Modeling Financial Time Series with Artificial Neural Networks

Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001

Forecasting Long-Term Government Bond Yields: An Application of Statistical and AI Models

This paper evaluates several artificial intelligence and classical algorithms on their ability of forecasting the monthly yield of the US 10-year Treasury bonds from a set of four economic indicators. Due to the complexity of the prediction problem, the task represents a challenging test for the algorithms under evaluation. At the same time, the study is of particular significance for the important and paradigmatic role played by the US market in the world economy. Four data-driven artificial intelligence approaches are considered, namely, a manually built fuzzy logic model, a machine learned fuzzy logic model, a self-organising map model and a multi-layer perceptron model. Their performance is compared with the performance of two classical approaches, namely, a statistical ARIMA model and an econometric error correction model. The algorithms are evaluated on a complete series of end-month US 10-year Treasury bonds yields and economic indicators from 1986:1 to 2004:12. In terms of prediction accuracy and reliability of the modelling procedure, the best results are obtained by the three parametric regression algorithms, namely the econometric, the statistical and the multi-layer perceptron model. Due to the sparseness of the learning data samples, the manual and the automatic fuzzy logic approaches fail to follow with adequate precision the range of variations of the US 10-year Treasury bonds. For similar reasons, the self-organising map model gives an unsatisfactory performance. Analysis of the results indicates that the econometric model has a slight edge over the statistical and the multi-layer perceptron models. This suggests that pure data-driven induction may not fully capture the complicated mechanisms ruling the changes in interest rates. Overall, the prediction accuracy of the best models is only marginally better than the prediction accuracy of a basic one-step lag predictor. This result highlights the difficulty of the modelling task and, in general, the difficulty of building reliable predictors for financial markets.interest rates; forecasting; neural networks; fuzzy logic.

A CIMB Stock Price Prediction Case Study with Feedforward Neural Network and Recurrent Neural Network

Artificial Neural Network (ANN) is one of the popular techniques used in stock market price prediction. ANN is able to learn from data pattern and continuously improves the result without prior information about the model. The two popular variants of ANN architecture widely used are Feedforward Neural Network (FFNN) and Recurrent Neural Network (RNN). The literature shows that the performance of these two ANN variants is studied dependent. Hence, this paper aims to compare the performance of FFNN and RNN in predicting the closing price of CIMB stock which is traded on the Kuala Lumpur Stock Exchange (KLSE). This paper describes the design of FFNN and RNN and discusses the performances of both ANNs

Nonlinearities In Stock Return Prediction: Evidence From South Africa

This research investigates the relationship between firm-specific style attributes and the cross-section of equity returns on the JSE Securities Exchange (JSE) over the period from 1 January 1997 to 31 December 2007. Both linear and nonlinear stock selection models are constructed based on the cross-section of equity returns with firm-specific attributes as model inputs. Both linear and nonlinear models identify book-value-to-price and cash flow-to-price as significant styles attributes that distinguish near-term future share returns on the JSE. The risk-adjusted performance of the nonlinear models is found to be comparable with that of linear models. In terms of artificial neural network modeling, the extended Kalman filter learning rule is found to outperform the traditional backpropagation approach. This finding is consistent with our prior findings on global stock selection

Prédiction de la tendance des actions basée sur les réseaux convolutifs graphiques et les LSTM

Abstract: As stocks have been developing over decades, the trend and the price of a stock are more often used for predictions in stock market analysis. In the field of finance, an accurate stock future trending can not only help decision-makers estimate the possibility of profit, but also help them avoid risks. In this research, we present a quantitative approach to predicting the trend of stocks in which a clustering model is employed to mine the stock trends patterns from historical stock price data. Stock series clustering is a special kind of time series clustering. We aim to find out the trend types, e.g. rising, falling and others, of a stock at time intervals, and then make use of the past trends to predict its future trend. The proposed prediction method is based on Graph Convolutional Neural Network for clustering and Long Short-Term Memory model for prediction. This method is suitable for the data clustering of unbalanced classes too. The experiments on real-world stock data demonstrate that our method can yield accurate forecasts. In the long run, the proposed method can be used to explore new possibilities in the research field of time series clustering, such as using other graph neural networks to predict stock trends.Comme les prix des actions évoluent au fil des décennies, la tendance et le prix d’une action sont souvent utilisés pour effectuer des prévisions en bourse. Bien anticiper la tendance future des actions peut non seulement aider les décideurs à mieux estimer les possibilités de profit, mais aussi les risques. Dans cette thèse, une approche quantitative est présentée pour prédire les fluctuations d’actions. L’approche se base sur une méthode de clustering pour identifier la tendance des actions à partir de leurs données historiques. C’est un type particulier de clustering appliqué sur des séries chronologiques. Il consiste à découvrir les tendances des actions sur des intervalles de temps, tel que des tendances haussières, des tendances baissières, et ensuite d’utiliser ces tendances pour prédire leurs états futurs. La méthode de prédiction proposée se base sur les réseaux de neurones convolutionnels graphiques et des réseaux récurrents mémoire pour la prédiction. Cette méthode fonctionne également sur des ensembles de données où la proportion des classes est déséquilibrée. Les données historiques des actions démontrent que la méthode proposée effectue des prévisions précises. La méthode proposée peut ouvrir une nouvelle perspective de recherche pour le clustering de séries chronologiques, notamment l’utilisation d‘autres réseaux de neurones graphiques pour prédire les tendances des actions

A factor augmented vector autoregressive model and a stacked de-noising auto-encoders forecast combination to predict the price of oil

The following dissertation aims to show the benefits of a forecast combination between an econometric and a deep learning approach. On one side, a Factor Augmented Vector Autoregressive Model (FAVAR) with naming variables identification following Stock and Watson (2016)1; on the other side, a Stacked De-noising Auto-Encoder with Bagging (SDAE-B) following Zhao, Li and Yu (2017)2 are implemented. From January 2010 to September 2018 Two-hundred-eighty-one monthly series are used to predict the price of the West Texas Intermediate (WTI). The model performance is analysed by Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE) and Directional Accuracy (DA). The combination benefits from both SDAE-B’s high accuracy and FAVAR’s interpretation features through impulse response functions (IRFs) and forecast error variance decomposition (FEVD)