Forecasting financial markets using neural networks: an analysis of methods and accuracy

This research examines andanalyzes the use of neural networks as a forecasting tool. Specifically a neural network's ability to predict future trends of Stock Market Indices is tested. Accuracy is compared against a traditional forecasting method, multiple linear regression analysis. Finally, the probability of the model's forecast being correct is calculated using conditional probabilities. While only briefly discussing neural network theory, this research determines the feasibility and practicality of usingneural networks as a forecasting tool for the individual investor. This study builds upon the work done by Edward Gately in his book Neural Networks for Financial Forecasting. This research validates the work of Gately and describes the development of a neural network that achieved a 93.3 percent probability of predicting a market rise, and an 88.07 percent probability of predicting a market drop in the S&P500. It was concluded that neural networks do have the capability to forecast financial markets and, if properly trained, the individual investor could benefit from the use of this forecasting tool.http://www.archive.org/details/forecastingfinan00kutsLieutenant, United States NavyApproved for public release; distribution is unlimited

Artificial Neural Networks in Financial Modelling

The study of Artificial Neural Networks derives from first trials to translate in mathematical models the principles of biological processing. An Artificial Neural Network deals with generating, in the fastest times, an implicit and predictive model of the evolution of a system. In particular, it derives from experience its ability to be able to recognize some behaviours or situations and to suggest how to take them into account. This work illustrates an approach to the use of Artificial Neural Networks for Financial Modelling; we aim to explore the structural differences (and implications) between one- and multi- agent and population models. In one-population models, ANNs are involved as forecasting devices with wealth-maximizing agents (in which agents make decisions so as to achieve an utility maximization following non-linear models to do forecasting), while in multipopulation models agents do not follow predetermined rules, but tend to create their own behavioural rules as market data are collected. In particular, it is important to analyze diversities between one-agent and one-population models; in fact, in building one-population model it is possible to illustrate the market equilibrium endogenously, which is not possible in one-agent model where all the environmental characteristics are taken as given and beyond the control of the single agent.artificial neural network, financial modelling, population model, market equilibrium.

Artificial Neural Networks in Finance Modelling

The study of Artificial Neural Networks derives from first trials to translate in mathematical models the principles of biological “processing”. An Artificial Neural Network deals with generating, in the fastest times, an implicit and predictive model of the evolution of a system. In particular, it derives from experience its ability to be able to recognize some behaviours or situations and to “suggest” how to take them into account. This work illustrates an approach to the use of Artificial Neural Networks for Financial Modelling; we aim to explore the structural differences (and implications) between one- and multi- agent and population models. In one-population models, ANNs are involved as forecasting devices with wealth-maximizing agents (in which agents make decisions so as to achieve an utility maximization following non- linear models to do forecasting), while in multi-population models agents do not follow predetermined rules, but tend to create their own behavioural rules as market data are collected. In particular, it is important to analyze diversities between one-agent and one-population models; in fact, in building one-population model it is possible to illustrate the market equilibrium endogenously, which is not possible in one-agent model where all the environmental characteristics are taken as given and beyond the control of the single agent. A particular application we aim to study is the one regarding “customer profiling”, in which (based on personal and direct relationships) the “buying” behaviour of each customer can be defined, making use of behavioural inference models such as the ones offered by Artificial Neural Networks much better than traditional statistical methodologies.Artificial Neural Network, Financial Modelling, Customer Profiling

Currency Exchange Rate Forecasting with Neural Networks

This is the published version. Copyright De GruyterThis paper presents the prediction of foreign currency exchange rates using artificial neural networks. Since neural networks can generalize from past experience, they represent a significant advancement over traditional trading systems, which require a knowledgeable expert to define trading rules to represent market dynamics. It is practically impossible to expect that one expert can devise trading rules that account for, and accurately reflect, volatile and rapidly changing market conditions. With neural networks, a trader may use the predictive information alone or with other available analytical tools to fit the trading style, risk propensity, and capitalization. Numerous factors affect the foreign exchange market, as they will be described in this paper. The neural network will help minimize these factors by simply giving an estimated exchange rate for a future day (given its previous knowledge gained from extensive training). Because the field of financial forecasting is too large, the scope in this paper is narrowed to the foreign exchange market, specifically the value of the Japanese Yen against the United States Dollar, two of the most important currencies in the foreign exchange market

On the use of neural networks for stock price forecasting

Having the ability to predict the price of a particular stock share is undoubtedly a major challenge, because of the complexity and implied volatility of the financial markets. This is a topic of great interest to researchers and market players, as the effectiveness of the forecast might translate into huge monetary gains. This work aims to demonstrate the use of neural networks for stock price forecasting. Two financial titles are considered: Microsoft and Apple. The initial choice of the predictor variables comprises the most used and referenced in the scientific papers published on this subject. This work demonstrates the importance of a careful selection of some of those variables for a good neural network performance.publishe

Forecasting the Portuguese stock market time series by using artificial neural networks

In this paper, we show that neural networks can be used to uncover the non-linearity that exists in the financial data. First, we follow a traditional approach by analysing the deterministic/stochastic characteristics of the Portuguese stock market data and some typical features are studied, like the Hurst exponents, among others. We also simulate a BDS test to investigate nonlinearities and the results are as expected: the financial time series do not exhibit linear dependence. Secondly, we trained four types of neural networks for the stock markets and used the models to make forecasts. The artificial neural networks were obtained using a three-layer feed-forward topology and the back-propagation learning algorithm. The quite large number of parameters that must be selected to develop a neural network forecasting model involves some trial and as a consequence the error is not small enough. In order to improve this we use a nonlinear optimization algorithm to minimize the error. Finally, the output of the 4 models is quite similar, leading to a qualitative forecast that we compare with the results of the application of k-nearest-neighbor for the same time serie

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

Forecasting foreign exchange rates with adaptive neural networks using radial basis functions and particle swarm optimization

The motivation for this paper is to introduce a hybrid Neural Network architecture of Particle Swarm Optimization and Adaptive Radial Basis Function (ARBF-PSO), a time varying leverage trading strategy based on Glosten, Jagannathan and Runkle (GJR) volatility forecasts and a Neural Network fitness function for financial forecasting purposes. This is done by benchmarking the ARBF-PSO results with those of three different Neural Networks architectures, a Nearest Neighbors algorithm (k-NN), an autoregressive moving average model (ARMA), a moving average convergence/divergence model (MACD) plus a naïve strategy. More specifically, the trading and statistical performance of all models is investigated in a forecast simulation of the EUR/USD, EUR/GBP and EUR/JPY ECB exchange rate fixing time series over the period January 1999 to March 2011 using the last two years for out-of-sample testing