Abstract
Modeling stock returns requires selections of
appropriate input variables. For an Artificial Neural Network,
the appropriate input variables have both linear and nonlinear
functional relationship with stock returns as output
variables. To capture the non-linear relationships, we propose
Weierstrass theorem. However, to estimate the relationships
for all possible combinations of input variables, especially for a
large set of variables, is too numerous for a simple exhaustive
search thus we use a Genetic Algorithm to approximate the
non-linear relationships between the prospective input
variables and the output variables. The result shows that the
Artificial Neural Networks with the selected variables based on
both linear and non-linear relationship perform better than the
ones with all possible variables for all but one out of the sample
of ten US stocks.
appropriate input variables. For an Artificial Neural Network,
the appropriate input variables have both linear and nonlinear
functional relationship with stock returns as output
variables. To capture the non-linear relationships, we propose
Weierstrass theorem. However, to estimate the relationships
for all possible combinations of input variables, especially for a
large set of variables, is too numerous for a simple exhaustive
search thus we use a Genetic Algorithm to approximate the
non-linear relationships between the prospective input
variables and the output variables. The result shows that the
Artificial Neural Networks with the selected variables based on
both linear and non-linear relationship perform better than the
ones with all possible variables for all but one out of the sample
of ten US stocks.
| Original language | English |
|---|---|
| Title of host publication | PACIIA 2009. Asia-Pacific Conference on Computational Intelligence and Industrial Applications, 2009. |
| Place of Publication | Piscataway, NJ |
| Publisher | IEEE |
| Pages | 186-189 |
| Number of pages | 4 |
| ISBN (Print) | 9781424446063 |
| DOIs | |
| Publication status | Published - 2009 |
| Externally published | Yes |
| Event | 2009 Asia-Pacific Conference on Computational Intelligence and Industrial Applications (PACIIA 2009), - Wuhan, China Duration: 28 Nov 2009 → 29 Nov 2009 |
Conference
| Conference | 2009 Asia-Pacific Conference on Computational Intelligence and Industrial Applications (PACIIA 2009), |
|---|---|
| Country/Territory | China |
| City | Wuhan |
| Period | 28/11/09 → 29/11/09 |