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

A Stock Market Trading System Using Deep Neural Network

The stock market prediction is a lucrativefield of interest withpromising profit and covered with landmines for the unprecedented. The mar-kets are complex, non-linear and chaotic in nature which poses huge difficultiesto predict the prices accurately. In this paper, a stock trading system utilizingfeed-forward deep neural network (DNN) to forecast index price of Singaporestock market using the FTSE Straits Time Index (STI) in t days ahead is pro-posed and tested through market simulations on historical daily prices. There are40 input nodes of DNN which are the past 10 days’opening, closing, minimumand maximum prices and consist of 3 hidden layers with 10 neurons per layer.The training algorithm used is stochastic gradient descent with back-propagationand is accelerated with multi-core processing. A trading system is proposedwhich utilizes the DNN forecasting results with defined entry and exit rules toenter a trade. DNN performance is evaluated using RMSE and MAPE. Theoverall trading system shows promising results with a profit factor of 18.67,70.83% profitable trades and Sharpe ratio of 5.34 based on market simulation ontest data

An Improved Stock Price Prediction using Hybrid Market Indicators

In this paper the effect of hybrid market indicators is examined for an improved stock price prediction. The hybrid market indicators consist of technical, fundamental and expert opinion variables as input to artificial neural networks model. The empirical results obtained with published stock data of Dell and Nokia obtained from New York Stock Exchange shows that the proposed model can be effective to improve accuracy of stock price prediction

Modelling tourism demand to Spain with machine learning techniques. The impact of forecast horizon on model selection

This study assesses the influence of the forecast horizon on the forecasting performance of several machine learning techniques. We compare the fo recastaccuracy of Support Vector Regression (SVR) to Neural Network (NN) models, using a linear model as a benchmark. We focus on international tourism demand to all seventeen regions of Spain. The SVR with a Gaussian radial basis function kernel outperforms the rest of the models for the longest forecast horizons. We also find that machine learning methods improve their forecasting accuracy with respect to linear models as forecast horizons increase. This results shows the suitability of SVR for medium and long term forecasting.Peer ReviewedPostprint (published version