Amarnath Rao

Banking professional with 3 years of experience in banking and capital markets, currently an Assistant Manager at HSBC focusing on credit analysis for alternative funds. Knowledge in financial modeling, data analysis, and capital markets research, with a Master’s in Data Science in progress and CFA Level I candidacy. Seeking banking roles mainly in equity or debt research to apply a strong mix of financial insight and technical expertise. Interested in other banking roles as well.

• Achieved HSBC Rising Star Award through effectively and quickly managing BAU.

CB1 Exemption – Institute and Faculty of Actuaries

Portfolio Optimization Using Machine Learning and Macroeconomic Indicators.

Tools: Google Colab, Jupyter Notebook, Python

Developed portfolio optimization models using machine learning (RF, XGBoost), deep learning (LSTM, CNN), and reinforcement learning (PPO). Integrated macroeconomic indicators (CPI, GDP, interest rates) with stock price data to capture non-linear market patterns. Conducted backtesting, rolling validation, and stress testing to improve robustness, outperforming traditional methods (MPT, CAPM) in Sharpe ratio and risk-adjusted returns.

Rental Price Prediction in Indian Cities
Tools: R Programming & Microsoft Excel
Built machine learning models to predict rental prices using real estate data from Kaggle (4,746 entries, 13 features). Performed extensive data cleaning (outlier handling, binning, log transformation) and feature engineering on categorical variables. Compared Linear Regression, Decision Tree, and XGBoost; XGBoost achieved 99.99% R² and MAE of 11.21 INR, outperforming others in accuracy.

Crop Image Classification using Deep Learning
Tools: Jupyter Notebook [TensorFlow, CNN, ResNet50, EfficientNet B0, ImageDataGenerator]
Developed three deep learning models (CNN, ResNet50, EfficientNet B0) to classify crop types (Banana, Corn, etc.) from a 6,000-image Kaggle dataset. Used image augmentation, dropout layers, and max pooling to reduce overfitting and improve generalizability. ResNet50 performed best, achieving highest precision and recall among models; identified key areas for model performance tuning.

Used Car Price Prediction
Tools: Jupyter Notebook [Pandas, Scikit-learn]
Built regression models to predict used car prices from 19,237 rows of structured data. Conducted end-to-end preprocessing: handled outliers, removed duplicates, performed one-hot encoding of categorical variables. Random Forest model achieved 95.9% R², outperforming XGBoost and MLR in accuracy and MAE; supported fair valuation for buyers and sellers.

Indian Society, Treasurer, SMK Bandar Puchong Jaya (B) Jan 2018 – Dec 2018
• Collection of Society funds from members.
• Keeping good accounting records of inflows and outflows
• Ensure all transaction receipts are recorded and safekept
Chess Club, Committee Member, SMK Bandar Puchong Jaya (B) Jan 2017 – Dec 2018
• Represented for School Level Chess tournament for 2 years.
Kadet Bomba, Committee Member, SMK Bandar Puchong Jaya (B) Jan 2017 – Dec 2018
• Assists the President in performing his or her duties.
• Ensure safety of fellow students during fire drills
• Volunteer in performing act in fire drill seminars