Market Microstructure Daniel Sungyeon Kim

1. Market MicrostructureDaniel Sungyeon Kim

2. Hanke and Hauser Q: What are stock spam e-mails?

3. Stock Spam Emails Recall:“pump and dump” strategy = “buy, lie, and sell high”  spam with false positive news = lie Q: For manipulators, what advantages do stock spam e-mails have over earlier generation print newsletters?

4. Internet Discussion Forums Q: What did earlier studies find about the impact of internet discussion forums on stock prices?

5. Target Stocks Q: What kind of stocks are commonly targeted by manipulators?

6. “Crummy” Database “Crummy” database = list of spam emails sent to large number of “trap” email accounts

8. Number of Spam Events • Few stocks have a lot of spams – many have few

12. Requirements for OTCBB vs. Pink Sheets

14. Returns, Turnover, and Volatility • Subscripts: p = prior day; s = spam day; f = following day • Returns: (prior = nothing, spam day: +1.9%, following = -1.9%) = temporary spike that goes away • Turnover: prior = +25.3%, spam day = +44.6%, following = +4.7% = rises on spammer purchase and on victim purchase/spammer sale • IR = Intraday Ratio of High/Low price = Volatility (prior = +35.4%, spam day = +15.8%, following = 0%) = spikes on spammer purchase and victim purchase/spammer sale

15. Bhattacharya, Holden, and Jacobsen Examine whether behavioral biases cause buy-sell imbalances on and around round numbers Round Number Effects: security price reaches or crosses a round number  generates a wave of buying or selling Consider three such effects: (1) Left-Digit Effect (2) Threshold Trigger Effect (3) Cluster Undercutting Effect

16. (1) Left-Digit Effect In consumer prices, how often do we see prices that end in nine?

17. Question Let’s think about why we see so many nine-ending prices What is the difference between: $7.00 $6.99?

18. (1) Left-Digit Effect Given that nine-ending price predominate in consumer prices, do we see the left-digit effect in the stock market? If investors view $6.99 as “much less” than $7.00, then Prediction: Excess buys (more buys than sells) at the “cheap” price of $6.99 and excess sells (more sells than buys) at the “expensive” prices of $7.00 and $7.01

19. (2) Threshold Trigger Effect People have a preference for round numbers Hierarchy of roundness: whole-dollars, half-dollars, quarters, dimes, nickels What is the threshold trigger effect? Investors select round number thresholds to trigger action

20. (2) Threshold Trigger Effect Suppose security analysis  value = $7.52 If price = $7.52  fair-priced Buy at $7.00 or $6.99 Sell at $8.00 or $8.01 Prediction: Excess buys on or below round numbers and excess sells on or above round numbers

21. (3) Cluster Undercutting Effect Limit orders are more frequent on round numbers Frequently limit sell at $7.00 = Ask New limit sell undercuts at $6.99 = Updated ask Market buy trades at $6.99 Conversely, frequently limit buy at $5.00 = Bid New limit buy undercuts at $5.01 = Updated bid Market sell trades at $5.01 Prediction: Excess buys below round numbers and excess sells above round numbers  No prediction on round numbers

22. Data 100 randomly selected stocks All their trades and quotes from TAQ database from 2001-2006 = decimal tick size era 137,335,376 trades If trade price > quote midpoint  buy If trade price < quote midpoint  sell If trade price = quote midpoint  discard

24. Figure 1 Y-axis: median (number of buys / number of sells) Excess buys at all price points one penny below a round number (.04, .09, .14, .19, etc.) and excess sells at all price points one penny above a round number (.01, .06, .11, .16, etc.) Amount of excess buys and excess sells is monotonic in the “roundness” of the adjacent round number  greatest excess above and below integers (.01, .99)  second greatest above and below half dollar (.49, .51)  next above and below quarters (.24, .26, .74, .76)  next above and below dimes (.09, .11, etc.)  next above and below nickels (.04, .06, etc.) These patterns are robust to how you measure it: Figure 2 = Shares Bought / Shares Sold Figure 3 = Dollars Bought / Dollars Sold

29. Interpretation of UnconditionalEvidence Excess buys below round numbers and excess sells above round numbers Could be one of two things: (1) Cluster Undercutting Effect (2) Buy-sell imbalance after crossing thresholds due to the Left-Digit Effect or the Threshold Trigger effect To distinguish between these two possibilities conditional analysis

30. Conditional Analysis Condition on the following price paths: Reach cases: Panel C: Ask Falls To Integer = Ask starts above integer, then drop to [.00] Panel D: Bid Rises To Integer = Bid starts below integer, then rises to [.00] Crossing cases: Panel A: Ask Falls Below Integer = Ask starts above integer, then drops to [.90, .99] Panel E: Bid Rises Above Integer = Bid starts below integer, then rises to [.01, .10] Opposite Cases = No Reach or Crossing: Panels B: Ask Rises While Staying Below Integer Panel F: Bid Falls While Staying Above Integer

32. Table 2 Panel A: Excess buying = positive Panel B: Excess buying  many not significant Panel C: Large excess buying = big positive Panel D: Large excess selling = big negative Panel E: Excess selling  many not significant Panel F: Excess selling = negative

33. Interpretation of Conditional Evidence Reach Cases: Strong Excess Buying in Ask Falls To Integer and Strong Excess Selling in Bid Rises To Integer case Crossing Cases: Much weaker Excess Buying in Ask Falls Below Integer and much weaker Excess Selling in Bid Rises Above Integer Strong reach cases  uniquely support left-digit effect and threshold trigger effect, because cluster undercutting has no prediction on round numbers Weak crossing cases  excess buys below round numbers and excess sells above round numbers are due to cluster undercutting, not the other two effects after crossing round number thresholds

34. Implications for Short-Term Returns For every buy trade, compute return to buying at the trade price and selling at bid price 24 hours later = Return to Buying For every sell trade, compute return to (short) selling at the trade price and buying at ask price 24 hours later = Return to Selling

38. Interpretation Buy-sell imbalances are a major determinant of 24-hour returns Summary: Behavioral biases buy-sell imbalances  short-term returns Excess buys below round numbers and excess sells above round numbers  due to cluster undercutting Excess Buying in Ask Falls Toand Excess Selling in Bid Rises To Integer  due to left-digit effect and round number effect

39. Grinblatt and Keloharju Q: What is the definition of Sensation Seeking?

40. Grinblatt and Keloharju • Examples: bungee jumping, roller coaster riding, risky driving, risky sex, gambling, drug and alcohol abuse, etc. • Trading fits = risky; new stock in port or change in position adds novelty/variety • Test if people who engage in other risky behavior are also the most frequent traders • Q: What proxy do they use for other risky behavior?

41. Overconfidence Q: What is the definition of Overconfidence?

42. Overconfidence • Two versions: (1) “I’m right” = high mean and (2) “I know I’m right” = low variance • Colleague Noah Stoffman surveyed F335 students in Indiana University prior to the first day of class – how well did they expect to do in class? What decile would your F335 performance be in? Where 1 = bottom 10%, .., 10 = top 10%. By definition, 10% of the class should be in each decile from 1 to 10. Here are the results:

43. On the final exam, I expect to perform • Above the class average • Roughly equal to the class average • Below the class average

44. More on Overconfidence  Both versions of overconfidence place too much weight on one’s own views  too much trading Q: What is source of proxy for overconfidence?

45. More on Overconfidence Overconfidence = “Self-confidence” (from psych test) – “Competence” In Finland, they make a huge amount of data available and don’t seem to care about privacy!! Disclaimers: (1) This is their research, not mine – don’t infer from their . results what my opinion is (2) As is always true, you should make up your own mind

50. Biais and Green Q: What is the predominate way in which corporate bonds are traded now?