- September 2, 2020
- Posted by: CFA Society India
- Category:BLOG, Careers, Events
Speaker: Suresh Krishnamurthy, CFA, Global Head of Research, CRISIL Global Research & Analytics
Moderator: Biharilal Deora, CFA, CIPM, Director, Abakkus Asset Manager LLP and Director, CFA Society India
Contributed By: Vikram Jhawar, CFA, Member, Public Awareness Committee, CFA Society India
Research and Analytics Industry can be a very promising choice for professionals in finance – both for freshers and experienced candidates. Research could be Buy-side or Sell-side and focus on a variety of sectors such as Pharmaceuticals, Oil & Gas or Technology. This is often coupled with the field of analytics to build financial models. Analytics, as the name suggests, focuses on utilizing technical skillsets such as R-Programming, Python and Excel-VBA to derive useful insights from data, automate routine tasks and inform research direction and interpretation. Analytics could focus on many other areas and does not have to be tied to research – especially at junior levels. In this blog, we look at the career opportunities this field offers, and the skills required to thrive in this industry.
Industry Background and Trends
Most of the Global banks and Investment Management firms have a research department which produces insights on sectors, industries and macroeconomic indicators. The field of research has existed for a while and continues to thrive. Analytics is a somewhat niche field but has rapidly built and evolved into a well-established field with its own unique opportunities and demands. Key factors driving the rapid growth in Research and Analytics are-
Rise in Customer Base
Globally, investment banks have witnessed an improved capitalization and revenue growth since the 2008 financial crisis. Large banks prefer to keep many of these functions in-house due to lower costs. They have been increasing their spending on data management, new-age analytics, and initiatives in business transformation and automation. Also, there is a growing requirement of niche specialized research in a rapidly evolving digital age.
Regulatory requirements have matured since 2008 financial crisis and nearing implementation by global as well as regional banks. This requires banks to set-up or outsource the implementation of regulatory standards.
Banks and the broader financial services industry participants have been investing in technology and operational revamp for long-term gains. There is an improved interest in utilizing big data analytics and data science to drive profitability and market differentiation, and automate routine processes using Machine Learning and Artificial Intelligence.
Let us look at the industry model in Research and Analytics. Financial Research and Analytics for global banks, other research houses and buy side firms are often outsourced or offshored. The large banks often have a mix of both in-house Research & Analytics capabilities and Outsourcing/Knowledge services partners in other global locations such as India.
3rd Party Offshoring – Global Research
In this model, KPOs and other 3rd party service providers manage entire research function on behalf of their clients. They act as a virtual extension of client teams. This model offers advantages of lower cost, diverse and skilled manpower and allows focus on core activities without compromising on compliance or operational requirements. A more recent offshoot of this style is the managed services model, where the partners manage client’s domestic research requirements leveraging the geographical expertise of 3rd party players.
In a Captive Model, Global banks and financial services providers have an in-house team to manage their Financial Research and Analytics needs. This model allows for more control over workflow, hiring of team and data & compliance related aspects. It also allows for a more integrated workforce within the organisation.
Onshore 3rd party Support/ Nearshore
In a Nearshore model, a 3rd party provides onshore analysts sitting at client locations for financial research and analytics support. This offers cost advantages along with a better integration with the client organisation. Client has better control over data and compliance aspects in this model.
Types of Research and Risk Analytics roles
Opportunities and roles in financial research exist within Investment Banks, Commercial Banks, and Buy-side firms. Some examples are as follows-
Research: Equity, Fixed-Income, Economic/Strategy, FX, Commodities, Quants etc.
Investment Banking & Capital Market: M&A, Loan Underwriting and Leveraged Finance.
Front Office Support: Sales and Marketing, Product Structuring, Trading support, Pricing model analytics, Fraud and Conduct risk.
Middle Office Support: Trade booking/re-booking, Collateral Management, Structured Trade Reviews.
Risk Management: Credit Risk, Market Risk, Operational Risk management, Portfolio Monitoring and Analytics.
Model Risk Management & Stress Testing: Model Development and Documentation, Model Performance Monitoring, Stress Testing Modelling and Validations, Scenario Generation, Execution and Review.
Career Paths and Progression
Career path and progression varies from firm to firm as well as the individual employee. However, the below are indicative of general hierarchy and progression in the field of Research and Analytics.
Data Analyst: Maintain databases, update financial models and provide earnings support. Usually offered to fresh graduates in finance/economics. An analyst at this level usually works for 2 years and then goes for higher studies such as MBA or Masters in Finance.
For designations of Research analyst and above, an employee usually holds a Master’s degree in Finance or other finance qualifications such as CA/CFA charter.
Research Analyst: Build financial models with guidance on revenue and forecasting, provide earnings support, company research etc. The job role requires upto 2 years of relevant research experience.
Senior Research Analyst: Build end to end financial models with minimal support, write detailed investment research reports, modelling and analysis of financial and industrial data, ad-hoc research support etc. The position requires 2-5 years of experience in equity/fixed Income research roles and proficiency in business intelligence tools.
Lead Analyst/Manager: Build end to end financial models with forecasts, conduct industry research for thematic top down stock selection, independent coverage of stocks and sectors, coach and mentor junior team members etc. The job role requires 5+ years of experience in sell-side/buy-side research roles and proficiency in business intelligence tools.
Associate Director: Independent management of accounts, client interactions and talent management of team as well as managing organization level outcomes. This designation requires 8+ years of experience in sell-side/buy-side research.
Director and Head of Research: Managing client relationship with multiple accounts and ownership of organization level outcomes. Directors have usually 12+ years of experience in BFSI research and managing teams.
Skills, Knowledge and Opportunities
The ability to continuously upgrade your skillset with evolving times is a key to rapid career growth in this field. Let us look at the Skills and Knowledge required to excel at different stages in your career in Research and Analytics.
At Analyst level, qualitative and quantitative research capabilities and valuation & modelling skills are a must. As one grows to become a Manager, team management and project delivery become imperative. Further up the command chain, an Associate Director is expected to manage an account – client outcomes, talent outcomes and organizational outcomes.
At Analyst level, exposure to financial markets coupled with quantitative skills such as statistical and programming knowledge is a must. As you grow up the ladder, managerial skills assume significance.
At Analyst level, project management, modelling, people oriented skills, domain knowledge of BFSI industry, strategic analysis and planning are sought. Higher up the chain, team management and driving organisation level changes become crucial.
At Analyst level, programming knowledge, data extraction & modelling, data visualization and interpretation are essential skills. Domain knowledge in Finance and overall industry is an added advantage. Managerial aspects take precedence as you grow up the ladder.
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