Surya Rajamani

I begin the development of my differentiated theses by thoroughly modeling the financial statements. I look to understand the revenue durability, margin structure, capital allocation efficiency, and true earnings power of a firm. I ground my differentiation in free-cash-flow robustness and balance sheet efficiency.

Recently, I initiated on META where I worked to value the potential AI-driven uplift in their family of apps (ARPU), which could drive meaningful long-term upside. My process helped me to further understand their growing internally funded AI capex spend and resilient FCF generation.

My research has historically focused on sectors that have (and will continue to have) experienced structural shifts in compute, automation, and data intensity. This focus can largely be attributed to a broader passion for technology, which started when I was much younger. From building PC's and being a part of several robotics clubs, I have always enjoyed innovation and the pursuit of curiosity.

I begin by assessing the long-term TAM of any given area as a way to also evaluate the runway for value-accretive reinvestments. Furthermore, I prioritize companies with expanding end markets, scalable cost structures, and a strong (demonstrated) ability to compound free cash flow over the long term.

Turning to ROIC, I focus on identifying business models with high and improving returns on invested capital (screened as ROIC/WACC or EVA). My analysis breaks ROIC down into margin structure, asset efficiency, and reinvestment rates to assess the long-term durability of systematic/economic profits.

Valuation is anchored on intrinsic value using multi-stage DCF (organic & inorganic growth, margin progression, reinvestment intensity, and TV assumptions, etc.). Moreover, I also incorporate forward-looking multiples (e.g., P/E 2GY, EV/EBITDA, EV/EBIT, etc.) relative to peers, cycle(s), and other growth-adjusted fundamentals to decipher if consensus is mispricing long-term earnings power.