealing characteristics. For example, overnight gap risks are greatly reduced. Intraday trading strategies tend to achieve better risk adjusted returns. However, academic literature on intraday trading strategies is relatively scarce compared to a significant amount of literature based on daily closing data. This may be partly related to the increased difficulty of dealing with intraday data. In the present paper we expand on a novel approach that builds an intraday trading strategy on open-high-low-close (OHLC) data. OHLC data is easily available from most database vendors. We use OHLC data to train neural networks that forecast the day's high and low of liquid US stocks and ETFs. The resulting long-short strategy tries to take advantage of the daily trading range of a security and exits all positions at the close. A volatility filter further improves risk-adjusted returns.
AB - Intraday trading has some appealing characteristics. For example, overnight gap risks are greatly reduced. Intraday trading strategies tend to achieve better risk adjusted returns. However, academic literature on intraday trading strategies is relatively scarce compared to a significant amount of literature based on daily closing data. This may be partly related to the increased difficulty of dealing with intraday data. In the present paper we expand on a novel approach that builds an intraday trading strategy on open-high-low-close (OHLC) data. OHLC data is easily available from most database vendors. We use OHLC data to train neural networks that forecast the day's high and low of liquid US stocks and ETFs. The resulting long-short strategy tries to take advantage of the daily trading range of a security and exits all positions at the close. A volatility filter further improves risk-adjusted returns.
KW - intraday trading
KW - Neural networks
KW - open-high-low-close data
UR - http://www.scopus.com/inward/record.url?scp=84880639970&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-32909-8_43
DO - 10.1007/978-3-642-32909-8_43
M3 - Conference contribution
AN - SCOPUS:84880639970
SN - 9783642329081
T3 - Communications in Computer and Information Science
SP - 423
EP - 432
BT - Engineering Applications of Neural Networks - 13th International Conference, EANN 2012, Proceedings
A2 - Yue, Shigang
A2 - Iliadis, Lazaros
T2 - 2012 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2012
Y2 - 26 October 2012 through 28 October 2012
ER -