My Financial Portfolio
Investment research, analysis, and lessons learned from wins and losses in the markets.
Disclaimer: This is not investment advice. Statistically, over 90% of active stock traders underperform the market over time, but I do it because I love the thrill of research, the intellectual challenge, and the learning curve. It keeps me sharp and passionate about markets.
Current Holdings
My portfolio is simple and focused: a total market index fund for broad exposure, and AMD as my only individual stock pick (as of right now). I believe in concentrated bets on high-conviction ideas, informed by my CS background.
AMD - Advanced Micro Devices
Due Diligence Analysis
AMD vs Intel
Both compete in x86 architectures (which I dove deep into in CS354, learning about memory management, caching, and assembly on x86 systems), but AMD has surged ahead in server CPU market share, hitting 39.4% recently, surpassing Intel's legacy dominance.
- Zen Architecture: Better multi-core efficiency and power usage
- Data Center Focus: Ideal for modern cloud computing demands
- Market Share Growth: Consistent gains in enterprise segment
AMD vs NVIDIA
NVDA dominates GPUs for AI training with a ~$4T market cap on $148B revenue, but AMD shines in inference (post-training AI deployment), where its MI325X and MI350 chips could capture 5%+ market share by late 2025.
- Valuation Gap: AMD's market cap significantly lower (~$300B)
- Growth Forecasts: EPS growth +53% (2025), +38% (2026)
- AI Inference Focus: Undervalued segment with massive potential
Investment Thesis
Bullish Case
- Undervaluation stems from NVIDIA hype overshadowing AMD's potential
- Integrated CPU-GPU approach positions AMD for AI edge computing and PCs
- ARM vs x86 insights from CS252: AMD experiments with ARM for edge devices
- If inference booms, AMD could double revenue in data centers
- Strong management and ecosystem partnerships
Risk Factors
- Supply chain vulnerabilities in semiconductor industry
- Intense competition from both Intel and NVIDIA
- Market timing risk for AI inference adoption
Past Biggest Winners
Early bets that paid off big. Each with due diligence from an early investor's lens.
PLTR - Palantir Technologies
CEO Deep Dive: Alex Karp
Invested early on CEO Alex Karp's visionary leadership. He's a firebrand disrupting data analytics from the ground up, known for his no-nonsense approach and philosophical perspectives on privacy. Palantir has evolved beyond a defense contractor to serve commercial and government sectors globally.
- Education: BA Haverford (1989), JD Stanford (1992), PhD Frankfurt (Neoclassical Social Theory)
- Background: Managed money for wealthy European clients, founded London investment firm
- Connections: Decades-long friendship with Peter Thiel (met at Stanford Law)
- Philosophy: Emphasizes human oversight in AI, making PLTR's AI ethics stand out
Investment Thesis
Government contracts provide moat; commercial expansion into healthcare and finance drove massive gains.
NET - Cloudflare
Investment Thesis
Early play on cybersecurity dominance. Edge computing and zero-trust security, replacing clunky VPNs.
META - Meta Platforms
Investment Thesis
2021 bet on Zuckerberg's metaverse vision, despite skepticism. CEO's long-term bets plus core ad revenue.
Key Insight: Zuckerberg's pivot to efficiency post-metaverse hype shows adaptability. This is underappreciated for future AI worlds.
TSLA - Tesla
Investment Thesis
Early investor on Full Self-Driving (FSD), best budget EV, and charging network dominance.
Past Losers & Lessons Learned
Learning from mistakes is key. Here are my biggest losses, with what I learned and how I'll apply it moving forward.
FISKER - Fisker Inc.
What Went Wrong
Overhyped EV startup; production delays tanked it. This was Henrik Fisker's second swing at building an EV empire. His first company, Fisker Automotive, failed in 2013 after battery fires, DoE loan defaults, and bankruptcy.
Lessons Learned
- Unproven founders with history of failure carry huge risks
- Hype in emerging sectors like EVs often masks poor execution
- SPAC deals without strong fundamentals are dangerous
Future Application
- Scrutinize management track records more deeply
- Demand proof of scalable production
- Focus on established players like TSLA
NCLH - Norwegian Cruise Line
What Went Wrong
COVID recovery bet gone wrong due to debt overload. The pandemic hammered travel stocks, and while I bet on a rebound, prolonged lockdowns and variants extended the pain, with high debt amplifying losses.
Lessons Learned
- Cyclical industries vulnerable to black swan events
- Overleveraged balance sheets turn downturns into disasters
- Recovery timelines often longer than expected
Future Application
- Stress-test investments for external shocks using scenario analysis
- Prioritize companies with low debt-to-equity ratios
- Diversify away from single-sector bets
CGC - Canopy Growth
What Went Wrong
Cannabis boom bust; regulatory hurdles crushed momentum. Jumped in during the 2018-2019 hype, but slow legalization, oversupply, and black market competition crushed margins.
Lessons Learned
- Emerging markets breed overvaluation
- Regulatory delays can kill momentum
- Competition from illicit sources erodes pricing power
Future Application
- Wait for clear regulatory tailwinds before entering nascent industries
- Use valuation metrics like EV/EBITDA to spot bubbles
- Apply contrarian signals from my VC Anti-Hype Engine
Crypto
Security First Approach
I don't hold any crypto, and even if I did, I wouldn't say so for security reasons. Wink wink. Better safe than sorry in this volatile space.
Smart investors never reveal their crypto holdings publicly.
Investment Philosophy
Research-Driven
Deep technical analysis combined with fundamental research, leveraging my CS background to understand technology companies.
Concentrated Conviction
Better to own a few companies you understand deeply than many you know superficially.
Long-Term Focus
Looking for companies that can compound value over years, not quarters.
Continuous Learning
Every investment is a learning opportunity, whether it wins or loses.