Projects

Big data has both high volume and high velocity – one way this manifests is as silos of in-situ data representing departments in banks that are very difficult to move and integrate to obtain a single coherent customer view. Further, the ability to perform data analytics – dynamically and in near real-time – of rapidly changing […]

  In the era of Finance Big Data, how can one conquer something so big and so vast? Management and learning in Finance Big Data should thus follow the holistic “Divide & Conquer” philosophy. We will develop a novel platform that supports all aspects of this philosophy, including workflow-based tools for content ingest and description, […]

This research project aims to transform unstructured textual content in multiple languages and formats into a structured form suitable for traditional analytic techniques in financial decision-making. The challenge is to extract semantically annotated facts in the form of relationships between concepts and entities mentioned in a stream of documents and social media. The task fits […]

  The research project aims to study investor behaviour and the dynamics of corporate ownership, especially during financial crises via complex network analysis and big data techniques. The researcher will study in depth large financial data sets, including a unique dataset of complete trading records from all Finnish investors on publicly traded domestic stocks along […]

This project seeks to analyse systemic risk from a network perspective, combining theoretical modelling, empirical  analysis, and practical (policy) applications. First, the project aims at contributing to the debate on fundamental questions such as: Is there a resilient architecture to the financial system? Should we put restrictions on institutions that are too big or too […]

  The objective of this project is to improve existing state-of-the-art financial econometric methods by quantifying an important determinant of asset prices, the arrival of economic information such as press-releases and newswire and newspaper items, and by using thus improving estimates of financial risk. First, this RP is based on our earlier joint paper (Engle, Hansen […]

  We uncover the structure of volatility in financial markets using ultra high-frequency data. Our recent work (Christensen et al. 2014) suggests that volatility over short time intervals may differ from what a vast amount of prior research has indicated and that jumps in asset prices account for only about one percent of the total […]

Information arrivals are of particular interest in finance. This project studies how announcements are related to the fundamental order book process. The objective is to provide empirical evidence and to model the determinants of order book dynamics and information asymmetry around information shocks and during a financial crisis. Secondly, given that there are investors who […]

When things happen, knowledge about the event and understanding of its importance in context propagates through a variety of world models, leading to patterns of behaviour that in turn affect the system. This task is to build representations and novel modelling techniques that allow these interactions to be instantiated, observed, and leveraged. The current work […]

The financial mood and confidence indexes should be more rapid, comprehensive, and cost-effective than existing surveybased indexes. Based on social media feeds, web search terms and query volumes, and online financial news/blogs, this RP will (i) develop big text and data mining algorithms to identify real-time financial market mood and confidence indexes; (ii) develop visualisation […]

The capital markets expose systematic risk factors (size, value, quality, volatility, carry, momentum, term structure,  illiquidity anomalies), which can be harvested persistently.23 Definition of multi-asset investment strategies builds on rigorous backtesting over the broadest possible set of data, identifying, constructing, and measuring optimal signals for allocating investments across the different strategies, building broadly based risk […]

Scaling-law has been observed in an extraordinary wide range of natural phenomena, from physics, biology, earth and planetary sciences, economics and finance, computer science, and demography to the social sciences. Scaling-law processes yield scaling properties for a broad range of values, sometimes for many orders of magnitude. Using the event-driven paradigm of directional changes and […]

The main objective is to develop a prototype framework for pricing and risk management using machine learning  algorithms and a large variety of heterogeneous and high-volume data, including tick-by-tick quotes of bond prices, market data underlying economic indicators (such as interest rates, foreign exchange rates, inflation rates, and commodity prices) and news feeds. This predictive analytics […]