Predicting Daily Probability Distributions of S&p500 Returns

  title={Predicting Daily Probability Distributions of S&p500 Returns},
  author={Andreas S. Weigend},
Most approaches in forecasting merely try to predict the next value of the time series. In contrast, this paper presents a framework to predict the full probability distribution. It is expressed as a mixture model: the dynamics of the individual states is modeled with so-called \experts" (potentially nonlinear neural networks), and the dynamics between the states is modeled using a hidden Markov approach. The full density predictions are obtained by a weighted superposi-tion of the individual… CONTINUE READING
Highly Cited
This paper has 53 citations. REVIEW CITATIONS


Publications citing this paper.
Showing 1-10 of 28 extracted citations

53 Citations

Citations per Year
Semantic Scholar estimates that this publication has 53 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 33 references

An inequality and associated maximization technique in statistical estimation

L. E. Baum
View 11 Excerpts
Highly Influenced

Time Series Prediction: Forecasting

A. S. Weigend, N. A. Gershenfeld
View 3 Excerpts
Highly Influenced

The combination of forecasts, Operations Research

J. M. Bates, C.W.J. Granger
View 3 Excerpts
Highly Influenced

Market risk using hidden Markov density predictions

Chin, A.S.E. andWeigend
View 1 Excerpt

Input-output HMMs for sequence processing

IEEE Trans. Neural Networks • 1996
View 1 Excerpt

Similar Papers

Loading similar papers…