About Me

I was just fifteen when I first got into the world of equities. My father, an investor, roped me in as his research partner. We spent countless evenings analyzing balance sheets, reading market trends, and debating which stocks were worth a second look.

Little did I know those simple father-son lessons would fuel a lifelong passion for investing, leading me to build a strong foundation and potentially grow into a confident successful investor. My goal is to merge fundamental research with quantitative methods, creating a unique approach that translates data into clear, strategic insights and investment decisions.

When I’m not crunching data or plotting the next investment move, you’ll probably find me with a ping pong paddle in hand. I’ve been hooked on the sport since I was twelve, competing in multiple state tournaments and still chasing that perfect backhand. You can often catch me at PingPod on West 37th or West 99th. I’m a passionate F1 fan cheering for Max Verstappen, and I love diving into strategy games like Call of Duty, Fortnite, FIFA, and F1.

Skills

Experience

Education

  • Finance
    Financial Modelling, Accounting, Bloomberg, Refinitiv Workspace
  • Analytics
    Python, R, Tableau, Mathematics, Statistics, Machine learning
  • Economics
    Macroeconomics, Microeconomics, Econometrics
  • Jan 2025 - Current
    Adjunct Associate (Teaching Blockchain) at Columbia University
  • Jun 2024 - Current
    Financial Analytics at Ascot Group LLC
  • Jan 2024 - May 2024
    Investment Banking Intern at Nova Capital
  • Jun 2023 - Jun 2024
    Financial Data Research Assistant at Columbia University
  • Mar 2022 - May 2022
    Business Analyst at AMN life Science
  • Jan 2021 - Jun 2021
    Equity Research Intern at CLSA
  • Jan 2020 - Mar 2020
    Capital Markets Consultant at Knight Frank
  • Sept 2022 - Dec 2023
    MSc in Applied Analytics - Columbia University
  • Aug 2019 - Aug 2020
    PGDM in Economics and Data Analytics - Gokhale Institue of Politics and Economics
  • Jan 2019
    Chartered Financial Analyst - Passed level 1 (CFA Institute)
  • May 2016 - June 2019
    BAF (Accounting and Finance) - Mumbai University

My Services

Investment Research

Leveraging a deep understanding of financial data science, econometrics, financial modeling and due diligence to optimize investment strategies

Economic Analysis

Harnessing advanced econometrics and time series modeling to predict business cycles, with a keen insight into global economic trends and their implications

Data Science

Combining applied analytics with machine learning techniques to drive actionable insights, optimize business processes, and enhance prediction accuracy across diverse domains

My Projects

r\wallstreetbets sentiment Analysis

Spearheaded an ARIMA-based Time Series Analysis to predict 10 tech stocks. Analyzed r/wallstreetbets sentiments (Affin: 0.34, Jocker: 0.10), noted 437 more puts than calls, hinting bearishness. Found 0.6 similarity between predictions and sentiments

Active Portfolio Management

Managed a $1Mn portfolio of 15 stocks, beating the S&P 500 index by 3%. Back tested using advanced models (Index, Mean-Variance, FAMA French, Black Litterman, CAPM, Equity Valuation), algorithmic trading, and machine learning (XGBoost, SVR, Neural Networks)

Real-Time Stock Trading Analysis with Apache Spark

Designed a real-time stock trading application using HTML, CSS, Python, and Apache Spark, and analyzed a multi-million dataset of 6875 stocks, incorporating risk management into trading strategies in under 1.5 minutes.

Credit Risk Modelling

Computed a Credit risk management model using L1, L2 penalized logistic regression with an accuracy of 93.4%. Predicted Loan delinquency paving the way for more informed credit monitoring

Revenue Breakdown Dashboard for Activision Blizzard

Created A Dashboard Using Tableau and R shiny. This dashboard Visualised the Revenue, Player-related Data. Visualization created helped get a perspective on the financials of the company

The Effect of Sustainable Tags in Fashion

Conducted a survey and applied hypothesis testing, including Two-Sample T-Test and ANOVA, revealing a significant increase of 42% in the likelihood of purchase and an improved product perception among respondents.

Decentralised Esports Betting Market

Leveraging blockchain technology, our Esports betting smart contract offers secure payments to winners, dynamic odds calculation, and betting insurance for a seamless and trustworthy betting experience

Spotify Music Rating Algorithm

Predicted Ratings of Spotify data which consisted of various aspects of a song. The best machine learning algorithm out of the 23 models which I ran to get the best fit was the hyper-tuned random forest algorithm. Engineering the supervised Random Forest algorithm gave an RMSE of 14.88767.

Mean-Variance Optimiser - Portfolio Management

Programmed a Python Mean-Variance Portfolio Optimizer utilizing SLSQP optimization for any number of stocks and achieved a Sharpe Ratio of 1.26 for the portfolio, consisting of 4 tech stocks (AMZN, IBM, CISCO, AAPL).

Contact Me

ij2243@columbia.edu

+1(917)216-7363

Download CV

Made by Ishu Jaswani