Options
A machine learning method for high-frequency data forecasting
Journal
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Date Issued
2014-01-01
Author(s)
López, Erick
Allende-Cid, Héctor
Abstract
In recent years several models for financial high-frequency
data have been proposed. One of the most known models for this type
of applications is the ACM-ACD model. This model focuses on modelling the underlying joint distribution of both duration and price changes
between consecutive transactions. However this model imposes distributional assumptions and its number of parameters increases rapidly (producing a complex and slow adjustment process). Therefore, we propose
using two machine learning models, that will work sequentially, based
on the ACM-ACD model. The results show a comparable performance,
achieving a better performance in some cases. Also the proposal achieves
a significatively more rapid convergence. The proposal is validated with
a well-known financial data set.
data have been proposed. One of the most known models for this type
of applications is the ACM-ACD model. This model focuses on modelling the underlying joint distribution of both duration and price changes
between consecutive transactions. However this model imposes distributional assumptions and its number of parameters increases rapidly (producing a complex and slow adjustment process). Therefore, we propose
using two machine learning models, that will work sequentially, based
on the ACM-ACD model. The results show a comparable performance,
achieving a better performance in some cases. Also the proposal achieves
a significatively more rapid convergence. The proposal is validated with
a well-known financial data set.
File(s)
