Divergent Market Expectations: A New Perspective on Technical Analysis & Algorithmic Trading

1. Topic 8 The Road to Technical Analysis & Algorithmic Trading

2. Introductory Remarks

3. The Dynamic Behavior of Prices The Effect of Trading Costs In a frictionless environment, prices would follow random walks Do they? If so, how valuable would technical analysis and algorithmic trading be? If not, would shares have unique fundamental values?

4. Do Shares Have Unique Values? Belief Equilibrium values exist Complex information translates into a single price Shares have fundamental values But This is not as simple as elementary economics

5. PRICE SELL BUY 0 QUANTITY Remember This One? The perfectly liquid, frictionless market solution P is Discovered Q is Discovered

6. Life Really is Not So Simple

7. The Standard Microstructure Model • Information traders • Liquidity traders • Noise traders

8. TraderEx Orders Come From 3 Types of Participants Liquidity Traders Information traders Noise Traders P* Is there a trend/ pattern? Quotes, Prices, Volume Is p*>offer or p*<bid? Trading Mechanism

9. Thoughts of a Efficient Market

10. What is Behind Standard Finance Models? • Markets are informationally efficient (EMH) • Shares have unique fundamental values • Informed investors form identical expectations • Homogeneous expectations

11. With Homogeneous Expectations • Information maps uniquely into security values (fundamental values) • If trades are triggered for liquidity reasons only, shares will trade at bid & ask quotes that are appropriate given the fundamentals • Aside from bid-ask bounce, prices will follow random walks Hmmm… What economic function is left for an exchange?

12. Traditional Support for Random Walk Burton Malkiel, A Random Walk Down Wall Street, 1973 “Technical analysis is anathema to the academic world. We love to pick on it. Our bullying tactics are prompted by two considerations: (1) the method is patently false; and (2) it's easy to pick on."

13. Investors are rational and prices reflect fundamental information Systematic patterns can be arbitraged away But… hmmm…wait a minute… Of Course, We All Know That…

14. But, What if Expectations Are Not Homogeneous?

15. A Second Opinion “The efficient market hypothesis is the most remarkable error in the history of economic theory” Lawrence Summers Subsequently U.S. Treasury Secretary The Wall Street Journal, 1987

16. An Earlier OpinionBernard M. Baruch, My Own Story,Henry Holt & Company, 1957, p. 84 • The prices of stocks – and commodities and bonds as well – are affected by literally anything and everything that happens in our world, from new inventions and the changing value of the dollar to vagaries of the weather and the threat of war or the prospect of peace. • But these happenings do not make themselves felt in Wall Street in an impersonal way, like so many jigglings on a seismograph. • What registers in the stock market’s fluctuations are not the events themselves but the human reactions to these events, how millions of individual men and women feel these happenings may affect the future.

17. With Whom Do You Agree? • Burton Malkiel • Lawrence Summers • Bernard Baruch ???

18. Another Perspective

19. A Deceptively Simple Question: What motivates individuals to trade? • Accepted academic answer • Informed traders • Liquidity traders • Noise traders • Perhaps we should add a fourth • Divergent expectations(people disagree…)

20. Divergent Expectations Has Implications For Understanding market structure & operations Assessing market quality Government regulatory policy Understanding volatlity

21. Edward M. Miller “Risk, Uncertainty, & Divergence of Opinion” Journal of Finance, Sept. 1977 “…it is implausible to assume that although the future is very uncertain, and the forecasts are very difficult to make, that somehow everyone makes identical estimates of the return and risk from every security. In practice, the very concept of uncertainty implies that reasonable men may differ in their forecasts.”

22. Complexity of Information • Information sets are typically huge, complex, & imprecise • Crudeness of our analytic tools • Price & quantity discovery may be more complicated than academicians previously thought • Technical analysis and algo trading may be valid Wow, did an academician say this?

23. Difficulty of Assessing Share Valuations With Precision Can a stock analyst or portfolio manager say with precision that the expected growth rate for XYZ is: • 7.000%, not • 7.545%?

24. Analyst Evaluation of XYZ Dividend one year from now = $1.35 Appropriate cost of eq. cap. = 10% (1) Growth rate (g) = 7.000% (2) Growth rate (g) = 7.545% Share price if g =7.000% = $45.00 Share price if g =7.545% = $55.00

25. Evidence of Divergent Expectations • Private information • Analyst recommendations commonly differ • Prevalence of short selling • Two large institutions trading with each other on an ATS (e.g., Posit, Pipeline or Liquidnet) • Neither is likely to be a liquidity or noise trader • Neither may presume to have an informational edge • They are simply “agreeing to disagree”

26. Representing Divergent Expectations in TraderEx Liquidity Traders Noise Traders P* Is there a trend/ pattern? Quotes, Prices, Volume Informed Traders Is p*>offer or p*<bid? P* + 10% = VH (the bulls) P* - 10% = VL (the bears) Do the informed Traders agree with each other? maybe not!

27. Price Discovery

28. The Inside Scoop onPrice Discovery A complex, protracted process Contributes to intra-day volatility Equilibrium depends on the sequence of order arrivals & on how orders are handled A coordination problem The quality of price discovery depends on trader behavior & market structure Divergent expectations underlie the complexity of price discovery

29. Divergent Expectations:A Simple Setting A company is facing a jury trial – its share value will be affected appreciably by the outcome Investors can have 1 of 2 expectations Some believe pr(acquittal) = .80 Some believe pr(acquittal) = .35 Shares are valued at $55 by those who expect acquittal $45 by those who expect conviction

30. Lets Be More Generic Bi-variate outcome: a decision will soon be made that will appreciably affect the value of a company Legal case: Acquit or convict Loan application: Grant or deny Takeover campaign: Win or loose shareholder votes Investors disagree about probability of positive outcome For bulls: positive expectation – stock is worth VH For bears: negative expectation – stock is worth VL The truth will soon be revealed

31. Bid-Ask Spread for k = 0.6 Price Determination in the Bi-variate Context VH = $55 (k percent of participants are bulls) VL = $45 (1-k percent of participants are bears) – A* – B* “Quote Setting and Price Formation in an Order Driven Market” Puneet Handa, Robert Schwartz, & Ashish Tiwari (HST) Journal of Financial Markets, August 2003

32. From Divergent Expectations to… Adaptive Valuations We have not heard much about this It implies endogeneity of the trading decision A “Wisdom of the Crowds” reality A crowd is more likely to reach a correct decision than any single member of the crowd assuming independence

33. Picture It This Way • 800 observes are guessing the number of beans in a jar (the jar holds a lot, say 2500) • Each observer looks at the jar individually and forms an estimate • The observers come up one at a time and disclose their expectations • Each observer’s expectation depends on his initial estimate and on what he observes others guessing • As more observers arrive, each places less weight on his initial estimate

34. Adaptive Valuations (AV) Imply With random equilibria, shares do not have unique values Random (multiple) equilibria Path dependency

35. Our Starting Point:The Handa, Schwartz, Tiwari Model Risk neutral participants Participants arrive in random sequence Order driven, limit order book market There are just two valuations: VH & VL k percent are bulls (VH) (1-k percent are bears (VL) Orders are placed w.r.t. VH, VL, and k

36. Bid-Ask Spread for k = 0.6 VH = $55 (k percent are bulls) VL = $45 (1-k percent are bears) – A* – B* HST Model Cont. • Market bid and offer prices for XYZ can be solved for if we know • VH, VL, & k • If we know VH & VL only, • Price discovery is equivalent to kdiscovery • Remember…

37. HST’s Optimal Bid (B*) and Offer (A*) B* = γ VL + (1-γ) VH A* =  VH + (1- ) VL where

38. What if k is Not Known? Orders are Based On • Each participant’s own assessment of information • Others’ assessments [ADAPTIVE VALUATIONS] • Others’ opinions are reflected in k, the % who are bulls

39. How Does Price Evolve When Everyone Uses This Basic Algorithm “The Dynamic Process of Price Discovery in an Equity Market,” J. Paroush, R. Schwartz & A. Wolf Working paper, 2008

40. Representative Price Paths Alternative Equilibrium Prices

41. News Price Discovery Efficient Vol Price Discovery Vol Volatility Consequences RC(t) = PC(t)/PC(t-1) Multiply and divide the RHS by VL(t)/ VL(t-1) Rearranging gives: RC(t) = [VL(t)/ VL(t-1)][PC(t) / VL(t)] ÷ [PC(t-1) / VL(t-1) ] • Volatility of RC(t) • = Vol [VL(t)/ VL(t-1)] + Vol [PC(t) / VL(t)÷ PC(t-1) / VL(t-1)]

42. Information Complexities: Consequences Investor Expectations Divergent Can change independently at any time Adaptive • Price Discovery • Random (multiple) equilibria • Path dependency • Accentuated intra-day volatility

43. Implications Volatility Intra-day & longer run bubbles and crashes Technical analysis Validation of a price Profitability of momentum and algorithmic trading Path dependency Importance of market structure The quality of a network Algorithmic trading

44. Algorithmic Trading

45. Question How Does Algo Trading Impact Price Discovery?

46. RememberFragmentation Has a Temporal Dimension An order can be fragmented Slicing, dicing, & shredding A breading ground for algo trading Goal: make an elephant look like an ant Warning: there can be negative consequences

47. The 5 little tranches From Elephants to Ants

48. If Algo Trading Helps Traders Individually, Does it Benefit Them Collectively? • Yes, in an electronic world, algos are essential • But some can lead to undesirable results • Lets look at one that might Slice & dice algos? VWAP algos? Momentum algos? Contrarian algos? • An algo for an ant…

49. An Algo for an Ant I’m lost. Where is my ant hill? Oh dear, I think I’ll follow the ant in front of me That ant is a momentum player…

50. The 5 little tranches An Algo for an Ant Lost Ant #1 (momentum player) ) #2… ???