Publications

The below list is a selected set of representative research publications with respect to the activities of RiskLab, which have been authored by its researchers and affiliates.

1.
Holopainen, M. & Sarlin, P. Toward robust early-warning models: A horse race, ensembles and model uncertainty. Quantitative Finance forthcoming (2017). http://doi.org/10.1080/14697688.2017.1357972
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Rönnqvist, S. & Sarlin, P. Bank distress in the news: Describing events through deep learning. Neurocomputing 264, 57--70 (2017). http://doi.org/10.1016/j.neucom.2016.12.110
1.
Mezei, J. & Sarlin, P. RiskRank: Measuring Interconnected Risk. Economic Modeling forthcoming (2017). http://doi.org/10.1016/j.econmod.2017.04.016
1.
Giudici, P., Sarlin, P. & Spelta, A. The multivariate nature of systemic risk: Direct and common exposures. Journal of Banking & Finance forthcoming (2017). http://doi.org/10.1016/j.jbankfin.2017.05.010
1.
Constantin, A., Peltonen, T. & Sarlin, P. Network linkages to predict bank distress. Journal of Financial Stability forthcoming (2017). http://doi.org/10.1016/j.jfs.2016.10.011
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Cerchiello, P. & Giudici, P. A Bayesian h index: how to measure research impact. Statistical Analysis and Data Mining forthcoming (2017).
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Previtali, P. & Cerchiello, P. Board effectiveness and governance innovation. A new application of a compliance system. Corporate Governance forthcoming (2017).
1.
Cerchiello, P., Giudici, P. & Nicola, G. Twitter data models of bank risk contagion. Neurocomputing forthcoming (2017).
1.
Akusok, A. et al. Adding reliability to ELM forecasts by confidence intervals. Neurocomputing 232–241 (2017). http://doi.org/10.1016/j.neucom.2016.09.021
1.
Mezei, J. & Brunelli, M. An inquiry into approximate operations on fuzzy numbers. International Journal of Approximate Reasoning 81, 147--159 (2017). http://doi.org/10.1016/j.ijar.2016.11.011
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Halaj, G., Peltonen, T. & Scheicher, M. How did the Greek credit event impact the credit default swap market? Journal of Financial Stability forthcoming (2017). http://doi.org/10.1016/j.jfs.2016.10.009
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Mezei, J. & Sarlin, P. Introduction to Machine Learning and Network Analytics in Finance Minitrack. in Proceedings of the 2017 Hawaii International Conference on System Sciences (HICSS) (IEEE Press, 2017).
1.
Mezei, J. & Sarlin, P. Possibilistic Clustering for Crisis Prediction: Systemic Risk States and Membership Degrees. in Proceedings of the 2017 Hawaii International Conference on System Sciences (HICSS) (IEEE Press, 2017).
1.
Mezei, J. & Sarlin, P. Aggregating expert knowledge for the measurement of systemic risk. Decision Support Systems 88, 38--50 (2016). http://doi.org/10.1016/j.dss.2016.05.007
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Cerchiello, P. & Giudici, P. Categorical network models for systemic risk measurement. Quality & Quantity 1–17 (2016). http://doi.org/10.1007/s11135-016-0354-x
1.
Forss, T. & Sarlin, P. From News to Company Networks: Co-occurrence, sentiment, and information centrality. in Proceedings of the 2016 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) (IEEE Press, 2016). http://doi.org/10.1109/SSCI.2016.7850022
1.
Sayfullina, L., Eirola, E., Komashinsky, D., Palumbo, P. & Karhunen, J. Android Malware Detection: Building Useful Representations. in IEEE 15th International Conference on Machine Learning and Applications (IEEE ICMLA 2016) (2016).
1.
Sovilj, D. et al. Extreme Learning Machine for Missing Data using Multiple Imputations. Neurocomputing 174, Part A, 220–231 (2016).
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Eirola, E., Akusok, A., Björk, K.-M., Johnson, H. & Lendasse, A. Predicting Huntington’s Disease: Extreme Learning Machine with Missing Values. in Proceedings of the International Conference on Extreme Learning Machines (ELM 2016) (2016).
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Akusok, A. et al. ELMVIS+: Improved Nonlinear Visualization Technique Using Cosine Distance and Extreme Learning Machines. Proceedings of ELM-2015 2, 357–369 (2016).
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Björk, K.-M. & Lundell, A. Global optimization of a portfolio adjustment problem under credibility measures. International Journal of Operational Research 25(4) (2016). http://doi.org/10.1504/IJOR.2016.075292
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Björk, K.-M. & Mezei, J. A heuristical solution method to separable nonlinear programming problems. International Journal of Mathematics in Operational Research 9(2) (2016). http://doi.org/10.1504/IJMOR.2016.078002
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Shynkevich, Y., Mcginnity, T., Coleman, S. & Belatreche, A. Forecasting movements of Health-Care stock prices based on different categories of news articles using multiple kernel learning. Decision Support Systems 85, 74--83 (2016). http://doi.org/10.1016/j.dss.2016.03.001
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El-Shagi, M., Schweinitz, G. & Lindner, A. Real Effective Exchange Rate Misalignment in the Euro Area: A Counterfactual Analysis. Review of International Economics 1 (2016). http://doi.org/10.1111/roie.12207
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El-Shagi, M. & Schweinitz, G. Qual VAR Revisited: Good Forecast, Bad Story. Journal of Applied Economics 2 (2016). http://doi.org/10.1016/S1514-0326(16)30012-5
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Cerchiello, P. & Giudici, P. Conditional graphical models for systemic risk estimation. Expert systems with applications 43, 165--174 (2016). http://doi.org/10.1016/j.eswa.2015.08.047
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Giudici, P. & Spelta, P. Graphical network models for international financial flows. Journal of Economics and Business statistics 34(1), 128--138 (2016). http://doi.org/10.1080/07350015.2015.1017643
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Heijmans, R., Heuver, R., Levallois, C. & van Lelyveld, I. Dynamic visualization of large financial networks. Journal of Network Theory in Finance 2(2), 57--79 (2016). http://doi.org/10.21314/JNTF.2016.017
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Sarlin, P. Editorial on Computational Tools for Systemic Risk Identification and Assessment. Intelligent Systems in Accounting, Finance and Management 23(1--2), 1--2 (2016). http://doi.org/10.1002/isaf.1389
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Mezei, J. & Sarlin, P. On interval-valued possibilistic clustering for a generalized objective function. in Proceedings of the 2016 IEEE World Congress on Computational Intelligence (IEEE WCCI) (IEEE Press, 2016). http://doi.org/10.1109/FUZZ-IEEE.2016.7737773
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Mezei, J. & Sarlin, P. On a generalized objective function for possibilistic fuzzy clustering. in Proceedings of the 2016 Conference on Information Processing and Management of Uncertainty (IPMU) (Springer-Verlag, 2016). http://doi.org/10.1007/978-3-319-40596-4_59
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Oet, P., Gramlich, D. & Sarlin, P. Evaluating measures of adverse financial conditions. Journal of Financial Stability 27, 234--249 (2016). http://doi.org/10.1016/j.jfs.2016.06.008
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Sarlin, P. Macroprudential oversight, risk communication and visualization. Journal of Financial Stability 27,  160--179 (2016). http://doi.org/10.1016/j.jfs.2015.12.005
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Sarlin, P. Visual Macroprudential Surveillance of Banks. Intelligent Systems in Accounting, Finance and Management 23(4), 257--264 (2016). http://doi.org/10.1002/isaf.1391
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Sarlin, P. & Peltonen, T. Introduction to Systemic Risk Analytics Minitrack. in Proceedings of the 2016 Hawaii International Conference on System Sciences (HICSS) (IEEE Press, 2016). http://doi.org/10.1109/HICSS.2016.221
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Kouontchou, P. et al. A R-SOM Analysis of the Link between Financial Market Conditions and a Systemic Risk Index based on ICA-factors of Systemic Risk Measures. in Proceedings of the 2016 Hawaii International Conference on System Sciences (HICSS) (IEEE Press, 2016). http://doi.org/10.1109/HICSS.2016.222
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Ramsay, B. & Sarlin, P. Ending over-lending: Assessing systemic risk with debt to cash flow. International Journal of Finance & Economics 21(1), 36--57 (2016). http://doi.org/10.1002/ijfe.1520
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Cerchiello, P. & Giudici, P. Big data analysis for financial risk management. Journal of Big Data (2015).
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Cerchiello, P. & Giudici, P. How to measure the quality of financial tweets. Quality & Quantity 1–19 (2015). http://doi.org/10.1007/s11135-015-0229-6
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Eirola, E. et al. Extreme Learning Machines for Multiclass Classification: Refining Predictions with Gaussian Mixture Models. in Advances in Computational Intelligence 9095, 153–164 (LNCS, 2015).
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Akusok, A. et al. Arbitrary Category Classification of Websites Based on Image Content. IEEE Computational Intelligence Magazine 10, 30–41 (2015).
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Akusok, A., Björk, K.-M., Miche, Y. & Lendasse, A. High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications. IEEE Access 1011–1025 (2015). http://doi.org/10.1109/ACCESS.2015.2450498
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Swaney, C., Akusok, A., Björk, K.-M., Miche, Y. & Lendasse, A. Efficient Skin Segmentation via Neural Networks: HP-ELM and BD-SOM. in (2015). http://doi.org/10.1016/j.procs.2015.07.317
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Akusok, A. et al. MD-ELM: Originally Mislabeled Samples Detection using OP-ELM Model. Neurocomputing 159, 242–250 (2015).
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Shynkevich, Y., Mcginnity, T., Coleman, S. & Belatreche, A. Predicting Stock Price Movements Based on Different Categories of News Articles. in (IEEE Press, 2015). http://doi.org/10.1109/SSCI.2015.107
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Shynkevich, Y., Mcginnity, T., Coleman, S. & Belatreche, A. Stock price prediction based on stock-specific and sub-industry-specific news articles. in (IEEE Press, 2015). http://doi.org/10.1109/IJCNN.2015.7280517
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El-Shagi, M. & Schweinitz, G. Risk and Return - Is there an Unholy Cycle of Ratings and Yields? Economics Letters 129, 49--51 (2015). http://doi.org/10.1016/j.econlet.2015.02.007
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Sani, A., Lazaric, A. & Ryabko, D. The Replacement Bootstrap for Dependent Data. in Proceedings of the 2015 IEEE International Symposium on Information Theory (IEEE Press, 2015). http://doi.org/10.1109/ISIT.2015.7282644
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Calabrese, C. & Giudici, P. Estimating bank default with generalised extreme value regressionmodels. Journal of the Operational Research Society 1--10 (2015). http://doi.org/10.1057/jors.2014.106
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Aymanns, C. & Georg, S. Contagious Synchronization and Endogenous Network Formation in Financial Networks. Journal of Banking & Finance 50(1) (2015). http://doi.org/10.1016/j.jbankfin.2014.06.030