Predicting the Stock Market

This paper presents a tuturial introduction to predictions of stock time series. The various approaches of technical and fundamental analysis is presented and the prediction problem is formulated as a special case of inductive learning. The problems with performance evaluation of near-random-walk processes are illustrated with examples together with guidelines for avoiding the risk of data-snooping. The connections to concepts like "the bias/variance dilemma", overtraining and model complexity are further covered. Existing benchmarks and testing metrics are surveyed and some new measures are introduced.

[1]  Donald B. Keim,et al.  Chapter 17 On the predictability of common stock returns: World-wide evidence , 1999, Finance.

[2]  D. Signorini,et al.  Neural networks , 1995, The Lancet.

[3]  Blake LeBaron,et al.  Evaluating Neural Network Predictors by Bootstrapping , 1994 .

[4]  David E. Rumelhart,et al.  Weight elimination and effective network size , 1994, COLT 1994.

[5]  Andreas S. Weigend,et al.  The Future of Time Series: Learning and Understanding , 1993 .

[6]  Gregory J. Wolff,et al.  Optimal Brain Surgeon and general network pruning , 1993, IEEE International Conference on Neural Networks.

[7]  Elie Bienenstock,et al.  Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.

[8]  D. Lowe,et al.  Time series prediction by adaptive networks: a dynamical systems perspective , 1991 .

[9]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[10]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..

[11]  Geoffrey E. Hinton.  Connectionist Learning Procedures , 1989, Artif. Intell..

[12]  H. White,et al.  Economic prediction using neural networks: the case of IBM daily stock returns , 1988, IEEE 1988 International Conference on Neural Networks.

[13]  S. Yakowitz.  NEAREST‐NEIGHBOUR METHODS FOR TIME SERIES ANALYSIS , 1987 .

[14]  J. Rissanen,et al.  Modeling By Shortest Data Description* , 1978, Autom..

[15]  Seymour Geisser,et al.  The Predictive Sample Reuse Method with Applications , 1975 .

[16]  H. Akaike.  A new look at the statistical model identification , 1974 .

[17]  P. Wedin,et al.  Regularization tools for training large feed-forward neural networks using automatic differentiation ∗ , 1998 .

[18]  John Moody,et al.  Architecture Selection Strategies for Neural Networks: Application to Corporate Bond Rating Predicti , 1995, NIPS 1995.

[19]  G. Andersson.  Volatility forecasting and efficiency of the Swedish call options market , 1995 .

[20]  John Moody,et al.  Prediction Risk and Architecture Selection for Neural Networks , 1994 .

[21]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[22]  Eric A. Wan,et al.  Time series prediction by using a connectionist network with internal delay lines , 1993 .

[23]  Andreas Weigend,et al.  On overfitting and the effective number of hidden units , 1993 .

[24]  B. LeBaron.  Technical Trading Rules and Regime Shifts in Foreign Exchange , 1991 .

[25]  D. Rumelhart,et al.  Generalization by weight-elimination applied to currency exchange rate prediction , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[26]  David E. Rumelhart,et al.  Predicting the Future: a Connectionist Approach , 1990, Int. J. Neural Syst..

[27]  Christian Lebiere,et al.  The Cascade-Correlation Learning Architecture , 1989, NIPS.

[28]  Yann LeCun,et al.  Optimal Brain Damage , 1989, NIPS.

[29]  B. Efron.  The jackknife, the bootstrap, and other resampling plans , 1987 .

[30]  Christopher M. Bishop,et al.  Current address: Microsoft Research, , 2022 .