Resampling in neural networks with application to financial time series
Notice bibliographique
Résumé
Neural networks provide powerful data analysis tools to handle various types of nonlinearities in many areas, especially in the area of financial time series prediction. The computationally oriented 'jackknife' and 'bootstrap' neural network learning algorithms are developed in this thesis to forecast noisy financial time series, in particular, the spot Canada/US foreign exchange rate. The daily traded foreign exchange rate (FX) is influenced by many factors. In the case of Canada and the US, capital will flow into the country with the preferable yield, directly influencing the spot Canada/US FX. Therefore, one set of variables always on the traders monitor, is the interest rate spreads between comparable secure and highly liquid assets; in particular, the short term interest rate spread. In this thesis, nonlinear transfer function models between the short term interest rate spread and the spot Canada/US FX are studied by using multi-layer feed-forward neural networks, in conjunction with 'back-propagation' ( BP) learning and associated statistical re-sampling methods of the ' jackknife' and the 'bootstrap'. Prediction of the spot Canada/US FX will be the focus. The basic modeling strategy is to build a forecasting model which satisfies both the "trader experience criteria" and the underlying mathematical/statistical structure. Several neural network predictive models are proposed using multi-layer feed-forward neural network architectures. In addition, the stability property of the nonlinear transfer predictive model is studied by using local stability analysis. Second, a comparative pre-test of the neural network model is constructed to evaluate the network performance and to select the 'best' model for further study. All of the testing models give us about 55%-60% accuracy of the directional forecast on the "out-of-sample test set". Comparing with the linear predictive model, a 2% to 5% gain is obtained by using nonlinear neural network models. Consequently, the separate neural network model explores the nonlinear structure between the spot Canada/US FX and short term interest rate spread, especially during the time period of negative interest rate spread. It also captures a corrective mean reversion when the Canadian dollar is under-valued or over-valued in the market. Furthermore, the comparative pre-test demonstrates the impact changes in the interest rate spread has on changes in the spot FX. Statistical data-based re-sampling methods such as 'jackknife ' and 'bootstrap' are studied in the case of cross-validation BP learning. Two 'grouped jackknife' and two ' bootstrap' cross-validation learning algorithms are proposed, using parametric and non-parametric nonlinear modeling methodologies. The results show that both 'grouped jackknife' and 'bootstrap' learning algorithms lead to robust and reliable forecasts along with the large amount of statistical information of the previous knowledge. (Abstract shortened by UMI.)
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».