Research Projects & Events

Reliability of Institutional Broker's Momentum & Oscillator Trading Strategies : An Empirical Study

  • Research aimed at examining the efficacy of trading strategies promoted by Institutional Brokers to Retail investors.

  • Python functions developed for 70 popular Trading Strategies used in brokerages such as Fidelity, Interactive Brokers etc.

  • Empirical Analysis including Portfolio Sorts, PCA, factor regressions were done to study efficacy of trading strategies.

Effect of Firm's Research Expenditure on Innovation Output : Joint work with Finance Ministry, India

  • Cross-country firm-level analysis to study the effect of research expenditure on innovation output of firms across nations.

  • Acknowledged for the contribution to "Innovation: Trending Up but Needs Thrust, Especially from the Private Sector" in the Economic Survey of India, 2020

Impact of Demonetization on Tax Aggressiveness Behaviour of Indian Firms : An Econometric Study

  • Econometric study to determine the impact of 2016 Demonetization on Tax Aggressiveness behavior of Indian firms

  • Tax aggression measures such as ETR, BTD, Discretionary Permanent Differences, Abnormal BTD were computed.

  • Difference-in-Difference Analysis on firms with varying exposure to informal economy Post-Demonetisation period .

Regularization based persistence study of financial risk factors & feature importance study

  • Applied roll-forward PCA for extracting principal risk factors amongst 50 financial features used in Kozak et.al (2020)

  • Investigated time-varying nature of top principal risk factors to determine the persistence of the risk factors.

  • Furthermore, applied PCA to construct eigen portfolios and compare the returns against Buy and Hold Strategy .

Guest Speaker : Application of Reinforcement Learning in Financial Markets (Microsoft Learn Series Event on AI)

(Invited as a Guest Speaker at Microsoft Student Learn Event ,hosted by Swati Rajwal ,Microsoft Student Ambassador at NSIT Delhi)

Application of Reinforcement Learning in Financial Markets.pptx

Workshop Highlights

  • Explained the concepts of Reinforcement Learning algorithms such as Q-value functions, Agent- Environment feedback loop, Deep Q-Network learning Agent, epsilon-greedy algorithm , Bellman Equations etc.

  • Pulled stock level price data from Yahoo Finance & built a RL trading bot with helper functions, Agent & Environment Classes and DQN Neural Network Architecture on Microsoft Azure using Python .