Risk, analytics & visualization

RiskLab is a research laboratory with an interdisciplinary lense for the study of risk through economics, finance and computer science.

Our research focuses on the use of analytics and visual interactive interfaces to provide insight into systemic risk.

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Visit our interactive, platform for visual systemic risk analytics.

RiskLab organizes a special session at CIFEr’16 on systemic risk analytics and at HICSS’17¬†on machine learning & networks in finance.

RiskLab, Bank of Finland & European Systemic Risk Board organize in October 2016 the 2nd Conference on Systemic Risk Analytics.

RiskLab research in the news: The Financial Threats That Machines Can See

Forbes mentions RiskLab:
SWIFT Gets Academic, But Usefully Academic

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    Publications

    1.
    Oet, P., Gramlich, D. & Sarlin, P. Evaluating measures of adverse financial conditions. Journal of Financial Stability forthcoming (2017). doi:10.1016/j.jfs.2016.06.008
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    Mezei, J. & Sarlin, P. Aggregating expert knowledge for the measurement of systemic risk. Decision Support Systems forthcoming (2017). doi:10.1016/j.dss.2016.05.007
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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).
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    Holopainen, M. & Sarlin, P. CrisisModeler: A Tool for Exploring Crisis Predictions. in Proceedings of the 2015 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) (IEEE Press, 2015). doi:10.1109/SSCI.2015.135
    1.
    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, 2017).
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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, 2017).
    1.
    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). doi:10.1109/HICSS.2016.221
    1.
    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). doi:10.1109/HICSS.2016.222
    1.
    Sarlin, P. Visual Macroprudential Surveillance of Banks. Intelligent Systems in Accounting, Finance and Management forthcoming (2017).
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    Sarlin, P. Macroprudential oversight, risk communication and visualization. Journal of Financial Stability forthcoming (2017). doi:10.1016/j.jfs.2015.12.005
    1.
    Rönnqvist, S. & Sarlin. Detect & Describe: Deep Learning of Bank Stress in the News. in Proceedings of the 2015 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) (IEEE Press, 2015). doi:10.1109/SSCI.2015.131
    1.
    Mezei, J., Wikström, R. & Carlsson, C. Aggregating linguistic expert knowledge in type-2 fuzzy ontologies. Applied Soft Computing 35(1), 911–920 (2015).
    1.
    Björk, K.-M. & Mezei, J. A heuristical solution method to separable nonlinear programming problems. International Journal of Mathematics in Operational Research forthcoming (2016).
    1.
    Björk, K.-M. & Mezei, J. An Economic Production Quantity Problem with Backorders and fuzzy cycle times. International journal of fuzzy systems 28(4), 1861–1868 (2015).
    1.
    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). doi:10.1002/ijfe.1520
    1.
    Nikou, S., Mezei, J. & Sarlin, P. A process view to evaluate and understand preference elicitation. Journal of Multi-Criteria Decision Analysis 22(5–6), 305–329 (2015). doi:10.1002/mcda.1544
    1.
    Rönnqvist, S. & Sarlin, P. Bank Networks from Text: Interrelations, Centrality and Determinants. Quantitative Finance 15(10), 1619–1635 (2015). doi:10.1080/14697688.2015.1071076
    1.
    Sarlin, P. & Nyman, H.J. The process of macropudential oversight in Europe. Global Policy 6(4), 389–407 (2015). doi:10.1111/1758-5899.12255
    1.
    Sarlin, P. Automated and Weighted Self-Organizing Time Maps. Knowledge and Information Systems 44(2), 493–505 (2015). doi:10.1007/s10115-014-0762-y
    1.
    Sarlin, P., Nikou, S., Mezei, J. & Bouwman, H. Visual Conjoint Analysis (VCA): A topology of preferences in multi-attribute decision making. Quality & Quantity 49(1), 385–405 (2015). doi:10.1007/s11135-014-9992-z.
    1.
    Björk, K.-M. & Mezei, J. A fuzzy MILP-model for the optimization of vehicle routing problem. Journal of Intelligent and Fuzzy System 26, 1349–1361 (2014).
    1.
    Laina, P., Nyholm, J. & Sarlin, P. Leading indicators of systemic banking crises: Finland in a panel of EU countries. Review of Financial Economics 24, 18–35 (2015).
    1.
    Rönnqvist, S. & Sarlin. From Text to Bank Interrelation Maps. in Proceedings of the 2014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) (IEEE Press, 2014). doi:10.1109/CIFEr.2014.6924053
    1.
    El-Shagi, M., Knedlik, T. & Schweinitz, G. Predicting financial crises: The (statistical) significance of the signals approach. Journal of International Money and Finance 35, 76–103 (2013). doi:10.1016/j.jimonfin.2013.02.001
    1.
    Sarlin, P. Data and Dimension Reduction for Visual Financial Performance Analysis. Information Visualization 14(2), 148–167 (2015). doi:10.1177/1473871613504102
    1.
    Clark, S., Sarlin, P., Sharma, A. & Sisson, S. Increasing dependence on foreign water resources? An assessment of trends in global virtual water flows using a self-organizing time map. Ecological Informatics 26(2), 192–202 (2015). doi:10.1016/j.ecoinf.2014.05.012
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    Björk, K.-M. & Lundell, A. Global optimization of a portfolio adjustment problem under credibility measures. International Journal of Operational Research forthcoming (2016).
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    Björk, K.-M. A Multi-item Fuzzy Economic Production Quantity Problem with a Finite Production Rate. International Journal of Production Economics 135, 702–707 (2012).
    1.
    Sarlin, P. Clustering the Changing Nature of Currency Crises in Emerging Markets: An Exploration with Self-Organising Maps. International Journal of Computational Economics and Econometrics 2(1), 24–46 (2011). doi:10.1504/IJCEE.2011.040575
    1.
    Betz, F., Oprica, S., Peltonen, T. A. & Sarlin, P. Predicting Distress in European Banks. Journal of Banking & Finance 45, 225–241 (2014). doi:10.1016/j.jbankfin.2013.11.041
    1.
    Marghescu, D., Sarlin, P. & Liu, S. Early-warning analysis for currency crises in emerging markets: A revisit with fuzzy clustering. Intelligent Systems in Accounting, Finance & Management 17(3–4), 143–165 (2010). doi:10.1002/isaf.317
    1.
    Mehrotra, A., Peltonen, T. & Santos Rivera, A. Modelling Inflation in China - A Regional Perspective. China Economic Review 21(2), 237–255 (2010). doi:10.1016/j.chieco.2009.06.010
    1.
    Sarlin, P. Visual monitoring of financial stability with a self-organizing neural network. in Proceedings of the International Conference on Intelligent Systems Design and Applications (ISDA 10) 248–253 (IEEE Press, 2010). doi:10.1109/ISDA.2010.5687256
    1.
    De Bondt, G., Santabarbara, D. & Peltonen, T. Booms and Busts in the Chinese Stock Market: Estimates Based on Fundamentals. Applied Financial Economics 21(5), 287–300 (2011). doi:10.1080/09603107.2010.530218
    1.
    Peltonen, T., Popescu, A. & Sager, M. Can Non-Linear Real Shocks Explain the Persistence of PPP Exchange Rate Disequilibria? International Journal of Finance and Economics 16(3), 290–306 (2011). doi:10.1002/ijfe.426
    1.
    Peltonen, T., Vansteenkiste, I. & Sousa, R. Fundamentals, Financial Factors and Investment in Emerging Markets. Emerging Markets Finance and Trade 47, 88–105 (2011). doi:10.2753/REE1540-496X4703S205
    1.
    Sarlin, P. Sovereign debt monitor: A visual Self-organizing maps approach. in Proceedings of IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr 11) 1–8 (IEEE Press, 2011). doi:978-1-4244-9933-5
    1.
    Sarlin, P. & Eklund, T. Fuzzy Clustering of the Self-Organizing Map: Some Applications on Financial Time Series. in Proceedings of the Workshop on Self-Organizing Maps (WSOM 11) 40–50 (Springer-Verlag, 2011). doi:10.1007/978-3-642-21566-7_4
    1.
    Sarlin, P. & Marghescu, D. Neuro-Genetic Predictions of Currency crises. Intelligent Systems in Accounting, Finance and Management 18(4), 145–160 (2011). doi:10.1002/isaf.328
    1.
    Sarlin, P. & Marghescu, D. Visual predictions of currency crises using self-organizing maps. Intelligent Systems in Accounting, Finance and Management 18(1), 15–38 (2011). doi:10.1002/isaf.321
    1.
    Knedlik, T. & Schweinitz, G. Macroeconomic Imbalances as Indicators for Debt Crises in Europe. Journal of Common Market Studies 50(5), 726–745 (2012). doi:10.1111/j.1468-5965.2012.02264.x
    1.
    Peltonen, T., Sousa, R. & Vansteenkiste, I. Wealth effects in Emerging Market Economies. International Review of Economics and Finance 24, 155–66 (2012). doi:10.1016/j.iref.2012.01.006
    1.
    Peltonen,T., Sousa, R. & Vansteenkiste, I. Investment in Emerging Markets. Empirical Economics 43(1), 97–119 (2012). doi:10.1007/s00181-011-0457-0
    1.
    Sarlin, P. Chance Discovery with Self-Organizing Maps: Discovering Imbalances in Financial Networks. in Advances in Chance Discovery (eds. Ohsawa, Y. & Abe, A.) 49–61 (Springer-Verlag, 2012). doi:10.1007/978-3-642-30114-8_4
    1.
    Sarlin, P., Yao, Z. & Eklund, T. A Framework for State Transitions on The Self-Organizing Map: Some Temporal Financial Applications. Intelligent Systems in Accounting, Finance and Management 19(1), 189–203 (2012). doi:10.1002/isaf.1328
    1.
    Sarlin, P. Visual tracking of the millennium development goals with a fuzzified self-organizing neural network. International Journal of Machine Learning and Cybernetics 3(3), 233–245 (2012). doi:10.1007/s13042-011-0057-5
    1.
    Yao, Z., Sarlin, P., Eklund, T. & Back, B. Temporal Customer Segmentation Using the Self-Organizing Time Map. in Proceedings of the International Conference on Information Visualisation (iV 12) 234–240 (IEEE Press, 2012). doi:10.1109/IV.2012.47
    1.
    Lo Duca, M. & Peltonen, T. Assessing systemic risks and predicting systemic events. Journal of Banking and Finance 37(7), 2183–2195 (2013). doi:10.1016/j.jbankfin.2012.06.010
    1.
    Holmbom, A., Rönnqvist, S., Sarlin, P., Eklund, T. & Back, B. Green vs. non-green customer behavior: A Self-Organizing Time Map over greenness. in Proceedings of the 13th IEEE International Conference on Data Mining Workshops (ICDMW’13) (IEEE Press, 2013). doi:10.1109/ICDMW.2013.103
    1.
    Sarlin, P. & Peltonen, T. Mapping the State of Financial Stability. Journal of International Financial Markets, Institutions & Money 26, 46–76 (2013). doi:10.1016/j.intfin.2013.05.002
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    Sarlin, P. & Rönnqvist, S. Cluster coloring of the Self-Organizing Map: An information visualization perspective. in Proceedings of the International Conference on Information Visualization (iV 13) (IEEE Press, 2013). doi:10.1109/IV.2013.72
    1.
    Sarlin, P. & Yao, Z. Clustering of the Self-Organizing Time Map. Neurocomputing 121, 317–327 (2013). doi:10.1016/j.neucom.2013.04.007
    1.
    Sarlin, P. Decomposing the Global Financial Crisis: A Self-Organizing Time Map. Pattern Recognition Letters 34, 1701–1709 (2013). doi:10.1016/j.patrec.2013.03.017
    1.
    Sarlin, P. Exploiting the Self-Organizing Financial Stability Map. Engineering Applications of Artificial Intelligence 26(5–6), 1532–1539 (2013). doi:10.1016/j.engappai.2013.01.002
    1.
    Sarlin, P. On policymakers’ loss functions and the evaluation of early warning systems. Economics Letters 119(1), 1–7 (2013). doi:10.1016/j.econlet.2012.12.030
    1.
    Sarlin, P. Self-Organizing Time Map: An Abstraction of Temporal Multivariate Patterns. Neurocomputing 99(1), 496–508 (2013). doi:10.1016/j.neucom.2012.07.011
    1.
    Sarlin, P. & Eklund, T. Financial Performance Analysis of European Banks Using a Fuzzified Self-Organizing Map. International Journal of Knowledge-Based and Intelligent Engineering Systems 17(3), 223–234 (2013). doi:10.3233/KES-130261
    1.
    Bussière, M., Peltonen, T. & Delle Chiaie, S. Exchange Rate Pass-Through in the Global Economy: the Role of Emerging Market Economies. IMF Economic Review 62, 146–178 (2014). doi:10.1057/imfer.2014.5
    1.
    Castrén, O. & Rancan, M. Macro-Networks: An application to euro area financial accounts. Journal of Banking & Finance 46, 43–58 (2014). doi:j.jbankfin.2014.04.027
    1.
    Peltonen, T., Vuillemey, G. & Scheicher, M. The Network Structure of the CDS market and its Determinants. Journal of Financial Stability 13, 118–133 (2014). doi:10.1016/j.jfs.2014.05.004
    1.
    Yao, Z., Sarlin, P., Eklund, T. & Back, B. Combining Visual Customer Segmentation and Response Modeling. Neural Computing & Applications 25, 123–134 (2014). doi:10.1007/s00521-013-1454-3
    1.
    Sarlin, P. A Weighted SOM for classifying data with instance-varying importance. International Journal of Machine Learning and Cybernetics 5(1), 101–110 (2014). doi:10.1007/s13042-013-0175-3
    1.
    Sarlin, P. On biologically inspired predictions of the global financial crisis. Neural Computing & Applications 24(3–4), 663–673 (2014). doi:10.1007/s00521-012-1281-y
    1.
    Sarlin, P. Mapping Financial Stability. (Springer, 2014).
    1.
    Sarlin, P. Evaluating a Self-Organizing Map for Clustering and Visualizing Optimum Currency Area Criteria. Economics Bulletin 31, 1483–1495 (2011).