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The forecasting performance of Cartesian ARIMA search and a vector‐valued state space model

The forecasting performance of Cartesian ARIMA search and a vector‐valued state space model The performance of Aoki’s state space algorithm and the Cartesian ARIMA search algorithm (CARlMA) of Östermark and Höglund is compared. The analysis is carried out on a set of stock prices on the Helsinki (Finland) and Stockholm (Sweden) Stock Exchanges. Demonstrates that the Finnish and Swedish stock markets differ in predictability of stock prices. With Finnish stock data, Aoki’s state space algorithm outperforms the subset of MAPE minimizing forecasts. In contrast, with Swedish stock data, ARIMA‐models of a fairly simple structure outperform Aoki’s algorithm. The stock markets are seen to differ in complexity of time series models as well as in predictability of individual asset prices. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Kybernetes Emerald Publishing

The forecasting performance of Cartesian ARIMA search and a vector‐valued state space model

Ralf Östermark
Kybernetes , Volume 29 (1): 22 – Feb 1, 2000
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21 pages

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Publisher
Emerald Publishing
Copyright
Copyright © 2000 MCB UP Ltd. All rights reserved.
ISSN
0368-492X
DOI
10.1108/03684920010308862
Publisher site
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Abstract

The performance of Aoki’s state space algorithm and the Cartesian ARIMA search algorithm (CARlMA) of Östermark and Höglund is compared. The analysis is carried out on a set of stock prices on the Helsinki (Finland) and Stockholm (Sweden) Stock Exchanges. Demonstrates that the Finnish and Swedish stock markets differ in predictability of stock prices. With Finnish stock data, Aoki’s state space algorithm outperforms the subset of MAPE minimizing forecasts. In contrast, with Swedish stock data, ARIMA‐models of a fairly simple structure outperform Aoki’s algorithm. The stock markets are seen to differ in complexity of time series models as well as in predictability of individual asset prices.

Journal

Kybernetes – Emerald Publishing

Published: Feb 1, 2000

Keywords: Cybernetics; Algorithms; Prediction; Efficiency

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