A Comparison of Forecasting Approaches for Capital Markets

Scott McDonald; SA Coleman; TM McGinnity; Yuhua Li; Ammar Belatreche

A Comparison of Forecasting Approaches for Capital Markets

Scott McDonald, SA Coleman, TM McGinnity, Yuhua Li, Ammar Belatreche

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

Abstract

In recent years, machine learning algorithms havebecome increasingly popular in financial forecasting. Theirflexible, data-driven nature makes them ideal candidates fordealing with complex financial data. This paper investigates theeffectiveness of a number of machine learning algorithms, andcombinations of these algorithms, at generating one-step aheadforecasts of a number of financial time series. We find thathybrid models consisting of a linear statistical model and a nonlinearmachine learning algorithm are effective at forecastingfuture values of the series, particularly in terms of the futuredirection of the series.

Original language	English
Title of host publication	Unknown Host Publication
Publisher	IEEE
Pages	32-39
Number of pages	8
Publication status	Published (in print/issue) - 27 Mar 2014
Event	IEEE Computational Intelligence for Financial Engineering and Economics - Duration: 27 Mar 2014 → …

Conference

Conference	IEEE Computational Intelligence for Financial Engineering and Economics
Period	27/03/14 → …

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

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title = "A Comparison of Forecasting Approaches for Capital Markets",

abstract = "In recent years, machine learning algorithms havebecome increasingly popular in financial forecasting. Theirflexible, data-driven nature makes them ideal candidates fordealing with complex financial data. This paper investigates theeffectiveness of a number of machine learning algorithms, andcombinations of these algorithms, at generating one-step aheadforecasts of a number of financial time series. We find thathybrid models consisting of a linear statistical model and a nonlinearmachine learning algorithm are effective at forecastingfuture values of the series, particularly in terms of the futuredirection of the series.",

author = "Scott McDonald and SA Coleman and TM McGinnity and Yuhua Li and Ammar Belatreche",

year = "2014",

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day = "27",

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note = "IEEE Computational Intelligence for Financial Engineering and Economics ; Conference date: 27-03-2014",

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AU - McDonald, Scott

AU - Coleman, SA

AU - McGinnity, TM

AU - Li, Yuhua

AU - Belatreche, Ammar

PY - 2014/3/27

Y1 - 2014/3/27

N2 - In recent years, machine learning algorithms havebecome increasingly popular in financial forecasting. Theirflexible, data-driven nature makes them ideal candidates fordealing with complex financial data. This paper investigates theeffectiveness of a number of machine learning algorithms, andcombinations of these algorithms, at generating one-step aheadforecasts of a number of financial time series. We find thathybrid models consisting of a linear statistical model and a nonlinearmachine learning algorithm are effective at forecastingfuture values of the series, particularly in terms of the futuredirection of the series.

AB - In recent years, machine learning algorithms havebecome increasingly popular in financial forecasting. Theirflexible, data-driven nature makes them ideal candidates fordealing with complex financial data. This paper investigates theeffectiveness of a number of machine learning algorithms, andcombinations of these algorithms, at generating one-step aheadforecasts of a number of financial time series. We find thathybrid models consisting of a linear statistical model and a nonlinearmachine learning algorithm are effective at forecastingfuture values of the series, particularly in terms of the futuredirection of the series.

M3 - Conference contribution

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EP - 39

BT - Unknown Host Publication

PB - IEEE

T2 - IEEE Computational Intelligence for Financial Engineering and Economics

Y2 - 27 March 2014

ER -

A Comparison of Forecasting Approaches for Capital Markets

Abstract

Conference

UN SDGs

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