Jason Laws

Higher order and recurrent neural architectures for trading the EUR/USD exchange rate

The motivation for this paper is to investigate the use of alternative novel neural network architectures when applied to the task of forecasting and trading the Euro/Dollar (EUR/USD) exchange rate. This is done by benchmarking three different neural network designs representing a Higher Order Neural Network (HONN), a Psi Sigma Network and a Recurrent Network (RNN) with three successful architectures, the traditional Multilayer Perceptron (MLP), the Softmax and the Gaussian Mixture (GM) models. More specifically, the trading performance of the six models is investigated in a forecast and trading simulation competition on the EUR/USD time series over a period of 8 years. These results are also benchmarked with more traditional models such as a moving average convergence divergence technical model (MACD), an autoregressive moving average model (ARMA) and a logistic regression model (LOGIT). As it turns out, the MLP, the HONN, the Psi Sigma and the RNN models all do well and outperform the more traditional models in a simple trading simulation exercise. However, when more sophisticated trading strategies using confirmation filters and leverage are applied, the GM network produces remarkable results and outperforms all the other network architectures.

Forecasting and trading the EUR/USD exchange rate with Gene Expression and Psi Sigma Neural Networks

Highlights? We investigate the use of Psi Sigma Neural Network and the Gene Expression. ? We benchmark their results with five different linear and non-linear models. ? We introduce a time-varying leverage strategy. The motivation for this paper is to investigate the use of two promising classes of artificial intelligence models, the Psi Sigma Neural Network (PSI) and the Gene Expression algorithm (GEP), when applied to the task of forecasting and trading the EUR/USD exchange rate. This is done by benchmarking their results with a Multi-Layer Perceptron (MLP), a Recurrent Neural Network (RNN), a genetic programming algorithm (GP), an autoregressive moving average model (ARMA) plus a naive strategy. We also examine if the introduction of a time-varying leverage strategy can improve the trading performance of our models.

Performance of Shariah-Compliant Indices in London and NY Stock Markets and their potential for diversification

This paper aims to contribute to the empirical literature on the performance of Sharia-Compliant Indices (SCIs) by evaluating the performance of a number of SCIs, in comparison to similar mainstream indices, as well as in comparison to other ethical funds. Furthermore, the paper will test for co-integration among the SCIs and the mainstream ones to establish whether there is any scope for diversification. The main findings are that SCIs offer an opportunity for portfolio diversification with mainstream indices and other ethical funds within the UK.

Trading futures spreads: an application of correlation and threshold filters

A clear motivation for this paper is the investigation of a correlation filter to improve the return/risk performance of spread trading models. A further motivation for this paper is the extension of trading futures spreads beyond the ‘Fair Value’ type of model used by Butterworth and Holmes (2002). The trading models tested are the following: the cointegration ‘fair value’ approach; reverse moving average (of which the results of the 20-day model are shown here); traditional regression techniques; and Neural Network Regression. Also shown is the effectiveness of two types of filter: a standard filter and a correlation filter on the trading rule returns. Results show that the best model for trading the WTI–Brent spread is the MACD model, which proved to be profitable, both in- and out-of-sample. This is evidenced by out-of-sample annualised returns of 26.35% for the standard filter and 26.15% for the correlation filter (inclusive of transactions costs).

Hedging effectiveness of stock index futures

Abstract The paper is concerned with the efficiency of hedging stock portfolios using futures stock indices covering the period January 1995–December 2001. The hedged portfolios consisted of the assets of seventeen investment companies quoted on the London Stock Exchange and two portfolios, which were assumed to match exactly the corresponding cash index. Two futures indices were used to hedge the funds namely FTSE100 and FTSE250 futures indices which are quoted on LIFFE. Weekly observations were used providing 365 observations for each variable. The total sample was split into two sections. The first 261 observations were used to estimate the optimal hedge ratio (i.e. the in-sample period) providing 260 returns for each variable and the remaining 104 (i.e. the post-sample period) observations utilised to check the efficiency of the estimated hedge ratio. In addition a second estimation window was tried using the last 30 observations of the in-sample period. A variety of methods were tried to estimate the optimal hedge ratio including ordinary least squares (OLS), methods allowing for the existence of Autoregressive Conditional Heteroskedasticity, and an Exponential Weighted Moving Average (EWMA). The general conclusions reached were that for the portfolios within the data set (i) that the EWMA method of estimation provided the best estimate of the optimal hedge (ii) the shorter estimation window was no more efficient than the longer window and (ii) the FTESE250 futures index was the best hedging vehicle for these portfolios.

Statistical Arbitrage and High-Frequency Data with an Application to Eurostoxx 50 Equities

The motivation for this paper is to apply a statistical arbitrage technique of pairs trading to high-frequency equity data and compare its profit potential to the standard sampling frequency of daily closing prices. We use a simple trading strategy to evaluate the profit potential of the data series and compare information ratios yielded by each of the different data sampling frequencies. The frequencies observed range from a 5-minute interval, to prices recorded at the close of each trading day.The analysis of the data series reveals that the extent to which daily data are cointegrated provides a good indicator of the profitability of the pair in the high-frequency domain. For each series, the in-sample information ratio is a good indicator of the future profitability as well.Conclusive observations show that arbitrage profitability is in fact present when applying a novel diversified pair trading strategy to high-frequency data. In particular, even once very conservative transaction costs are taken into account, the trading portfolio suggested achieves very attractive information ratios (e.g. above 3 for an average pair sampled at the high-frequency interval and above 1 for a daily sampling frequency).

Modelling and trading the EUR/USD exchange rate at the ECB fixing

The motivation for this paper is to investigate the use of alternative novel neural network (NN) architectures when applied to the task of forecasting and trading the euro/dollar (EUR/USD) exchange rate, using the European Central Bank (ECB) fixing series with only auto-regressive terms as inputs. This is done by benchmarking four different NN designs representing a higher-order neural network (HONN), a Psi Sigma Network and a recurrent neural network with the classic multilayer perception (MLP) and some traditional techniques, either statistical such as an auto-regressive moving average model, or technical such as a moving average convergence/divergence model, plus a naïve strategy. More specifically, the trading performance of all models is investigated in a forecast and trading simulation on the EUR/USD ECB fixing time series over the period 1999–2007 using the last one and half years for out-of-sample testing, an original feature of this paper. We use the EUR/USD daily fixing by the ECB as many financial institutions are ready to trade at this level and it is therefore possible to leave orders with a bank for business to be transacted on that basis. As it turns out, the MLP does remarkably well and outperforms all other models in a simple trading simulation exercise. However, when more sophisticated trading strategies using confirmation filters and leverage are applied, the HONN network produces better results and outperforms all other NN and traditional statistical models in terms of annualized return.

Trading futures spread portfolios: applications of higher order and recurrent networks

This paper investigates the modelling and trading of oil futures spreads in the context of a portfolio of contracts. A portfolio of six spreads is constructed and each spread forecasted using a variety of modelling techniques, namely, a cointegration fair value model and three different types of neural network (NN), such as multi-layer perceptron (MLP), recurrent, and higher order NN models. In addition, a number of trading filters are employed to further improve the trading statistics of the models. Three different filters are optimized on an in-sample measure of down side risk-adjusted return, and these are then fixed out-of-sample. The filters employed are the threshold filter, correlation filter, and the transitive filter. The results show that the best in-sample model is the MLP with a transitive filter. This model is the best performer out-of-sample and also returns good out-of-sample statistics.

Modelling commodity value at risk with higher order neural networks

The motivation for this article is to investigate the use of a promising class of Neural Network (NN) models, Higher Order Neural Networks (HONNs), when applied to the task of forecasting the 1-day ahead Value at Risk (VaR) of the brent oil and gold bullion series with only autoregressive terms as inputs. This is done by benchmarking their results with those of a different NN design, the Multilayer Perceptron (MLP), an Extreme Value Theory (EVT) model along with some traditional techniques, such as an Autoregressive Moving Average Model-Generalized Autoregressive Conditional Heteroscedasticity (ARMA-GARCH) (1,1) model and the RiskMetrics volatility. In addition to these, we also examine two hybrid NNs-RiskMetrics volatility models. More specifically, the forecasting performance of all models for computing the VaR of the brent oil and the gold bullion is examined over the period 2002 to 2008 using the last year for out-of-sample testing. The evaluation of our models is done by using a series of backtesting algorithms and two loss functions: a violation ratio calculating when the realized return exceeds the forecast VaR and an average squared violation magnitude function, firstly introduced in this article, computing the average magnitude of the violations. As it turns out, the hybrid HONNs-RiskMetrics model does remarkably well and outperforms all other models in forecasting the VaR of gold and oil at both the 5% and 1% confidence levels, providing an accurate number of independent violations which also have the lowest magnitude on average. The pure HONNs and MLPs along with the hybrid MLP-RiskMetrics model also give satisfactory forecasts in most cases.

Modelling and trading the realised volatility of the FTSE100 futures with higher order neural networks

The motivation for this article is the investigation of the use of a promising class of neural network (NN) models, higher order neural networks (HONNs), when applied to the task of forecasting and trading the 21-day-ahead realised volatility of the FTSE 100 futures index. This is done by benchmarking their results with those of two different NN designs, the multi-layer perceptron (MLP) and the recurrent neural network (RNN), along with a traditional technique, RiskMetrics. More specifically, the forecasting and trading performance of all models is examined over the eight FTSE 100 futures maturities of the period 2007–2008 using the realised volatility of the last 21 trading days of each maturity as the out-of-sample target. The statistical evaluation of our models is done by using a series of measures such as the mean absolute error, the mean absolute percentage error, the root-mean-squared error and the Theil U-statistic. Then we apply a simple trading strategy to exploit our forecasts based on trading at-the-money call options on FTSE 100 futures. As it turns out, HONNs demonstrate a remarkable performance and outperform all other models not only in terms of statistical accuracy but also in terms of trading efficiency. We also note that both the RNNs and MLPs provide sufficient results in the trading application in terms of cumulative profit and average profit per trade.