坦佩雷理工大学招大数据金融博士研究生# DataSciences - 数据科学
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http://bigdatafinance.eu/two_positions_available/
Tampere University of Technology is the coordinator of the BigDataFinance
Marie Skłodowska-Curie European Innovative Training Network. There are
altogether 13 positions available for doctoral students, and currently we
are seeking to fill 2 of those positions at Department of Industrial
Management/Research Group on Financial Engineering and one at Department of
Signal Processing.
Tampere University of Technology (TUT) is an active scientific community of
2,000 employees and more than 10,000 students. The University operates in
the form of a foundation and has a long-standing tradition of collaboration
with other research institutions and business life. Many of the fields of
research and study represented at the University play a key role in
addressing global challenges. Internationality is an inherent part of all
the University’s activities. Welcome to join us at TUT!
Job description:
We are looking for talented, creative and highly motivated researchers. A
suitable background for these open positions includes Econometrics, Finance,
Quantitative Finance, Data Engineering, Knowledge Engineering, Statistics,
Signal Processing, Artificial Intelligence, Machine Learning, Physics and
other related areas. Fluent written and spoken English and solid programming
(C/C++/Python/R/Matlab) and sufficient data engineering skills (e.g. SQL,
Hadoop or Spark) are required. Excellent skills in statistics, applied
mathematics and data science are essential. Skills in financial analysis are
acknowledged. If separately asked from a candidate, a suitable English
language proficiency test may be required.
Three position available (each 36 months) at Tampere University of
Technology in the following research projects.
Position 1 (ESR 4)
(WP2) Complex Networks in Finance
Research project: Complex Network Analysis in Stock Markets
Objectives: This 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
with background information on traders’ transactions and their attributes (
e.g., individual/institutional, male/female, location, and size of the
position with unique trader IDs) from 1995 to 2009 (covering the Millennium
IT bubble and recent financial crises). The first part of this RP will
provide solid empirical results on investor networks by linking traders with
similar portfolio rebalancing and trading strategies. We aim to (i) study
how empirical investor networks change during crises and to (ii) identify
the determinants of different rebalancing and trading strategies (e.g., is
it announcements or volatility that drives a certain group of investors to
trade). The second part will analyse the determinants and dynamics of
corporate ownership during financial crises.
Expected Results: We expect to provide empirical evidence on the
determinants of ownership base and dynamics, behavioural differences between
different investor groups (e.g., major institutional investors and
individual small-scale investors), how ownership structure reflects the
industrial sector of the stocks (e.g., energy sector vs IT during the
Millennium IT bubble), and how different investors react to news
announcement and process the public information. This data-intensive
analysis is very essential to gaining an understanding of the empirical
properties of the financial markets and the behaviour of investors.
Financial supervisory bodies can benefit from the study to understand the
impacts of macro variables on stock markets and to advise monetary policy
makers. Private sectors can use the results to obtain insight into and
advice on corporate strategies. Companies can use these results to
understand how ownership base affects the dynamics of the underlying stock
and investors to predict the nature of information diffusion in financial
markets. Two journal publications in Finance and a PhD Manuscript will be
completed (or at least submitted).
Position 2 (ESR 8)
(WP3) Financial Econometrics with High-Frequency Data and News Announcements
Research project: Order Books Dynamics and Announcement Effects during
Financial Crisis
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 may take the advantage of inside information before its
publication, the objective is to spot inside traders’ proactive actions
from highfrequency order book data. These topics will be addressed by using
extensive data sets over the recent financial crisis and by introducing a
new class of limit order book models with infinite-activity time-changed Lé
vy processes that can capture variation in the business activity. Though
some prominent researchers have recently addressed some questions about
liquidity available in the Treasury order book markets at news arrivals (see
Engle, R. F., M. Fleming, E. Ghysel and G. Nguyen (2012), “Liquidity,
Volatility, and Flights to Safety in the U.S. Treasury Market: Evidence from
a New Class of Dynamic Order Book Models.” Working Paper, Federal Reserve
Bank of New York Staff Reports.), liquidity at information shocks has not
been studied in depth with data from Equity markets–perhaps because of the
technical challenges of managing massive stock order book data sets. This RP
fills this gap by using ultra high-frequency limit order book data from
Nordic and US Nasdaq.
Expected Results: This project provides a framework that can be used to (i)
study the liquidity dynamics around information arrivals to help the
scientific community to develop reliable and robust models and theories for
order book markets and (ii) seek evidence of information leakage before
public news announcement to identify abnormalities in the order flow caused
by information leakage, which serves as a tool not only in trading and risk
management but also in financial supervision. Two journal submissions (
Finance/Operation Research) and a PhD manuscript will be carried out.
For more information, please contact:
Professor Juho Kanniainen
Financial Engineering Research Group/ Industrial Management
Tampere University of Technology
[email protected]
/* */
+358 407 074 532
Tampere University of Technology is the coordinator of the BigDataFinance
Marie Skłodowska-Curie European Innovative Training Network. There are
altogether 13 positions available for doctoral students, and currently we
are seeking to fill 2 of those positions at Department of Industrial
Management/Research Group on Financial Engineering and one at Department of
Signal Processing.
Tampere University of Technology (TUT) is an active scientific community of
2,000 employees and more than 10,000 students. The University operates in
the form of a foundation and has a long-standing tradition of collaboration
with other research institutions and business life. Many of the fields of
research and study represented at the University play a key role in
addressing global challenges. Internationality is an inherent part of all
the University’s activities. Welcome to join us at TUT!
Job description:
We are looking for talented, creative and highly motivated researchers. A
suitable background for these open positions includes Econometrics, Finance,
Quantitative Finance, Data Engineering, Knowledge Engineering, Statistics,
Signal Processing, Artificial Intelligence, Machine Learning, Physics and
other related areas. Fluent written and spoken English and solid programming
(C/C++/Python/R/Matlab) and sufficient data engineering skills (e.g. SQL,
Hadoop or Spark) are required. Excellent skills in statistics, applied
mathematics and data science are essential. Skills in financial analysis are
acknowledged. If separately asked from a candidate, a suitable English
language proficiency test may be required.
Three position available (each 36 months) at Tampere University of
Technology in the following research projects.
Position 1 (ESR 4)
(WP2) Complex Networks in Finance
Research project: Complex Network Analysis in Stock Markets
Objectives: This 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
with background information on traders’ transactions and their attributes (
e.g., individual/institutional, male/female, location, and size of the
position with unique trader IDs) from 1995 to 2009 (covering the Millennium
IT bubble and recent financial crises). The first part of this RP will
provide solid empirical results on investor networks by linking traders with
similar portfolio rebalancing and trading strategies. We aim to (i) study
how empirical investor networks change during crises and to (ii) identify
the determinants of different rebalancing and trading strategies (e.g., is
it announcements or volatility that drives a certain group of investors to
trade). The second part will analyse the determinants and dynamics of
corporate ownership during financial crises.
Expected Results: We expect to provide empirical evidence on the
determinants of ownership base and dynamics, behavioural differences between
different investor groups (e.g., major institutional investors and
individual small-scale investors), how ownership structure reflects the
industrial sector of the stocks (e.g., energy sector vs IT during the
Millennium IT bubble), and how different investors react to news
announcement and process the public information. This data-intensive
analysis is very essential to gaining an understanding of the empirical
properties of the financial markets and the behaviour of investors.
Financial supervisory bodies can benefit from the study to understand the
impacts of macro variables on stock markets and to advise monetary policy
makers. Private sectors can use the results to obtain insight into and
advice on corporate strategies. Companies can use these results to
understand how ownership base affects the dynamics of the underlying stock
and investors to predict the nature of information diffusion in financial
markets. Two journal publications in Finance and a PhD Manuscript will be
completed (or at least submitted).
Position 2 (ESR 8)
(WP3) Financial Econometrics with High-Frequency Data and News Announcements
Research project: Order Books Dynamics and Announcement Effects during
Financial Crisis
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 may take the advantage of inside information before its
publication, the objective is to spot inside traders’ proactive actions
from highfrequency order book data. These topics will be addressed by using
extensive data sets over the recent financial crisis and by introducing a
new class of limit order book models with infinite-activity time-changed Lé
vy processes that can capture variation in the business activity. Though
some prominent researchers have recently addressed some questions about
liquidity available in the Treasury order book markets at news arrivals (see
Engle, R. F., M. Fleming, E. Ghysel and G. Nguyen (2012), “Liquidity,
Volatility, and Flights to Safety in the U.S. Treasury Market: Evidence from
a New Class of Dynamic Order Book Models.” Working Paper, Federal Reserve
Bank of New York Staff Reports.), liquidity at information shocks has not
been studied in depth with data from Equity markets–perhaps because of the
technical challenges of managing massive stock order book data sets. This RP
fills this gap by using ultra high-frequency limit order book data from
Nordic and US Nasdaq.
Expected Results: This project provides a framework that can be used to (i)
study the liquidity dynamics around information arrivals to help the
scientific community to develop reliable and robust models and theories for
order book markets and (ii) seek evidence of information leakage before
public news announcement to identify abnormalities in the order flow caused
by information leakage, which serves as a tool not only in trading and risk
management but also in financial supervision. Two journal submissions (
Finance/Operation Research) and a PhD manuscript will be carried out.
For more information, please contact:
Professor Juho Kanniainen
Financial Engineering Research Group/ Industrial Management
Tampere University of Technology
[email protected]
/* */
+358 407 074 532