Work Description

I am working in the R&D Department at Borsa İstanbul as a senior specialist. As a part of my work, I’m dealing with the new trends on capital markets and finance sector. Transformation and revenue diversification are the two hot trends in capital markets. With the transformation of global stock exchanges into high-tech companies and searching for high yields, using big data in the strategic and operational workflow has been a vital component of stock exchanges. In this context, as Borsa İstanbul, we are trying to constitute data analytics by using order book data in order to increase the depth of Turkish capital markets.
RStudio Conference

The RStudio Conference by Mette Langaas named “Teaching statistics - with all learning resources written in R Markdown” is about using R as a new teaching method. In the YouTube video, she makes brief introduction about R Markdown and its modules, explains teaching approaches with using R, exemplifies R Markdown issues and shares the observations on her students.

Example 1: R for Data Analytics

High Frequency Trading and, as a subset of it, Algorithmic Trading, are the two mainstream of buy side companies. Algorithms is a decision originated from the behaviors of investors and reflects the pattern of orders and execution at a financial market. In this stochastic models data analytics are the factors that are used to get results about investment decision. As a programming language, R can be used to constitute these kind of data analytics from big data sets of order book.

Example 2: R for Surveillance

Surveillance is used in order to find the manipulations and market abuses. Before big data management tools, this monitoring was executed manually. After the development of new big data management tools, this monitoring for surveillance has been executed automatically by using AI tools. In this context R can be used for designing surveillance models by using R machine learning libraries.

Example 3: R for Robo-advisor, a fintech model

Robo-advisors (robo-advisers) are digital platforms that provide automated, algorithm-driven financial planning services with little to no human supervision. A typical robo-advisor collects information from clients about their financial situation and future goals through an online survey, and then uses the data to offer advice and/or automatically invest client assets. R also can be used to define the these kind of automated investment decisions on behalf of clients (investors) by using financial data, customer data and third part data such as social media sentiments.