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Neural network time series forecasting of financial markets
Book Reviews / Journal of Forecasting 11 (1995) 599-604
there is a presumption that they are fixed and estimated once. It seems to me that, in the time domain in particular, it is much more desirable, and indeed much more practical, to design procedures which explicitly allow for parameter updating as new observations become available. For 'modern econometrics' this requires just the application of devices like the Kalman filter in a non-linear setting. The equivalent methods for N L D need some thought, and there is also the need for some thought about the implications of models where the parameters and perhaps the generating function may change. I would commend this book to applied researchers who are concerned with models in the time domain. It offers a good overview for anyone who needs to gain a better understanding of m o d e r n methods, which may be used to investigate the processes under study, or who just needs to try and generate better forecasts. For mathematical statisticians whose focus is more on traditional methods, this is a useful and thought provoking synthesis of two approaches. Inspire of the technical material, it has much to offer. I look forward to the next conference. This book certainly improved my knowledge of econometric techniques and non-linear dynamics, and has given me a much clearer view of their current strengths and shortcomings. It is therefore a pleasure to thank Nigel Meade for giving me the opportunity to review it. Chris Adcock
School of Economic & Business Studies The University of Westminster London, UK
E Michael Azoff, Neural Network Time Series Forecasting of Financial Markets, (John Wiley, Chichester) hardback, 194 pp., £34.95, ISBN 0471 943568. The focus of the book is neural networks applied to the prediction of financial time series. The introduction covers the spectrum of theories
601
underlying the study of financial time series, from the efficient market hypothesis to the detection of chaotic behaviour. Chapter 2 gives a description of the multi-layer perceptron, a supervised neural network, and the back propagation algorithm. This chapter acquaints the reader with the necessary neural network terminology and the basic concepts. Preparation of the time series data before it is offered to the neural network is the next topic. This covers outlier detection and possible transformation of skewed data through to using principal components analysis to reduce the dimensionality of the input data. The importance of the design of the neural network is explained in the next chapter. The elements discussed include: the choice of cost function, (for example, is root mean square error or some sort of binary measure more appropriate for a given situation?) and the design of the network (in terms of the number and size of the hidden layers and the form of the transfer function). Chapter 5 describes some properties of random walks. Chapter 6 is called Benchmarks. Several time series are modelled by neural networks, the series include sunspot numbers, future prices on Standard and Poor's 500 Index futures prices on the £US$ exchange rate. They are benchmarks in the sense that details of the data input preparation and the network design are given, so that an analyst should be able to duplicate the results. For the general reader they will serve as worked examples. A F O R T R A N program is given in the appendix, which will allow the results to be reproduced. This is a neural network in its recall mode only, the optimisation algorithms are not given. The final chapter discusses the use of forecasts in the context of a futures trading strategy. The appendices give useful details on back propagation methods and sources of information on neutral network journals, books and packages. The book is generally easy to read and would allow a novice to neural networks to use an appropriate package to produce forecasts of a given time series. In terms of evaluating the forecasts produced the novice would be on weaker ground, this is because little reference is made to other forecasting approaches. Of the
602
Book Reviews / Journal of Forecasting I1 (1995)599-604
series used as benchmarks; the sunspot data has been heavily modelled in the literature and it would have been informative to compare the neural network forecasts with other those of other models. Nigel Meade The Management School Imperial College, London, UK
Apostos-Paul Refenes, Editor, Neural Networks in the Capital Markets, (John Wiley, Chichester) hardback, 392 pp., £34.95, $55.95, ISBN 0471 943649. This is an edited work of 25 chapters, divided into five parts. Part one is about neural networks in general. The remaining parts relate to applications in different markets: equity, foreign exchange, bonds and a residual part on macroeconomic and corporate performance. The preface by Refenes is an unashamed plug for neural networks. The dichotomy in modelling approaches is presented as linear and stochastic models versus nonlinear and completely or partially deterministic systems. One hostage to fortune is 'Once nonlinear feedback mechanisms were introduced in market description, many price fluctuations could be explained without reference to stochastic effects'. The case for neural networks will not be strengthened by overselling the significance of their contribution. The first part of the book, written in the main by Refenes, includes a general description of neural networks and looks at network design, data modelling and model testing strategies. These topics are covered clearly and this part of the book would serve as a good general introduction to anyone wanting to become familiar with neural networks. The chapter on network design relates the various control mechanisms, such as choice of cost function and architecture of the network, against the desirable properties of the network's performance: a well fitting model, in and out of sample and a stable predic-
tion performance. The final chapter in this part applies 'computational learning theory' to the topic of network design. Although less applied than the preceding chapters, this chapter introduces different and useful ways of looking at how a neural network or other algorithms learn. In the section applied to equity markets, the first chapter is concerned with modelling the returns on an asset using arbitrage pricing theory (APT) (or ATP in several instances). The problem is to predict 'outperformance' of an asset using three independent 'factors' from nonmarket sources such as company accounts. The neural network approach is found to substantially outperform multiple regression, both in and out of sample. The problem tackled was nontrivial as can be seen, using data from 143 shares over 72 months, the neural networks took 'three to four days' (on a SUN4 workstation) to converge. The superior performance of the neural network is attributed to its smoother interpolation. Diagrams show that the marginal relationship between the dependent variable, 'outperformance', and each 'factor' according to the neural network is very non-linear; curves with three, four or five turning points are shown. If this neutral network model is close to reality, it is not surprising that linear regression was not competitive. Unfortunately the identity of the factors is not revealed, otherwise this data set would make a valuable test bed for different modelling approaches. The next chapter is a straightforward attempt at forecasting share prices using neutral networks, as a test of the efficient market hypothesis. The results are inconclusive and the chapter has the air of work in progress. Chapter nine follows the same theme, but making stronger claims for their forecasts. The experimental procedure was unclear and I found the presentation of results confusing (not helped by tables of MADs to nine decimal places). The remaining chapters cover index tracking and forecasting gold futures. The section on foreign exchange (FX) markets is introduced by a chapter describing the size and importance of this market and only general comments about the potential contribution of
there is a presumption that they are fixed and estimated once. It seems to me that, in the time domain in particular, it is much more desirable, and indeed much more practical, to design procedures which explicitly allow for parameter updating as new observations become available. For 'modern econometrics' this requires just the application of devices like the Kalman filter in a non-linear setting. The equivalent methods for N L D need some thought, and there is also the need for some thought about the implications of models where the parameters and perhaps the generating function may change. I would commend this book to applied researchers who are concerned with models in the time domain. It offers a good overview for anyone who needs to gain a better understanding of m o d e r n methods, which may be used to investigate the processes under study, or who just needs to try and generate better forecasts. For mathematical statisticians whose focus is more on traditional methods, this is a useful and thought provoking synthesis of two approaches. Inspire of the technical material, it has much to offer. I look forward to the next conference. This book certainly improved my knowledge of econometric techniques and non-linear dynamics, and has given me a much clearer view of their current strengths and shortcomings. It is therefore a pleasure to thank Nigel Meade for giving me the opportunity to review it. Chris Adcock
School of Economic & Business Studies The University of Westminster London, UK
E Michael Azoff, Neural Network Time Series Forecasting of Financial Markets, (John Wiley, Chichester) hardback, 194 pp., £34.95, ISBN 0471 943568. The focus of the book is neural networks applied to the prediction of financial time series. The introduction covers the spectrum of theories
601
underlying the study of financial time series, from the efficient market hypothesis to the detection of chaotic behaviour. Chapter 2 gives a description of the multi-layer perceptron, a supervised neural network, and the back propagation algorithm. This chapter acquaints the reader with the necessary neural network terminology and the basic concepts. Preparation of the time series data before it is offered to the neural network is the next topic. This covers outlier detection and possible transformation of skewed data through to using principal components analysis to reduce the dimensionality of the input data. The importance of the design of the neural network is explained in the next chapter. The elements discussed include: the choice of cost function, (for example, is root mean square error or some sort of binary measure more appropriate for a given situation?) and the design of the network (in terms of the number and size of the hidden layers and the form of the transfer function). Chapter 5 describes some properties of random walks. Chapter 6 is called Benchmarks. Several time series are modelled by neural networks, the series include sunspot numbers, future prices on Standard and Poor's 500 Index futures prices on the £US$ exchange rate. They are benchmarks in the sense that details of the data input preparation and the network design are given, so that an analyst should be able to duplicate the results. For the general reader they will serve as worked examples. A F O R T R A N program is given in the appendix, which will allow the results to be reproduced. This is a neural network in its recall mode only, the optimisation algorithms are not given. The final chapter discusses the use of forecasts in the context of a futures trading strategy. The appendices give useful details on back propagation methods and sources of information on neutral network journals, books and packages. The book is generally easy to read and would allow a novice to neural networks to use an appropriate package to produce forecasts of a given time series. In terms of evaluating the forecasts produced the novice would be on weaker ground, this is because little reference is made to other forecasting approaches. Of the
602
Book Reviews / Journal of Forecasting I1 (1995)599-604
series used as benchmarks; the sunspot data has been heavily modelled in the literature and it would have been informative to compare the neural network forecasts with other those of other models. Nigel Meade The Management School Imperial College, London, UK
Apostos-Paul Refenes, Editor, Neural Networks in the Capital Markets, (John Wiley, Chichester) hardback, 392 pp., £34.95, $55.95, ISBN 0471 943649. This is an edited work of 25 chapters, divided into five parts. Part one is about neural networks in general. The remaining parts relate to applications in different markets: equity, foreign exchange, bonds and a residual part on macroeconomic and corporate performance. The preface by Refenes is an unashamed plug for neural networks. The dichotomy in modelling approaches is presented as linear and stochastic models versus nonlinear and completely or partially deterministic systems. One hostage to fortune is 'Once nonlinear feedback mechanisms were introduced in market description, many price fluctuations could be explained without reference to stochastic effects'. The case for neural networks will not be strengthened by overselling the significance of their contribution. The first part of the book, written in the main by Refenes, includes a general description of neural networks and looks at network design, data modelling and model testing strategies. These topics are covered clearly and this part of the book would serve as a good general introduction to anyone wanting to become familiar with neural networks. The chapter on network design relates the various control mechanisms, such as choice of cost function and architecture of the network, against the desirable properties of the network's performance: a well fitting model, in and out of sample and a stable predic-
tion performance. The final chapter in this part applies 'computational learning theory' to the topic of network design. Although less applied than the preceding chapters, this chapter introduces different and useful ways of looking at how a neural network or other algorithms learn. In the section applied to equity markets, the first chapter is concerned with modelling the returns on an asset using arbitrage pricing theory (APT) (or ATP in several instances). The problem is to predict 'outperformance' of an asset using three independent 'factors' from nonmarket sources such as company accounts. The neural network approach is found to substantially outperform multiple regression, both in and out of sample. The problem tackled was nontrivial as can be seen, using data from 143 shares over 72 months, the neural networks took 'three to four days' (on a SUN4 workstation) to converge. The superior performance of the neural network is attributed to its smoother interpolation. Diagrams show that the marginal relationship between the dependent variable, 'outperformance', and each 'factor' according to the neural network is very non-linear; curves with three, four or five turning points are shown. If this neutral network model is close to reality, it is not surprising that linear regression was not competitive. Unfortunately the identity of the factors is not revealed, otherwise this data set would make a valuable test bed for different modelling approaches. The next chapter is a straightforward attempt at forecasting share prices using neutral networks, as a test of the efficient market hypothesis. The results are inconclusive and the chapter has the air of work in progress. Chapter nine follows the same theme, but making stronger claims for their forecasts. The experimental procedure was unclear and I found the presentation of results confusing (not helped by tables of MADs to nine decimal places). The remaining chapters cover index tracking and forecasting gold futures. The section on foreign exchange (FX) markets is introduced by a chapter describing the size and importance of this market and only general comments about the potential contribution of