Mining for critical stock price movements using temporal power laws and integrated autoregressive models
Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet
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in: International Journal of Information and Decision Sciences, Jahrgang 6, Nr. 3, 2014, S. 211 - 225.
Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet
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TY - JOUR
T1 - Mining for critical stock price movements using temporal power laws and integrated autoregressive models
AU - Jacobs, Jürgen
PY - 2014
Y1 - 2014
N2 - This paper investigates the practical applicability of the log-periodic power law model to forecast large drawdowns of stock prices and compares its performance with the performance of the classical integrated autoregressive time series model. Both models are fitted to the daily closing prices of the Dow Jones index. In the case of the log-periodic power law model an alarm is issued if any fit conforming to theoretically motivated parameter restrictions can be found. In the case of the integrated autoregressive model an alarm is issued if structural breaks are observed at the end of the fit interval. It is shown that both models are successful in predicting upcoming stock market crises. The log-periodic power law model is superior in filtering out extreme drawdowns. However, its performance is highly dependent on the fit procedure.
AB - This paper investigates the practical applicability of the log-periodic power law model to forecast large drawdowns of stock prices and compares its performance with the performance of the classical integrated autoregressive time series model. Both models are fitted to the daily closing prices of the Dow Jones index. In the case of the log-periodic power law model an alarm is issued if any fit conforming to theoretically motivated parameter restrictions can be found. In the case of the integrated autoregressive model an alarm is issued if structural breaks are observed at the end of the fit interval. It is shown that both models are successful in predicting upcoming stock market crises. The log-periodic power law model is superior in filtering out extreme drawdowns. However, its performance is highly dependent on the fit procedure.
KW - Sustainability sciences, Management & Economics
KW - Autoregressive time series
KW - Log-periodic power law
KW - Speculative bubble
KW - Stock market crash
U2 - 10.1504/IJIDS.2014.064447
DO - 10.1504/IJIDS.2014.064447
M3 - Journal articles
VL - 6
SP - 211
EP - 225
JO - International Journal of Information and Decision Sciences
JF - International Journal of Information and Decision Sciences
SN - 1756-7017
IS - 3
ER -