Impact of automation, surging layoffs in the financial sector, the rise of robots and threat to our future, the list of such articles on the net seems endless.
But have you ever been in a similar situation?
Have you ever been at the risk of a layoff for want of better skilled, technologically enhanced workforce?
What did you do then?
Those from an MBA-Finance background evidently aspired to master their chosen field of work. Choosing to go for an MBA in finance would mean you are good at numbers, strategies, and money-making in general.
But what if, after certain years of work and excellence at it, you feel saturated again?
Either situation, an expected threat of layoff or a saturation from existing profile, both need a career upgrade.
Considering to upgrade your knowledge and career options?
Become a Quant.
As financial securities become increasingly complex, it is still interesting to note that it is the people who understand the algorithmic trading strategies and are responsible for incorporating the same in algorithms.
Complex mathematical and financial models are drafted, interpreted and put to use by computerized mechanisms. There has been a steady growth in demand for people who not only understand the complex mathematical models that price these securities, but who can enhance them to generate profits and reduce risk. These individuals are known as quantitative analysts. To be specific, the people behind quantitative trading strategies are referred to as quants and quant traders.
Quantitative analysts design and implement complex models that allow financial firms to price and trade securities. They are employed primarily by investment banks and hedge funds, but sometimes also by commercial banks, insurance companies and management consultancies, in addition to financial software and information providers.
How to become a Quant?
Quants employ programmatic languages to deploy discretionary methods of trading. Primary methods of trading include traditional algorithmic trading strategies. The same strategies which you may have acquired after a keen observation of the market data.
Though technology is employed for pulling and utilizing data and for coding it into a computer understandable language, it is us humans who put their brains into framing the strategy and coding the same in a programming language.
Appropriate education and related job experience forms the base when learning a new trade. Picking up the basics of algo trading shall be easier for those who have studied maths and programming languages prior to the MBA program.
Skill set needed to become an algorithmic trader-expertise in trading, financial markets and programming language supported by trading platforms viz. Python, R or Matlab.
Comparison between the curriculum for MBA-Finance and Executive Program in Algorithmic Trading
Reportedly, 40% of the participants who enroll for learning programs in Algorithmic Trading come from a finance background and 42% algorithmic trading aspirants are educated in technology and computer sciences. It is easier for candidates from these two backgrounds to grasp the additional knowledge required to become a quant.
A comparative analysis of the MBA Finance curriculum and the topics covered in algo trading training programs is a must to understand how much extra effort you will need to put in.
Given below is an account of the MBA curriculum, these are topics you would have already covered during the management program.
- Behavioural finance, Business Analysis and Valuation, Capital Expenditure Planning and Control, Derivatives, Taxation, Financial Modelling, Risk Management, Securities, International Business Economics, Insurance
- Economics: Basic Econometrics, Demand and Business Forecasting, Entrepreneurship, Firms, Markets and Global Dynamics, Managing Partnerships, Banking and Microfinance
- Marketing: Advertising and Sales Promotion, Business-to-Business Marketing, Competition and Globalisation, Consumer Behaviour, International Marketing, Pricing Management, Pricing and Brand Management, Services Marketing and Strategic Marketing
- Organisational Behaviour: Assertiveness Training, Balanced Scorecard, Building learning Organisations, Corporate Governance, Cross-Cultural Management, Leadership Influence and Power, Management of Creativity and Relationships, Transactional Analysis
- Information Systems: Management Information Systems, Business and Data Communications Networks, Business Intelligence and Data Mining, Business Modelling Through System Dynamics, Cyber Law, Data Structures, Decision Support Systems, Intellectual Property Rights, OOPS, and Software Project Management)
The topics that are marked in bold within the MBA curriculum are applicable for algo-trading as well.
Apart from these topics, here’s a collation of the additional information that you will need before setting up an algo trading desk:
- In the Algo Trading module, participants are introduced to fundamentals and basic concepts of Statistics, Options & derivatives and financial markets
- Options pricing models and options Greeks and their applications
- Dispersion trading concepts, implementation and roadblocks
- Designing of risk management tool that shows sensitivity of options portfolio to different conditions, allowing the trader to modify his/her portfolio under different conditions
- Quantitative trading paradigms popular in algorithmic trading such as statistical arbitrage, market microstructure, trend following, momentum based, market making, machine learning etc
- Introduction to System Architecture and Execution Strategies for Algorithmic Trading, Basic programming in R, Python or Matlab to create technical indicators and backtesting models
- Using time series analysis for volatility estimation and prediction, creating models in R
- Using latest packages to code strategies in different programming languages
- Infrastructure, capital and business and regulatory requirements for setting up an algorithmic trading desk
Preliminary knowledge of the tools required for coding and trading is provided in detail. That includes a module on application of algorithmic trading strategies in MS Excel, application of statistics in predicting future stock prices and approximations of risk/award.
Participants can also opt to complete a project under mentorship of a practitioner/trader that involves ideation and creation of a trading strategy. Project topic qualifies for area specialization and enhanced learning.
Answering a few basic queries that one may have:
How many MBA’s have enrolled in QuantInsti® for EPAT™ till date?
Every year, approximately 40% of students who enroll for EPAT™ come from a finance and/or management background.
What are some of these EPAT™ians doing currently?
After finishing EPAT™, many of them start their own trading setup. Some of them join quant analyst roles or trading roles in a finance firm, depending on the interest and expertise.
How does EPAT™ help an MBA explore different domains?
With managerial skills already in hand, learning quantitative techniques is a benefit for those who want to build a career in quantitative trading.
The holistic curriculum of EPAT™ is designed in a way that it gives an opportunity to explore trading and quant roles.
If you want to learn various aspects of Algorithmic trading then check out the Executive Programme in Algorithmic Trading (EPAT™). The course covers training modules like Statistics & Econometrics, Financial Computing & Technology, and Algorithmic & Quantitative Trading. EPAT™ equips you with the required skill sets to be a successful trader. Enroll now!