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Projects arkistot - BigDataFinance Skip to content Menu Home People Projects Research Publications Beneficiaries & Partners Events BigDataFinancePrimingTraining Event on Textual Data in Finance Data Science in Finance High-Frequency Data Econometrics Winter School onRamifiednetworks in finance Blog Contact Projects 4 years, 13 research projects. RP1: Distributed and Real-Time Machine Learning for Financial DataWringer(WP1) 03.11.2016 Big data has both upper volume and upper 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 consumer view. Further, the worthiness to perform data analytics – dynamically and in near real-time – of rapidly waffly […] RP2: Divide and Conquer Deep Learning for Big Data in Finance (WP1) 03.11.2016   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, […] RP3: Deep Knowledge Extraction from Financial, Business, and Social Text (WP1) 03.11.2016 This research project aims to transform unstructured textual content in multiple languages and formats into a structured form suitable for traditional supersensual techniques in financial decision-making. The rencontre is to pericope semantically annotated facts in the form of relationships between concepts and entities mentioned in a stream of documents and social media. The task fits […] RP4:RamifiedNetworkWringerin Stock Markets (WP2) 03.11.2016   The research project aims to study investor behaviour and the dynamics of corporate ownership, expressly during financial crises via ramified network wringer and big data techniques. The researcher will study in depth large financial data sets, including a unique dataset of well-constructed trading records from all Finnish investors on publicly traded domestic stocks withal […] RP5: Systemic Risk and Financial Networks (WP2) 03.11.2016 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 tracery to the financial system? Should we put restrictions on institutions that are too big or too […] RP6: Modelling and Forecasting the Joint Distribution ofWindfallReturns with News (WP3) 03.11.2016   The objective of this project is to modernize existing state-of-the-art financial econometric methods by quantifying an important determinant of windfall prices, the inrush 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 older joint paper (Engle, Hansen […] RP7: Identifying the Structure of Volatility Using High-Frequency and News Data (WP3) 03.11.2016   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 value of prior research has indicated and that jumps in windfall prices worth for only well-nigh one percent of the total […] RP8: Order Books Dynamics and Announcement Effects during FinancialSlipperiness(WP3) 03.11.2016 Information arrivals are of particular interest in finance. This project studies how announcements are related to the fundamental order typesetting process. The objective is to provide empirical vestige and to model the determinants of order typesetting dynamics and information lopsidedness virtually information shocks and during a financial crisis. Secondly, given that there are investors who […] RP9: Characterising Financial Markets from Event-driven Perspective (WP3) 03.11.2016 When things happen, knowledge well-nigh the event and understanding of its importance in context propagates through a variety of world models, leading to patterns of behaviour that in turn stupefy the system. This task is to build representations and novel modelling techniques that indulge these interactions to be instantiated, observed, and leveraged. The current work […] RP10: Identify Financial Market Mood Indexes (WP4) 03.11.2016 The financial mood and conviction indexes should be increasingly 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 conviction indexes; (ii) develop visualisation […] RP11: Smart Beta Investing – A Data-Driven Strategy to Exploit Systematic Risk Factors in the Financial Markets (WP4) 03.11.2016 The wanted 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 wideness the variegated strategies, towers widely based risk […] RP12:UpperFrequency Trading Risk Management Tools Based on Scaling Law (WP4) 03.11.2016 Scaling-law has been observed in an no-go wide range of natural phenomena, from physics, biology, earth and planetary sciences, economics and finance, computer science, and taking to the social sciences. Scaling-law processes yield scaling properties for a wholesale range of values, sometimes for many orders of magnitude. Using the event-driven paradigm of directional changes and […] RP13: Machine Learning Algorithms for Risk Management in Trading Activities (WP4) 03.11.2016 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 yoke prices, market data underlying economic indicators (such as interest rates, foreign mart rates, inflation rates, and thingamabob prices) and news feeds. This predictive analytics […] Popular tags algorithms deject priming corporate ownership data econometric models ESR extracted knowledge finance financial slipperiness financial markets financial volatility job recruitment research risk management self-data velocity volatility volume Copyright BigDataFinance 2017 | All rights reserved | Login WordPress Download Manager - Best Download Management Plugin Close