Peter Sarlin

Peter Sarlin
Hanken School of Economics · Department of Finance and Statistics

PhD

About

112
Publications
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2,069
Citations

Publications

Publications (112)
Article
Full-text available
In this paper, we focus our attention on leveraging the information contained in financial news to enhance the performance of a bank distress classifier. The news information should be analyzed and inserted into the predictive model in the most efficient way and this task deals with the issues related to Natural Language interpretation and to the a...
Article
Full-text available
Recurring financial instabilities have led policymakers to rely on early-warning models to signal financial vulnerabilities. These models rely on ex-post optimization of signaling thresholds on crisis probabilities accounting for preferences between forecast errors, but come with the crucial drawback of unstable thresholds in recursive estimations....
Article
Full-text available
This paper uses macro‐network to measure the interconnectedness of the banking sector and relates it to banking crises in Europe. Beyond cross‐border financial linkages of the banking sector, the macronetwork also accounts for financial linkages to the other main financial and nonfinancial sectors within the economy. We find that a more central pos...
Article
Purpose Recent technological and digital developments have opened new avenues for customer data utilization in insurance services. One form of this data transformation is automated chatbots that provide convenient access to data leveraged through a discussion-like interface. The purpose of this paper is to uncover how insurance chatbots support cu...
Article
This paper presents first steps towards robust models for crisis prediction. We conduct a horse race of conventional statistical methods and more recent machine learning methods as early-warning models. As individual models are in the literature most often built in isolation of other methods, the exercise is of high relevance for assessing the rela...
Article
In this paper we focus our attention on the exploitation of the information contained in financial news to enhance the performance of a classifier of bank distress. Such information should be analyzed and inserted into the predictive model in the most efficient way and this task deals with all the issues related to text analysis and specifically an...
Preprint
In this paper we focus our attention on the exploitation of the information contained in financial news to enhance the performance of a classifier of bank distress. Such information should be analyzed and inserted into the predictive model in the most efficient way and this task deals with all the issues related to text analysis and specifically an...
Article
To understand the relationship between news sentiment and company stock price movements, and to better understand connectivity among companies, we define an algorithm for measuring sentiment-based network risk. The algorithm ranks companies in networks of co-occurrences, and measures sentiment-based risk, by calculating both individual risks and ag...
Preprint
To understand the relationship between news sentiment and company stock price movements, and to better understand connectivity among companies, we define an algorithm for measuring sentiment-based network risk. The algorithm ranks companies in networks of co-occurrences, and measures sentiment-based risk, by calculating both individual risks and ag...
Article
To capture systemic risk related to network structures, this paper introduces a measure that complements direct exposures with common exposures, as well as compares these to each other. Trying to address the interconnected nature of financial systems, researchers have recently proposed a range of approaches for assessing network structures. Much of...
Article
We create a tool for visual surveillance of the European banking system from a macroprudential perspective. The tool performs visual dynamic clustering with the self-organizing time map (SOTM) to visualize evolving multivariate data from two viewpoints: (i) multivariate cluster structures, and (ii) univariate drivers of changes in structures. In as...
Article
Building on the literature on systemic risk and financial contagion, the paper introduces estimated network linkages into an early-warning model to predict bank distress among European banks. We use multivariate extreme value theory to estimate equity-based tail-dependence networks, whose links proxy for the markets’ view of bank interconnectedness...
Conference Paper
Possibilistic clustering methods have gained attention in both applied and theoretical research. In this paper, we formulate a general objective function for possibilistic clustering. The objective function can be used as the basis of a mixed clustering approach incorporating both fuzzy memberships and possibilistic typicality values to overcome va...
Article
The policy objective of safeguarding financial stability has stimulated a wave of research on systemic risk analytics, yet it still faces challenges in measurability. This paper models systemic risk by tapping into expert knowledge of financial supervisors. We decompose systemic risk into a number of interconnected segments, for which the level of...
Article
Full-text available
Timely identification and anticipation of adverse conditions in the financial system are critical for macroprudential policy. However, there is no consensus on how to evaluate the quality of systemic measures. This paper provides a framework to compare measures of systemic conditions. We illustrate the proposed tests with a case study of US measure...
Article
While many models are purposed for detecting the occurrence of events in complex systems, the task of providing qualitative detail on the developments is not usually as well automated. We present a deep learning approach for detecting relevant discussion in text and extracting natural language descriptions of events. Supervised by only a small set...
Preprint
Full-text available
While many models are purposed for detecting the occurrence of significant events in financial systems, the task of providing qualitative detail on the developments is not usually as well automated. We present a deep learning approach for detecting relevant discussion in text and extracting natural language descriptions of events. Supervised by onl...
Article
This paper proposes RiskRank as a joint measure of cyclical and cross-sectional systemic risk. RiskRank is a general-purpose aggregation operator that concurrently accounts for risk levels for individual entities and their interconnectedness. The measure relies on the decomposition of systemic risk into sub-components that are in turn assessed usin...
Preprint
This paper proposes RiskRank as a joint measure of cyclical and cross-sectional systemic risk. RiskRank is a general-purpose aggregation operator that concurrently accounts for risk levels for individual entities and their interconnectedness. The measure relies on the decomposition of systemic risk into sub-components that are in turn assessed usin...
Article
This paper focuses on multi-attribute design problems in which several quantitative and/or qualitative attributes are simultaneously involved. Preference elicitation methods dealing with decision-makers’ preferences are often employed to solve such problems. The present paper proposes a conceptual framework that considers preference elicitation as...
Research
Full-text available
Timely identification of coincident systemic conditions and forward-looking capacity to anticipate adverse developments are critical for macroprudential policy. Despite clear recognition of these factors in literature, an evaluation methodology and empirical tests for the information value of coincident measures are lacking. This paper provides a t...
Article
The 2007–2008 financial crisis has paved the way for the use of macroprudential policies in supervising the financial system as a whole. This article views macroprudential oversight in Europe as a process, a sequence of activities with the ultimate aim of safeguarding financial stability. To conceptualize a process in this context, we introduce the...
Article
Full-text available
News is a pertinent source of information on financial risks and stress factors, which nevertheless is challenging to harness due to the sparse and unstructured nature of natural text. We propose an approach based on distributional semantics and deep learning with neural networks to model and link text to a scarce set of bank distress events. Throu...
Technical Report
Full-text available
Early-warning models most commonly optimize signaling thresholds on crisis probabilities. The ex-post threshold optimization is based upon a loss function accounting for preferences between forecast errors, but comes with two crucial drawbacks: unstable thresholds in recursive estimations and an in-sample overfit at the expense of out-ofsample perf...
Article
This paper presents first steps toward robust early-warning models. We conduct a horse race of conventional statistical methods and more recent machine learning methods. As early-warning models based upon one approach are oftentimes built in isolation of other methods, the exercise is of high relevance for assessing the relative performance of a wi...
Article
This paper investigates leading indicators of systemic banking crises in a panel of 11 EU countries, with a particular focus on Finland. We use quarterly data from 1980Q1 to 2013Q2, in order to create a large number of macro-financial indicators, as well as their various transformations. We make use of univariate signal extraction and multivariate...
Article
Building on the literature on systemic risk and financial contagion, the paper introduces estimated network linkages into an early-warning model to predict bank distress among European banks. We use multivariate extreme value theory to estimate equity-based tail-dependence networks, whose links proxy for the markets' view of bank interconnectedness...
Article
Full-text available
The policy objective of safeguarding financial stability has stimulated a wave of research on systemic risk analytics, yet it still faces challenges in measurability. This paper models systemic risk by tapping into expert knowledge of financial supervisors. The model builds on the decomposition of systemic risk into a number of interconnected segme...
Article
Full-text available
Probabilistic topic modeling is a popular and powerful family of tools for uncovering thematic structure in large sets of unstructured text documents. While much attention has been directed towards the modeling algorithms and their various extensions, comparatively few studies have concerned how to present or visualize topic models in meaningful wa...
Article
Full-text available
In the wake of the still ongoing global financial crisis, interdependencies among banks have come into focus in trying to assess systemic risk. To date, such analysis has largely been based on numerical data. By contrast, this study attempts to gain further insight into bank interconnections by tapping into financial discourse. We present a text-to...
Article
Water resources are continually redistributed across international borders as a result of virtual water flows associated with global trade, where ‘virtual water’ is the term describing water used in the production of commodities. This transfer of virtual water allows some countries to rely heavily on the water resources of other countries without h...
Chapter
An understanding of all elements in the macroprudential oversight process is obviously crucial for safeguarding financial stability. While providing a basis, such a framework is still highly dependent on the underlying data. Access to complete, accurate, and timely data is central not only for policymakers to make good economic policy, but also for...
Chapter
Data and dimension reduction techniques hold promise for representing data in easily understandable formats, as has been shown by their wide scope of applications. Data reductions provide summarizations of data by compressing information into fewer partitions, whereas dimension reductions provide low-dimensional overviews of similarity relations in...
Chapter
Early identification of financial instabilities is of interest for a wide spectrum of decision-makers for a wide range of reasons. It is needless to say that the recent occurrences of instability have stimulated efforts in understanding and predicting financial stress. The work in this book has provided a wide range of tools for macroprudential ove...
Chapter
The standard Self-Organizing Map (SOM), while having merit for the task at hand, may be extended in multiple directions, not the least to better meet the demands set by macroprudential oversight and data. Along these lines, with a key focus on temporality, this chapter first discusses the literature on time in SOMs. This is followed by extensions t...
Chapter
The provided models for macroprudential oversight have thus far concerned assessing the cross-sectional or temporal dimensions close to isolation. In this chapter, we turn the focus to exploring cross-sectional dynamics. The Self-Organizing Time Map (SOTM) provides means for visual dynamic clustering and thus also for illustrating dynamics in cross...
Chapter
This chapter exploits the Self-Organizing Financial Stability Map (SOFSM) for tasks in macroprudential oversight. The SOFSM was created in Chap. 7, whereas the Self-Organizing Map (SOM) extensions used for exploiting it were introduced in Chap. 6. The tasks performed with the SOFSM are two, risk identification and assessment, of which the former is...
Chapter
Paraphrasing Milton Friedman’s statement about Keynesians, Borio (2011) stated “We are all macroprudentialists now”. This chapter provides an overview of macroprudential oversight. It focuses first on the definition of financial systems and financial (in)stability, as well as fragilities in financial systems, and the concept of systemic risk. After...
Chapter
Data and dimension reduction techniques, and particularly their combination for Data-Dimension Reductions (DDR), have in many fields and tasks held promise for representing data in an easily understandable format. However, comparing methods and finding the most suitable one is a challenging task. In the previous chapter, we discussed the aim of dim...
Article
This paper discusses the role of risk communication in macroprudential oversight and of visualization in risk communication. Beyond the soar in availability and precision of data, the transition from firm-centric to system-wide supervision imposes obvious data needs. Moreover, broad and effective communication of timely information related to syste...
Article
Early-warning models provide means for ex ante identification of elevated risks that may lead to a financial crisis. This paper taps into the early-warning literature by introducing biologically inspired models for predicting systemic financial crises. We create three models: a conventional statistical model, a back-propagation neural network (NN)...
Article
This paper proposes an approach denoted visual conjoint analysis (VCA). Conjoint analysis is commonly used in marketing to understand consumers’ decision criteria, particularly why consumers prefer and select certain products and their variations. Yet, little efforts have been made to provide visual means for exploring and visualizing preferences a...
Article
This paper introduces the ratio of debt to cash flow (D/CF) of nations and their economic sectors to macroprudential analysis, particularly as an indicator of systemic risk and vulnerabilities. While leverage is oftentimes linked to the vulnerability of a nation, the stock of total debt and the flow of gross savings is a less explored measure. Cash...
Article
The ongoing global financial crisis has shown the importance of a system-wide, or macroprudential, approach to safeguarding financial stability. An essential part of macroprudential oversight concerns the tasks of early identification and assessment of risks and vulnerabilities. Yet, while risk identification and assessment oftentimes makes use of...
Book
This book approaches macroprudential oversight from the viewpoint of three tasks. The focus concerns a tight integration of means for risk communication into analytical tools for risk identification and risk assessment. Generally, this book explores approaches for representing complex data concerning financial entities on low-dimensional displays....
Article
Full-text available
This paper introduces the notion of public collaborative processes (PCPs) to the field of Business Process Management (BPM). PCPs involve multiple organizations with a common objective, where a number of dispersed organizations cooperating under various unstructured forms take a collaborative approach to reaching the final goal. This paper exemplif...
Conference Paper
Companies have traditionally used segmentation approaches to study and learn more about their customer base. One area that has attracted considerable amounts of research in recent years is that of green customer behavior. However, the approaches used have often been static clustering approaches and have focused on identifying green vs. non-green cu...
Article
Full-text available
This paper proposes schemes for automated and weighted Self-Organizing Time Maps (SOTMs). The SOTM provides means for a visual approach to evolutionary clustering, which aims at producing a sequence of clustering solutions. This task we denote as visual dynamic clustering. The implication of an automated SOTM is not only a data-driven parametrizati...
Article
This article assesses the suitability of data and dimension reduction methods, and data–dimension reduction combinations, for visual financial performance analysis. Motivated by no comparable quantitative measure of all aspects of dimension reductions, this article attempts to capture the suitability of methods for the task through a qualitative co...
Article
A key starting point for financial stability surveillance is understanding past, current and possible future risks and vulnerabilities. Through temporal data and dimensionality reduction, or visual dynamic clustering, this paper aims to present a holistic view of cross-sectional macro-financial patterns over time. The Self-Organizing Time Map (SOTM...
Article
Customer relationship management is a central part of Business Intelligence, and sales campaigns are often used for improving customer relationships. This paper uses advanced analytics to explore customer behavior during sales campaigns. We provide a visual, data-driven and efficient framework for customer-segmentation and campaign-response modelin...
Conference Paper
There is an increasing interest in green consumer behavior. These consumers are ecologically conscious and interested in buying environmentally friendly products. Earlier efforts at identifying these consumers have relied upon questionnaires based on demographic and psychographic data. Most of the studies have concluded that it is not possible to i...
Article
Full-text available
A lot of attention in supply chain management has been devoted to understanding customer requirements. What are customer priorities in terms of price and service level, and how can companies go about fulfilling these requirements in an optimal way? New manufacturing technology in the form of 3D printing is about to change some of the underlying ass...
Article
Full-text available
In the wake of the ongoing global financial crisis, interdependencies among banks have come into focus in trying to assess systemic risk. To date, such analysis has largely been based on numerical data. By contrast, this study attempts to gain further insight into bank interconnections by tapping into financial discussion. Co-mentions of bank names...
Article
Full-text available
This paper takes an information visualization perspective to visual representations in the general SOM paradigm. This involves viewing SOM-based visualizations through the eyes of Bertin's and Tufte's theories on data graphics. The regular grid shape of the Self-Organizing Map (SOM), while being a virtue for linking visualizations to it, restricts...
Article
This paper enhances the visualization and extraction of information on the self-organizing financial stability map (SOFSM). The SOFSM uses the self-organizing map to represent a multidimensional financial stability space on a two-dimensional grid and allows monitoring economies in the financial stability cycle represented by four states. The SOFSM...
Article
This paper introduces a new loss function and Usefulness measure for evaluating early warning systems (EWSs) that incorporate policymakers’ preferences between issuing false alarms and missing crises, and individual observations. The novelty derives from three enhancements: (i) accounting for unconditional probabilities of the classes, (ii) computi...
Conference Paper
Understanding dynamics in multivariate data before, during and after events, i.e. time-to-event data, is of central importance in a wide range of tasks, such as the path to and afterlife of a failure of a financial institution or country and diagnosis of a disease. The main task of this paper is to provide a solution to exploring dynamics across ma...
Article
The paper develops an early-warning model for predicting vulnerabilities leading to distress in European banks using both bank and country-level data. As outright bank failures have been rare in Europe, we introduce a novel dataset that complements bankruptcies and defaults with state interventions and mergers in distress. The signals of the early-...
Article
In this chapter, we introduce the Self-Organizing Map (SOM) from the viewpoint of Chance Discovery. The SOM paradigm supports several principal parts of Chance Discovery: visualization of temporal multivariate data, discovering rare clusters bridging frequent ones, detecting the degree of event rarity or outliers, and dealing with continuously evol...
Article
This paper presents a weighted self-organizing map (WSOM) that combines the advantages of the standard SOM paradigm with learning that accounts for instance-varying importance. While the learning of the classical batch SOM weights data by a neighborhood function, it is here augmented with a user-specified instance-specific importance weight for cos...
Article
This paper adopts and adapts Kohonen's standard Self-Organizing Map (SOM) for exploratory temporal structure analysis. The Self-Organizing Time Map (SOTM) implements SOM-type learning to one-dimensional arrays for individual time units, preserves the orientation with short-term memory and arranges the arrays in an ascending order of time. The two-d...
Conference Paper
Visual clustering provides effective tools for understanding relationships among clusters in a data space. This paper applies an adaptation of the standard Self-Organizing Map for visual temporal clustering in exploring the customer base and tracking customer behavior of a department store over a 22-week period. In contrast to traditional clusterin...
Article
Self-organizing maps (SOMs) have commonly been used in temporal applications. This paper enhances the SOM paradigm for temporal data by presenting a framework for computing, summarizing and visualizing transition probabilities on the SOM. The framework includes computing matrices of node-to-node and node-to-cluster transitions and summarizing maxim...
Article
Self-organizing maps (SOM) have been commonly used in temporal financial applications. This paper enhances the SOM paradigm for temporal data by presenting a framework for computing, summarizing and visualizing transition probabilities on the SOM. The framework includes computing matrices of node-to-node and node-to-cluster transitions and summariz...
Article
We create a neuro-genetic (NG) model for predicting currency crises by using a genetic algorithm for specifying (1) the combination of inputs, (2) the network configuration and (3) the training parameters for a back-propagation artificial neural network (ANN). The performance of the NG model is evaluated by comparing it with standalone probit and A...
Article
The paper uses the Self-Organizing Map for mapping the state of financial stability and visualizing the sources of systemic risks as well as for predicting systemic financial crises. The Self-Organizing Financial Stability Map (SOFSM) enables a two-dimensional representation of a multidimensional financial stability space that allows disentangling...
Conference Paper
Due to the recent wave of bank failures, stress tests have been conducted on banks within the European Union. The stress tests, however, only consider the adequacy of a bank's capital ratios, whereas the general financial performance of individual banks is disregarded. In this paper, we use the Self-Organizing Map (SOM) to perform a visual multidim...
Conference Paper
The Millennium Development Goals (MDGs) represent commitments to reduce poverty and hunger, and to tackle ill- health, gender inequality, lack of education, lack of access to clean water and environmental degradation by 2015. The eight goals of the Millennium Declaration are tracked using 21 benchmark targets, measured by 60 indicators. This paper...
Conference Paper
The Self-organizing map (SOM) has been widely used in financial applications, not least for time-series analysis. The SOM has not only been utilized as a stand-alone clustering technique, its output has also been used as input for second-stage clustering. However, one ambiguity with the SOM clustering is that the degree of membership in a particula...
Conference Paper
In the 1980s and at the turn of last century, severe global waves of sovereign defaults occurred in less developed countries. To date, the forecasting and monitoring results of debt crises are still at a preliminary stage, while the issue is at present highly topical. This paper explores whether the application of the Self-organizing map (SOM), a n...

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