RESEARCH FIELDNeurosciences › Neuroinformatics
RESEARCHER PROFILEFirst Stage Researcher (R1)
APPLICATION DEADLINE26/02/2021 23:00 - Europe/Brussels
LOCATIONFrance › Paris
TYPE OF CONTRACTTemporary
HOURS PER WEEK35
OFFER STARTING DATE01/09/2021
EU RESEARCH FRAMEWORK PROGRAMMEH2020 / Marie Skłodowska-Curie Actions COFUND
MARIE CURIE GRANT AGREEMENT NUMBER945304
“Artificial intelligence for the Sciences” (AI4theSciences) is an innovative, interdisciplinary and intersectoral PhD programme, led by Université Paris Sciences et Lettres (PSL) and co-funded by the European Commission. Supported by the European innovation and research programme Horizon 2020-Marie Sklodowska-Curie Actions, AI4theSciences is uniquely shaped to train a new generation of researchers at the highest academic level in their main discipline (Physics, Engineering, Biology, Human and Social Sciences) and master the latest technologies in Artificial Intelligence and Machine Learning which apply in their own field.
26 doctoral students will join the PSL university's doctoral schools in 2 academic cohorts to carry out work on subjects suggested and defined by PSL's scientific community. The 2020 call will offer up to 15 PhD positions on 24 PhD research projects. The candidates will be recruited through HR processes of high standard, based on transparency, equal opportunities and excellence.
Description of the PhD subject: “Impact of human cognitive traits on financial market formation”
Context - Motivation
Understanding the interactions of individuals with large-scale social and economic structures, such as financial markets is a key 21st century issue. On the one hand, we need to grasp how macroscopic dynamics of the markets may shape individual decision-making and on the other hand, how individual cognitive traits, such as bounded rationality and biases, impact and structure market dynamics.
This project aims to characterize this relation using agent-based models (ABMs) that link the individual to macro levels. The fields of ABM in finance and cognitive neuroscience have both been increasingly impacted by breakthroughs in AI and machine learning. Combining these two fields is of special significance to social and financial research, notably to ABM and other price formation models in that machine learning can embed human characteristics identified in neuroeconomics in financial market models, and vice-versa.
The project will investigate human cognitive traits and biases relevant to financial decision-making and notably in trading. The project will use the ABM “SYMBA” that has been developed by the MNC team at the LNC2 integrated into behavioural experiments geared to examine behaviour of human subjects in a controlled laboratory setting. The experiments will be designed to analyse what traits are apparent in different market conditions as simulated by the ABM. The model and analysis methods will then be used to assess cognitive traits that are emergent in a real market settings, gleaned from a data base of trader patterns provided by our unique collaboration with Swissquote Bank SA. We will examine our overarching scientific hypothesis is that human cognitive traits are instrumental to formation of macroscopic market dynamics. The proposed collaboration between cognitive scientists, computational neuroscientists and econophysicists comes at the right time of maturity, as the ample preliminary work on the technical platform as well as expertise of the collaborating consortium ensures project feasibility.
Scientific Objectives, Methodology & Expected results
The project will focus on three main aims:
- Aim 1: SYMBA Simulations with biased agents
- Aim 2: SYMBA Experiments with naive and professional subjects
- Aim 3: Validating prediction of A1 and A2 on Swissquote data
Aim 1: Modelling human cognitive trait impact on market formation
We will work on development of SYMBA simulations and model itself, especially with respect to its machine learning features. We will extend the model with agent portfolio diversification and short selling transactions as well as to extend it with multiple reinforcement learning and AI algorithms as indicated by human data. SYMBA will be extended with agents implementing human cognitive traits to profile the experiments and in turn, optimized from gathered human data. SYMBA will be used to analyse trader data from the Swissquote data base. We will extend SYMBA to model four important human traits (implemented as agent traits/biases) that are impactful to financial market structures: learning rate, delay discounting, loss aversion and confirmation bias. We will implement the cognitive traits via their associated variables in the reinforcement learning algorithms, and introduce variations to analyse in detail their role in the emergence of three macro-phenomena: Price formation, bubble formation and market crash formation. These market phenomena will be quantitatively modelled from stock exchange data (e.g. the 2015 Black Monday S&P500 and Nasdaq composite crash, 2008 NYSE financial industry stock bubble).
Aim 2: Human Experimental aspects
We will study naive subjects and professional traders in an experimental setting represented by markets generated via the simulation of SYBMA agents. We will test humans ubjects in the behavioural experimental laboratory of the ENS - PSL, while they trade stocks in a virtual platform represented by SYMBA agents. The main experiment will involve a 2×2 factorial design. The first factor will be represented by the subject tested: naivesubjects vs. professional traders (recruited thanks to our contact with financial industry). The second factor will be the psychological traits of the SYMBA agents against which our subjects will trade. Specifically we will be generate two types of markets: one will include SYMBA agents whose psychological traits guarantee market stability and the other will include SYMBA agents whose psychology induces market instability. The exact psychological traits what will be determined in Aim 1 through extensive model simulations. The results (i.e., sequences of choices made by the experimental subjects) will be analysed through the lens of the SYMBA agent algorithm and we will be able to assess which value the key parameters (learning rate, discount, cognitive biases) take in the tested population and specifically assess: i-the differences between naive subjects and professional traders and ii-how the decision-making strategy is affected by the market type.
Aim 3: Trader behaviour data analysis
We will confront the output of the model with trader-resolved data from a broker, Swissquote. We will build on SYMBA to implement trader patterns derived from the extensive trader data. We will leverage large databases of trader-resolved data (several millions of orders), first to test the presence of the human traits found in the above experiments, and then to characterize the determinants of these traits (e.g. gender, age, trading experience, activity, types of assets traded, ...). Most crucially, Swissquote clients include both retail and professional traders. These findings, in turn, will be essential to validate SYMBA framework as well as suggest additional ingredients in order to better account for the richness of the heterogeneity of market participants. Aim 3 will therefore provide essential insights regarding what level and which kind of trader heterogeneity are needed to reproduce stylized facts of financial markets dynamics faithfully.
Candidate will have an opportunity to spend research internships with collaborators at the Higher School of Economics in Moscow (Russia) and/or at the MILA Montreal (Canada) funded by the NSF Accelnet network grant.
Boris Gutkin and Damien Challet
Created in 2012, Université PSL is aiming at developing interdisciplinary training programmes and science projects of excellence within its members. Its 140 laboratories and 2,900 researchers carry out high-level disciplinary research, both fundamental and applied, fostering a strong interdisciplinary approach. The scope of Université PSL covers all areas of knowledge and creation (Sciences, Humanities and Social Science, Engineering, the Arts). Its eleven component schools gather 17,000 students and have won more than 200 ERC. PSL has been ranked 36th in the 2020 Shanghai ranking (ARWU).
- Opportunity to conduct academic research in a top 100 university in the world.
- High-quality doctoral training rewarded by a PhD degree, prepared within Ecole Normale Supérieure - PSL and delivered by PSL.
- Access to cutting-edge infrastructures for research & innovation.
- Appointment for a period of 36 months (job contract delivered by the involved component school of PSL) based on a salary of 3100 € gross employer (including employer tax) per month or approximately a 2228 € gross salary per month.
- Job contract under the French labour legislation in force, respecting health and safety, and social security: 35 hours per week contract, 25 days of annual leave per year (“congés annuels”). Eventual complementary activities may be accepted or proposed by the co-supervisors (maximum of 64h/year for teaching, 32 day/year for specific missions).
- Short stay(s) or secondment in France or abroad are expected.
- An international environment supported by the adherence to the European Charter & Code.
- Access to AI training package, with a strong interdisciplinary focus, together with a Career development Plan.
- Applicants must have a Master’s degree (or be in the process of obtaining one) or have a University degree equivalent to a European Master’s (5-year duration) to be eligible at the time of the deadline of the relative call.
- There is no nationality or age criteria, but applicants must not have resided or carried out their main activity (work, studies, etc.) in France for more than 12 months in the 3 years immediately before the deadline of the call (MSCA Mobility rule).
- Applicants must declare to be available to start the programme on schedule.
For submitting your online application, go to: https://www.psl.eu/recherche/grands-projets-de-recherche/projets-europee...
The online application should contain the following documents:
- English translated transcripts from the Master’s degree (or equivalent 5-year degree). A copy of the Master’s degree or a certificate of achievement will be required later on for the final registration.
- International curriculum vitae and a cover letter explaining the reasons that lead him/her to prepare a PhD, why he/she applies to this offer and his/her professional project (guidelines will be given to the applicants in order to help him/her in the writing of his/her letter).
- Two academic reference letters.
- A statement duly signed on the mobility rules, availability, and conflicts of interest.
The applicants can only apply to one PhD project among the available ones. Multiple applications of one candidate will automatically make all his/her applications ineligible.
The applications will be analysed by the Management Team for eligibility and completeness. Afterwards, the applications will be reviewed by the Selection Committee. In the pre-selection round (March-April 2021), applicants will be rated using a scoring system based on 3 criteria (academic excellence, experience, motivation, and qualities). A shortlist of qualified applicants will be interviewed during the selection round (June 2021) to further assess their qualifications and skills according to the predefined selection criteria.
All information regarding the applications (criteria, composition of the Selection Committee, requirements) can be found on the website of the programme, in greater detail.
The selection and recruitment processes of the PhD student will be in accordance with the European Charter for Researchers and Code of Conduct of the Recruitment of Researchers. The recruitment process will be open, transparent, impartial, equitable, and merit based. There will be no discrimination based on race, gender, sexual orientation, religion of belief, disability, or age.
The Laboratoire de Neurosciences Cognitives & Computationnelles (LNC2) is a research unit associated with the French national biomedical research institute INSERM and ENS - PSL. The LNC2 research efforts strive to shed light on the biological foundations of human mental functions and their eventual degradations in neurological and neuropsychiatric diseases. This research is based on experimental psychology, computational modelling and neuroimaging. LNC2 is composed of eight research teams focused on the various aspects of cognitive and computational neuroscience. This project implicates a collaboration between two teams: Mathematics of Neural Circuits (Boris Gutkin team leader), whose research is in mathematical modelling in neuroscience and Human Reinforcement Learning (Stefano Palminteri team leader) with a focus on modelling motivated decision making, bounded rationality in behavioral economics and their underlying neural processes. There are numerous seminars and unit-wide dissemination events organized at the LNC2, providing a rich and interactive intellectual environment for both faculty and trainees.
The Ecole Normale Supérieure - PSL is a leading multidisciplinary institution that focuses on training through research. The ENS - PSL defines and applies scientific and technological research policies, from a multidisciplinary and international perspective and counts close relationships with prestigious partners, in France and abroad. It encompasses fourteen teaching and research departments, spanning the main humanities, sciences, and disciplines. The ENS - PSL currently has a staff of almost 800 lecturers, ENS - PSL, CNRS or associated researchers, and post-doc researchers. Within its Departments, the ENS - PSL includes 40 research units identified as ENS - PSL, INSERM or INRIA, encompassing ENS - PSL and CNRS agents as well as 300 foreign researchers and 650 doctoral students. The ENS - PSL respects the principles of the European Charter and Code for Researchers and is engaged in the HRS4R certification.
Web site for additional job details
Required Research Experiences
YEARS OF RESEARCH EXPERIENCE1 - 4
REQUIRED EDUCATION LEVELNeurosciences: Master Degree or equivalent
REQUIRED LANGUAGESENGLISH: Excellent
- Candidate expected to have quantitative training (Physics, Applied Mathematics, Engineering, Computer Science).
- Knowledge/interest in neuroscience, behavioural economics, fintech.
EURAXESS offer ID: 580403
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