Wanted for IDRC’s Hong Kong client QRC™ (update: now closed)

IDRC is building a QRC for its Hong Kong based quant trading/technology client. Our client is a start-up with secured funding from Hong Kong government as well as private investors.

We are currently looking to hire ONE person with BFSI/Capital markets IT development skills - specifically C#, Web Technologies, Databases, and C++ (all these skills are simultaneously required).

The job is based out of Mumbai, India, and is expected to start in mid-April 2013.

Candidate should have 2-3 year relevant full-time work experience, and relevant education and certifications. Work experience in buy-side stock-selection, trading systems/platforms will be highly preferred. Computer Science degrees with CFA/FRM certifications will be highly preferred. Candidate will be required to share samples of work upfront, alongside resume.

The job strongly requires value-added idea generation, and superb documentation, communication and presentation skills. The candidate will be required to demonstrate these.

Candidates meeting these requirements can expect top-decile-in-category remuneration. Over qualified candidates with much higher experience must refrain from applying. Further openings are expected in the future for more experienced people.

Candidates can write to us on talent@idrcglobal.com, with a cover letter and mentioning their current remuneration.

Candidates are strongly suggested to create a descriptive profiles on LinkedIn and using that 'follow' IDRC on LinkedIn. This will keep you posted on our activities, including job posts. We regularly visit the list of IDRC followers to match them with our relevant promotions and campaigns. (To do so, click on top right button on IDRC LinkedIn page).

Amit Batra Interview with Analytics India

IDRC managing partner Amit Batra spoke about current trends in analytics in an interview with Analytics India Magazine. Read to learn about the productivity edge obtained through the use of analytics in business - as well as about the wider impact of analytics as a social force and an emerging institution

New Research Project

IDRC has initiated a new project aimed at developing software tools to determine the value of the analysis of chart techniques. Momentum-driven returns refer to the general principle, according to which stocks, which have historically outperformed over a period of length m, will do so in future.

The overall research framework is formally defined as follows: Let U be the universe of charts (sequences of prices over the interval [0, T] of m stocks. We say that momentum (L, S) works, if for all t in [0, T] the average return for all stocks with over-average performance in the historic time period L the performance in period S is also above average. We show that under very mild conditions, for any time series of returns, and independently of the market, there exist L and S, where momentum works. However we also show that there also have to be H not equal to L and K not equal to S, in which momentum does not work. Based on these basic considerations the IDRC project is to empirically determine the values of L and S in different markets.

If you would like to learn more about the project please contact us at research@idrcglobal.com or +49(0)6084 609623.

What is Analytics

Analytics is the application of computer technology, operational research, and statistics to solve problems in business and industry. Analytics is carried out within an information system: while, in the past, statistics and mathematics could be studied without computers and software, analytics has evolved from the application of computers to the analysis of data and this takes place within an information system or software environment.

Mathematics underpins the algorithms used in analytics—the science of analytics is concerned with extracting useful properties of data using computable functions (see Church-Turing thesis), and typically will involve extracting properties from large data bases (see data mining). Analytics therefore bridges the disciplines of computer science, statistics, and mathematics.[1]

A simple definition of analytics can be "the science of analysis". A practical definition, however, would be that analytics is the process of developing optimal or realistic decision recommendations based on insights derived through the application of statistical models and analysis against existing and/or simulated future data. Business managers may choose to make decisions based on past experiences or rules of thumb, or there might be other qualitative aspects to decision making; but unless there are data involved in the process, it would not be considered analytics.

Common applications of analytics include the study of business data using statistical analysis in order to discover and understand historical patterns with an eye to predicting and improving business performance in the future.[2] Also, some people use the term to denote the use of mathematics in business. Others hold that the field of analytics includes the use of operations research, statistics, and probability. However, it would be erroneous to limit the field of analytics to only statistics and mathematics.

Analytics closely resembles statistical analysis and data mining, but tends to be based on modeling involving extensive computation. Some fields within the area of analytics are enterprise decision management, retail analytics, marketing analytics, predictive science, strategy science, credit risk analysis, and fraud analytics.

Examples of Analytics Applications

Portfolio analysis

A common application of business analytics is portfolio analysis. In this, a bank or lending agency has a collection of accounts of varying value and risk. The accounts may differ by the social status (wealthy, middle-class, poor, etc.) of the holder, the geographical location, its net value, and many other factors. The lender must balance the return on the loan with the risk of default for each loan. The question is then how to evaluate the portfolio as a whole.

The least risk loan may be to the very wealthy, but there are a very limited number of wealthy people. On the other hand there are many poor that can be lent to, but at greater risk. Some balance must be struck that maximizes return and minimizes risk. The analytics solution may combine time series analysis, with many other issues in order to make decisions on when to lend money to these different borrower segments, or decisions on the interest rate charged to members of a portfolio segment to cover any losses among members in that segment.

Marketing optimization

Marketing has evolved from a creative process into a highly data-driven process. Marketing organizations use analytics to determine the outcomes of campaigns or efforts and to guide decisions for investment and consumer targeting. Demographic studies, customer segmentation, conjoint analysis and other techniques allow marketers to use large amounts of consumer purchase, survey and panel data to understand and communicate marketing strategy.

Web analytics allows marketers to collect session-level information about interactions on a website. Those interactions provide the web analytics information systems with the information to track the referrer, search keywords, IP address, and activities of the visitor. With this information, a marketer can improve the marketing campaigns, site creative content, and information architecture.

The Challenges for Analytics Experts

In the industry of commercial analytics software, an emphasis has emerged on solving the challenges of analyzing massive, complex data sets, often when such data is in a constant state of change. Such data sets are commonly referred to as big data. Whereas once the problems posed by big data were only found in the scientific community, today big data is a problem for many businesses that operate transactional systems online and, as a result, amass large volumes of data quickly.[3]

The analysis of unstructured data types is another challenge getting attention in the industry. Unstructured data differs from structured data in that its format varies widely and cannot be stored in traditional relational databases without significant effort at data transformation.[4] Sources of unstructured data, such as email, the contents of word processor documents, PDFs, geospatial data, etc., are rapidly becoming a relevant source of business intelligence for businesses, governments and universities.[5] For example, in Britain the discovery that one company was illegally selling fraudulent doctor's notes in order to assist people in defrauding employers and insurance companies,[6] is an opportunity for insurance firms to increase the vigilance of their unstructured data analysis. The McKinsey Global Institute estimates that big data analysis could save the American health care system $300 billion per year and the European public sector €250 billion.[7]

These challenges are the current inspiration for much of the innovation in modern analytics information systems, giving birth to relatively new machine analysis concepts such as complex event processing, full text search and analysis, and even new ideas in presentation.[8] One such innovation is the introduction of grid-like architecture in machine analysis, allowing increases in the speed of massively parallel processing by distributing the workload to many computers all with equal access to the complete data set.[9]


  1. Kohavi, Rothleder and Simoudis (2002). "Emerging Trends in Business Analytics". Communications of the ACM 45 (8): 45-48.
  2. Davenport, T.H. (2006). "Competing on Analytics". Harvard Business Review.
  3. Naone, Erica. "The New Big Data". Technology Review, MIT. Retrieved August 22, 2011.
  4. Inmon, Bill (2007). Tapping Into Unstructured Data. Prentice-Hall. ISBN 978-0132360296.
  5. Wise, Lyndsay. "Data Analysis and Unstructured Data". Dashboard Insight. Retrieved February 14, 2011.
  6. "Fake doctors' sick notes for Sale for £25, NHS fraud squad warns". London: The Telegraph. Retrieved August 2008.
  7. "Big Data: The next frontier for innovation, competition and productivity as reported in Building with Big Data". The Economist. May 26, 2011. Retrieved May 26, 2011.
  8. Ortega, Dan. "Mobililty: Fueling a Brainier Business Intelligence". IT Business Edge. Retrieved June 21, 2011.
  9. Khambadkone, Krish. "Are You Ready for Big Data?". InfoGain. Retrieved February 10, 2011.

IDRC and UnRisk enter a synergistic affilation

Last week, IDRC entered into an affiliation with UnRisk, a renowned Austrian provider of sophisticated analytics platforms and valuation & risk models. With this, IDRC expects to capture demand from buy-side and corporate treasuries, as well as audit and accounting firms, for quant analytics offered in cost-efficient service framework.

We package know-how of our ground breaking technology by partnering with select and most talented groups like IDRC, said Mr. Herbert Exner, UnRisk’s boss. The partnership will “synergistically allow packaging UnRisk’s analytics, with IDRC’s client-centric service framework, the QRC™", noted Mr. Amit Batra, IDRC's president. Both leaders agree on fundamental business philosophy and are passionate about quality and innovation.

IDRC is set to tap highly demanded services of exotic OTC & structured derivative valuation and risk quantification, thus bringing client-acknowledgement to UnRisk’s product range from India, Russia, UK and Germany. 

Follow formal announcement and learn more about UnRisk on UnRisk’s official blog and website.

Follow IDRC on twitter. To learn more about IDRC visit www.idrcglobal.com.