Fintech Study Guide - Lecture notes Lectures 1-14 PDF

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All lecture notes from entire course (taken fall 2018) with Professor Howell...

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LECTURE 1: Introduction & Fintech Landscape Entrepreneurial Finance: 1. Startups often need to grow rapidly, with zero profit/losses a. Why? i. Network Effects ii. Scale economies/first-mover advantage b. Angel/VC Equity investment plays a crucial role where debt or bootstrapping is infeasible Typical Startup Characteristics (differentiating them from small businesses) 1. Young 2. Risky (large potential reward, but success unlikely) 3. Growing fast (or planning to grow fast) 4. Unprofitable 5. Few capital/physical assets 6. Value of firm tied to specific human capital Information Frictions 1. Search frictions: little public info available about startup 2. Information asymmetry: entrepreneur typically knows more than investor about quality of his project a. No operating history or cash flows to use for valuation 3. Info frictions in three major domains a. Sourcing: Where do I find a deal? (Search frictions) b. Diligence: Is this a scam? (Info asymmetry) c. Monitoring: Is the entrepreneur doing what he said he would do before I invested? (Info asymmetry) Implications of Info Frictions for Startup Investing 1. Sourcing: personal networks matter a lot 2. Diligence/Monitoring: Angel/VC investment deals tend to be local a. Face to face interaction b. Investor visits startup often, meets regularly with leadership 3. Diligence: share risk and diligence cost by syndicating together 4. Monitoring: Investors have control – hold board seats a. Actively support management (e.g. help find employees and strategic partners) 5. Capital markets for private firms (including startups) are “deal markets” 6. Investment deal negotiations a. Bilateral b. One-off (bespoke) c. High transaction costs (e.g. lawyer)  lumpy investments (>$100K) 7. Angel/VC investors balance 3 goals a. Diversification across investments b. Scale in each investment c. Management/transaction costs

Typical Venture Trajectory 1. Identify opportunity  acquire resources (people, money, advisers)  execute business plan (pivot if necessary)  harvest success Typical Financing Flow 1. Friends/family  angel investors  venture capital funding (often in multiple rounds – staging)  debt if needed  Exit (IPO: usually best case for equity holders; Acquisition) Successful Entrepreneurship is Hard 1. Roughly 600,000 businesses with employees are started each year in the US 2. Roughly 3,000 receive VC funding for 1st time a. Means that only 0.05% of businesses get VC funding 3. Between 100 and 200 companies go public each year a. Means that fewer than 0.04% of businesses go public Rise in US Venture Capital 1970-2015 1. Left: fraction of public firms & share of market cap with VC backing 2. Right: investment by VC’s (amount and fraction of total US) VC-backed Firms Important to Economic Growth: Job Creation & Innovation 1. Fraction of public firm employment, R&D, and patenting VC-backed Firms are Household Names

1. No financial services companies 2. VC’s are making major plays in fintech today 3. Will the future of financial services need be VC backed? VC is a “Hits” Business 1. Returns (“carry”) come from small portion of investments a. Most portfolio companies fail b. Median fund returns are low if not negative c. Big returns historically from IPO’s (and after!) 2. Deal structures/price paid relatively minor role in outcomes 3. What matters: terminal share that the investor owns, company performance, capital markets at exit Stylized Facts about VC Investing 1. Low barriers to entry, but only a small group of top-tier firms get the best deals 2. Tend to herd into hot sectors 3. Cyclical returns, but sticky outflows 4. Not clear how well the system works for LPs (limited partnership) a. But social returns very high Finance Income and Intermediated Assets as Share of GDP

1. Big market (2017 GDP = $19.5 trillion; financial income = $1.4 trillion) 2. After adjusting for quality, no decline in recent decades in cost of intermediating assets (income/quantity) 3. Suggests that there is room for (disruptive?) efficiency improvements Rise of the Megabanks 1. From 2001-2010, number of banks and amount of assets each bank has largely consolidated Fintech  Catchall Term for Techs/Startups Trying to “Disrupt” Financial Services 1. “Internet/Silicon Valley” coming for financial services a. As it came for mail, bookstores, music, news 2. Startups finding ways to do what parts of the existing system do at 1/10 to 1/100 the price 3. More focus on empowering the customer 4. Most exciting: Disintermediation (e.g. P2P insurance, ICO’s) 5. Most common: doing something incumbents do, but cheaper and better a. E.g. payments, mobile banking b. & with better code…data/machine learning-driven How Startups Disrupt Incumbents 1. Incumbents say... a. “They don’t have our balance sheet.” “Our brand is highly trusted.” “Supply chain is complex.” “This is a highly regulated industry.” “We’ve been doing this for 140 years.” “Clients trust us. They won’t trust some startup.” “We have a cost advantage.” “Our risk management capabilities are proven.” 2. Most startups fail, it’s the ones that succeed that become a problem a. i.e. Amazon, eBay, etc. i. Caveat: the banks have existing brands, existing scale, and a low cost of funds ii. JP Morgan market cap = $292 billion; Citigroup = $207 billion iii. They can eat (acquire) the biggest startups as an afternoon snack Why Now? 1. Changing IT/AI/data tech a. Larger network effects: faster path to a billion users b. Lower costs of launching startup: because of AWS, etc. can rent and rapidly scale computing power c. Blockchain technology 2. Lower interest rates (important for startups that need capital pools to, e.g. lend) 3. Regulation: scrutiny, complexity, cost increased after financial crisis; made it harder for big banks to experiment 4. Changing wealth demographics/geographies a. Consumers expect digital, mobile b. Asians and Africans leapfrogging directly to mobile/digital payment Four Views of the Current Fintech Bubble 1. It’s all hype, not too much will change 2. Wall Street becomes a Palo Alto outpost, and banks become diminished utilities 3. Big banks will gobble up all the upstarts and will become meaningfully different entities 4. Blockchain will decentralize and disintermediate financial services, returning rents and power to the people LECTURE 2: Equity Crowdfunding Personal Finance 1. Startups bringing Silicon Valley focus on UI/UX to banking and wealth management 2. Automated investing services (Robo-advising)

3. Holistic data integration for financial planning (combine insurance, utility bill, checking account, Netflix, etc.; e.g. Mint” 4. Banking: New tech to replace outdated legacy bank infrastructure a. Future is online only; we don’t need branches b. Proactive relationship with user: predictive, offer options (e.g. there’s a cheaper utility bill available) Startup Approaches to Banking 1. Front-end: Consumer, commercial internet only a. “Digital Challenger Banks” successful in UK, e.g. Atom (solely has smartphone based) b. US has fewer because the cost of acquiring customers is apparently high in US (e.g. BankMobile, Moven, Chime, Qapital) 2. Back-end: API (integrate with bank infrastructure), analytics a. E.g. Plaid: “API for banking data” Insurance 1. New providers/platforms a. Oscar, Lemonade (“peer” groups disperse profits to charity of choice), Friendsurance, Guevara (peer-to-peer) 2. AI applications especially large in underwriting 3. Value add relative to incumbents (e.g. Metlife, Northwestern, Mutual, Prudential): Cheaper, easy buying experience Institutional Investment/Research 1. AI driven hedge fund startups (Sentient) a. E.g. Numerai: blockchain-based crowdsourced algorithm 2. Social platforms (Stocktwits) a. E.g. eToro: Follow investments of successful lead investors (a bit like angel syndicates) 3. Data/analytics (Kensho, Second Measure, Dataminr) 4. Trade finance platforms: some crypto-based (e.g. TRADEIX trade finance platform for banks, LedgerX clearing and trading for digital currency swaps and options) Lending 1. Small loans unprofitable for big banks (esp. post-Dodd Frank) a. What was profitable: credit card based lending b. Small business loans of income $200K per year for past 3 years, or > $1million in net worth) b. Startup files form D with SEC before starting to solicit c. Company responsible for verifying that all investors are accredited, else banned for a year from fundraising 3. Title III: “Retail” Crowdfunding a. Startups can raise up to $1million in a 12-month period b. Must use SEC registered platform (online intermediary) c. Must be a US company d. Must disclose some financial info and report regularly e. Anyone can invest on an up to lesser of 10K or 10% of income f. No special purpose vehicles 4. Has this been successful? a. Started in 5/2016, 178 offerings in 2016 (99 successful), 481in 2017 (200 successful), end of 2017 - 36 approved intermediary platforms 5. Accredited under Title II most widely used a. Angel investing gone online b. Mostly very curated: Only small fraction of applicants allowed on platforms i. This is shift; in early days, there was a spirit of letting anyone fundraise ii. CircleUp, Crowdfunder shifted to intense curating c. Only a small fraction funded AngelList 1. Today: largest, most prestigious marketplace for startup investing ($160 mil raised in 2015 for 441 startups) 2. History maps evolution of crowdfunding a. Social network for investors and startups (2010)  Database (2012)  Syndicates – angels coinvest with a leader angel, AL takes 5% (2013)  $25million fund to invest in AngelList syndicates/angels (2014)  Recruiting (2015) 3. Graph shows cumulative # os successful syndicated vs non-syndicated deals on AngelList Special Purpose Vehicles (SPVs) 1. AngelList and other crowdfunding platforms increasingly use “Special Purpose Vehicles” (SPVs) to syndicate (combine many small investors) 2. An SPV is the legal entity representing the syndicate a. It allows many passive investors to be represented as one entity (on cap table, board) 3. Founders negotiate with one lead investor 4. All follower investors buy equity in an SPV 5. Key benefit: Startup doesn’t have to manage 100s of individual investors Can Crowdfunding Address the 3 Information Frictions? 1. Sourcing, Diligence, Management/Transaction Costs a. Crowdfunding platforms in theory address sourcing and transaction costs b. Syndicates (SPVs) around lead investor; Address Diligence i. Reputation (repeated game) & carry from follower investors (incentives) Real Estate Crowdfunding 1. Reliable numbers are hard to find but ~$3.5 billion in 2016; likely much higher in 2017, was only $130 million in 2014! 2. 125 real estate crowdfunding sites (e.g. RealtyShares, Rich Uncles, Fundrise, RealtyMogul)

a. Mostly: accredited investors who take stakes in house flipping b. Marketplaces benefit from scale, likely a few will shake out 3. Fundrise pioneered real estate crowdfunding a. Shifted entirely in 2015-2016 to e REITs using Reg A+ IPO LECTURE 3: Valuation Soft Aspects of Valuation 1. Valuation (and allocations of value) depends on price 2. Price depends on bargaining power a. Who has more? Entrepreneur or investor? b. Should derive from who has more power over whether the venture succeeds 3. Valuation is hard a. Much uncertainty; novelty = poor comparables (which are at the heart of valuation) b. Without analytical rigor, you’re just gambling, but numbers depend on assumptions Valuation Methods 1. Discounted Cash Flow (DCF/NPV) method 2. Comparables (method of multiples/relative valuation) 3. Real Options 4. Investor buys % of firm for $ amount  can infer valuation Net Present Value 1. NPV = sum of discounted future cash flows (CF) ; discounted at periodic rate of return (r) 2. NPV =

CF 1 CF 2 CF 3 + +… + 2 1+r ( ) (1+r ) (1+ r)3

3. CFt= EBITt * (1-tax) + DEPRt – CAPEXt – ∆NWCt + ot 4. R = WACC = (D/V) * rd * (1-tax) + (E/V) * re **DCF formula will not be on tests ** ot = increases in taxes payable, wages payable, etc ** V = D + E Benefits of DCF 1. Technically sounds 2. Less prone to market distortions (e.g. bubbles) than other methods Why DCF Doesn’t Work Well with Startups 1. None or very short operating history 2. Calculating r is tough as you must use publicly traded companies to create a beta (3 problems) a. Business models are rarely truly similar b. Need a liquidity discount, can easily buy and sell the startup (how to calculate?) c. Capital structure not in steady state, usually no debt. Need to unlever and relever the beta, without knowing the target capital structure 3. High uncertainty (fat tailed distribution) a. Not clear that taking an average of possible cash flow scenarios is the right approach Comparables Analysis (Relative Valuation) 1. “Ballpark” Valuation by comparing to firms with similar “value characteristics” a. e.g. risk, growth rate, capital structure 2. Multiple =

What you pay for asset What you get∈return

3. Example: Suppose in Neighborly’s industry, enterprise value is on average 5 times revenue (multiple =

EV Revenue

= 5)

a. We forecast Neighborly will have $50 million in revenue when we exit b. What is the implied enterprise value? $250 million 4. Steps: a. Pick multiple (e.g. P/E, EV/patents) b. Pick comparable firms (sector/size/region; public/private) c. Adjust to fit your story (growth/risk) VC Method 1. In exchange for $25 million today, what does the company have to return to me in the future? a. This depends on… i. Opportunity cost: What else could you do with your money? ii. Risk: How uncertain is the company’s success? iii. Time to exit: How long will it take the company to have an IPO/large acquisition? iv. Value at exit: How much will the company be worth in a great state of the world? 2. What fraction of a company does the VC need for a given investment? (need valuation to answer this) a. Focus on whether the venture can deliver needed return, not exact valuation b. Most VC backed startups deliver little to shareholders, but ignore these lesser payoffs c. Focus on plausible exit value in scenario with max payoff i. Usually use a multiple ii. Ignore intermediate cash flows iii. Discount terminal valuation by “required return” 3. Steps a. Estimate terminal value (TV, company value in year you forecast exit) i. What is value in a “successful exit”: IPO or competitive sale ii. What year? Strong positive cash flows iii. Approaches: DCF, comps, successful exits in industry (shortcut) iv. Example: Neighborly TV = $1 million b. Discount TV by required internal rate of return (discount rate or IRR) i. 30-80% (higher for earlier rounds); note: IRR is the “kitchen sink”: includes risk, opportunity cost of capital ii. Discounted TV =

TV (1+IRR) years

=

1,000,000 (1.4)3

= 364.4

iii. Suppose time to exit = 3 years & IRR = 40% c. Calculate the required stake at exit (% ownership) i. Required Final Stake =

Investment 25 =0.0686=6.9 % = Discounted TV 364.4

ii. Alternatively: at exit, how much must our stake be worth? 1. Stake value at exit = (1 + IRR)years * investment = $68.6 million 2. Required final stake =

Stake value at exit 68.6 = =6.9% TV 1000

iii. Post-money valuation: Includes new capital 1. =

Investment 25 = =$ 364.4 million Ownership Stake 0.0686

iv. Pre-money Valuation = Post-money Valuation – Investment 1. = 364.4 – 25 = $339.4 million a. “sweat equity” if first round d. If expect additional funding rounds before exit, estimate dilution and required current stake i. What if Neighborly will need more financing before exit? ii. Initial investment will be diluted and you must adjust valuation downward/ask for additional equity iii. Method: start from last round and work backward iv. Why stage investments (have multiple rounds)? 1. Retain leverage, ability to monitor 2. Staging is a form of experimentation, investor has the option to continue if the experiment goes well v. Neighborly will need to raise a further $30 million next year, in a series C round (one year after our $25 million Series B round). What stake does the Series C investor require? Suppose they use a 30% IRR vi. Retention Ratio =

1 1 = =0.951 1+future ownership 1+0.051

1. Future ownership = % of firm that other stakeholders will be given vii. Required current ownership (Series B) = 1.

¿

Required final ownership Retention Ratio

0.0686 =0.0 72=7.2 %(vs 6.9 % ) 0.951

viii. Alternative Approach: 1. Adjusted TV = TV * (1 – Future Ownership) 2. Redo required stake in earlier round a. Adjusted TV = 1000 * (1-0.051) = 949 b. Required final stake = investment/discounted TV =

25 /949 =0.072 3 (1.4 ) **order depends on goal of calculation 4. Review a. What is 2nd round post-money valuation? Pre-money? i. Post-money = 30/.0507 = $591.7 million ii. Using DTV = 1000/(1+.3)^2 = $591.7 million iii. Pre-money = 591.7-30 = $561.7 a. What is updated 1st round post-money valuation? Pre-money? a. Post-money = 25/.072 = $346 million, b. Using DTV = 949.3/(1+.4)^3 = $346 million c. Pre-money = 346-25 = $321 million VCs Technically Transact in Shares 1. Given X shares outstanding before round, how many new shares (#Snew) are required? a. Required current ownership = b. Price per share =

¿ Snew new ¿S +X

Investment new ¿S

2. Pre-money valuation = #Soriginal * Price per share 3. Assume there are 1 mill shares outstanding before the first financing round (Series B) How many new shares are issued in Series B (after accounting for dilution from Series C)?

a. Stake =

New shares New shares = =0.072 New Shares+ Shares Outstanding New Shares +1,000,000 i. New Shares = 77, 586 4. What is the Price per share? a.

Investment 25,000,000 =$ 322 per share = 77,586 New Shares

5. What is the Pre-money valuation using the # original shares? a. Pre-money = original shares * Price = 1,000,000 *322 = $322 million 6. How many shares are outstanding after Series C? a.

New shares =0.0507 ; New Shares=57,551 New Shares+ 1,077,586

b. Shares outstanding = 1,077,586 + 57,551 = 1,135,137 7. What is the Price per share in Series C? a.

30,000,000 =$ 521 per share 57,551

Comparing DCF and VC Methods 1. VC Method: a. Only need “success scenario” TV b. No probabilities, intermediate cash flows, or betas c. High & arbitrary required returns 30-80% 2. DCF Method: a. Average inherent value across scenarios b. Explicitly model all intermediate cash flows and probabilities of success and failure c. Discount rate from CAPM $200K-ish to invest b. Charge 1-3% of portfolio value every year c. Advisor rebalances regularly and minimizes taxes What do Robo-Advisors Offer? 1. Initial entrants: Betterment and Wealthfront 2. Low initial investment requirement which enables less wealthy people to get investment advice 3. Competitive and transparent fees a. E.g. Wealthfront takes 0.25% per year, first $10,000 managed free; traditionally Morgan Stanley and JP Morgan charged >0.35% 4. Offer range of investment options, portfolio management features (e.g. tax loss harvesting”) 5. Made possible, in theory, by very low marginal costs a. Because “robo”, the algorithm makes decisions rather than each customer receiving human advice What Does This Mean for the Robo-Advisor Business Model? 1. Scale is crucial

2. With $10bn in AUM, revenue is only $25mil 3. Customer acquisition costs have thus far been high, on the order of $300 per customer a. This implies that with no other costs, need a $120,000 account to break even b. Wealthfront’s average account is more like $50,000 4. Meaningful profitability requires AUM in $100s of billions Strategy Options 1. Many (e.g. Betterment) have added human financial planners to the digitial advice 2. Wealthfront is sticking to the no-human model 3. Wealthfront has added products over the years a. Retirement planning, stock selling for employees of newly IPO’d companies, loans (as % of your account value), high quality tax minimization, building an “entire banking and brokerage platform” Acorns 1. Targetin...