Financial Applications of Neural Networks

Most Important Publications

  1. ZHANG, G., B.E. PATUWO and M.Y. HU, 1998. Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting. [Cited by 129]
  2. Interest in using artificial neural networks (ANNs) for forecasting has led to a tremendous surge in research activities in the past decade. While ANNs provide a great deal of promise, they also embody much uncertainty. Researchers to date are still not certain about the effect of key factors on forecasting performance of ANNs. This paper presents a state-of-the-art survey of ANN applications in forecasting. Our purpose is to provide (1) a synthesis of published research in this area, (2) insights on ANN modeling issues, and (3) the future research directions.
  3. TAM, K.Y. and M.Y. KIANG, 1992. Managerial Applications of Neural Networks: The Case of Bank Failure Predictions. Management Science. [Cited by 186]
  4. This paper introduces a neural-net approach to perform discriminant analysis in business research. A neural net represents a nonlinear discriminant function as a pattern of connections between its processing units. Using bank default data, the neural-net approach is compared with linear classifier, logistic regression, kNN, and ID3. Empirical results show that neural nets is a promising method of evaluating bank conditions in terms of predictive accuracy, adaptability, and robustness. Limitations of using neural nets as a general modeling tool are also discussed.
  5. TRIPPI, Robert R. and Efraim TURBAN, 1996. Neural networks in finance and investing: using artificial intelligence to improve real-world performance. Chicago: Irwin Professional Pub. [Cited by 99]
  6. BOOK 1 Book review: International Journal of Forecasting, Volume 13, Issue 1, March 1997, Pages 144-146
  7. AZOFF, E.M., 1994. Neural network time series forecasting of financial markets. Chichester; New York: Wiley. [Cited by 114]
  8. [book] A neural network is a computer program that can recognise patterns in data, learn from this and (in the case of time series data) make forecasts of future patterns. There are now over 20 commercially available neural network programs designed for use on financial markets and there have been some notable reports of their successful application. However, like any other computer program, neural networks are only as good as the data they are given and the questions that are asked of them. Proper use of a neural network involves spending time understanding and cleaning the data: removing errors, preprocessing and postprocessing. This book takes the reader beyond the 'black-box' approach to neural networks and provides the knowledge that is required for their proper design and use in financial markets forecasting - with an emphasis on futures trading. Comprehensively specified benchmarks are provided (including weight values), drawn from time series examples in chaos theory and financial futures. The book covers data preprocessing, random walk theory, trading systems and risk analysis. It also provides a literature review, a tutorial on backpropagation, and a chapter on further reading and software. For the professional financial forecaster this book is without parallel as a comprehensive, practical and up-to-date guide to this important subject.
  9. SWANSON, N.R. and H. WHITE, 1997. A Model Selection Approach to Real-Time Macroeconomic Forecasting Using Linear Models and Artificial Neural Networks. The Review of Economics and Statistics. [Cited by 80]
  10. We take a model selection approach to real-time macroeconomic forecasting using linear and nonlinear models. True ex-ante forecasting are constructed by using unrevised as opposed to fully revised data. Model selection as well as model performance measures are considered.
  11. REFENES, Apostolos-Paul, 1995. Neural networks in the capital markets. Chichester; New York: Wiley. [Cited by 98]
  12. BOOK
  13. WILSON, Rick L. and Ramesh SHARDA, 1994. Bankruptcy prediction using neural networks. Decision Support Systems. [Cited by 100]
  14. Prediction of firm bankruptcies have been extensively studied in accounting, as all stakeholders in a firm have a vested interest in monitoring its financial performance. This paper presents an exploratory study which compares the predictive capabilities for firm bankruptcy of neural networks and classical multivariate discriminant analysis. The predictive accuracy of the two techniques is presented within a comprehensive, statistically sound framework, indicating the value added to the forecasting problem by each technique. The study indicates that neural networks perform significantly better than discriminant analysis at predicting firm bankruptcies. Implications of our results for the accounting professional, neural networks researcher and decision support system builders are highlighted.
  15. SZKUTA, B.R., L.A. SANABRIA and T.S. DILLON, 1999. Electricity price short-term forecasting using artificial neural networks. IEEE Transactions on Power Systems. [Cited by 56]
  16. This paper presents the system marginal price (SMP) short-term forecasting implementation using the artificial neural networks (ANN) computing technique. The described approach uses the three-layered ANN paradigm with backpropagation. The retrospective SMP real-world data, acquired from the deregulated Victorian power system, was used for training and testing the ANN. The results presented in this paper confirm considerable value of the ANN based approach in forecasting the SMP.
  17. SAAD, E.W., D.V. PROKHOROV and D.C. WUNSCH, 1998. Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks.. IEEE Transactions on Neural Networks. [Cited by 60]
  18. Three networks are compared for low false alarm stock trend predictions. Short-term trends, particularly attractive for neural network analysis, can be used profitably in scenarios such as option trading, but only with significant risk. Therefore, we focus on limiting false alarms, which improves the risk/reward ratio by preventing losses. To predict stock trends, we exploit time delay, recurrent, and probabilistic neural networks (TDNN, RNN, and PNN, respectively), utilizing conjugate gradient and multistream extended Kalman filter training for TDNN and RNN. We also discuss different predictability analysis techniques and perform an analysis of predictability based on a history of daily closing price. Our results indicate that all the networks are feasible, the primary preference being one of convenience.
    The three networks showed comparable results.
    Predicting short term stock trends based on history of daily closing prices is possible using any of the three different networks discussed here. SaadProkhorovWunsch98
  19. TKACZ, G., 2001. Neural Network Forecasting of Canadian GDP Growth.. International Journal of Forecasting. [Cited by 35]
  20. The objective of this paper is to improve the accuracy of financial and monetary forecasts of Canadian output growth by using leading indicator neural network models. We find that neural networks yield statistically lower forecast errors for the year-over-year growth rate of real GDP relative to linear and univariate models. However, such forecast improvements are less notable when forecasting quarterly real GDP growth. Neural networks are unable to outperform a naive no-change model. More pronounced non-linearities at the longer horizon is consistent with the possible asymmetric effects of monetary policy on the real economy.
  21. KUAN, C.M. and T. LIU, 1995. Forecasting exchange rates using feedforward and recurrent neural networks. Journal of Applied Econometrics. [Cited by 77]
  22. In this paper we investigate the out-of-sample forecasting ability of feedforward and recurrent neural networks based on empirical foreign exchange rate data. A two-step procedure is proposed to construct suitable networks, in which networks are selected based on the predictive stochastic complexity (PSC) criterion, and the selected networks are estimated using both recursive Newton algorithms and the method of nonlinear least squares. Our results show that PSC is a sensible criterion for selecting networks and for certain exchange rate series, some selected network models have significant market timing ability and/or significantly lower out-of-sample prediction error relative to the random walk model.
  23. SWANSON, N.R. and H. WHITE, 1995. A Model-Selection Approach to Assessing the Information in the Term Structure Using Linear Models …. Journal of Business & Economic Statistics. [Cited by 76]
  24. We take a model selection approach to the question of whether forward interest rates are useful in predicting future spot rates, using a variety of out-of-sample forecast-based model selection criteria: forecast mean squared error, forecast direction accuracy, and forecast-based trading system profitability. We also examine the usefulness of a class of novel prediction models called "artificial neural networks," and investigate the issue of appropriate window sizes for rolling-window-based prediction methods. Results indicate that the premium of the forward rate over the spot rate helps to predict the sign of future changes in the interest rate. Further, model selection based on an in-sample Schwarz Information Criterion (SIC) does not appear to be a reliable guide to out-of-sample performance, in the case of short-term interest rates. Thus, the in-sample SIC apparently fails to offer a convenient shortcut to true out-of-sample performance measures.
  25. WIDROW, B., D.E. RUMELHART and M.A. LEHR, 1994. Neural networks: applications in industry, business and science. Communications of the ACM. [Cited by 83]
  26. Just four years ago, the only widely reported commercial application of neural network technology outside the financial industry was the airport baggage explosive detection system [27] developed at Science Applications International Corporation (SAIC). Since that time scores of industrial and commercial applications have come into use, but the details of most of these systems ate considered corporate secrets and are shrouded in secrecy. This hastening trend is due in part to the availability of an increasingly wide array of dedicated neural network hardware. This hardware is either in the form of accelerator cards for PCs and workstations or a large number of integrated circuits implementing digital and analog neural networks either currently available or in the final stages of design. An assortment of tools and development systems is provided by the manufacturers of most of these products.
  27. GARCIA, R. and R. GENCAY, 2000. Pricing and hedging derivative securities with neural networks and a homogeneity hint. Journal of Econometrics. [Cited by 39]
  28. We estimate a generalized option pricing formula that has a functional shape similar to the usual Black-Scholes formula by a feedforward neural network model. This functional shape is obtained when the option pricing function is homogeneous of degree one with respect to the underlying asset price (St) and the strike price (K). We show that pricing accuracy gains can be made by exploiting this generalized Black-Scholes shape. Instead of setting up a learning network mapping the ratio St/K and the time to maturity (t) directly into the derivative price, we break down the pricing function into two parts, one controlled by the ratio St/K, the other one by a function of time to maturity. The results indicate that the homogeneity hint always reduces the out-of-sample mean squared prediction error compared with a feedforward neural network with no hint. Both feedforward network models, with and without the hint, provide similar delta-hedging errors that are small relative to the hedging performance of the Black-Scholes model. However, the model with hint produces a more stable hedging performance.
  29. BACK, A.D. and A.S. WEIGEND, 1997. A first application of independent component analysis to extracting structure from stock returns. International Journal of Neural Systems. [Cited by 58]
  30. This paper explores the application of a signal processing technique known as independent component analysis (ICA) or blind source separation to multivariate financial time series such as a portfolio of stocks. The key idea of ICA is to linearly map the observed multivariate time series into a new space of statistically independent components (ICs). We apply ICA to three years of daily returns of the 28 largest Japanese stocks and compare the results with those obtained using principal component analysis. The results indicate that the estimated ICs fall into two categories, (i) infrequent large shocks (responsible for the major changes in the stock prices), and (ii) frequent smaller fluctuations (contributing little to the overall level of the stocks). We show that the overall stock price can be reconstructed surprisingly well by using a small number of thresholded weighted ICs. In contrast, when using shocks derived from principal components instead of independent components, the reconstructed price is less similar to the original one. ICA is shown to be a potentially powerful method of analyzing and understanding driving mechanisms in financial time series. The application to portfolio optimization is described in Chin and Weigend (1998).
  31. WHITE, H., 1988. Economic prediction using neural networks: The case of IBM daily stock returns. [Cited by 112]
  32. Abstract: "This paper reports some results of an on-going project using neural network modelling and learning techniques to search for and decode nonlinear regularities in asset price movements. We focus here on the case of IBM common stock daily returns. Having to deal with the salient features of economic data highlights the role to be played by statistical inference and requires modifications to standard learning techniques which may prove useful in other contexts."
  33. WEST, D., 2000. Neural network credit scoring models. Computers & Operations Research. [Cited by 35]
  34. This paper investigates the credit scoring accuracy of "ve neural network models: multilayer perceptron, mixture-of-experts, radial basis function, learning vector quantization, and fuzzy adaptive resonance. The neural network credit scoring models are tested using 10-fold crossvalidation with two real world data sets. Results are benchmarked against more traditional methods under consideration for commercial applications including linear discriminant analysis, logistic regression, k nearest neighbor, kernel density estimation, and decision trees. Results demonstrate that the multilayer perceptron may not be the most accurate neural network model, and that both the mixture-of-experts and radial basis function neural network models should be considered for credit scoring applications. Logistic regression is found to be the most accurate of the traditional methods.
  35. HUANG, Z., et al., 2004. Credit rating analysis with support vector machines and neural networks: a market comparative study. Decision Support Systems. [Cited by 9]
  36. Corporate credit rating analysis has attracted lots of research interests in the literature. Recent studies have shown that Artificial Intelligence (AI) methods achieved better performance than traditional statistical methods. This article introduces a relatively new machine learning technique, support vector machines (SVM), to the problem in attempt to provide a model with better explanatory power. We used backpropagation neural network (BNN) as a benchmark and obtained prediction accuracy around 80% for both BNN and SVM methods for the United States and Taiwan markets. However, only slight improvement of SVM was observed. Another direction of the research is to improve the interpretability of the AI-based models. We applied recent research results in neural network model interpretation and obtained relative importance of the input financial variables from the neural network models. Based on these results, we conducted a market comparative analysis on the differences of determining factors in the United States and Taiwan markets.
  37. CHAKRABORTY, K., et al., 1990. Forecasting the behavior of multivariate time series using neural networks. [Cited by 91]
  38. This paper presents a neural network approach to multivariate time-series analysis. Real world observations of flour prices in three cities have been used as a benchmark in our experiments. Feedforward connectionist networks have been designed to model flour prices over the period from August 1972 to November 1980 for the cities of Buffalo, Minneapolis, and Kansas City. Remarkable success has been achieved in training the networks to learn the price curve for each of these cities, and thereby to make accurate price predictions. Our results show that the neural network approach leads to better predictions than the autoregressive moving average (ARMA) model of Tiao and Tsay [TiTs89]. Our method is not problem-specific, and can be applied to other problems in the field of dynamical system modeling, recognition, prediction and control.
  39. COATS, P. and L.F. FANT, 1993. Recognizing financial distress patterns using a neural network tool. Financial Management. [Cited by 73]
  40. Financial Management, vol., 22, no 3, automne 1993, p. 142-155
  41. WERBOS, Paul J., 1988. Generalization of backpropagation with application to a recurrent gas market model.. Neural Networks. [Cited by 102]
  42. Backpropagation is often viewed as a method for adapting artificial neural networks to classify patterns. Based on parts of the book by Rumelhart and colleagues, many authors equate backpropagation with the generalized delta rule applied to fully-connected feedforward networks. This paper will summarize a more general formulation of backpropagation, developed in 1974, which does more justice to the roots of the method in numerical analysis and statistics, and also does more justice to creative approaches expressed by neural modelers in the past year or two. It will discuss applications of backpropagation to forecasting over time (where errors have been halved by using methods other than least squares), to optimization, to sensitivity analysis, and to brain research. This paper will go on to derive a generalization of backpropagation to recurrent systems (which input their own output), such as hybrids of perceptron-style networks and Grossberg/Hopfield networks. Unlike the proposal of Rumelhart, Hinton, and Williams, this generalization does not require the storage of intermediate iterations to deal with continuous recurrence. This generalization was applied in 1981 to a model of natural gas markets, where it located sources of forecast uncertainty related to the use of least squares to estimate the model parameters in the first place.
  43. GOONATILAKE, Suran and Philip TRELEAVEN, 1995. Intelligent systems for finance and business. Chichester; New York: Wiley. [Cited by 55]
  44. BOOK
  45. KIMOTO, T., et al., 1990. Stock market prediction system with modular neural networks. Proceedings of the International Joint Conference on Neural …. [Cited by 77]
  46. This paper discusses a buying and selling timing prediction system for stocks on the Tokyo Stock Exchange and analysis of internal representation. It is based on modular neural networks[1][1]. We developed a number of learning algorithms and prediction methods for the TOPIX(Tokyo Stock Exchange Prices Indexes) prediction system. The prediction system achieved accurate predictions and the simulation on stocks trading showed an excellent profit. The prediction system was developed by Fujitsu and Nikko Securities. AbstractPlus: A discussion is presented of a buying- and selling-time prediction system for stocks on the Tokyo Stock Exchange and the analysis of internal representation. The system is based on modular neural networks. The authors developed a number of learning algorithms and prediction methods for the TOPIX (Tokyo Stock Exchange Prices Indexes) prediction system. The prediction system achieved accurate predictions, and the simulation on stocks trading showed an excellent profit
  47. WALCZAK, S., 2001. An empirical analysis of data requirements for financial forecasting with neural networks. Journal of Management Information Systems. [Cited by 22]
  48. Neural networks have been shown to be a promising tool for forecasting financial time series. Several design factors significantly impact the accuracy of neural network forecasts. These factors include selection of input variables, architecture of the network, and quantity of training data. The questions of input variable selection and system architecture design have been widely researched, but the corresponding question of how much information to use in producing high-quality neural network models has not been adequately addressed. In this paper, the effects of different sizes of training sample sets on forecasting currency exchange rates are examined. It is shown that those neural networks-given an appropriate amount of historical knowledge-can forecast future currency exchange rates with 60 percent accuracy, while those neural networks trained on a larger training set have a worse forecasting performance. In addition to higher-quality forecasts, the reduced training set sizes reduce development cost and time.
  49. REFENES, A.N., A. ZAPRANIS and G. FRANCIS, 1994. Stock performance modeling using neural networks: A comparative study with regression models. Neural Networks. [Cited by 56]
  50. We examine the use of neural networks as an alternative to classical statistical techniques for forecasting within the framework of the APT (arbitrage pricing theory) model for stock ranking. We show that neural networks outperform these statistical techniques in forecasting accuracy terms, and give better model fitness in-sample by one order of magnitude. We identify intervals for the network parameter values for which these performance figures are statistically stable. Neural networks have been criticised for not being able to provide an explanation of how they interact with their environment and how they reach an outcome. We show that by using sensitivity analysis, neural networks can provide a reasonable explanation of their predictive behaviour and can model their environment more convincingly than regression models.
  51. KIM, K. and I. HAN, 2000. Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert Systems with Applications. [Cited by 26] (4.69/year)
  52. This paper proposes genetic algorithms (GAs) approach to feature discretization and the determination of connection weights for artificial neural networks (ANNs) to predict the stock price index. Previous research proposed many hybrid models of ANN and GA for the method of training the network, feature subset selection, and topology optimization. In most of these studies, however, GA is only used to improve the learning algorithm itself. In this study, GA is employed not only to improve the learning algorithm, but also to reduce the complexity in feature space. GA optimizes simultaneously the connection weights between layers and the thresholds for feature discretization. The genetically evolved weights mitigate the well-known limitations of the gradient descent algorithm. In addition, globally searched feature discretization reduces the dimensionality of the feature space and eliminates irrelevant factors. Experimental results show that GA approach to the feature discretization model outperforms the other two conventional models. Expert Systems with Applications, Volume 19, Number 2, August 2000, pp. 125-132(8) ME: The study compares a GA approach to feature discretization, the linear transformation with the backpropagation neural network and the linear transformation with ANN trained by GA and found that the first method outperfrmed the other two conventional models.
  53. Andrea Beltratti, Sergio Margarita and Pietro Terna, 1996. Neural networks for economic and financial modelling. [Cited by 44]
  54. BOOK
  55. ODOM, M.D. and R. SHARDA, 1990. A neural network model for bankruptcy prediction. Neural Networks, 1990., 1990 IJCNN International Joint …. [Cited by 70]
  56. A neural network model is developed for prediction of bankruptcy, and it is tested using financial data from various companies. The same set of data is analyzed using a more traditional method of bankruptcy prediction, multivariate discriminant analysis. A comparison of the predictive abilities of both the neural network and the discriminant analysis method is presented. The results show that neural networks might be applicable to this problem.
  57. FERNANDEZ-RODRIGUEZ, F., C. GONZALEZ-MARTEL and S. , 2000. On the profitability of technical trading rules based on artificial neural networks: Evidence from the Madrid stock market. Economics Letters. [Cited by 23]
  58. In this paper we investigate the profitability of a simple technical trading rule based on Artificial Neural Networks (ANNs). Our results, based on applying this investment strategy to the General Index of the Madrid Stock Market, suggest that, in absence of trading costs, the technical trading rule is always superior to a buy-and-hold strategy for both ‘‘bear’’ market and ‘‘stable’’ market episodes. On the other hand, we find that the buy-and-hold strategy generates higher returns than the trading rule based on ANN only for a ‘‘bull’’ market subperiod.



[number in square brackets indicates number of Google results]