Finance Flashcards
Talk about Allowance for Corporate Equity
De Mooji and Deveurex (2010):
- ACE: relief for equity to match debt. Doesn’t impact NPV - neutral tax - doesn’t distort scale of investment as reduce effective marginal tax rate to zero.
- Method: calculate notional rate of return * equity.
- include old equity: how treat it?
- Tax rate increases to maintain revenue: impacts location. Unless inc consumption tax instead
Talk about CBIT
- disallows exemption of interest from taxation
- not neutral tax as taking return to capital so alter NPV
Tax on economic rent impact i) investment decisions?
ii) Location decisions?
i) No - doesn’t impact WACC so doesn’t impact NPV
ii) Yes - if mobile activity
Why FFC helpful?
Fama and French (2012) reject CAPM and 3 factor model in their tests of global CAPM
- 4 factor plausible to use as the evaluation of fund performance (CAPM is unable to do)
- matches past data to the returns in the present
- if size only as lack of info then in future may be futile at explaining returns
Equity premium puzzle - evidence US luck
Siegel’s (2002) data from roughly 1900 to 2000, all countries from the UK (6.1%) to Japan (9.3%) to India (11.3%) have equity premiums - not solely US luck.
Explanations equity premium puzzle: mismeasurement of consumption
Savoy (2011): measure garbage - more volatility that GDP consumption methods.
- coefficient on risk aversion from 81 to 17
Mehra and Prescott (2003):
- credit constraints, young shut out market, priced by select group with different marginal rate of substitution of consumption
Solutions to excess volatility puzzle
Price-to-price feedback theory (Shiller 2003):
- speculative price rises, attract attention - media enhancing this
- Kahneman and Tversky (1979): representativeness heuristic - predict closeness to past patterns not probability new pattern will match past.
- E.g. Tulip Mania in Holland in 1630s
Smart money - need to offset for theoretical models to have relevance in stock market
- can’t assume all solve complicated stochastic optimisation models - irrational fixations
- e.g. short when long
- Miller (1977): short sale constraints, want limited liability, lose more than initial investment. NYSE 77-00: 0.14-1.91% all stocks.
- One model: Smart money amplify effect of feedback traders - buy ahead of them in anticipation of the price increases they will cause (De Long et al 1990)
Permanent vs transitory: if assume p dividend cut they over estimate as sceptical - info asymmetries.
- Rare disaster (Barrow 2006)
Explain trade-off theory
- MM with taxes
- Lever until MB = MC
- Kaplan (1989): in 1980s 10-20% firm value (depend if personal taxes offset)
- if no costs to adjusting cap structure, then at target debt ratio
- bankruptcy and agency costs between shareholders and creditors
- small, little taxable income, intangibles: lower MB
Explain pecking order theory
Myers and Majiluf (1984):
- Asymmetric information: managers know more about risks, value, prospects
- weighted average to reflect uncertainty
- reluctant to issue when price is too low, investors understand this and interpret a decision to issue shares as a signal that manager views the stock as undervalued
- Akerlof (1970): market for lemons
- IPO priced below market value to induce people to buy
- equity as last resort
- equity only issued when debt capacity runs out and financial distress threatens
Evidence for trade / pecking order
- intangibles low ratio: PandG TO
- related to industry (Bradley et al 1984) TO
- Devereux (15): 1% = 1% TO
- profitable: borrow the least (Wald 1999) - negative correlation debt levels and profitability. Less opportunities? More cash use to finance - PO
- equity issuance price drop but not debt (less exposed to errors in firm value as first claims) (Mayer 1990) and more for fewer security analysts following (DeMello and Ferris 2000) PO
- Myers (2001): external less than 20% of investment and most of that is debt PO
- Loughan and Ritter (2015): leaving money on the table - indirect cost in issuing - value of shares jump from IPO price. PO
- Campello (2010): 1050 CFOs - 86% pass up +NPV in crisis as credit constraint and costly to issue equity at bargain price for old shareholders > NPV of investment if cash didn’t suffice
- Barclay and Smith (2005): profitability immediate, move back to target. Info costs, minimising taxes, contracting costs
Weakness of pecking order theory.
Forgo profit maximising behaviour: not max shareholder value
Testing empirically TO and PO
Rajan and Zingales (1995):
- tangible assets
- profitability
Between countries vs within : different tax levels
- issues over investor protection
Age and debt:
- negative relationship rejects prediction of TOT but accords with PO
CEOs
- come into problem in past
- no theory
CAPM assumptions
1) mean-variance preferences
2) homogenous investors
3) no transaction costs
4) lending/borrowing at risk-free rate
Problems in testing CAPM
Roll (1977) critique: what is the market portfolio - use proxies.
- Jagganathan and Wang (1993): add human capital in market portfolio using n as proxy, more cross-sectional variance explained
Finding beta:
- daily data too much noise
- Jagganathan and Wang (1993): vary over business cycle. When collect data. More explained with time-varying betas
- Hawawini (83): beta biased downwards for small firms as non-trading
- unlevered (business risk - desired) vs levered (business and financial risk)
Expectations - ex ante
Joint hypothesis testing problem: bad pricing or bad asset pricing model?
Evidence against CAPM
Alpha = 0 if on SML (graphical depiction of CAPM) - Jensen’s alpha: find s.d. alpha large and not zero, not a blip that is arbitraged away, persist.
BJS (1972): find SML flatter. No risk-free rate or using proxies?
Other factors have explanatory power beyond beta:
- Banz (1981): size effect. Don’t know or illiquid - don’t fully disregard CAPM. Reinganum (81): 20% per year
- Jegadeesh and Titman (1993) momentum effect - do well, do well or poorly. continuing positive alpha for 6 months
- risk factors or misplaced portfolios?
- FF 93: argue they are proxies for distress (non-diversifiable risk factor)
What is FF and FFC models
Argue from ICAPM to motivate using other factors but don’t say why those factors. Multiple time periods: multiple beta coefficients.
FF 93:
- 3 factor: market risk, SMB,HML
FFC 97: add in momentum
- PR1YR: winners - losers
FFC claim explain 90% of portfolio returns whilst CAPM 70%
What is APT
Arbitrage pricing theory
- Ross (1976): beyond mean-variance.
- not specific risk
- another not testable theory
What is equity premium puzzle
Mehra and Prescott (1985):
- from 89-78
- observed equity premium cannot be rationalised by a reasonable level of risk aversion in a class of consumption based asset pricing models
- looked at coefficient and and assumed iso elastic preferences. Found to be 40, 10 deemed maximum.
- pervasive high sharpe ratio
Explanations for equity premium puzzle (in short)
- r too low (Arrow 71)
- Rare disaster (Barrow 2006)
- Survival bias (Brown et al 95) - ex post are those who survived
- Good luck (Cochrane 2001)
- ex ante vs ex post: valuation inaccuracies
- Borrowing constraints and taxes - different marginal rates of substitution of consumption (M and P 03)
- mismeasurement of consumption
- Narrow framing / loss aversion (Bernatzi and thaler 95)
Explain rare disaster
Barrow (2006): investors rationally worried about small chance of eco catastrophe.
Siegel and Thaler (1997): most financial holocausts that destroy stock value, have hyperinflation or govt appropriating most real value of debt so worse off in bonds.
Explain loss aversion
Kahneman and Tversky (1979): prospect theory where more sensitive to losses than gains of same magnitude.
MM proposition and assumptions
MM with tax
Modigliani-Miller (1958): firm value is independent of market structure. If not then an arbitrage opportunity would exist where investors could ‘home make’ the firm’s leverage - financial innovation would extinguish any deviation from predicted income.
- value depends on future cash flows not how split these up for providers of capital
Assumptions: costless default, perfect capital markets, homogenous expectations, no tax, cash-flows independent of cap structure
With tax: lower WACC
- Vlevered = Vunlevered + NPV tax shield
- NPV = NPV interest * t
Indirect costs of default
Debt overhang (Myers 1977): pass-up on +ve NPV projects as
Excess volatility puzzle explanation
Shiller (1990): stock prices more volatile than NPV of dividends
- looks at crash of 1987, fall 22%
- deeper than that of FX overshooting or price-stickiness
Dynamic trade-off theory
Fischer et al (1989):
- don’t adjust ratio every period as adjustment costs
- assessing external market or financial constraints (e.g. dividend payouts)
- Devereux: close about 24% gap each year
Lemmon et al (2008): highly persistent ratio over time
Explain indirect costs of bankrupcy
Due to agency problems:
- Graham and Harvey (2001): survey 392 CFOs - are concerned about these when make investing and financing decisions.
Debt-overhang (1977):
- forego +ve NPV projects as share benefits with creditors but shareholders full cost
Asset substitution:
- raise risk once debt in place.
Implicit incentives (Ju & Ou-Yang 2014):
- inc asset base = dec probability of default = inc future PV future investment
- raise risk, debt more costly, less NPV interest shield
Alternative theories of capital structure
Rajan and Zingales (1995):
- institutional differences: bankruptcy law, historical role
- jurisdictions determine how treat rights of equity, debt holders when liquidation occurs.
Bertrand and Schoar (2003): biases
- behavioural: tracked careers of CFOs/ CEOs.
- styles persisted as move
- old conservative, young MBA aggressive
- past experience
- representativeness heuristics: closest match the past patterns that they’ve witness.
Find tax benefit in other ways e.g. RandD is expensed
- Bradley et al (1984)
Jensen: free-cash theory
- putting firms on a diet
- agency costs in letting self-interest manager have the realm to allocate resources without legal binding to provide a set return. Debt ligations so not inefficient or invest below the average cost of capital
Evidence of tax competition
US: 35% - 15% 2018
- Apple $40bn tax bill
UK: 2010 govt most competitive in G20
Reason tax advantage for interest and its issue
Cost of business vs renumeration to capital
De Moo (2012): - both payments are a return to capital
Discriminates against risky businesses as require lower leverage - bad for growth
Desire to equalise the tax treatment of debt and equity as cost of debt became more apparent in the recent financial crisis - increases systematic risk - deepens effect even if didn’t cause
Issue with taxing MNCs
Devereux and Vella (2014):
- 1920s compromise inapprioriate in digital economy
Results in tax competition
Change location decisions
- transfer pricing (arms-length principle)
- Voget 15: no. 2.5%, assets 1.6%.
Change financing decisions
- equity vs debt (Devereux 15 - race-to-bottom - taxes strategic complement)
Solution to tax avoidance
OECD BEPS AP - agreed in 2015
- loopholes vs fundamental
- incentive incompatibility: undermine consensus
Auerbach et al (2010): destination-based tax
- immobile activity of the customer
- Giles (2018): winners (deficits), losers (surplus, tax havens)
- world transition
World CAPM theory
Karolyi and Stulz (2003):
- world vs market beta
- fortunes not perfectly positively correlated
- non-diversifable become idiosyncratic risk
- domestic security line too flat
- lower variance, same expected return
Home bias: asymmetric info explanation
Coval and Moskovitz (1999): prefer geographically proximate. US twice as much Canada as Germany but smaller in world portfolio. Explain 1/3.
Adhearne et al (2001):
- important explain underweighting by US investors, fraction of a country’s mkt cap that corresponds to firms ADR programs. Certification effect = dec info asymmetries due to adopt GAAP.
- 1994-2000 constant weight.
Beale et al (07): bias dec sharply end of 1990s, they link with regional integration and time-varying globalisation.
Buffet: never understand culture, tax laws or customs as well as US. Close in Britain
Benefits of diversifying globally
Lewis (1999): 20-100% lifetime consumption
Goetzmann & Kumar (2004):
- earns 2.4% less if least diversified.
Home bias
French and Poterba (1991):
- US 94%, Japan 98%, UK 82%
- MRS equalised
Lewis (1999):
- no level of risk aversion can justify these levels, not on efficient frontier
Mishra (2015):
- model vs data approach: computing benchmarked weights
- Model: weight in world market cap - doesn’t explicitly model returns just trust done that
- Data: time series of returns and compute benchmarks weights from mean-variance optimisation - mistrust of CAPM
Home bias explanations
- cost vs benefits (index funds - minimal transactional costs)
- hedging domestic consumption: RER - Fidora 07 20%. Lewis better hedged with foreign equity
- Institutional differences (Dalquist et al 2003) - world float. Small proportion have poor protection
- Info asymmetries
- Behavioural: Goetzmann and Kumar 04): patriotic.
Next financial crisis possibilities
- Shadow banking (outside periphery of reg)
- Cryptocurrencies - Carney
- Cyber attacks
Kay (2016): regulatory measures addressed (not very effectively) to the past rather than the next crisis.
Proposed regulation to crisis
- levies (Devereux 13 - 1-1.5% inc in equity - asset ratio - heterogeneity levies - reduce risk of safe banks but not risky banks - sub-pop don’t reduce risk
- ring-fencing (Vickers 11)
- equity cushion (Admati 13): inc capital on hand and reduce chances of bank runs (less liquidity risk -> which would turn into solvency risk)
- investor pay model
Error model of credit ratings
Skreta and Veldkamp (2008):
- if errors distributed symmetrically
- at least one overoptimistic estimate and systematically choose highest
- investor pay: choose most reliable
Bank run model
Diamond and Dybvig (1983):
- 2 NE for depositors
- induce (s,s) need to have deposit insurance
- incentive to gamble
- accompanied by regulation
CEO incentives for crisis
Falenbrach and Stulz (2011):
- CEOs didn’t reduce holding of shares in anticipation of crisis
- suffered losses
- Banks with CEO incentives better aligned performed no better
“Too much debt”
- 2004 SEC allowed IB use internal models to assess credit risk and corresponding capital charges -> higher leverage than commercial
- pressure for high returns: Goldman sachs 40.7% in 2006, Deutsche wanted 25%
“Too much risk”
- implicit guarantees: ‘too big to fail’
- A and R (09): OBSE - regulatory arbitrage - so not capital buffers - repackaged so AAA so less capital
- sub-prime - good oversecuritised so left went for even more risky which was fine unless house prices fell
- Reinhart and Rogoff (2008): ‘this time is different syndrome’ - justified as financial innovation and inflow of capital from Asia savings glut
“Too little transparency”
- credit ratings - White (09): 50% AA defaulted - not expected for that investment grade - incentives - AAA trigger happy
- if investors were chief purchases of risk then fall housing market not = financial crisis
- Keys et al (2010): originate-to-distribute channel, little disclosure, lax screening as insufficient ‘skin in the game’
- Sold sec products to SIV but bank provided guarantee
- A/R 09: legitimate securitisation to spread risk into hand of large investors - kept concentrated in financial institutions - of the $1.25trillion ABS loss only 4.5% w investors
- Sinn et al: dangerous network externalities created systematic risk
Efficient market hypothesis definition and quote
The stock prices reflect all information that is available to investors, thus they are fairly priced and the market price will be such that investors earn just their required returns, no superior returns.
Samuelson (1965): if one could be sure the price will rise, it would have already risen.
Important distinction: ex ante - before realisation of price - can be wrong ex post. Efficiency about investors using info correctly to form expectations.
Forms of EMH and ways to test each
Weak: past prices
- see if follow random walk of correlation
Semi: past prices and public info
- if abnormal returns after an announcement. On announcement, adjustment is immediate and complete.
Strong: all private info.
- no superior manager who consistently outperforms
EMH authors
Malkiel (2005):
- 10 best active funds in 60s (doubled), underperformed in next decade
- not above average returns w/o accepting >risks (risk-adjusted returns) - allows bubbles.
Grossman and Stiglitz (1980):
- need non-efficient as gathering info is costly
- incentive to acquire info vs how its spread
Jensen (1978): more sensible version: prices reflect all info where MB acting on info = MC
Fama: joint hypothesis problem. Efficient or market wrong? Can’t test ex-ante.
Talk about Glass-Steagall Act
1933:
- by separating retail banks were prohibited from using depositor funds for risky investments
- 4000 banks failed in the Great Depression
- Repealed in 1999
- Should be reinstated?
- protect those who use banking for everyday activities: payments, deposits, overdrafts, small business loans.
- investors need to be punished / go bankrupt: trading, IPO
What is home bias
Principle of diversification to reduce risk should induce an internationally diversified portfolio.
Relative to full diversification, portfolios are typically biased towards the home country of the investor.
Home bias is holding too high a proportion of portfolios in the home country of the investor relative to what world CAPM would suggest for full diversification.
Home bias: RER explanation
Fidora et al (2007): international investment induces RER and inflation risk - if PPP doesn’t hold then FX volatility may make investors wish to hold domestic assets.
- Evidence home bias fallen for mature companies but still pronounced in emerging - higher FX volatility - short-run departures from PPP are present
- home-equity better hedges this risk.
- Lewis: domestic specific risk better hedged with foreign equity - deepens puzzle.
Home bias: corporate governance explanations
Dahlquist et al (2003): countries with poor investor protection then controlling investors need to have large stakes, leaving little equity available to the market.
- so world float market (those not controlling shareholders) differ sharply from world market portfolio
- small portion of countries that exhibit this
- US not invest in developing but why not more in UK, Germany etc where strong
Why behavioural finance theories mean need to rethink economic models
- Assume mean-variance preferences
Basel III regulation
CET 1 / RWA > 4.5%
- if at the margin of this then if levies inc capital, allow inc of risk
Constrasts TO and PO
Differ on what the primary incentive for choosing a particular capital structure:
- TO: risk and return (max value of shares). Balancing PV(tax shield) with cost of distress.
- PO: reducing costs of the approach itself.
IPO explanation for PO
increased costs of equity makes debt relatively cheaper and, therefore, it is why debt is used overwhelmingly to finance
Lemmon et al (2008)
- Other studies focused US companies
- used compustat data (global), ratios stable over 2 decades, therefore key factors are time invariant
- info asym and distress costs should dec overtime
- suggest institutional factors
My conclusion on PO and TO
Since many factors could explain capital structure, newer research may actually be showing these are not conflicting approaches at all.
Premium - Arrow’s point
alpha: willingness to substitute consumption between successive yearly periods
- if a = 2 then r = 3.7%
- 71: argued theoretical ground should be approx 1 so still a puzzle
Theories for equity premium solve the puzzle?
Acknowledge that most of what underlies asset pricing models i false.
- doesn’t necessarily solve all of it - theories providing more fit
What CAPM tells you
Expected return to compensate for the given risk. To know the risk need accurate betas.
My point re size effect
Hawawini (83): beta biased downwards for small firms when using daily returns due to non-trading. Appear less risky than they really are.
- Banz size effect: find size can explain cross-sectional variation on a particular set of assets better than beta
- maybe not as actual size matters but size is a proxy for a more accurate beta -> don’t fully disregard CAPM
- if can use more accurate betas then size as a factor will become futile
What motivates empirical asset pricing models
Don’t make assumption about people’s behaviours.
- assume care about covariance with other factors as well as mean–variance
- not based on theoretical investor concerns so unsatisfactory as better alternatives to CAPM
CCAPM
- relaxes one period and have multi-period world
- concerned with utility of lifetime consumption and the uncertainty of this
- r = a + BC + e
- consumption beta: coefficient of regression of asset returns and consumption growth rather than market portfolio
Explain Bernatzi and thaler
- not rational so price idiosyncratic - narrow framing
- prospect theory - loss aversion - greater more frequent check returns, discomfort if check regularly
- BandT combine these two theories: l a over f w
- charge high premium since high stock market -> volatility in returns on financial wealth -> discomfort -> only hold if compensated by higher average returns
Why Wald problematic for TO
Higher profitability then more table income o shield and that the firm can service more debt without risking financial distress
- other tax driven tactics e.g. RandD
PO removing asymmetric info
- Bad companies could produce numbers that no one can verify to gain funding. Credit ratings only say stuff about the best, not future investment opportunities
PO vs TO in practice
PO works better for large mature firms whereas smaller growth firms face high costs of financial distress
Personal investor taxes
Barclay and Smith: (1-tc)(1-te) = (1-td)
- tc: personal investor taxes - average of capital gains and dividend tax rate
- can offset gains from interest tax shield
Price-to-price disproved by the random walk
- Not much day-to-day serial correlation as other factors feed directly into short-run changes, tendency for prices to continue in the same direction over intervals of 6 months to a year but to reverse over longer intervals
- Jagadeesh and Titman: momentum effect
- ‘lag’ may lead to bubbles, causing excess volatility
