Abstract
A systematic algorithm based on data smoothing and the Bayes' theorem is proposed to predict the increase or decrease of a financial time series, which can be used in trading financial products when decisions need to be made between long and short positions. The algorithm compares the observed product values with those in the history to find a similar pattern with the maximum likelihood, based on which future up-down movement of the value is predicted. Empirical studies with S&P 500 Index and stocks of several companies show that the proposed methodology improves the rate of the correct predictions by about 30% or more, relative to naive prior probability or moving average convergence divergence predictions.
| Original language | English |
|---|---|
| Pages (from-to) | 197-210 |
| Number of pages | 14 |
| Journal | Economic Computation and Economic Cybernetics Studies and Research |
| Volume | 51 |
| Issue number | 3 |
| Publication status | Published - 2017 Jan 1 |
Keywords
- Bayes' theorem
- Empirical study
- Financial time series
- Maximum likelihood estimation
- Numerical prediction method
- Smoothing
ASJC Scopus subject areas
- Economics and Econometrics
- Computer Science Applications
- Applied Mathematics
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Cite this
Moon, K. S., Kim, H., & Kim, H. (2017). A prediction methodology for the change of the values of financial products. Economic Computation and Economic Cybernetics Studies and Research, 51(3), 197-210.