This paper investigates the use of Higher Order Neural Networks using a number of architectures to forecast the Gasoline Crack spread. The architectures used are Recurrent Neural Network and Higher Order Neural Networks; these are benchmarked against the standard MLP model. The final models are judged in terms of out-of-sample annualised return and drawdown, with and without a number of trading filters.
The results show that the best model of the spread is the recurrent network with the largest out-of-sample returns before transactions costs, indicating a superior ability to forecast the change in the spread. Further the best trading model of the spread is the Higher Order Neural Network with the threshold filter due a superior in- and out-of-sample risk/return profile.
In this paper, we examine the use of Neural Network Regression (NNR) and alternative forecasting techniques in financial forecasting models and financial trading models. In both types of applications, NNR models results are benchmarked against simpler alternativě approaches to ensure that there is indeed added value in the use of these more complex models.
The idea to use a nonlinear nonparametric approach to predict financial variables is intuitively appealing. But whereas some applications need to be assessed on traditional forecasting accuracy criteria such as root mean squared errors, others that deal with trading financial markets need to be assessed on the basis of financial criteria such as risk adjusted return. Accordingly, we develop two different types of applications. In the first one, using monthly data from April 1993 through June 1999 from a UK financial institution, we develop alternativě forecasting models of cash flows and cheque values of four of its major customers. These models are then tested out-of-sample over the period July 1999-April 2000 in terms of forecasting accuracy.
In the second series of applications, we develop financial trading models for four major stock market indices (S&P500, FTSEIOO, EUROSTOXX50 and NIKKEI225) using daily data from 31 January 1994 through 4 May 1999 for in-sample estimation and leaving the period 5 May 1999 through 6 June 2000 for out-of-sample testing. In this case, the trading models developed are not assessed in terms of forecasting accuracy, but in terms of trading efficiency via the use of a simulated trading strategy.
In both types of applications, for the periods and time series concerned, we clearly show that the NNR models do indeed add value in the forecasting process.
The purpose of this paper is twofold. Firstly, to investigate the merit of estimating probability density functions rather than level or classification estimations on a one-day-ahead forecasting the task of the silver time series.
This is done by benchmarking the Gaussian mixture neural network model (as a probability distribution predictor) against two other neural network designs representing a level estimator (the Mulit-layer perceptron network [MLP]) and a classification model (Softmax cross entropy network model [SCE]). In addition, we also benchmark the results against standard forecasting models, namely a naive model, an autoregressive moving average model (ARMA) and a logistic regression model (LOGIT).
The second purpose of this paper is to examine the possibilities of improving the trading performance of those models by applying confirmation filters and leverage.
As it turns out, the three neural network models perform equally well generating a recognisable gain while ARMA benchmark model, on the other hand, seems to have picked up the right rhythm of mean reversion in the silver time series, leading to very good results. Only when using more sophisticated trading strategies and leverage, the neural network models show an ability to identify successfully trades with a high Sharpe ratio and outperform the ARMA model.
The main aim of this paper is to forecast gold and silver daily returns with advanced regression analysis using various linear and non-linear models.
ARMA models are used as a linear benchmark for comparison purposes with established non-linear models such as Nearest Neighbours and MultiLayer Perceptron (MLP), and Higher Order Neural Networks (HONN) whose application to financial markets is quite new. All models are assessed using statistical criteria such as correct directional change as well as financial criteria such as risk adjusted return. The main aim is to find which of these models generate the best returns and if nonlinear models can be used for generating excess returns in the precious metals market. This is achieved by implementing a trading simulation where the forecast is translated into a trading signal. Profit statistics are calculated taking into account transaction costs.
It is concluded that, for the January 2000-May 2006 period under review, nonlinear models like MLPs and HONNs did outperform the linear ARMA models. In the end, the performance of both MLP and HONN models showed the presence of nonlinearities in the gold and silver prices as it was found that nonlinear models can be effectively used for generating excess returns in these markets.