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Economics student at UT Austin. Focused on capital markets, financial modeling, and the kind of analysis that holds up under pressure. I've worked across private equity, hedge funds, and investment banking — and I write code to make the work faster and sharper.
I'm a second-year Economics student at the University of Texas at Austin, graduating May 2028, with minors in Accounting and Entrepreneurship. My work sits at the intersection of financial analysis and quantitative tooling — I care about getting the logic right before trusting the output.
Across four internships I've learned that the most valuable skill isn't any specific model — it's the ability to question assumptions, stress-test inputs, and communicate what the numbers actually say. I've built LBO models, written investment memos, automated reporting pipelines in Python, and pitched ideas directly to partners.
Outside of finance I write code — mostly Python for data work and automation — and I read broadly across markets and economic history. I'm affiliated with the Kay Bailey Hutchison Energy Center and the McCombs Real Estate Center at UT Austin.
Representing the student energy community — coordinating industry events, engaging senior energy advisors, and bridging students with professionals across oil & gas, renewables, and energy policy.
Leading workshops on real estate financial modeling, cap rate analysis, debt structuring, and investment strategy. Building curriculum for undergraduate members new to CRE finance.
Competed against teams from top programs. Collaborated on market research, underwriting assumptions, and final investment recommendations presented to a panel of industry judges.
Supporting transaction analysis, valuation work, and deal materials across middle-market engagements. Assisting senior bankers on pitch decks and financial models for M&A and capital raise mandates. Exposure to the full deal lifecycle — from initial screening through close materials.
Conducted market and competitive intelligence for a Texas-based data center operator, benchmarking 15+ regional peers on pricing, customer concentration, and capacity utilization. Identified growth opportunities tied to AI-driven colocation demand. Built scenario models covering working capital and balance sheet dynamics across growth, base, and stress cases. Supported GTM strategy for hyperscaler customer acquisition.
Conducted industry research on U.S. multifamily housing — demand drivers, supply constraints, rent growth, and macro risk factors. Supported two live transactions:
$2M Multifamily — Potential LBO: Organized historical financials from CIM, benchmarked comparable transactions, contributed to senior review materials.
$1.5M Print Media — Potential LBO: Built and adjusted LBO model focusing on operating assumptions, leverage cases, and IRR/MOIC sensitivity tables. Compiled competitive landscape and comparable valuation metrics.
Streamlined monthly reporting via Python automation, reducing data compilation time by 23% and improving alpha attribution accuracy and compliance tracking. Leveraged SQL for data extraction across portfolio reporting workflows. Drafted investment memos and quarterly portfolio updates covering 30+ discretionary strategies. Conducted preliminary diligence on 15+ private investment ideas across software, energy, and healthcare. Pitched 5 ideas to partners and investors, covering thesis, variant perception, and key risks.
Finance-adjacent tools, data pipelines, and models. Some are small automations, some are more involved. All of them started because I wanted to understand something better — or make a tedious process faster and less error-prone.
Built at PNTHR Funds to replace a manual monthly reporting workflow. Pulls raw position data via SQL, reconciles against benchmark returns, computes alpha attribution by strategy, and outputs formatted Excel reports automatically. Handles edge cases like partial-month entries and mid-period strategy reclassifications.
Outcome — 23% reduction in data compilation time · improved compliance tracking accuracyBuilt during the Evermount Partners engagement to benchmark a Texas data center operator against 15+ regional peers. Structured pricing, capacity, and customer concentration data into a comparative view of market positioning, with scenario overlays for AI-driven demand projections and hyperscaler customer sizing.
Outcome — used directly in partner-level strategy discussionsFull three-statement LBO model for a $1.5M lower middle market print media acquisition. Includes dynamic debt schedules, multiple leverage cases, and a 2D IRR/MOIC sensitivity table across entry multiple and revenue growth assumptions. Built to support formal partner review at Fund Launch Partners.
Outcome — reviewed by senior partners as part of formal deal screening processA personal tool for filtering public equities across valuation, momentum, and quality factors. Pulls market data via API, computes ratios (EV/EBITDA, P/FCF, ROIC, net debt), and ranks a watchlist against configurable thresholds. Designed to surface ideas worth looking at more closely — not to replace judgment.
Ongoing — personal idea generation toolAlso on GitHub
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All repos: github.com/vishanthashok
Essays on markets, investment strategy, and the frameworks I find useful for thinking clearly about capital. Not a traditional newsletter — more like a working notebook made public. Published on Substack.
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Open to conversations about investing, strategy, internships, or markets in general. If you have an interesting problem or just want to think through an idea — reach out. I'll respond.