The presence of an active capital market is a huge boost to economic growth. Significant growth is witnessed in the Indian capital in terms of value and volume of transactions. Traditional roles in Capital Market focused on analysis which involved studying financial statements, focusing on industry trends, economic data, and global trends to make decisions. There is a shift in investment firms to depend on data-driven decisions, trying to combine fundamental investing with quantitative models popularly referred to as Algo and Quant trading. Algo trading is the process of using a computer program to follow a set of instructions for place a trade to generate profits at a high frequency which is beyond the capacity of a human trader. The strategies developed are based on a set of pre-defined rules and are based on timing, price or quantity, or a model. Algo trading makes markets more liquid and makes trading more systematic by ruling out emotional human biases.
Technical Analysis is the second module of the super specialization: Technical analysis is the forecast of future price movements based on the past price movements The course empowers students to identify the opportunities in the market and plan optimal entry and exit points and thereby trade and invest successfully. The concept of technical analysis is old but relevant across time and the reason being price action in markets reflects human nature and technical principles can be applied to any freely traded entity in any time frame.
Standard electives are offered in capital markets like Introduction to Financial System and Markets (level1), Introduction to Financial Services, Security Analysis, and Portfolio Management, Derivatives, Finance lab, Investment Banking, Financial Modeling, and Business Valuation. In addition, JAGSoM offers Super Specialization in Capital Market
Equity Trading Strategies and back testing
Python for Financial Markets
Introduction to Machine learning for trading
Quantitative portfolio management strategies
Options trading strategies and back testing
Technical Analysis
Professor - Finance at JAGSoM
An academician with 21 years of teaching, research, and corporate training experience. Dr.Sridevi was part of a faculty led research project on Mutual Funds- a study on expense ratio to SEBI, which led to many actions by the regulator. She worked on content development project related to capital markets for a leading MNC. Dr.Sridevi currently teaches Indian Financial system and Markets at JAGSoM.
Associate Professor, JAGSoM
Before his entry into academics, Dr Raghuram was with Zacks Research Private Limited (the Indian subsidiary of Zacks Investment Research, Inc., Chicago, USA) worked in the area of Quantitative Equity Portfolio Management (QEPM).
Dr Raghuram is a PhD from the Institute of Management, Nirma University, Ahmedabad. He formulated and defended his PhD thesis proposal as an exchange research scholar at the Mexico state University, USA. He has held full time faculty positions earlier with schools like FLAME, SIBM and SASTRA
Dr Raghuram’s research interests include anomalies of the Capital Asset Pricing Model (CAPM), the Fama-French three-factor model and the Adaptive Market Hypothesis.
Prof. Jitender is a prolific equity researcher, portfolio advisor and a senior executive running financial organizations. He has over 16 years of experience in the fields of Equities, Mutual Funds, Derivatives, Fund Management and Research with various financial firms including an Asset Management Company. He also serves as a Visiting Faculty to several tier-1 Management Institutes for more than ten years. Prof. Jitender holds CFA designation from CFA institute
A very popular Professor in Finance Area, Dr. Bagga is a practicing Chartered Accountant, Financial Consultant, Auditor & Valuation Analyst, Senior Partner at N.B. & Co., a CA firm. He is also a trainer with NISM, ICICI Securities and Imarticus. He is a Strategic HR Consultant, Committee Member at ICAI, having a rich experience of more than 25 years.
Senior Associate, Research and Content at Quantinsti
Ashutosh holds a Masters in Statistics with distinction from the London School of Economics (LSE) and is a Certified FRM (GARP). He is also the co-author of A rough and ready guide to Algorithmic Trading (QuantInsti, 2020). Ashutosh has more than a decade of experience in the area of financial derivatives trading and quant finance. Apart from contributing to the overall content development at QuantInsti, he teaches Python in our flagship programme EPAT.
Associate, Research and content at Quantinsti
Jay comes with several years of experience in the BFSI industry across various roles and is actively engaged in content development for quant finance courses. is passionate about algo trading. He comes with a natural flair to programming given his formal computer science background. He enjoys developing automated trading systems and active research interests in applications of machine learning models in various aspects of trading.
He is expert in building backtest systems and options trading.
Jay is the co-author of Python Basics: With Illustrations from the Financial Markets (2019) and holds a Bachelors’ in Computer Engineering.
Quant Researcher, Quantinsti
Rekhit has studied Computer Engineering and completed his PGDM from IIM Indore.
At Quantra, he researches trading strategies and how they are applied in the real world. From swing trading to momentum trading as well as position sizing, he likes to use them on particular stocks and analyse their performance
Quant researcher
Ishan Shah leads the content & research team at Quantra by QuantInsti. Prior to that, he worked with Barclays in the Global Markets team & with Bank of America Merrill Lynch. He has a rich experience in financial markets spanning across various asset classes in different roles.
He is expert in data modelling, statistics, machine learning and natural language processing, Statistical Arbitrage and Options.
Head Research and Content at Quantinsti
Vivek teaches Python for data analysis, building quant strategies and time series analysis to our students across the world. He has over a decade of experience across India, Singapore and Canada in industry, and academia.
He has trained mid and senior-level executives in corporate finance, financial modeling, quantitative finance and portfolio theory at Larsen & Toubro, Credit Suisse and other organizations.
He is the co-author of Python Basics: With Illustrations from the Financial Markets (2019) and A rough and ready guide to Algorithmic Trading (2020).
CMT, CFTE
Technical and Derivatives Analyst at BOB capital.
Viraj teaches technical analysis. His job involves Observing patterns of the stock market to make calculated predictions about its future performance and shares high-probability trading opportunities. He trains students in identifying entry and exit opportunities using technical analysis.
Stock edge
Trading Screeners for Equity
the objective of the study is to use technical indicators to screen stocks to ascertain if the stocks change their trend on an intraday or swing basis. The group used application of Python to create the source code which ran the functions from various libraries to back test on 6 years of data of the pre-defined stock universe.
Masters Portfolio services
Quantitative portfolio management using ML
The group created a long, short momentum portfolio of stocks in the Indian Market that have futures and options trading. Applied fundamental filters to sort the stocks first and then applied momentum using ML.
Quantinsti
Swing trading strategies for the Indian Equity market
The group created, tested and paper-traded a systematic investment/trading strategy using Python and its data science stack. The group prepared and submitted a report with details on the data set used, techniques deployed, detailed results obtained on in-sample and out-of-sample data
Quantinsti
Quantum Momentum strategies
The group Created a long-only and a long-short momentum portfolio of stocks and created three different models that try to capture momentum in the market using the following approaches.
1. Technical indicators
2. Time series returns
3. Cross sectional returns and Uses ranking algorithms to rank the instruments for trading.
irage
Analysis of Indian futures Market
The group analysed how the futures market characteristics have changed over time i.e., pre covid & post covid with specific reference to select Nifty 50 & Bank Nifty stocks.
The group used python to source the past 5-year data.
