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Mining for critical stock price movements using temporal power laws and integrated autoregressive models

Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet

Standard

Mining for critical stock price movements using temporal power laws and integrated autoregressive models. / Jacobs, Jürgen.
in: International Journal of Information and Decision Sciences, Jahrgang 6, Nr. 3, 2014, S. 211 - 225.

Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet

Harvard

Jacobs, J 2014, 'Mining for critical stock price movements using temporal power laws and integrated autoregressive models', International Journal of Information and Decision Sciences, Jg. 6, Nr. 3, S. 211 - 225. https://doi.org/10.1504/IJIDS.2014.064447

APA

Jacobs, J. (2014). Mining for critical stock price movements using temporal power laws and integrated autoregressive models. International Journal of Information and Decision Sciences, 6(3), 211 - 225. https://doi.org/10.1504/IJIDS.2014.064447

Vancouver

Jacobs J. Mining for critical stock price movements using temporal power laws and integrated autoregressive models. International Journal of Information and Decision Sciences. 2014;6(3):211 - 225. doi: 10.1504/IJIDS.2014.064447

Bibtex

@article{efa956eaec0b490b9b0502d1c4e45982,
title = "Mining for critical stock price movements using temporal power laws and integrated autoregressive models",
abstract = "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.",
keywords = "Sustainability sciences, Management & Economics, Autoregressive time series, Log-periodic power law, Speculative bubble, Stock market crash",
author = "J{\"u}rgen Jacobs",
year = "2014",
doi = "10.1504/IJIDS.2014.064447",
language = "English",
volume = "6",
pages = "211 -- 225",
journal = " International Journal of Information and Decision Sciences",
issn = "1756-7017",
publisher = "Inderscience Enterprises Ltd",
number = "3",

}

RIS

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 -

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