Part 1: Training Sets & ASG Transforms

Rilwan Adewoyin

Papers from arXiv.org

Abstract: In this paper, I discuss a method to tackle the issues arising from the small data-sets available to data-scientists when building price predictive algorithms that use monthly/quarterly macro-financial indicators. I approach this by training separate classifiers on the equivalent dataset from a range of countries. Using these classifiers, a three level meta learning algorithm (MLA) is developed. I develop a transform, ASG, to create a country agnostic proxy for the macro-financial indicators. Using these proposed methods, I investigate the degree to which a predictive algorithm for the US 5Y bond price, predominantly using macro-financial indicators, can outperform an identical algorithm which only uses statistics deriving from previous price. This was an undergraduate project, subsequently the research was not exhaustive.

Date: 2017-12, Revised 2020-05
New Economics Papers: this item is included in nep-big and nep-cmp
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/1801.05752 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1801.05752

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().