Het effect van conflicten op financiële markten met hoge geweldpleging
Na 9/11, de financiële de New York Stock Exchange en Dow Jones bleef gesloten voor meerdere dagen, de langste shutdown sinds 1933. Als de markt uiteindelijk toch opende, de markt viel met 684 punten, een val van 7,1%, het grootste verlies in ruilgeschiedenis voor een handelsdag. Dit gegeven doet de vraag oppoppen of elke markt zo reageert op een terroristische aanval of een andere politiek conflict.
Het resultaat van een terroristische aanval op een Europees land
Er zijn rijkelijke studies uitgevoerd in het verleden die bewijs leverden dat de financiële markten significant negatief reageren na een terroristische aanval (Rigobon & Sack, 2005; Schneider & Troeger, 2006 and Zussman, Zussman, & Nielsen, 2008). De meeste studies concentreren zich echter op lokale markten en afzonderlijke bedrijven. Zo zijn er interessante studies uitgevoerd die bewijs leverden voor een significante positieve impact op de wapenindustrie na een terroristische aanval (Apergis & Apergis, 2016). In dit onderzoek wordt er echter bewijs gevonden dat een terroristische aanval geen significant effect heeft op de Europese totale markt. Dit kan enerzijds worden verklaard dat de Europese markt zeer gediversifieerd is of anderzijds dat de Europese financiële markt toch niet zo geïntegreerd is.
Het effect van conflicten op de Indische financiële markt
Aangezien er veel bewijs is dat politieke conflicten de lokale (westelijke) markten significant negatief beïnvloeden is het interessant om markten te onderzoeken waar geweldpleging vaker voorkomt. In westelijke echter komen politieke conflicten en terroristische niet vaak voor. Daarom disconteren investeerders de markten niet naargelang. Een land waar er wel veel spanning bestaat op deze vlakken is Israël. Voor deze financiële markt is echter bewijs gevonden dat investeerder weldegelijk weten dat er een reële kans is op een aanval en dus de marktprijs navenant disconteren (Yonamine ,2013). Een gelijkaardig land is India en staat op de 18e plaats van de gevaarlijkste plaatsen om zaken te doen volgens de Conflict and Political Violence Index in 2014 (Dhoot, 2014). Dit is dus een land dat niet zo extreem is als Israël maar ook niet zo vredevol is als de meeste westerse landen. In dit onderzoek vinden we echter een significant negatief effect op de Indische financiële markt met de hoeveelheid van de conflicten. Dit wilt dus zeggen dat de Indische investeerder de Indische index niet disconteert en negatief reageert wanneer er zich conflicten voordoen. Het onderzoek geeft dus weer dat de conflicten weldegelijk relevant blijft voor bepaalde landen met hoge varianties van geweldpleging, maar niet voor allemaal.
Ahmed, R. A., & Shabri, A. Bin. 2014. Daily crude oil price forecasting model using arima, generalized
autoregressive conditional heteroscedastic and Support Vector Machines. American Journal of
Applied Sciences. https://doi.org/10.3844/ajassp.2014.425.432.
Alberg, D., Shalit, H., & Yosef, R. 2008. Estimating stock market volatility using asymmetric GARCH
models. Applied Financial Economics. https://doi.org/10.1080/09603100701604225.
Apergis, E., & Apergis, N. 2016. The 11/13 Paris terrorist attacks and stock prices: The case of the
international defense industry. Finance Research Letters.
https://doi.org/10.1016/j.frl.2016.03.002.
Bishop, C. 2013. Mixture Density Networks. Journal of Chemical Information and Modeling.
https://doi.org/10.1017/CBO9781107415324.004.
Bollerslev, T. 1986. Generalized Autorregresive Conditional Hetersoskedasticity. Journal of
Econometrics. https://doi.org/10.1016/0304-4076(86)90063-1.
Brounen, D., & Derwall, J. 2010. The impact of terrorist attacks on international stock markets.
European Financial Management. https://doi.org/10.1111/j.1468-036X.2009.00502.x.
Brown, S. J., & Warner, J. B. 1985. Using daily stock returns. The case of event studies. Journal of
Financial Economics. https://doi.org/10.1016/0304-405X(85)90042-X.
Charles, A., & Darné, O. 2006. Large shocks and the September 11th terrorist attacks on international
stock markets. Economic Modelling. https://doi.org/10.1016/j.econmod.2006.03.008.
Chojnacki, S., Ickler, C., Spies, M., & Wiesel, J. 2012. Event Data on Armed Conflict and Security: New
Perspectives, Old Challenges, and Some Solutions. International Interactions.
https://doi.org/10.1080/03050629.2012.696981.
Clemen, R. T. 1989. Combining forecasts: A review and annotated bibliography. International Journal
of Forecasting. https://doi.org/10.1016/0169-2070(89)90012-5.
Davis, M. 2017. How September 11 Affected The U.S. Stock Market. Investopedia.
https://www.investopedia.com/financial-edge/0911/how-september-11-affec….
aspx.
Dhankar, R. S., & Chakraborty, M. 2007. Non-linearities and GARCH effects in the emerging stock
markets of South Asia. Vikalpa. https://doi.org/10.1177/0256090920070303.
Dhoot, V. 2014. India does better on global conflict, political violence index. The Economic Times.
https://economictimes.indiatimes.com/news/politics-and-nation/india-doe…-
political-violence-index/articleshow/34775107.cms.
Dunis, C. L., & Huang, X. 2005. Forecasting and Trading Currency Volatility: An Application of
Recurrent Neural Regression and Model Combination. Applied Quantitative Methods for
Trading and Investment. https://doi.org/10.1002/0470013265.ch4.
Dunis, C. L., Laws, J., & Evans, B. 2016. Modelling and trading the gasoline crack spread: A non-linear
story. Derivatives and Hedge Funds. https://doi.org/10.1057/9781137554178.
Dunis, C. L., Laws, J., & Karathanasopoulos, A. 2012. Modelling and trading the Greek stock market
with hybrid ARMA-neural network models. Springer Optimization and Its Applications.
https://doi.org/10.1007/978-1-4614-3773-4_4.
Dunis, C. L., Laws, J., & Sermpinis, G. 2010. Modelling and trading the EUR/USD exchange rate at the
ECB fixing. European Journal of Finance. https://doi.org/10.1080/13518470903037771.
Eck, K. 2012. In data we trust? A comparison of UCDP GED and ACLED conflict events datasets.
Cooperation and Conflict. https://doi.org/10.1177/0010836711434463.
Elshendy, M., Fronzetti Colladon, A., Battistoni, E., & Gloor, P. A. 2018. Using four different online
media sources to forecast the crude oil price. Journal of Information Science.
https://doi.org/10.1177/0165551517698298.
Engle, R. F. 1982. Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of
United Kingdom Inflation. Econometrica. https://doi.org/10.2307/1912773.
Fulcher, J., Zhang, M., & Xu, S. 2006. Application of Higher-Order Neural Networks to Financial Time-
Series Prediction. Artificial Neural Networks in Finance and Manufacturing.
https://doi.org/10.4018/978-1-59140-670-9.
Galla, D., & Burke, J. 2018. Predicting social unrest using GDELT. Lecture Notes in Computer
Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in
Bioinformatics). https://doi.org/10.1007/978-3-319-96133-0_8.
Galtung, J., & Ruge, M. H. 1965. The structure of foreign news : the presentation of the Congo, Cuba
and Cyprus crisis in four Norwegian Newspapers. Journal of Peace Reasearch.
https://doi.org/10.1159/0000102124.
Gilbert, E., & Karahalios, K. 2010. Widespread Worry and the Stock Market. ICWSM.
https://doi.org/10.1.1.220.9231.
Giles, C. L., & Maxwell, T. 1987. Learning, invariance, and generalization in high-order neural
networks. Applied Optics. https://doi.org/10.1364/AO.26.004972.
Hippert, H. S., Pedreira, C. E., & Souza, R. C. 2000. Combining neural networks and ARIMA models
for hourly temperature forecast. Proceedings of the IEEE-INNS-ENNS International Joint
Conference on Neural Networks, 2000 (IJCNN 2000) , Vol. IV.
https://doi.org/10.1136/emermed-2013-203373.
Kamijo, K., & Tanigawa, T. 1990. Stock price pattern recognition-a recurrent neural network approach.
1990 IJCNN International Joint Conference on Neural Networks.
https://doi.org/10.1109/IJCNN.1990.137572.
King, G., & Lowe, W. 2003. An Automated Information Extraction Tool for International Conflict Data
with Performance as Good as Human Coders: A Rare Events Evaluation Design. International
Organization. https://doi.org/10.1017/S0020818303573064.
Kolaric, S., & Schiereck, D. 2016. Are stock markets efficient in the face of fear? Evidence from the
terrorist attacks in Paris and Brussels. Finance Research Letters.
https://doi.org/10.1016/j.frl.2016.05.003.
Kwak, H., & An, J. 2014. A First Look at Global News Coverage of Disasters by Using the GDELT
Dataset. International Conference on Social Informatics, 300–308.
Leblang, D., & Mukherjee, B. 2005. Government partisanship, elections, and the stock market:
Examining American and British stock returns, 1930-2000. American Journal of Political
Science. https://doi.org/10.1111/j.1540-5907.2005.00155.x.
Leetaru, K., & Schrodt, P. A. 2012. GDELT: Global Data on Events, Location and Tone. International
Studies Association.
Lindemann, A., Dunis, C. L., & Lisboa, P. 2005. Level estimation, classification and probability
distribution architectures for trading the EUR/USD exchange rate. Neural Computing and
Applications. https://doi.org/10.1007/s00521-004-0462-8.
Makridakis, S. 1989. Why combining works? International Journal of Forecasting.
https://doi.org/10.1016/0169-2070(89)90017-4.
Makridakis, S., Andersen, A., Carbone, R., Fildes, R., Hibon, M., et al. 1982. The accuracy of
extrapolation (time series) methods: Results of a forecasting competition. Journal of
Forecasting. https://doi.org/10.1002/for.3980010202.
Mohammadi, H., & Su, L. 2010. International evidence on crude oil price dynamics: Applications of
ARIMA-GARCH models. Energy Economics. https://doi.org/10.1016/j.eneco.2010.04.009.
Newbold, P., & Granger, C. W. J. 1974. Experience with Forecasting Univariate Time Series and the
Combination of Forecasts. Journal of the Royal Statistical Society. Series A (General).
https://doi.org/10.2307/2344546.
O‟Brien, S. P. 2010. Crisis early warning and decision support: Contemporary approaches and
thoughts on future research. International Studies Review. https://doi.org/10.1111/j.1468-
2486.2009.00914.x.
O‟Loughlin, J., Witmer, F. D. W., Linke, A. M., & Thorwardson, N. 2010. Peering into the Fog of War:
The Geography of the WikiLeaks Afghanistan War Logs, 2004-2009. Eurasian Geography and
Economics. https://doi.org/10.2747/1539-7216.51.4.472.
Palm, F. C., & Zellner, A. 1992. To combine or not to combine? issues of combining forecasts. Journal
of Forecasting. https://doi.org/10.1002/for.3980110806.
Rigobon, R., & Sack, B. 2005. The effects of war risk on US financial markets. Journal of Banking
and Finance. https://doi.org/10.1016/j.jbankfin.2004.06.040.
Schneider, G., & Troeger, V. E. 2006. War and the world economy: Stock market reactions to
international conflicts. Journal of Conflict Resolution.
https://doi.org/10.1177/0022002706290430.
Schrodt, P. A. 2012. Precedents, Progress, and Prospects in Political Event Data. International
Interactions. https://doi.org/10.1080/03050629.2012.697430.
Tenti, P. 1996. Forecasting foreign exchange rates using recurrent neural networks. Applied Artificial
Intelligence. https://doi.org/10.1080/088395196118434.
Terui, N., & Van Dijk, H. K. 2002. Combined forecasts from linear and nonlinear time series models.
International Journal of Forecasting. https://doi.org/10.1016/S0169-2070(01)00120-0.
Tiňo, P., Schittenkopf, C., & Dorffner, G. 2001. Financial volatility trading using recurrent neural
networks. IEEE Transactions on Neural Networks. https://doi.org/10.1109/72.935096.
Tseng, F. M., Yu, H. C., & Tzeng, G. H. 2002. Combining neural network model with seasonal time
series ARIMA model. Technological Forecasting and Social Change.
https://doi.org/10.1016/S0040-1625(00)00113-X.
Weidmann, N. B., & Ward, M. D. 2010. Predicting conflict in space and time. Journal of Conflict
Resolution. https://doi.org/10.1177/0022002710371669.
Winkler, R. L. 1989. Combining forecasts: A philosophical basis and some current issues.
International Journal of Forecasting. https://doi.org/10.1016/0169-2070(89)90018-6.
Wu, H. D. 2000. Systemic determinants of international news coverage: A comparison of 38 countries.
Journal of Communication. https://doi.org/10.1111/j.1460-2466.2000.tb02844.x.
Yermack, D. 1997. Good timing: CEO stock option awards and company news announcements.
Journal of Finance. https://doi.org/10.1111/j.1540-6261.1997.tb04809.x.
Yonamine, J. E. 2013. A nuanced study of political conflict using the Global Datasets of Events
Location and Tone (GDELT) dataset. ProQuest Dissertations and Theses.
Zammit-Mangion, A., Dewar, M., Kadirkamanathan, V., & Sanguinetti, G. 2012. Point process
modelling of the Afghan War Diary. Proceedings of the National Academy of Sciences.
https://doi.org/10.1073/pnas.1203177109.
Zhang, G. P., & Qi, M. 2005. Neural network forecasting for seasonal and trend time series. European
Journal of Operational Research. https://doi.org/10.1016/j.ejor.2003.08.037.
Zhang, P. G. 2003. Time series forecasting using a hybrid ARIMA and neural network model.
Neurocomputing. https://doi.org/10.1016/S0925-2312(01)00702-0.
Zhang, X., Fuehres, H., & Gloor, P. A. 2011. Predicting Stock Market Indicators Through Twitter “I
hope it is not as bad as I fear.” Procedia - Social and Behavioral Sciences.
https://doi.org/10.1016/j.sbspro.2011.10.562.
Zussman, A., Zussman, N., & Nielsen, M. Ø. 2008. Asset market perspectives on the Israeli-
Palestinian conflict. Economica. https://doi.org/10.1111/j.1468-0335.2007.00607.x.