This document outlines part of my investment strategy development process and past investment journey, showcasing key milestones and performance highlights. A significant component of this journey involves the use of quantitative tools and in-depth analysis.
Key Features
- Python 2.7 Compatibility: Ensures robustness and backward compatibility.
- Attribution Analysis: Focuses on the attribution of excess returns relative to benchmarks.
Contributing
We welcome contributions to enhance the project. You can participate by:
- Reporting issues or bugs.
- Suggesting improvements and enhancements.
- Sharing your experiences in portfolio management.
- Making a donation to support the project.
StrBeta
Performance Metrics
- Compound Annual Growth Rate (CAGR): ~25%
- Maximum Drawdown (MDD): ~-12%
- Sharpe Ratio: Between 1.5 and 1.8
Note: Returns may vary due to missing data on some dates.
| Metric | MSCI World | Portfolio 01 | Portfolio 02 |
|---|---|---|---|
| Total Return | 3.16% | 6.60% | 5.75% |
| Excess Return | N/A | 3.44% | 2.59% |
| # of Holdings | 1,632 | <100 | <100 |
Detailed daily returns from March to May 2019 are provided below for further analysis.
Holdings Breakdown (Ciphered)
- Sectors: B41211, B4129, B41225, B41221, B41222, B41216, B4123
- Holdings: A455, D2111, A11612, A319620, B2, B1, A31313, A132614, H4, A334, A01410, A665, B1148, A67, A115, A6516, F2, G15157
Str01 - Compound Return: 2009-2017
| Year | Top Ten Portfolio | TOPIX | Alpha (%) | Winner |
|---|---|---|---|---|
| 2009-2010 | n/a | n/a | n/a | n/a |
| 2010-2011 | -6.17% | -9.83% | 3.66% | Top Ten |
| 2011-2012 | 15.03% | 1.37% | 13.66% | Top Ten |
| 2012-2013 | 17.46% | 23.66% | -6.20% | TOPIX |
| 2013-2014 | 15.67% | 2.58% | -6.91% | TOPIX |
| 2014-2015 | 37.19% | 30.54% | 6.65% | Top Ten |
| 2015-2016 | -3.68% | -10.07% | 6.39% | Top Ten |
| 2016-2017 | 33.39% | 18.80% | 14.59% | Top Ten |
| 2017-2018 | 34.93% | 15.33% | 19.60% | Top Ten |
Total Compound Return from 2009-2017: 212.92% (vs. 103.61% for the TOPIX Index)
My research process identified a significant mispricing in the electricity stock at $7, a conviction strengthened as market panic drove the price even lower to $0.96. However, recognizing the inherent difficulty in acting decisively on research, I observed from the sidelines while those with greater risk tolerance and conviction realized returns exceeding 2500% as the stock rebounded to $25 within two months. This experience underscored the critical distinction between identifying an opportunity and successfully capitalizing on it.
Bond ETF Fund of Funds Performance (Relative Return)
| Metric | Portfolio | Benchmark 01 | Benchmark 02 |
|---|---|---|---|
| Annualized Return | -2.39% | -3.90% | -5.51% |
| Annualized Volatility | 5.55% | 5.68% | 9.54% |
| Excess Returns | N/A | +1.51% | +3.12% |
Quarterly NAV Performance (2020)
| Year | Q1 (NAV 3/9) | Q2 | Q3 | Q4 |
|---|---|---|---|---|
| 2020 | 10.00 | 0.00% | -0.20% | 1.00% |
| Bmrk01 | 10.00 | -4.97% | 0.80% | 0.78% |
| Bmrk02 | 10.00 | -1.47% | 0.72% | -1.45% |
Start Date: March 9, 2020
Focus: A-Rated U.S. Bond ETFs
Benchmarks: Bloomberg Barclays Global Aggregate Total Return Index (Bmrk01), iShares Core US Aggregate (Bmrk02)
Real Estate Fund of Funds Performance (Absolute Return)
Key Performance Metrics
| Metric | Value |
|---|---|
| Annualized Total Return | 7.68% |
| Annualized Volatility | 2.96% |
| Maximum Drawdown | -1.54% |
| Sharpe Ratio | 2.59 |
Monthly Returns (2020)
| Month | Return |
|---|---|
| January | 1.24% |
| February | 0.53% |
| March | 0.47% |
| April | -0.04% |
| May | 0.17% |
| June | 0.06% |
| July | 0.32% |
| August | -0.19% |
| September | -0.22% |
| October | 0.06% |
Start Date: June 2020
- Private Banking & Institutional Mandates: Managed within strict guidelines for risk exposure and asset allocation.
- Bond Fund: Focuses on A-rated U.S. Bonds and balancing risk constraints with performance optimization.
- Real Estate Fund: Prioritizes minimal rebalancing frequency and aims to maximize returns while managing drawdowns.
Performance data demonstrates proficiency in managing institutional portfolios under stringent constraints and optimizing for risk-adjusted returns.