Automated trading with performance weighted random forests and seasonality

By Ash Booth, Enrico H. Gerding, Frank McGroarty. 2014

149 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Finance, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Flavio Luiz de Moraes Barboza
2. Edward I. Altman
3. Herbert Kimura

In Finance

1. Flavio Luiz de Moraes Barboza
2. Edward I. Altman
3. Balaji Rajagopalan
4. Herbert Kimura
5. Xiaojun Sun
6. Yalin Lei
7. Tuong Le
8. Mi Young Lee

In Management Science and Operations Research

1. Antoine Jean-Pierre Tixier
2. Flavio Luiz de Moraes Barboza
3. Edward I. Altman
4. Balaji Rajagopalan
5. Herbert Kimura

Citations in the corpus

• Application of machine learning to construction injury prediction (2016, background): “[6], forecast air traf fi c delays [56], analyze the risk of mortgage prepayment [47], determine the likelihood that a customer will cease doing business with a company [75], predict horse race outcomes [77], and to evaluate the likelihood of being elected to the baseball hall of fame [33].”
• Machine learning models and bankruptcy prediction (2017, methodology): “AC CE PT ED M AN US CR IP T A more detailed discussion on random forests, including a more rigorous mathematical description, is 180 found in Breiman (2001); Booth et al. (2014), and Calderoni et al.”