Managerial Economics

1. Managerial Economics Lecture Eleven: Alternative theories of finance

2. Recap • Conventional CAPM finance theory • Derived by applying conventional economic theory to finance • Utility maximising individual, budget line of available investments • BUT it neatly separates finance theory • (Modigliani-Miller “dividend irrelevance” theorem etc.) • Firm’s value independent of how finances investment • Finance therefore doesn’t affect the economy • Empirical data manifestly refutes CAPM • We need a really new theory of finance… • For complete coverage, do Behavioural Finance with me and Craig Ellis next semester; • But for an overview…

3. The really new finance… • Two key aspects • Economics & Finance not separable • How firm finances its investments does affect value • How investment financed affects economic outcomes • Finance does affect the economy. • Back to Schumpeter… • Behaviour of finance markets • Not “random walk” and “fundamental value” but • “Fractal walk” & “speculators speculating on speculators” • First, Schumpeter’s finance-real economy link • Entrepreneurs must borrow to finance innovations • Credit thus plays essential role in economy’s boom/bust cycle

4. Schumpeter’s model: money has real effects • In Schumpeter’s model, entrepreneurs start new firms • No retained earnings, capital, workers • “…the carrying out of new combinations takes place through the withdrawal of services of labor and land from their previous employments… • this again leads us to … the heresy that money, and … other means of payment, perform an essential function, • hence that processes in terms of means of payment are not merely reflexes of processes in terms of goods. • In every possible strain, with rare unanimity, even with impatience and moral and intellectual indignation, a very long line of theorists have assured us of the opposite.” (Schumpeter p. 95)

5. Schumpeter’s model: money has real effects • Conventional interpretation of money emphasises • Money simply a “veil over barter” • Money plays no essential role • Double all prices & incomes, no-one better or worse off • Schumpeter accepts above as true for existing products, production techniques, etc., in general equilibrium • But new products, new methods, disturb “the circular flow”. Money plays essential role in this disequilibrium phenomenon • Affects the price level and output • Doubling all prices & incomes would make some better off, some worse • Those with debts would be better off • Including entrepreneurs…

6. Schumpeter’s model: money has real effects • Conventional theory suffers from “barter illusion” • Existing producers using existing production methods exchanging existing products • “Walras’ Law” applies • Major role of finance is initiating new products / production methods etc.; • For these equilibrium-disturbing events, classic “money a veil over barter” concept cannot apply. • “From this it follows, therefore, that in real life total credit must be greater than it could be if there were only fully covered credit. The credit structure projects not only beyond the existing gold basis, but also beyond the existing commodity basis.” (101) • “Walras’ Law” therefore false for growing economy…

7. Schumpeter’s model: credit has real effects • “[T]he entrepreneur needs credit … • [T]his purchasing power does not flow towards him automatically, as to the producer in the circular flow, by the sale of what he produced in preceding periods. • If he does not happen to possess it … he must borrow it… He can only become an entrepreneur by previously becoming a debtor… • his becoming a debtor arises from the necessity of the case and is not something abnormal, an accidental event to be explained by particular circumstances. What he first wants is credit. Before he requires any goods whatever, he requires purchasing power. He is the typical debtor in capitalist society.” (102)

8. Schumpeter’s model: credit has real effects • In normal productive cycle, income from production finances purchases; credit can be used, but not essential • “[T]he decisive point is that we can, without overlooking anything essential, represent the process within the circular flow as if production were currently financed by receipts.” (104) • Effectively, Say’s Law applies: “supply creates its own demand” • Aggregate demand equals aggregate supply (with maybe some sectors above, some sectors below) • But credit-financed entrepreneurs very different • Expenditure (demand) not financed by current receipts (supply) but by credit • Aggregate Demand exceeds Aggregate Supply

9. Schumpeter’s model: credit has real effects • Credit finance for entrepreneurs thus endogenous: not “deposits create loans” but “loans create deposits”: • “[I]n so far as credit cannot be given out of the results of past enterprise … it can only consist of credit means of payment created ad hoc, which can be backed neither by money in the strict sense nor by products already in existence... • It provides us with the connection between lending and credit means of payment, and leads us to what I regard as the nature of the credit phenomenon.” (106)

10. Schumpeter’s model: credit has real effects • “[G]iving credit involves creating purchasing power, and newly created purchasing power is of use only in giving credit to the entrepreneur,… credit is essentially the creation of purchasing power for the purpose of transferring it to the entrepreneur, but not simply the transfer of existing purchasing power. • The creation of purchasing power characterises, in principle, the method by which development is carried out in a system with private property and division of labor. • By credit, entrepreneurs are given access to the social stream of goods before they have acquired the normal claim to it.” (106-107) • Credit irrelevant to equilibrium economics, but essential to disequilibrium process of economic development:

11. Schumpeter’s model: credit has real effects • “… credit is not essential in the normal circular flow, because … it can be assumed there that all purchases of production goods by producers are cash transactions or that in general whoever is a buyer previously sold goods of the same money value…” • However “it is certain that there is such a gap to bridge in the carrying out of new combinations. To bridge it is the function of the lender, and he fulfils it by placing purchasing power created ad hoc at the disposal of the entrepreneur. • Then those who supply production goods need not "wait" and yet the entrepreneur need advance them neither goods nor existing money. Thus the gap is closed which would otherwise make development extraordinarily difficult, if not impossible in an exchange economy where private property prevails.” (107) • So process of innovation & change breaches Say’s Law: in growing, changing economy • Demand exceeds receipts from current sales • Difference financed by credit (debt) to entrepreneurs

12. Schumpeter’s model: credit has real effects • Say’s Law & Walras’ Law apply in circular flow, but not entrepreneurial credit-financed activity: • “In the circular flow, from which we always start, the same products are produced every year in the same way. For every supply there waits somewhere in the economic system a corresponding demand, for every demand the corresponding supply. All goods are dealt in at determined prices with only insignificant oscillations, so that every unit of money may be considered as going the same way in every period. A given quantity of purchasing power is available at any moment to purchase the existing quantity of original productive services, in order then to pass into the hands of their owners and then again to be spent on consumption goods.” (108)

13. Aside: “Marx with different adjectives” • Schumpeter’s thinking here very similar to Marx • Marx argued there were two “Circuits of Capital” • Commodity—Money—Commodity • Equivalent to Schumpeter’s “circular flow” • Essentially Say’s Law applies • Sellers only sell in order to buy • Money—Commodity—Money • Equivalent to Schumpeter’s entrepreneurial function • Say’s Law doesn’t apply: “The capitalist throws less value in the form of money into the circulation than he draws out of it... Since he functions ... as an industrial capitalist, his supply of commodity-value is always greater than his demand for it. If his supply and demand in this respect covered each other it would mean that his capital had not produced any surplus-value... His aim is not to equalize his supply and demand, but to make the inequality between them ... as great as possible.” (Marx 1885: 120-121) • Schumpeter’s point: • Capitalist “throws in” borrowed money • Succeeds if can repay debt and pocket some of the gap

14. Schumpeter’s model: credit has real effects • “If now credit means of payment … are created and placed at the entrepreneur's disposal, then … his purchasing power [takes] its place beside the total previously existing. • Obviously this does not increase the quantity of productive services existing in the economic system. Yet "new demand" becomes possible in a very obvious sense. • It causes a rise in the prices of productive services. From this ensues the "withdrawal of goods" from their previous use…” (108) • Aggregate Demand exceeds Aggregate Supply; Say’s Law violated in move from stationary state • Sum of excess demands negative (not zero as in Walras’ Law) • Credit-financed demand a source of price inflation…

15. Schumpeter’s model: credit has real effects • “Just as when additional gas streams into a vessel the share of the space occupied by each molecule of the previously existing gas is diminished by compression, so the inflow of new purchasing power into the economic system will compress the old purchasing power. • When the price changes which thus become necessary are completed, any given commodities exchange for the new units of purchasing power on the same terms as for the old, only the units of purchasing power now existing are all smaller than those existing before and their distribution among individuals has been shifted.” (109) • This inflation • Isn’t necessarily a bad thing • Can be reversed by dynamics of economic development

16. Schumpeter’s model: credit has real effects • “credit inflation .. is distinguished from … inflation for consumptive purposes by a very essential element… • The entrepreneur must not only legally repay money to his banker, but he must also economically repay commodities to the reservoir of goods … • after a period at the end of which his products are on the market and his productive goods used up - he has, if everything has gone according to expectations, enriched the social stream with goods whose total price is greater than the credit received and than the total price of the goods directly and indirectly used up by him. • Hence the equivalence between the money and commodity streams is more than restored, the credit inflation more than eliminated, the effect upon prices more than compensated for.” (110)

17. Schumpeter’s model: credit has real effects • “Furthermore, the entrepreneur can now repay his debt (amount credited plus interest) at his bank, and normally still retain a credit balance (= entrepreneurial profit) that is withdrawn from the purchasing-power fund of the circular flow.” (111) • So dynamic view of economy • Overturns “money doesn’t have real effects” bias of neoclassicals/monetarists • Breaches “supply creates its own demand” Say’s Law view of self-equilibrating economy • Breaches Walras’ Law “if n-1 markets in equilibrium, nth also in equilibrium” general equilibrium analysis • Links finance and economics: without finance there would not be economic growth, but • Finance can affect economic growth negatively as well as positively (if entrepreneurial expectations fail)…

18. The really new finance… • Dynamic vision of economics overturns equilibrium truths • Ditto realistic view of finance markets: not equilibrium but disequilibrium dynamics… • Three highly unrealistic assumptions essential to CAPM: • Investors agree on values of all shares • Perceptions of values are correct • All investors have limitless access to risk free finance • Outcome: shares follow “random walk” • Really new finance rejects all these assumptions: • Investors disagree on values of shares • Future uncertain & perceptions of future wrong • Differing access to finance • Outcome: shares follow “fractal walk”

19. The really new finance… • Several as yet not integrated aspects • Behavioural Finance • Investors don’t make “rational utility maximising” decisions when confronted with risky financial choices • Inefficient Finance • Finance markets themselves not efficient • “Minority Game” • Financial Markets as game in which you win by being in the minority • Fractal Finance • Statistical properties of markets “fractal” and “power law”, not random • Last (empirical) aspect first…

20. Fractal Finance • Remember last week Fama (1969)—when still believer in CAPM—noted “large daily price changes tend to be followed by large daily changes. The signs of the successor changes are apparently random”…? • A characteristic of “fractal” distributions • Many elements interact with each other; and • Interactions nonlinear: small movements cause large ones • Example: earthquakes • Caused by movement of “tectonic plates” • Movement of one plate against another builds up tension • Earthquake releases tension in one spot • Makes other releases elsewhere more likely • One big movement—followed by others • Eventually settles down; then cycle renews

21. Fractal Finance • Results: • Earthquakes “cluster” • Long period of small quakes; then • Sudden large quake • Lots of large aftershocks • Eventually calm returns; • Then tension builds up again before next release • No “average size” earthquake • Quakes of all scales occur • Big quake just a small quake that does not stop • “Self-similarity” • Close up, small-scale quake effects look like big quakes on larger scale… • Same phenomena found in stock market data

22. Fractal Finance • Volatility clustering • Periods of high volatility not randomly distributed but clustered together • No “average” size daily/weekly/yearly movement • Averages & standard deviations can be calculated • But data does not fit means, deviations etc. • Highly skewed • Many more large events than predicted • Self-similarity • Intra-day pattern in a day looks like • Daily pattern in a month • Monthly pattern in a decade…

23. Fractal Finance • Example: NASDAQ over two time periods: which one is longer?

24. Fractal Finance • What’s the point? • Randomly generated pattern would have decreasing volatility as time scale increased: • Variance of random distribution scales to square root of time scale • Variance of actual financial time series scales linearly with time • No “average” scale of movement at any time scale: • Random distribution: huge movements can be ruled out • Actual financial time series: huge movements can and do occur: • 10% fall of DJIA on “Black Friday” in 1929 • 25% fall of ASX on “Black Tuesday” in 1987 • 14% fall of NASDAQ in April 2000…

25. Fractal Finance • “If we take a graph of the S&P 500 index …, and place it above a graph of an uncorrelated biased random walk with the same overall bias, at first glance they seem almost identical. • When we look closer, however, we notice the graph of the S&P 500 has occasional large fluctuations (e.g. the huge drop that took place on Black Monday in October, 1987 (when most world markets lost 20-30% of their value over a period of 1-2 days). • We do not see this kind of large fluctuation in the biased random walk graph because the probability of taking a very large number of random steps in the same direction (which would be necessary for a large fluctuation) is exponentially small.” (Stanley, Physica A 2000: 9)

26. Fractal Finance • If market obeyed CAPM, prices would follow “random walk” along upward trend • Deviations from trend would fit within normal distribution • Defined average • Dispersion described by standard deviation • If market fractal, prices follow “power law” • Number of movements of some size related to size raised to some power:

27. Fractal Finance • Basic model “the sandpile” (Per Bak) • Tip sand onto ground; forms a sandpile • Lots of little local “avalanches” all the time • But generally sandpile grows uniformly • Until slope reaches some critical level • Next sandgrain causes pile-wide avalanche • Pile collapses to well below critical shape • Additional sand reforms shape till critical point • “Big avalanche is a small avalanche that doesn’t stop…” • Number of avalanches of given size roughly equals size raised to a power…

28. Fractal Finance • Same idea in markets: • Generally rising price level; • Small “crashes” all the time • Systemic critical level approached • Next “small” crash sets of systemic crash • Number of “crashes” (or “bubbles”) of given size roughly equals size raised to some power • Size measure: daily percentage movement of index • Fractal market prediction: number per century of daily crashes of (e.g.) 10% roughly equals 0.1 raised to some power • Take logs:

29. Fractal Finance • Power law fit Dow Jones: Power law predicts6 10% daily movementsper century Actual number was 8 1 means 101=10events per century -1 means 10-1=10% daily change • Does this tell us anything the EMH doesn’t?

30. Fractal Finance • Power law fits stock market data • Gaussian fit hopeless: • Far more extreme events than random change predicts…

31. Fractal Finance • “Random walk” prediction OK for small movements • +/-3% 780 reality v 718 random prob. • Hopeless for large • +/-6%: 57 v 1 • +/- 8%: 11 v 1 in a million chance… -2 means 10-2: onesuch event predictedevery century 11 lastcentury 10-6: 1 event predictedevery 1 million centuries Actual number 57 10-1.1:8% change -1.2 means 10-1.2=6% daily change

32. Fractal Finance • Other statistical properties found: • “Tsallis’s q” • Sornette’s “Log-periodic” crashes… • Most research done by physicists (“econophysicists”) • Characterise how the market behaves • Large movements • Clearly interactions between agents • Like interactions between grains of sand in sandpile—one grain pushes several others that push others… chain reaction to avalanche • Doesn’t explain why market behaves this way • Over to “behavioural finance”…

33. Behavioural Finance • CAPM based on “rational utility maximising behaviour” • Expected utility hypothesis: • Given two risky outcomes, agent chooses one that maximises expected value: • Problem 1: You have to choose between two alternatives: • A: 50% chance of $100 and 50% chance of nothing • B: 75% chance of $200 and 25% chance of -$100 • Which would you choose?... • Write your choice down: • A or B?

34. Behavioural Finance • According to economic theory you should choose B: • EVA= . 5 x 100 + . 5 x 0 = 50 • EVB= .75 x 200 + .25 x -100 = 150-25 = 125 • Unfortunately, in experiments, most choose A over B • Theory modified to take into account “risk averse” behaviour: • People seek to maximise not EV, but subjective utility of EV, taking risk preference into account • Same basic relation applies: can break down utility of gamble into odds times utility of components • Modified theory describes people who choose A over B as “risk averse”; B over A as “risk seeking” • But still theory doesn’t work: experiments show people choose “risk averse” bundle some times, “risk seeking” others

35. Behavioural Finance • Problem 2: You have to choose between two alternatives: • A: do nothing • B accept gamble with outcome either X or Y: • X: a 50 per cent chance to win $150, and • Y: a 50 per cent chance to lose $100. • What would you choose: option A or option B? • Would your choice change if your overall wealth were lower by $100? • Write your choice down: • A or B? • Would your choice change?

36. Behavioural Finance • Problem 3: You have to choose between two alternatives: • A: Lose $100 with certainty • B accept gamble with outcome either X or Y: • X: a 50 per cent chance to win $50, and • Y: a 50 per cent chance to lose $200 • What would you choose: option A or option B? • Would your choice change if your overall wealth were higher by $100? • Write your choice down • A or B? • Would your choice change?

37. Behavioural Finance • Majority of experimental subjects choose: • Problem 2: A—don’t gamble • Problem 3: B—accept gamble • Pattern contradicts expected utility theory: • 2A is “risk-averse” choice • U(0) > U( 0.5 x 150 + 0.5 x -100)=U(EV[75-50]) • U(0) > U(EV[25]) • 3B is “risk-seeking”!: • U(-100) < U(0.5 x 50 + 0.5 x -200)=U(EV[25-100]) • U(-100) < U(EV[-75]) • “$100 addition to wealth” question allows next step: • U(0) < U(EV[25]) • “Preference reversal”: most experimental subjects don’t behave “rationally” (as economists define rational) • Another example at end of lecture

38. Behavioural Finance • Behavioural finance theorists interpretation • People aren’t rational as economists define it • Economists • linear trade-off: losses and gains weighted equally • Absolute wealth position all that matters • Economic thought involves rational non-emotional calculation • Behavioural finance • Nonlinear tradeoff: losses weighted more than gains • Relative wealth position matters • Economic thought involves emotional intuition as well as rationality • Intuition much faster but can sometimes be incorrect

39. Behavioural Finance • Psychologist Kahneman won 2003 Nobel Prize for Economics • Argues for two reasoning systems in humans • Intuition • Reason • Neoclassical economics models behaviour “as if” only rational system exists, but both exist & are used in economic & financial decisions…

40. Behavioural Finance • Intuitive, emotional, relative judgments lie behind standard choices by experimental subjects: • “the very abrupt switch from risk aversion to risk seeking could not plausibly be explained by a utility function for wealth. Preferences appeared to be determined by attitudes to gains and losses, defined relative to a reference point… We therefore proposed an alternative theory of risk, in which the carriers of utility are gains and losses—changes of wealth rather than states” (Kahneman Nobel Prize lecture: 1456) • In “prospect theory”, people react more to losses than gains: pain of loss weighted more heavily than pleasure of gain

41. Behavioural Finance • “The value function is defined on gains and losses and is characterized by three features: • (1) it is concave in the domain of gains, favoring risk aversion; • (2) it is convex in the domain of losses, favoring risk seeking; • (3) most important, the function is sharply kinked at the reference point, and loss-averse—steeper for losses than for gains by a factor of about 2–2.5.” (1456) • “Rational choice” model that dominates conventional economics & finance thus unsuitable for real people:

42. Behavioural Finance • “The rational agent of economic theory would be described, in the language of the present treatment, as endowed with a single cognitive system that has the logical ability of a flawless System 2 and the low computing costs of System 1… • The [behavioural finance] model of the agent … has a different architecture The core ideas … are • the two-system structure, [intuition & reason] • the large role of System 1 [intuition] • and … extreme context-dependence… • The central characteristic of agents is not that they reason poorly but that they often act intuitively…” (1469) • Applied to stock market: Haugen’s “Inefficient Markets Hypothesis”

43. Inefficient Markets Hypothesis • Emphasises emotional component of investor decision-making • “Fad” (or Schumpeterian innovation) makes some industry sector or firm “growth stocks” • Valued above average Price to Book value (P/B) • Other unpopular “value” stocks ignored • Valued below average P/B ratio • Growth stocks inevitably disappoint • Value stocks often “surprise” • Repeated on scale of individual firms • Poor performing firm undervalued; good one overvalued • “Reversion to mean” causes “star” to disappoint, “dog” to outperform • Series of reports needed before trend spotted

44. Inefficient Markets Hypothesis • Institutional investors forced by need to match index to purchase broad portfolio • Non-institutional investors can profit by • Buying value stocks: Low P/B ratio & low earnings volatility • Timing entrance/exit from market • Many structural anomalies in stock market returns: • The “incredible January effect” • Rise of market almost every January • 95% of gains in December-April • Selective buy-in sell-out works • Some sample data from Bob Haugen (main proponent IMH) http://www.bobhaugen.com/

45. 25% 20% 15% Annualized Rate of Return 10% 5% 0% 1 2 3 4 5 6 7 8 9 10 High Book/Market Low Book/Market Inefficient Markets Hypothesis • Haugen’s plot of Fama-French B/M ratios and future returns… Value Growth

46. Cumulative Wealth $2,000,000 $1,500,000 $1,000,000 2.47% Return 15.18% Return $500,000 $0 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 Inefficient Markets Hypothesis • Cumulative effect of Value vs growth investment ‘30-’64

47. 24 21.78 22 19.05 20 18.43 18 16.04 16 14 12 10 8.62 7.09 8 5.93 4.88 6 3.76 4 2.46 2.49 2 0.62 0 Return on Sales Asset Growth Equity Growth Return on Return on Total Capital Market- Book Ratio Equity "Excellent Companies" "Unexcellent Companies" Inefficient Markets Hypothesis • Mean reversion: “today’s excellent companies” do badly

48. 280 230 180 130 80 1981 1982 1983 1984 1985 1986 Inefficient Markets Hypothesis • If you invested $100 in each group of companies in 1981… Unexcellent Companies 297.5 181.6 Excellent Companies • “Unexcellent companies” portfolio far better • Reversion to the mean: poorer companies had more room to improve

49. High Yield Low Yield 100 High P.E. 90 Low P.E. S&P 500 80 70 60 Cumulative Value of $1 Invested 50 40 30 20 10 0 1970 1980 1974 1972 1994 1968 1986 1992 1978 1982 1976 1988 1990 1984 1996 Year Inefficient Markets Hypothesis • Investing in Low P/E companies far better than Index

50. Behavioural Finance • Behavioural economics emphasises emotional, non-rational aspects of human decision making; • But 2nd explanation of behavioural economics results: • Economics falsely applies risk theory to uncertainty • What economists call rational isn’t rational in uncertain world… • Expected value theory originally developed to interpret gambling: behaviour in risky games (roulette, cards,…) • Applied to economics after work on “Games & Economic Behaviour” by mathematician John von Neumann & economist Oskar Morgenstern • BUT: • von Neumann & Morgenstern used risk to build numerical theory of consumer choice…