Forecasting the critical points of stock markets’ indices using log-periodic power law
Languages of publication
This article presents Log-Periodic Power Law and considers its usefulness as a forecasting tool on the financial markets. One of the estimation methods of this function was presented and six models were built, based on time series of the DJIA and the WIG20. Estimated models were utilized to predict crashes of those indices. The variations between the actual values of analyzed indices observed in the forecasted period and values observed in the actual period of their downturn were assembled to assess the results. In three cases, relative errors were below 5%; and in three cases, they were higher than 15%.
- Brée D.S., Joseph N.L. (2010), Fitting the Log Periodic Power Law to financial crashes: a critical analysis, arXiv:1002.1010v1.
- Feigenbaum J.A., Freund P.G.O. (1996), Discrete scaling in stock markets before crashes, International Journal of Modern Physics 10: 3737–3745.
- Jacobsson E. (2009), How to Predict Crashes in Financial Markets with the Log-Periodic Power Law, Stockholm University, Stockholm.
- Johansen A., Ledoit O., Sornette D. (2000), Crashes as critical points, International Journal of Theoretical and Applied Finance 3: 219–255.
- Lubiński M., Analiza koniunktury i badanie rynków, Elipsa, Warszawa 2004.
- Sornette D. (2002), Why Stock Markets Crash: Critical Events in Complex Financial Systems, Princeton University Press, Princeton.
- Sornette D., Sammis C.G. (1995), Complex critical exponents from renormalization group theory of earthquakes: Implications for earthquake predictions, Journal de Physique (France) I, 5: 607–619.
Publication order reference