Practical investment with the long-short game

Najim Al-Baghdadi; David Lindsay; Yuri Kalnishkan; Sian Lindsay

Practical investment with the long-short game

Najim Al-Baghdadi, David Lindsay, Yuri Kalnishkan, Sian Lindsay

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this paper we apply the aggregating algorithm, an on-line prediction with expert advice algorithm, to real-world foreign exchange trading data with the aim of finding investment strategies with optimal returns. We consider the Long-Short game first introduced in Vovk and Watkins (1998) and it’s implementation, including the derivation of expert predictions from model trading data. Furthermore, we propose modifications to improve the practical performance of the game with respect to well-known portfolio performance indicators.

Original language	English
Title of host publication	Conformal and Probabilistic Prediction and Applications
Subtitle of host publication	COPA 2020
Publisher	Proceedings of Machine Learning Research
Pages	209-228
Number of pages	20
Volume	128
Publication status	Published - Sept 2020
Event	The 9th Symposium on Conformal and Probabilistic Prediction with Applications: COPA 2020 - Online Duration: 9 Sept 2020 → 11 Sept 2020 https://cml.rhul.ac.uk/copa2020/

Publication series

Name	Proceedings of Machine Learning Research

Conference

Conference	The 9th Symposium on Conformal and Probabilistic Prediction with Applications: COPA 2020
Abbreviated title	COPA 2020
Period	9/09/20 → 11/09/20
Internet address	https://cml.rhul.ac.uk/copa2020/

Keywords

Prediction with Expert Advice
Online Learning
Aggregating Algorithm
Portfolio Optimisation
Long-Short Game
Foreign Exchange
Currency Trading

Access to Document

http://proceedings.mlr.press/v128/

Cite this

@inproceedings{1e5061726f8841db83feeea0e3ab48ac,

title = "Practical investment with the long-short game",

abstract = "In this paper we apply the aggregating algorithm, an on-line prediction with expert advice algorithm, to real-world foreign exchange trading data with the aim of finding investment strategies with optimal returns. We consider the Long-Short game first introduced in Vovk and Watkins (1998) and it{\textquoteright}s implementation, including the derivation of expert predictions from model trading data. Furthermore, we propose modifications to improve the practical performance of the game with respect to well-known portfolio performance indicators. ",

keywords = "Prediction with Expert Advice, Online Learning, Aggregating Algorithm, Portfolio Optimisation, Long-Short Game, Foreign Exchange, Currency Trading",

author = "Najim Al-Baghdadi and David Lindsay and Yuri Kalnishkan and Sian Lindsay",

year = "2020",

month = sep,

language = "English",

volume = "128",

series = "Proceedings of Machine Learning Research ",

publisher = "Proceedings of Machine Learning Research",

pages = "209--228",

booktitle = "Conformal and Probabilistic Prediction and Applications",

note = "The 9th Symposium on Conformal and Probabilistic Prediction with Applications: COPA 2020, COPA 2020 ; Conference date: 09-09-2020 Through 11-09-2020",

url = "https://cml.rhul.ac.uk/copa2020/",

}

Al-Baghdadi, N, Lindsay, D, Kalnishkan, Y & Lindsay, S 2020, Practical investment with the long-short game. in Conformal and Probabilistic Prediction and Applications: COPA 2020. vol. 128, Proceedings of Machine Learning Research , Proceedings of Machine Learning Research, pp. 209-228, The 9th Symposium on Conformal and Probabilistic Prediction with Applications: COPA 2020, 9/09/20. <http://proceedings.mlr.press/v128/>

Practical investment with the long-short game. / Al-Baghdadi, Najim; Lindsay, David; Kalnishkan, Yuri et al.
Conformal and Probabilistic Prediction and Applications: COPA 2020. Vol. 128 Proceedings of Machine Learning Research, 2020. p. 209-228 (Proceedings of Machine Learning Research ).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Practical investment with the long-short game

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AB - In this paper we apply the aggregating algorithm, an on-line prediction with expert advice algorithm, to real-world foreign exchange trading data with the aim of finding investment strategies with optimal returns. We consider the Long-Short game first introduced in Vovk and Watkins (1998) and it’s implementation, including the derivation of expert predictions from model trading data. Furthermore, we propose modifications to improve the practical performance of the game with respect to well-known portfolio performance indicators.

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KW - Online Learning

KW - Aggregating Algorithm

KW - Portfolio Optimisation

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KW - Currency Trading

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