Forecasting financial time series

The world went through weeks of financial turbulence in stock markets and investors were overcome by fears fuelled by more bad news, while countries continued their attempts to calm the markets with more injection of funds. By these very disturbed times, even if traders hope extreme risk aversion has passed, an investor would like predict the future of the market in order to protect his portfolio and a speculator would like to optimize his tradings. This thesis describes the design of numerical models and algorithms for the forecasting of financial time series, for speculation on a short time interval. To this aim, we will use two models: - " Price Forecasting Model " forecasts the behavior of an asset for an interval of three hours. This model is based on Functional Clustering and smoothing by cubic-splines in the training phase to build local Neural models, and Functional Classification for generalization, - " Model of Trading " forecasts the First Stopping time, when an asset crosses for the first time a threshold defined by the trader. This model combines a Price Forecasting Model for the prediction of market trend, and a Trading Recommendation for prediction of the first stopping time. We use an auto-adaptive Dynamic State Space Model, with Particle Filters and Kalman-Bucy Filters for parameter estimation.(FSA 3) -- UCL, 200

Classification et prédiction fonctionnelles d'actifs boursiers en intraday

Nous présentons une méthode d’analyse fonctionnelle pour la prédiction de séries temporelles. A partir de la décomposition des dynamiques en clusters, nous construisons des modèles locaux pour la prédiction de l’évolution des séries à partir des données du passé. Un modèle probabiliste est utilisé pour la combinaison des prédictions locales. Cette méthode peut être appliquée à tout problème de prédiction de séries temporelles mais elle est particulièrement adaptée aux données avec des dépendances non linéaires et des clusters, tels que les séries financières. La méthode a été appliquée à la prédiction des séries boursières de données en "tick par tick"

Forecasting high and low of financial time series by particle filters and Kalman filters

The analysis of financial time series is very useful in the economic world. This paper deals with a data-driven empirical analysis of financial time series. In this paper we present a forecasting method of the first stopping times, when the prices cross for the first time a "high" or "low" threshold defined by the trader, based on an empirical functional analysis of the past "tick data" of the series, without resampling. An originality of this method is that it does not use a theoretical financial model but a non-parametric space state representation with non-linear RBF neural networks. Modelling and forecasting are made by Particles systems and Kalman filters. This method can be applied to any forecasting problem of stopping time, but is particularly suited for data showing nonlinear dependencies and observed at irregularly and randomly spaced times like financial time series of «tick data» do. The method is applied to the forecasting of stopping times of "high" and "low" of financial time series in order to be useful for speculator

Modelling and Forecasting financial time series of «tick data» by functional analysis and neural networks

The analysis of financial time series is of primary importance in the economic world. This paper deals with a data-driven empirical analysis of financial time series. The goal is to obtain insights into the dynamics of series and out-of-sample forecasting. In this paper we present a forecasting method based on an empirical functional analysis of the past of series. An originality of this method is that it does not make the assumption that a single model is able to capture the dynamics of the whole series. On the contrary, it splits the past of the series into clusters, and generates a specific local neural model for each of them. The local models are then combined in a probabilistic way, according to the distribution of the series in the past. This forecasting method can be applied to any time series forecasting problem, but is particularly suited for data showing nonlinear dependencies, cluster effects and observed at irregularly and randomly spaced times like high-frequency financial time series do. One way to overcome the irregular and random sampling of "tick-data" is to resample them at low-frequency, as it is done with "Intraday". However, even with optimal resampling using say five minute returns when transactions are recorded every second, a vast amount of data is discarded, in contradiction to basic statistical principles. Thus modelling the noise and using all the data is a better solution, even if one misspecifies the noise distri- bution. The method is applied to the forecasting of financial time series of «tick data» of assets on a short horizon in order to be useful for speculator

Modelling and Forecasting financial time series of "tick data" by functional analysis and neural networks

The analysis of financial time series is of primary importance in the economic world. This paper deals with a data-driven empirical analysis of financial time series. The goal is to obtain insights into the dynamics of series and out-of-sample forecasting. In this paper we present a forecasting method based on an empirical functional analysis of the past of series. An originality of this method is that it does not make the assumption that a single model is able to capture the dynamics of the whole series. On the contrary, it splits the past of the series into clusters, and generates a specific local neural model for each of them. The local models are then combined in a probabilistic way, according to the distribution of the series in the past. This forecasting method can be applied to any time series forecasting problem, but is particularly suited for data showing nonlinear dependencies, cluster effects and observed at irregularly and randomly spaced times like high-frequency financial time series do. One way to overcome the irregular and random sampling of "tick-data" is to resample them at low-frequency, as it is done with "Intraday". However, even with optimal resampling using say five minute returns when transactions are recorded every second, a vast amount of data is discarded, in contradiction to basic statistical principles. Thus modelling the noise and using all the data is a better solution, even if one misspecifies the noise distribution. The method is applied to the forecasting of financial time series of «tick data» of assets on a short horizon in order to be useful for speculators

Modelling and forecasting of financial time series of "tick data" by functional analysis and neural networks

A functional method for time series forecasting is presented. Based on the splitting of the past dynamics into clusters, local models are built to capture the possible evolution of the series given the last known values. A probabilistic model is used to combine the local predictions. The method can be applied to any time series forecasting problem, but is particularly suited to data showing nonlinear dependencies, cluster effects, and observed at irregularly and randomly spaced times as financial series of "tick data" do. The method is applied to the forecasting of financial time series of tick data of IBM asset

Prédiction de séries temporelles financières par double carte de Kohonen et modèles RBFN locaux: application à la prédiction de l'indice boursier DAX30

Une méthode générale de prédiction de séries temporelles est présentée. Son principe est de modéliser l'évolution passée d'une série en utilisant des cartes de Kohonen. Les régions définies par ces cartes sont ensuite modélisées par des modèles locaux. Des prédictions locales sont combinées en une seule prédiction en utilisant un modèle stochastique. Cette méthode, conçue pour la prédiction de séries financières, est appliquée à la prédiction des rendements de l’index DAX30

Time series forecasting with SOM and local non-linear models - Application to the DAX30 index prediction

A general method for time series forecasting is presented. Based on the splitting of the past dynamics into clusters, local models are built to capture the possible evolution of the series given the last known values. A probabilistic model is used to combine the local predictions. The method can be applied to any time series prediction problem, but is particularly suited to data showing non-linear dependencies and cluster e#ects, as many financial series do. The method is applied to the prediction of the returns of the DAX30 index