Mapping financial stability

The ongoing global financial crisis has demonstrated the importance of a systemwide, 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 that eventually may lead to a systemic financial crisis. Thriving tools are crucial as they allow early policy actions to decrease or prevent further build-up of risks or to otherwise enhance the shock absorption capacity of the financial system. In the literature, three types of systemic risk can be identified: i ) build-up of widespread imbalances, ii ) exogenous aggregate shocks, and iii ) contagion. Accordingly, the systemic risks are matched by three categories of analytical methods for decision support: i ) early-warning, ii ) macro stress-testing, and iii ) contagion models. Stimulated by the prolonged global financial crisis, today's toolbox of analytical methods includes a wide range of innovative solutions to the two tasks of risk identification and risk assessment. Yet, the literature lacks a focus on the task of risk communication. This thesis discusses macroprudential oversight from the viewpoint of all three tasks: Within analytical tools for risk identification and risk assessment, the focus concerns a tight integration of means for risk communication. Data and dimension reduction methods, and their combinations, hold promise for representing multivariate data structures in easily understandable formats. The overall task of this thesis is to represent high-dimensional data concerning financial entities on lowdimensional displays. The low-dimensional representations have two subtasks: i ) to function as a display for individual data concerning entities and their time series, and ii ) to use the display as a basis to which additional information can be linked. The final nuance of the task is, however, set by the needs of the domain, data and methods. The following ve questions comprise subsequent steps addressed in the process of this thesis: 1. What are the needs for macroprudential oversight? 2. What form do macroprudential data take? 3. Which data and dimension reduction methods hold most promise for the task? 4. How should the methods be extended and enhanced for the task? 5. How should the methods and their extensions be applied to the task? Based upon the Self-Organizing Map (SOM), this thesis not only creates the Self-Organizing Financial Stability Map (SOFSM), but also lays out a general framework for mapping the state of financial stability. This thesis also introduces three extensions to the standard SOM for enhancing the visualization and extraction of information: i ) fuzzifications, ii ) transition probabilities, and iii ) network analysis. Thus, the SOFSM functions as a display for risk identification, on top of which risk assessments can be illustrated. In addition, this thesis puts forward the Self-Organizing Time Map (SOTM) to provide means for visual dynamic clustering, which in the context of macroprudential oversight concerns the identification of cross-sectional changes in risks and vulnerabilities over time. Rather than automated analysis, the aim of visual means for identifying and assessing risks is to support disciplined and structured judgmental analysis based upon policymakers' experience and domain intelligence, as well as external risk communication.

Prediction of market switching and delisting events from OMX First North Nordic multilateral stock exchange

This thesis studies the predictability of market switching and delisting events from OMX First North Nordic multilateral stock exchange by using financial statement information and market information from 2007 to 2012. This study was conducted by using a three stage process. In first stage relevant theoretical framework and initial variable pool were constructed. Then, explanatory analysis of the initial variable pool was done in order to further limit and identify relevant variables. The explanatory analysis was conducted by using self-organizing map methodology. In the third stage, the predictive modeling was carried out with random forests and support vector machine methodologies. It was found that the explanatory analysis was able to identify relevant variables. The results indicate that the market switching and delisting events can be predicted in some extent. The empirical results also support the usability of financial statement and market information in the prediction of market switching and delisting events.

Visual analytics for behavioral and niche market segmentation

Companies require information in order to gain an improved understanding of their customers. Data concerning customers, their interests and behavior are collected through different loyalty programs. The amount of data stored in company data bases has increased exponentially over the years and become difficult to handle. This research area is the subject of much current interest, not only in academia but also in practice, as is shown by several magazines and blogs that are covering topics on how to get to know your customers, Big Data, information visualization, and data warehousing. In this Ph.D. thesis, the Self-Organizing Map and two extensions of it – the Weighted Self-Organizing Map (WSOM) and the Self-Organizing Time Map (SOTM) – are used as data mining methods for extracting information from large amounts of customer data. The thesis focuses on how data mining methods can be used to model and analyze customer data in order to gain an overview of the customer base, as well as, for analyzing niche-markets. The thesis uses real world customer data to create models for customer profiling. Evaluation of the built models is performed by CRM experts from the retailing industry. The experts considered the information gained with help of the models to be valuable and useful for decision making and for making strategic planning for the future.

FUTURES OF FINNISH FOOD SYSTEM BY 2050. The perspective of resilience

Modern food systems face complex global challenges such as climate change, resource scarcities, population growth, concentration and globalization. It is not possible to forecast how all these challenges will affect food systems, but futures research methods provide possibilities to enable better understanding of possible futures and that way increases futures awareness. In this thesis, the two-round online Delphi method was utilized to research experts’ opinions about the present and the future resilience of the Finnish food system up to 2050. The first round questionnaire was constructed based on the resilience indicators developed for agroecosystems. Sub-systems in the study were primary production (main focus), food industry, retail and consumption. Based on the results from the first round, the future images were constructed for primary production and food industry sub-sections. The second round asked experts’ opinion about the future images’ probability and desirability. In addition, panarchy scenarios were constructed by using the adaptive cycle and panarchy frameworks. Furthermore, a new approach to general resilience indicators was developed combining “categories” of the social ecological systems (structure, behaviors and governance) and general resilience parameters (tightness of feedbacks, modularity, diversity, the amount of change a system can withstand, capacity of learning and self- organizing behavior). The results indicate that there are strengths in the Finnish food system for building resilience. According to experts organic farms and larger farms are perceived as socially self-organized, which can promote innovations and new experimentations for adaptation to changing circumstances. In addition, organic farms are currently seen as the most ecologically self-regulated farms. There are also weaknesses in the Finnish food system restricting resilience building. It is important to reach optimal redundancy, in which efficiency and resilience are in balance. In the whole food system, retail sector will probably face the most dramatic changes in the future, especially, when panarchy scenarios and the future images are reflected. The profitability of farms is and will be a critical cornerstone of the overall resilience in primary production. All in all, the food system experts have very positive views concerning the resilience development of the Finnish food system in the future. Sometimes small and local is beautiful, sometimes large and international is more resilient. However, when probabilities and desirability of the future images were questioned, there were significant deviations. It appears that experts do not always believe desirable futures to materialize.

FUTURES OF FINNISH FOOD SYSTEM BY 2050. The perspective of resilience

Modern food systems face complex global challenges such as climate change, resource scarcities, population growth, concentration and globalization. It is not possible to forecast how all these challenges will affect food systems, but futures research methods provide possibilities to enable better understanding of possible futures and that way increases futures awareness. In this thesis, the two-round online Delphi method was utilized to research experts’ opinions about the present and the future resilience of the Finnish food system up to 2050. The first round questionnaire was constructed based on the resilience indicators developed for agroecosystems. Sub-systems in the study were primary production (main focus), food industry, retail and consumption. Based on the results from the first round, the future images were constructed for primary production and food industry sub-sections. The second round asked experts’ opinion about the future images’ probability and desirability. In addition, panarchy scenarios were constructed by using the adaptive cycle and panarchy frameworks. Furthermore, a new approach to general resilience indicators was developed combining “categories” of the social ecological systems (structure, behaviors and governance) and general resilience parameters (tightness of feedbacks, modularity, diversity, the amount of change a system can withstand, capacity of learning and self- organizing behavior). The results indicate that there are strengths in the Finnish food system for building resilience. According to experts organic farms and larger farms are perceived as socially self-organized, which can promote innovations and new experimentations for adaptation to changing circumstances. In addition, organic farms are currently seen as the most ecologically self-regulated farms. There are also weaknesses in the Finnish food system restricting resilience building. It is important to reach optimal redundancy, in which efficiency and resilience are in balance. In the whole food system, retail sector will probably face the most dramatic changes in the future, especially, when panarchy scenarios and the future images are reflected. The profitability of farms is and will be a critical cornerstone of the overall resilience in primary production. All in all, the food system experts have very positive views concerning the resilience development of the Finnish food system in the future. Sometimes small and local is beautiful, sometimes large and international is more resilient. However, when probabilities and desirability of the future images were questioned, there were significant deviations. It appears that experts do not always believe desirable futures to materialize.