There has been an explosion in the velocity, variety and volume of financial data. Social media activity, mobile interactions, server logs, real-time market feeds, customer service records, transaction details, information from existing databases – there’s no end to the flood. To make sense of these giant data sets, companies are increasingly turning to data scientists for answers. (1) If you are interested how data science work for finance, join our next Meetup – Data Science Finance.
Factor Models in Finance
In this talk Metodi Nikolov, a Quantitative Researcher, will be reviewing, without being exhaustive, the usage of factor models in finance – from the simplest single factor linear regression models, through latent variables and beyond. The focus will not be put solely on stocks but rather, we will be exploring other data types. The hope is to give the listeners an appreciation for the different ways the models can be applied. Time permitting, we would also discuss fitting the models.
Building a Quant Finance Research Pipeline from Scratch: Some Lessons Learned in Battle
Georgi Kirov will share a common market-neutral statistical arbitrage framework. It will help showcase the many different ways to structure a systematic research project. From data reconciliation and signal backtesting to optimization and execution, what are some principled ways to evaluate and compare ML ideas? This process inevitably depends on the characteristics of a specific strategy, for instance, if it is liquidity-taking or liquidity-making. If time permits, fast deployment of promising research ideas will also be discussed.
Metodi Nikolov is a quantitative researcher and for the past 15 years, Metodi has been developing, implementing and running models within the Fintech and risk management software industries as part of the strong teams at FinAnalytica, BISAM and FactSet where he was the lead quantitative researcher.
Metodi holds a B.Sc. and a M.Sc. from Sofia University and is currently working towards his Ph.D. in the field of Probability and Statistics. His skills include working with R, MATLAB, C/C++, Java, Python among others.
Georgi Kirov has been applying Machine Learning for the better part of the last 10 years, mostly in Finance and in Medical Imaging. He is currently leading a systematic research team with a focus on Energy Futures. He has studied and done research at Paris Dauphine, ULB Brussels and UC Berkeley.
Except for the MeetUp, everyone is invited to share what s/he is doing in the field of Data Science – We believe that sharing is caring! So come and join us for knowledge sharing! : )
Data and Time: 08.10.2019 (TUE), 19:00 -21:30
Location: Leanplum– bulevard “Knyaginya Maria Luiza” 2, 1000 Sofia Center, Sofia
The event is open and the registration is mandatory – Book a ticket to reserve a place and to get the materials afterwards.
This event is powered by Telelink and Ontotext. We want to thank them for the support and their contribution to the Society. Special thanks to Leanplum for being our host for the event!
This event is fully booked.