Details
| Original language | English |
|---|---|
| Title of host publication | ICIS 2010 Proceedings |
| Subtitle of host publication | Thirty First International Conference on Information Systems |
| Publication status | Published - 2010 |
| Event | 31st International Conference on Information Systems, ICIS 2010 - Saint Louis, MO, United States Duration: 12 Dec 2010 → 15 Dec 2010 |
Abstract
The recent financial crisis showed the need for more robust decision support systems. In this paper, we introduce a novel type of recurrent artificial neural network, the shared layer perceptron, which allows forecasts that are robust by design. This is achieved by not over-fitting to a specific variable. An entire market is forecast. By training not one, but many networks, we obtain a distribution of outcomes. Further, multi-step forecasts are possible. Our system uses hidden states to model internal dynamics. This allows the network to build a memory and hardens it against external shocks. Using a single shared weight matrix offers the possibility of interpreting system output. An often cited disadvantage of neural networks, the black box character, is not an issue with our approach. We focus on two case studies: determining value at risk and transaction decision support. We also present other applications, including load forecast in electricity networks.
Keywords
- Artificial neural networks, Decision support systems (DSS), Financial forecast
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
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ICIS 2010 Proceedings: Thirty First International Conference on Information Systems. 2010.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Robust decision support systems with matrix forecasts and shared layer perceptrons for finance and other applications
AU - Von Mettenheim, Hans Jörg
AU - Breitner, Michael H.
N1 - Copyright: Copyright 2012 Elsevier B.V., All rights reserved.
PY - 2010
Y1 - 2010
N2 - The recent financial crisis showed the need for more robust decision support systems. In this paper, we introduce a novel type of recurrent artificial neural network, the shared layer perceptron, which allows forecasts that are robust by design. This is achieved by not over-fitting to a specific variable. An entire market is forecast. By training not one, but many networks, we obtain a distribution of outcomes. Further, multi-step forecasts are possible. Our system uses hidden states to model internal dynamics. This allows the network to build a memory and hardens it against external shocks. Using a single shared weight matrix offers the possibility of interpreting system output. An often cited disadvantage of neural networks, the black box character, is not an issue with our approach. We focus on two case studies: determining value at risk and transaction decision support. We also present other applications, including load forecast in electricity networks.
AB - The recent financial crisis showed the need for more robust decision support systems. In this paper, we introduce a novel type of recurrent artificial neural network, the shared layer perceptron, which allows forecasts that are robust by design. This is achieved by not over-fitting to a specific variable. An entire market is forecast. By training not one, but many networks, we obtain a distribution of outcomes. Further, multi-step forecasts are possible. Our system uses hidden states to model internal dynamics. This allows the network to build a memory and hardens it against external shocks. Using a single shared weight matrix offers the possibility of interpreting system output. An often cited disadvantage of neural networks, the black box character, is not an issue with our approach. We focus on two case studies: determining value at risk and transaction decision support. We also present other applications, including load forecast in electricity networks.
KW - Artificial neural networks
KW - Decision support systems (DSS)
KW - Financial forecast
UR - http://www.scopus.com/inward/record.url?scp=84870973061&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84870973061
SN - 9780615418988
BT - ICIS 2010 Proceedings
T2 - 31st International Conference on Information Systems, ICIS 2010
Y2 - 12 December 2010 through 15 December 2010
ER -