Dynamic Interrelations Among Major World Stock Markets: A Neural Network Analysis
Notice bibliographique
Résumé
ABSTRACT This paper investigates the application of artificial neural networks to the dynamic interrelations among major world stock markets. The database for this study consists of daily stock market indices of major world stock markets. These stock market indices are: Canada, France, Germany, Japan, United Kingdom (UK), the United States (US), and the world excluding US (World). Based on the criteria of Root Mean Square Error, Maximum Absolute Error, and the value of the objective function, it is found that Multilayer Perceptron models with logistic activation functions predict daily stock returns better than traditional Ordinary Least Squares and General Linear Regression models. Furthermore, it is found that a multilayer perceptron with five units in the hidden layer better predicts the stock indices for USA, France, Germany, UK and World than a neural network with two hidden elements. It is concluded that neural systems can be used as an alternative tool for financial analysis. JEL: C3, C32, C45, C5, C63, F3, G15 Keywords: Neural networks; Major stock markets; Dynamic interrelations; Forecasting I. INTRODUCTION Neural networks are powerful forecasting tools that draw on the most recent developments in artificial intelligence research. They are non-linear models that can be trained to map past and future values of time series data and thereby extract hidden structures and relationships that govern the data. Neural networks are applied in many fields such as computer science, engineering, medical and criminal diagnostics, biological investigation, and economic research They can be used for analysing relations among economic and financial phenomena, forecasting, data filtration, generating time-series, and optimization (Hawley, Johnson, and Raina, 1990; White, 1998; White 1996; Tema, 1997; Cogger, Koch and Lander, 1997; Cheh, Weinberg, and Yook, 1999; Cooper, 1999; Hu and Tsoukalas, 1999; Moshiri, Cameron, and Scuse, 1999; Shtub and Versano, 1999; Garcia and Gencay, 2000; and Hamm and Brorsen, 2000). This paper investigates the application of artificial neural networks to the dynamic interrelations among major world stock markets.' These stock market indices are: Canada, France, Germany, Japan, United Kingdom (UK), the United States (US), and the world excluding US (World). Based on the criteria of Root Mean Square Error (RMSE), Maximum Absolute Error (MAE), and the value of the objective function the model is compared to other statistical methods such as Ordinary Least Squares (OLS) and General Linear Regression Model (GLRM). Neural networks have found ardent supporters among various avant-garde portfolio managers, investment banks and trading firms. Most of the major investment banks, such as Goldman Sachs and Morgan Stanley, have dedicated departments to the implementation of neural networks. Fidelity Investments has set up a mutual fund whose portfolio allocation is based solely on recommendations produced by an artificial neural network. The fact that major companies in the financial industry are investing resources in neural networks indicates that artificial neural networks may serve as an important method of forecasting. Artificial neural networks are information processing systems whose structure and function are motivated by the cognitive processes and organizational structure of neuro-biological systems. The basic components of the networks are highly interconnected processing elements called neurons, which work independently in parallel (Consten and May, 1996). Synaptic connections are used to carry messages from one neuron to another. The strength of these connections varies. These neurons store information and learn meaningful patterns by strengthening their inter-connections. When a neuron receives a certain number of stimuli, and when the sum of the received stimuli exceeds a certain threshold value, it fires and transmits the stimulus to adjacent neurons (Soh1, 1995). …
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».