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See all. MSM approaches learning as a practical exercise and it presents necessary elements that are required in the professional world. I really enjoyed my study at MSM and will come back for more relevance courses in the future. Abdul Salam from Indonesia, participant Project Management and Project Finance program October has been a critical year for global economic development.
To achieve the objectives, a research programme structured into four scientific Work Packages WP has been set up. WP1, Data Science in Finance. The goals of this Data Science WP are i to extract knowledge from data and to produce data samples, products, and features for further analysis, and ii to develop and implement techniques from machine learning, signal processing, and statistics for data analysis in finance. In particular, novel machine learning methods specially designed for big data financial analysis will be developed and applied to learning the characteristics of financial markets and extracting knowledge from textual data on different computational platforms.
This WP not only provides academically and practically important and original methods for learning from massive data sets but is also of crucial importance to the other WPs in this network that utilise large data sets in financial modelling and risk management. WP2, Complex Networks in Finance.
Research in International Business and Finance
Importantly, visualisation algorithms, which are vital to efficiently and effectively use Big Data, will be developed and used. In WP2, analysis is model-free; the overall aim is to understand the features of real financial system by letting the data speak.
For example, one RP in WP2 studies how empirical investor networks and corporate ownership bases change around news arrivals during financial crises; another RP analyses systemic risk from a network perspective, combining theoretical modelling, empirical analysis and practical policy applications to answer very topical question such as if there is a resilient architecture of the financial system and if we should put restrictions to institutions that are too-big or too connected to fail.
Clearly, WP2 depends on WP1 extract features from the original data sets.
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WP3 represents model-driven research in that it aims to develop and use econometric models with high-frequency stock prices and limit orders and massive data sets on macro and firm-specific news arrivals. Overall, this WP seeks to create empirically valid models augmented with news indicators to model the effects and interactions between news arrivals, volatility, correlations, and order book dynamics.
These new models link the news arrival data to the high-frequency financial series, providing new approaches for risk management in WP4. WP3 directly employs knowledge extraction techniques from WP1.