CHAPTER ONE Performance Leakage and Value Discounts on the TSX

1
CHAPTER ONEPerformance Leakage and Value
Discounts on the TSX

• Lawrence Kryzanowski and Skander Lazrak
• Concordia University Brock University,
  respectively

2
Reading Questions

• What costs should an investor consider when
  executing a transaction?
• What type of trader is most concerned about the
  quoted spread and how is it calculated?
• What type of trader is most concerned about the
  effective spread and how is it calculated?
• What metric is available to measure price impact
  and how is it calculated?
• What information is obtained from examining the
  quoted depth?

3
Reading Questions Contd

• Are the various spread measures normally
  distributed for the security types on the TSX?
• Which security types have the lowest and highest
  relative spreads, on average, on the TSX?
• Describe how trades are classified as buyer- or
  seller-initiated when this needs to be inferred
  from quote trade data and what is the accuracy
  of doing so?
• Describe the time-series behavior of the number
  of trades over time?
• Discuss three implications of the findings of
  this study?

4
Performance Leakage and Value Discounts on the TSX

• Performance affected by trade execution quality
• Trade costs commissions, fees, execution
  opportunity costs
• Execution quality depends upon investors
  investment style and trading demands

5
Execution Quality

• Imputed from data since not observable
• Difference between actual trade execution price
  fair value estimate
• Pre- and post-trade measures
• Metrics include quoted, effective realized
  spreads, quoted depth, number of trades trading
  volume (number of shares dollar value)

6
Quoted Spreads and Depth

• Quoted Spread
• Important for (e.g., momentum) traders whose
  informational advantage becomes stale quickly
• Quoted (Inside) Depth
• Important for traders whose trade sizes exceed
  the quoted inside depth

7
Effective Realized Spreads Price Impact

• Effective Spread
• Important for (e.g., value) traders whose
  informational advantage does not become stale
  quickly
• Realized Spread
• A measure of the market-makers profit if s/he
  rebalances inventory shortly after making the
  trade
• Price Impact Difference between effective
  realized spreads

8
Studied Sample

• All 2,300 listed securities on TSX with data for
  first 2 months of 2008
• Security types common shares (CDN USD)
  preferreds units warrants asset-linked notes
• Examine all valid trades and quotes
• Over 197 million quotes 25 million trades

9
Execution Quality Estimates

• Nearly all distributions are right skewed so that
  the Median lt Mean
• For all three relative spread measures
• Lowest averages and ? Common shares with prices
  gt 5 and NT_NO
• Highest averages and ? Warrants USD
• Considerable variation intra inter security
  type
• E.g., intra quoted spreads for common shares with
  prices gt 5 range from 0.03 to 16.45

10
Execution Quality Estimates Contd

• Quote depth averages ?
• Lowest highest Warrants Common gt5
• Traded volume (number shares) averages ?
• Lowest highest NT_NO Common lt5
• Traded volume () averages ?
• Lowest highest NT_NO (except Warrants for
  median) Common gt5
• Number of trades averages ?
• Lowest highest NT_NO Common gt5

11
Execution Quality Estimates Contd

• Intertemporal variation appearance in top 3 ranks
  for the 6 liquidity measures
• Warrants USD frequently appear
• Common gt5 never appears
• Common lt5 has solo appearance
• Units appears in top 3 for 2 measures
• NT_NO relatively higher for all six liquidity
  measures.

12
Conclusion

• Net benefits of trade impacted by material
  variation in potential actual trade execution
  costs intra inter security type
  intertemporally (including global shocks)
• Valuation discount estimates by security type
  important for asset valuators investors for
  nongranular fund holdings
• Value discount or performance drag estimates
  important for assessing reported portfolio
  performance, particularly under adverse market
  conditions

13
CHAPTER TWOInformed Trading in Parallel Auction
and Dealer Markets The Case of the London Stock
Exchange

• Pankaj J. Jain
• Christine Jiang
• Thomas McInish
• Nareerat Taechapiroontong

14
Reading Questions

• At one time the London Stock Exchange was losing
  trades to other European exchanges. Why?
• Describe the main characteristics of the London
  Stock Exchanges SEATS and dealer markets.
• Who can trade on the SEATS and dealer markets and
  why? Describe a type of order that you might
  decide to route to each market?
• Which trading platform within the London Stock
  Exchange has a higher permanent price impact and
  why?
• Which trading platform within the London Stock
  Exchange has the ability to screen out informed
  traders and why?

15
Reading Questions

• How have the dealers obligations to provide
  liquidity on the London Stock Exchange changed
  over time?
• What do you know about transparency (pre- and
  post-trade) on the London Stock Exchange?
• Provide your opinion about the various optimal
  institutional design features of a stock
  exchange.
• Which trading platforms on the London Stock
  Exchange are associated with lower temporary
  price impact and total price impact?
• List some key characteristics of a trade that
  determine its price impact.

16
Objectives and Contributions

• Test whether intensity of trader anonymity is
  correlated with trading with informed traders
  (adverse selection)
• Use permanent price impact (PPI) of trades to
  gauge information content of orders in 2 parallel
  markets
• Provide evidence based on unique structure of the
  LSE.
• Compare adverse selection problem between
  parallel anonymous Auction market and
  non-anonymous voluntarily Dealer market.
• Fully time-synchronized markets and no
  firm-specific differences (same firms)

16
17
Previous Studies on Trader Anonymity

• Survey Institutional investors prefer to trade
  in anonymous automated execution systems that
  provide low disclosure of identity of the company
  submitting the orders.
• Economides and Schwartz (1995) and Schwartz and
  Steil (1996)
• Theory Negotiated dealer market serves as a
  screening device to eliminate informed trades.
• Seppi (1990) and Pagano and Roell (1992)
• Professional non-anonymous relationship between
  specialist and brokers reduces the adverse
  selection problem.
• Benveniste et al. (1992)
• Off-exchange dealers are likely to cream skim
  order flow and divert informed orders to
  on-exchange market.
• Easley, Kiefer and OHara (1996)
• Upstairs dealer market facilitates searching and
  matching of order flow.
• Seppi (1990), Burdett and OHara (1987) and
  Grossman (1992)

17
18
Order flow of SETS stocks on the London Stock
Exchange
18
19
Comparison of SETS and Dealer
markets in 2000
 
19
20
Data selection and processing

• Main data source London transaction data service
• Compustat global file used for Market
  capitalization
• SETS stocks FTSE 100 or FTSE 250 in 2000
• Trading days gt80 days in 2000
• Sample stocks 177
• Delete 28 stocks for methodological problem
• Final sample 149
• Trade Reports File contains
• Trade direction (buy or sell)
• Trade location (SETS or Dealer)
• Code that identifies each counterparty, but
  there no information concerning their actual
  identity
• Standard trade and quote filters are applied

20
21
Methodology

• Keim and Madhavan (1996) and Booth et al.
  (2002)s price impact measures
• Permanent price impact BSln (PA/PB) inform.
  content
• Temporary price impact BSln (PT/PA) liquidity
  cost
• Total price impact BSln (PT/PB)
• Note BS is buy/sell indicator PB,PA,PT are
  before, after, and trade prices

21
22
Fig. 1. Cumulative average returns around large
GBP trades. We identify the 5 of trades that
have the greatest GBP value.  We label each of
these trades, in turn, as trade 0.  For each
trade 0, we identify the twenty previous trades,
trades -1 through -21, and the subsequent 21
trades, trades 1 through 21.  We calculate the
return for each trade from -20 to 20 as the
difference in the log of the trade price minus
the log of the previous trade price.  These
returns are averaged and cumulated beginning with
trade -20. Mean values of cumulative average
returns are plotted above.
22
23
Table 4. Information Differences on SETS and
Dealer
Permanent Price Impact Permanent Price Impact Temporary Price Impact Temporary Price Impact
Independent variables Independent variables Coefficient t-statistics Coefficient t-statistics
Intercept -0.2224 -1.74 1.3699 12.19
SETS 0.2849 21.62 -0.1956 -16.92
Cap 0.0008 0.08 -0.0024 -0.26
Price 0.0274 2.00 -0.1060 -8.82
Volatility 0.1031 4.90 0.1176 6.37
Freq -0.0399 -4.77 -0.0180 -2.45
Size 0.0311 2.28 -0.0948 -7.93

Adj. R2 0.7062 0.7836
F-value 119.97 175.58
23
24
Conclusions

• Regulators of the London Stock Exchange
  accomplished their goals of providing efficient
  markets by offering alternative trading venues.
• Dealers compete effectively with SETS
• More number of trades on SETS
• Larger trade size on dealer market
• Price impact measures suggest that SETs trades
  have larger information content
• Dealers effectively screen out informed traders
  or charge them more for providing liquidity

24
25
CHAPTER THREEMomentum Trading for the Private
Investor

• Alexander Molchanov
• Philip Stork
• Massey University

26
Reading Questions

1. Is there momentum in the worlds largest shares
   returns?
2. Are these momentum profits economically
   meaningful?
3. Do the momentum returns depend on ranking and
   holding periods?
4. How different is the magnitude of the momentum
   effect in various regions?
5. Is momentum a disappearing phenomenon?
6. Is trading volume a useful variable in
   determining momentum profits?
7. How robust are momentum profits to trading costs?

27
Motivation

• Momentum effect is one of the best-known return
  predictability patterns
• Jegadeesh and Titman (1993) Past winners keep
  outperforming past losers
• Behavioural finance has several potential
  explanations (Daniel, Hirshleifer and Subramanyam
  (1998), Hong and Stein (1999))
• Concensus is yet to be reached
• Simplicity of a momentum trading strategy makes
  it attractive to a private investor

28
Data

• We use only the largest shares in the most liquid
  markets
• Transaction costs, bid/ask spreads and lending
  fees are likely to be lower for these shares
• They are more likely to come into private
  investors spotlight (Barber and Odean, 2008)
• Constituents of five major indices, covering
  years 1992 2008
• Dow Jones Euro Stoxx 50, DJIA 30, Dow Jones
  Nordic 30, SP/ASX 50, SP/TSX 60
• Survivorship bias is (mostly) removed

29
Momentum Profits

• Top five winners are bought and bottom five
  losers sold short
• Both ranking and holding periods are varied from
  one to twelve months
• One month is skipped between ranking and holding
  periods
• Momentum returns are observed in all markets, but
  the statistical significance varies

30
Momentum Profits

• Australia and Canada exhibit highest momentum
  returns both in terms of magnitude (up to 31)
  and statistical significance (T-Value up to 4.31)
• For other markets, even though returns are mostly
  positive, significance is not reached
• Momentum returns generally increase with an
  increase in ranking period length

31
Robustness Checks

• Is momentum a disappearing phenomenon?
• Sample is split in two periods 1992 1999 and
  2000 2008
• Returns decrease in Europe, US and Canada (in the
  latter case remaining economically meaningful)
• Returns increase greatly in Australia and remain
  virtually unchanged in Nordic stocks
• Do momentum returns depend on the size of
  momentum portfolio?
• Returns increase for extreme winners/losers, but
  at a cost of greater volatility

32
Volume Filter

• Momentum returns have been linked to trading
  volume (Conrad, Hameed and Niden (1994), Lee and
  Swaminathan (2000))
• Shares which show extreme volume changes (10 or
  -50) are filtered out
• Volume filter works for small-size portfolios
• For larger portfolios, the difference is negative
  and small

33
Private Investor Trading

• The following transaction costs are assumed
• Four times 0.3 and twice the bid-ask spread of
  0.3, a total of 1.8 per six months
• Stock lending fee of 1 and miscellaneous costs
  of 0.4
• Total transaction costs of 5 per annum
• The actual costs are likely to be less than our
  estimates
• Canadian and Australian momentum strategies are
  robust to trading costs and yield more than 10
  per annum

34
Conclusions

• Momentum effect is one of the better-known market
  anomalies
• We document significant momentum returns, robust
  to a number of tests
• Momentum profits are significant in Australia and
  Canada, net of trading costs

35
CHAPTER FOURTrading in Turbulent Markets Does
Momentum Work?

• Tim A. Herberger
• Daniel M. Kohlert
• University of Bamberg

36
Reading Questions

• Do momentum strategies involving NYSE stocks
  deliver positive abnormal returns in the period
  from 1994 to 2009?
• Are some momentum strategies involving NYSE
  stocks more profitable than others in the period
  from 1994 to 2009?
• How do transaction costs affect profits of
  momentum strategies involving NYSE stocks in the
  period from 1994 to 2009?
• Do profits of momentum strategies involving NYSE
  stocks depend more on poorly performing loser
  stocks or on well performing winner stocks in
  the period from 1994 to 2009?

37
Momentum Strategy

• Trading strategy that buys stocks that have
  performed well in the previous J months
  (winners) and sells stocks that have performed
  poorly in the same period (losers)
• Based on different combinations of ranking
  periods (J) and holding periods (K)
• J and K usually between 3 to 12 months
• Self-financing due to long/short approach

38
Empirical Evidence (Selection)

• Jegadeesh and Titman (1993) First report of
  positive and statistically significant returns of
  momentum strategies for the U.S. stock market
• Moskowitz and Greenblatt (1999) Strong
  contribution of industry momentum to momentum in
  stock returns
• Rouwenhorst (1998) Positive market-adjusted
  abnormal returns for a sample of 12 European
  stock markets
• Chui et al. (2000) Positive market-adjusted
  abnormal returns for a sample of 8 Asian stock
  markets
• Glaser and Weber (2003) Positive market-adjusted
  abnormal returns for the German stock market

39
Data Set

• Monthly stock prices over the period from
  12/31/94 to 05/31/09 for all stocks listed on the
  New York Stock Exchange (NYSE) excluding
• American Depository Receipts (ADRs)
• Real Estate Investment Trusts (REITs)
• Closed-end funds
• Delisted stocks
• Weighted NYSE index used as market proxy
• Source Datastream

40
Methodology

• Three strategies
• Short-term (J 3, K 3)
• Medium-term (J 6, K 6)
• Long-term (J 12, K 12)
• Winner portfolio Best-performing percent of NYSE
  stocks
• Loser portfolio Worst-performing percent of NYSE
  stocks
• Overlapping portfolios
• Portfolio formation one month after ranking

41
Methodology

• Gross return
• Market-adjusted abnormal return
• Market-adjusted abnormal return after transaction
  costs

42
Results
Average Monthly Returns of Momentum Portfolios in the Period from 1994 to 2009 P1 Low-return loser portfolio, P2 High-return winner portfolio, J Ranking period (in months), K Holding period (in months), P2 P1 Zero-cost momentum portfolio, t-statistics in parentheses Average Monthly Returns of Momentum Portfolios in the Period from 1994 to 2009 P1 Low-return loser portfolio, P2 High-return winner portfolio, J Ranking period (in months), K Holding period (in months), P2 P1 Zero-cost momentum portfolio, t-statistics in parentheses Average Monthly Returns of Momentum Portfolios in the Period from 1994 to 2009 P1 Low-return loser portfolio, P2 High-return winner portfolio, J Ranking period (in months), K Holding period (in months), P2 P1 Zero-cost momentum portfolio, t-statistics in parentheses Average Monthly Returns of Momentum Portfolios in the Period from 1994 to 2009 P1 Low-return loser portfolio, P2 High-return winner portfolio, J Ranking period (in months), K Holding period (in months), P2 P1 Zero-cost momentum portfolio, t-statistics in parentheses Average Monthly Returns of Momentum Portfolios in the Period from 1994 to 2009 P1 Low-return loser portfolio, P2 High-return winner portfolio, J Ranking period (in months), K Holding period (in months), P2 P1 Zero-cost momentum portfolio, t-statistics in parentheses
Ranking Period (J)   Holding Period (K) Holding Period (K) Holding Period (K)
Ranking Period (J) Portfolio 3 6 12
         
3 Loser (P1) -0.0167    
  Winner (P2) 0.0038    
  Winner Loser (P2 - P1) 0.0205    
  (t-stat) (4.16)    
         
6 Loser (P1)   -0.0187  
  Winner (P2)   0.0032  
  Winner Loser (P2 - P1)   0.0219  
  (t-stat)   (5.93)  
         
12 Loser (P1)     -0.0091
  Winner (P2)     -0.0064
  Winner Loser (P2 - P1)     0.0027
  (t-stat)     (1.12)
43
Results
Abnormal Monthly Returns of Momentum Portfolios Using the Average NYSE Return as the Market Proxy in the Period from 1994 to 2009 P Return of momentum portfolio, J Ranking period (in months), K Holding period (in months), M Average market proxy return, P M Abnormal return, Panel A Zero transaction costs, Panel B Transaction costs .2 percent, Panel C Transaction costs .5. Panel D Transaction costs 1, t-statistics for monthly return differences in parentheses Abnormal Monthly Returns of Momentum Portfolios Using the Average NYSE Return as the Market Proxy in the Period from 1994 to 2009 P Return of momentum portfolio, J Ranking period (in months), K Holding period (in months), M Average market proxy return, P M Abnormal return, Panel A Zero transaction costs, Panel B Transaction costs .2 percent, Panel C Transaction costs .5. Panel D Transaction costs 1, t-statistics for monthly return differences in parentheses Abnormal Monthly Returns of Momentum Portfolios Using the Average NYSE Return as the Market Proxy in the Period from 1994 to 2009 P Return of momentum portfolio, J Ranking period (in months), K Holding period (in months), M Average market proxy return, P M Abnormal return, Panel A Zero transaction costs, Panel B Transaction costs .2 percent, Panel C Transaction costs .5. Panel D Transaction costs 1, t-statistics for monthly return differences in parentheses Abnormal Monthly Returns of Momentum Portfolios Using the Average NYSE Return as the Market Proxy in the Period from 1994 to 2009 P Return of momentum portfolio, J Ranking period (in months), K Holding period (in months), M Average market proxy return, P M Abnormal return, Panel A Zero transaction costs, Panel B Transaction costs .2 percent, Panel C Transaction costs .5. Panel D Transaction costs 1, t-statistics for monthly return differences in parentheses Abnormal Monthly Returns of Momentum Portfolios Using the Average NYSE Return as the Market Proxy in the Period from 1994 to 2009 P Return of momentum portfolio, J Ranking period (in months), K Holding period (in months), M Average market proxy return, P M Abnormal return, Panel A Zero transaction costs, Panel B Transaction costs .2 percent, Panel C Transaction costs .5. Panel D Transaction costs 1, t-statistics for monthly return differences in parentheses Abnormal Monthly Returns of Momentum Portfolios Using the Average NYSE Return as the Market Proxy in the Period from 1994 to 2009 P Return of momentum portfolio, J Ranking period (in months), K Holding period (in months), M Average market proxy return, P M Abnormal return, Panel A Zero transaction costs, Panel B Transaction costs .2 percent, Panel C Transaction costs .5. Panel D Transaction costs 1, t-statistics for monthly return differences in parentheses Abnormal Monthly Returns of Momentum Portfolios Using the Average NYSE Return as the Market Proxy in the Period from 1994 to 2009 P Return of momentum portfolio, J Ranking period (in months), K Holding period (in months), M Average market proxy return, P M Abnormal return, Panel A Zero transaction costs, Panel B Transaction costs .2 percent, Panel C Transaction costs .5. Panel D Transaction costs 1, t-statistics for monthly return differences in parentheses Abnormal Monthly Returns of Momentum Portfolios Using the Average NYSE Return as the Market Proxy in the Period from 1994 to 2009 P Return of momentum portfolio, J Ranking period (in months), K Holding period (in months), M Average market proxy return, P M Abnormal return, Panel A Zero transaction costs, Panel B Transaction costs .2 percent, Panel C Transaction costs .5. Panel D Transaction costs 1, t-statistics for monthly return differences in parentheses Abnormal Monthly Returns of Momentum Portfolios Using the Average NYSE Return as the Market Proxy in the Period from 1994 to 2009 P Return of momentum portfolio, J Ranking period (in months), K Holding period (in months), M Average market proxy return, P M Abnormal return, Panel A Zero transaction costs, Panel B Transaction costs .2 percent, Panel C Transaction costs .5. Panel D Transaction costs 1, t-statistics for monthly return differences in parentheses Abnormal Monthly Returns of Momentum Portfolios Using the Average NYSE Return as the Market Proxy in the Period from 1994 to 2009 P Return of momentum portfolio, J Ranking period (in months), K Holding period (in months), M Average market proxy return, P M Abnormal return, Panel A Zero transaction costs, Panel B Transaction costs .2 percent, Panel C Transaction costs .5. Panel D Transaction costs 1, t-statistics for monthly return differences in parentheses Abnormal Monthly Returns of Momentum Portfolios Using the Average NYSE Return as the Market Proxy in the Period from 1994 to 2009 P Return of momentum portfolio, J Ranking period (in months), K Holding period (in months), M Average market proxy return, P M Abnormal return, Panel A Zero transaction costs, Panel B Transaction costs .2 percent, Panel C Transaction costs .5. Panel D Transaction costs 1, t-statistics for monthly return differences in parentheses Abnormal Monthly Returns of Momentum Portfolios Using the Average NYSE Return as the Market Proxy in the Period from 1994 to 2009 P Return of momentum portfolio, J Ranking period (in months), K Holding period (in months), M Average market proxy return, P M Abnormal return, Panel A Zero transaction costs, Panel B Transaction costs .2 percent, Panel C Transaction costs .5. Panel D Transaction costs 1, t-statistics for monthly return differences in parentheses Abnormal Monthly Returns of Momentum Portfolios Using the Average NYSE Return as the Market Proxy in the Period from 1994 to 2009 P Return of momentum portfolio, J Ranking period (in months), K Holding period (in months), M Average market proxy return, P M Abnormal return, Panel A Zero transaction costs, Panel B Transaction costs .2 percent, Panel C Transaction costs .5. Panel D Transaction costs 1, t-statistics for monthly return differences in parentheses Abnormal Monthly Returns of Momentum Portfolios Using the Average NYSE Return as the Market Proxy in the Period from 1994 to 2009 P Return of momentum portfolio, J Ranking period (in months), K Holding period (in months), M Average market proxy return, P M Abnormal return, Panel A Zero transaction costs, Panel B Transaction costs .2 percent, Panel C Transaction costs .5. Panel D Transaction costs 1, t-statistics for monthly return differences in parentheses Abnormal Monthly Returns of Momentum Portfolios Using the Average NYSE Return as the Market Proxy in the Period from 1994 to 2009 P Return of momentum portfolio, J Ranking period (in months), K Holding period (in months), M Average market proxy return, P M Abnormal return, Panel A Zero transaction costs, Panel B Transaction costs .2 percent, Panel C Transaction costs .5. Panel D Transaction costs 1, t-statistics for monthly return differences in parentheses Abnormal Monthly Returns of Momentum Portfolios Using the Average NYSE Return as the Market Proxy in the Period from 1994 to 2009 P Return of momentum portfolio, J Ranking period (in months), K Holding period (in months), M Average market proxy return, P M Abnormal return, Panel A Zero transaction costs, Panel B Transaction costs .2 percent, Panel C Transaction costs .5. Panel D Transaction costs 1, t-statistics for monthly return differences in parentheses Abnormal Monthly Returns of Momentum Portfolios Using the Average NYSE Return as the Market Proxy in the Period from 1994 to 2009 P Return of momentum portfolio, J Ranking period (in months), K Holding period (in months), M Average market proxy return, P M Abnormal return, Panel A Zero transaction costs, Panel B Transaction costs .2 percent, Panel C Transaction costs .5. Panel D Transaction costs 1, t-statistics for monthly return differences in parentheses
Ranking Period (J)   Panel A Panel A Panel A   Panel B Panel B Panel B   Panel C Panel C Panel C   Panel D Panel D Panel D
Ranking Period (J)    Holding Period (K)   Holding Period (K)   Holding Period (K)     Holding Period (K)  Holding Period (K)  Holding Period (K)   Holding Period (K) Holding Period (K) Holding Period (K)   Holding Period (K) Holding Period (K) Holding Period (K)
Ranking Period (J)   3 6 12   3 6 12   3 6 12   3 6 12
                                 
3 Market Return (M) 0.0026       0.0026       0.0026       0.0026    
  Momentum Return (P) 0.0209       0.0181       0.0140       0.0071    
  Abnormal Return (P - M) 0.0183       0.0156       0.0114       0.0045    
  (t-stat) (-3.31)       (-2.82)       (-2.08)       (-0.82)    
                                 
6 Market Return (M)   0.0015       0.0015       0.0015       0.0015  
  Momentum Return (P)   0.0223       0.0210       0.0189       0.0154  
  Abnormal Return (P - M)   0.0208       0.0195       0.0174       0.0139  
  (t-stat)   (-5.10)       (-4.77)       (-4.27)       (-3.41)  
                                 
12 Market Return (M)     0.0021       0.0021       0.0021       0.0021
  Momentum Return (P)     0.0030       0.0023       0.0013       -0.0004
  Abnormal Return (P - M)     0.0009       0.0002       -0.0008       -0.0025
  (t-stat)     (-0.30)       (-0.07)       (0.28)       (0.87)
44
Results

• Significantly positive abnormal market-adjusted
  returns for short- and medium-term momentum
  strategies
• Medium-term strategy delivers highest performance
• Significant abnormal returns for short- and
  medium-term strategies after transaction costs of
  .2 and .5 percent, and for medium-term strategy
  after 1 percent transaction costs
• No significant returns at all for long-term
  strategy
• Profits mainly driven by loser portfolios

45
Conclusion

• Generation of superior returns using momentum
  strategies possible over the period from 12/31/94
  to 05/31/09
• Despite, or precisely because of turbulent market
  environment

46
CHAPTER FIVEThe Financial Futures Momentum

• Juan Ayora and Hipòlit Torró
• Universitat de València

47
Main Objectives

• Testing the existence of the momentum effect in
  financial futures markets.
• Researching the influence of futures volatility,
  trading volume, and open interest on momentum
  strategy performance.

48
Testing Momentum Effect (I) Procedure

• Formation periods (F) and holding periods (H) are
  set at 1, 3, 6, 12 and 24 months.
• In each F period, futures are grouped in three
  portfolios P1, P2 and P3
• P1 contains past winners
• P3 contains past losers
• P2 contains the remaining contracts.
• We then compute portfolio returns for each
  holding period.
• Finally, the momentum portfolio return is
  calculated as P1-P3. That is, by taking long
  positions in the past winning contracts, and
  short positions in the past losing contracts.
• The momentum effect exists if the average return
  of this portfolio is positive and significantly
  different from zero.

49
Testing Momentum Effect (II) Results
Return averages and t-statistics (above)
50
Momentum, Volatility, Trading Volume, and Open
Interest

• Bessembinder and Seguin (1993) results
• Positive relationship between trading volume and
  volatility.
• Negative relationship between open interest and
  volatility.
• Cross-effect an increase in open interest
  reduces trading volume influence on volatility.
• Abnormally high trading volume associated with
  positive shocks in prices.
• Next, we analyse implications on momentum
  returns.

51
Momentum Returns and Futures Volatility
Return averages and t-statistics (above)
52
Momentum Returns and Trading Volume
Return averages and t-statistics (above)
53
Momentum Returns and Open Interest
Return averages and t-statistics (above)
54
Momentum Returns, Open Interests, and Volume
Return averages and t-statistics (above)
55
Conclusions

• Momentum strategies for 6 and 12 months produce
  significant returns.
• Momentum effect is more persistent in highly
  volatile futures contract portfolios.
• Momentum effect is more persistent in futures
  portfolios with low relative increase in trading
  volume.
• Momentum effect is more persistent in futures
  portfolios with high relative increase in open
  interest
• When open interest and traded volumes are allowed
  to interact, the momentum effect is more
  persistent in futures portfolios with high
  relative increase in traded volume, and low
  relative increase in open interest.
• 6 and 12 month momentum strategies returns are
  shown to be abnormal
• Risk-adjusted momentum returns using CAPM and
  Fama and French three-factor model only partially
  explain momentum strategy returns.
• Bootstrap test shows that mean and Sharpe ratios
  are above the percentile 99.5 of the empirical
  distribution.

56
CHAPTER SIXOrder Placement Strategies in
Different Market Structures A Primer

• Giovanni Petrella
• Catholic University (Milano, Italy)

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57
Reading Questions

• When should you submit a limit order?
• When should you place a market order?
• Will an aggressive trader use limit orders?
  Explain your answer.
• What is the winner's curse for a limit order
  trader?
• What effect does the bid-ask spread have on the
  decision to take or offer liquidity?
• Explain the factors affecting the probability of
  limit order execution.
• What is a liquidity event?
• What is the difference between efficient and
  inefficient return volatility?
• Which is the type of order that you would use
  when the stock price is mean reverting? Explain
  your answer.
• Which is the type of order that you would use if
  a news is expected to arrive? Explain your answer.

58
Order Placement Strategy

• The order placement strategy refers to
• the type of orders (i.e., to trade via limit
  orders, market orders or a combination of both),
• the size of the orders,
• and the timing of the orders
• The order placement strategy depends on the
  relative merits and costs of limit orders and
  market orders
• What are the costs and returns from placing limit
  orders?

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Costs of Trading by Limit Orders

• Risk of adverse informational change (also known
  as ex post regret or cost of being bagged or
  winners curse)
• Heads You Win
• The market moves against the limit order trader
  (thats why I will regret the execution)
• Bearish news has caused the price of the stock to
  fall and my buy limit order executes
• Bullish news has caused the price of the stock to
  rise and my sell limit order executes
• Risk of limit order not executing (i.e., non
  execution cost)
• Tails I Lose
• Bullish news has caused the price of the stock to
  rise and my buy limit order does not execute
• Bearish news has caused the price of the stock to
  fall and my sell limit order does not execute
• So, why did I place that limit order?

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Benefits of Limit Orders A Better Price

• Limit order traders hope to get better prices
• Buyers who submit limit orders hope to buy at the
  bid
• If they had submitted a buy market order instead,
  they would pay the ask price (which is higher
  than the bid)
• Sellers who submit limit orders hope to sell at
  the ask
• If they had submitted a sell market order
  instead, they would receive the bid price (which
  is lower than the ask)
• but they do not always realize their hopes
• Limit order traders receive better prices only if
  their order actually trades
• If the market moves away from their limit price,
  they may never trade
• If they still want to trade, they will have to
  chase the price by raising their bid or
  lowering their offer
• This would make the final price actually worse
  than the price that they would have obtained had
  they used market orders

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When a Liquidity Event Occurs!

• A liquidity event is the arrival of a trader on
  the other side of the market who is buying
  liquidity (and is not an informed trader)
• I posted a buy limit order and an impatient
  seller arrived
• I posted a sell limit order and an impatient
  buyer arrived
• Alternatively, you can look a the same event as
  mean reversion in the pricing process
• I posted a buy limit order and the price first
  goes down and then up
• I posted a sell limit order and the price first
  goes up and then down
• Again, why did I place that limit order?
• Because I expected that sufficient mean reversion
  would offset the costs that might result from
  informational change

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Order Placement Strategy in a Continuous
Order-Driven Market

• Should I submit
• A market order?
• A limit order?
• If a limit order, how should I price it?
• The decision is made with respect to
• Gains from trading
• A buy (sell) market order would pay (receive) a
  higher (lower) price
• A buy (sell) limit order would pay (receive) a
  lower (higher) price
• Probability of a limit order executing
• A market order would be executed with certainty
• A limit order would not be executed with certainty

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Factors Affecting the Probability of Order
Execution (I)

• Price aggressiveness
• For buy orders, the higher is the limit price,
  the higher is the probability of execution
• For sell orders, the lower is the limit price,
  the higher is the probability of execution
• Demand/supply equilibrium
• The larger is the size of the depth on the buy
  (sell) side, the lower is the probability of
  execution for a buy (sell) limit order
• The market order arrival rate
• The larger is the arrival rate of market order on
  the opposite side of the market, the higher is
  the probability of execution for a limit order

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Factors Affecting the Probability of Order
Execution (II)

• Duration of the order
• The longer is the duration of the order, the
  higher is the probability of execution
• Volatility
• The larger is price volatility, the higher is the
  probability of execution for a limit order
• This does not imply that order execution will be
  profitable, this just means that higher
  volatility entails higher probability of
  execution
• Execution is profitable for a limit order trader
  if the volatility that triggered the limit order
  execution is temporary
• Execution is unprofitable for a limit order
  trader if the volatility that triggered the limit
  order execution is permanent

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Implications for Trading Strategies in a
Continuous Order-Driven Market

• High volatility days or high volatility stocks
  make convenient to use limit orders
• You might even place limit orders on both sides
  of the market to benefit from accentuated
  intra-day volatility
• When operating in this manner you resemble a
  dealer (buy at the bid and sell at the ask)
• Relatively patient traders place limit orders
• This is called Passive Trading Strategy (PTS)
• Large investors may prefer PTS because this
  strategy does not cause market prices to change
  (market impact) as the orders are naturally
  absorbed by the market
• Relatively eager traders place market orders

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Continuous Order-Driven Markets vs. Call Auctions

• Immediacy
• Waiting time is lower in continuous order driven
  markets
• Price discovery
• Prices are better in call markets then in
  continuous markets
• Prices are volatile in continuous markets because
  of news (efficient volatility) and order
  imbalance (inefficient volatility)
• Traders get their orders executed depending on
  the current market conditions at the time of the
  submission
• In a call auction, the terms of your trade will
  depend on the market conditions at the end of the
  pre-opening phase (which implies that you may get
  more orders on the opposite side of the market,
  and thus you may get better conditions)

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Continuous Order-Driven Markets vs. Call Auctions

• Trading volume
• Volume is higher in continuous markets
• Some traders that do not get execution in a call
  market, may execute their trades in a continuous
  market (with zero surplus)
• If on the opposite side of the market, there are
  impatient traders demanding liquidity
• In short
• Continuous markets sacrifice surplus (i.e.,
  expected profits) for immediacy
• Call auction markets sacrifice immediacy for a
  better price discovery process

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Implications for Trading Strategies in a Call
Auction

• There is no bid-ask spread in a call auction
  because all executed orders clear at the same
  price
• Executed orders receive price improvement (or
  positive surplus or positive expected profits) in
  a call auction
• Buy orders priced above the single clearing price
  and sell orders priced below it receive the price
  improvement
• Orders priced exactly at the clearing price do
  not receive price improvement
• Traders are more aggressive in a call auction
• The limit price affects the probability of
  execution, but it does not set the price

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CHAPTER SEVENProfitability of Technical Trading
Rules in an Emerging Market

• Dimitris Kenourgios
• University of Athens, Department of Economics
• Spyros Papathanasiou
• Hellenic Open University, School of Social
  Sciences

70
Reading Questions

• Why the profitability of technical trading
  strategies does not support the Efficient Market
  Hypothesis?
• Describe why and how traders use moving average
  trading rules.
• Why academics have main concerns regarding the
  validity of technical analysis?
• Describe how the bootstrap methodology overcomes
  traditional statistical problems of financial
  time series.
• What is the reason for the use of a generalized
  autoregressive conditional heteroskedasticity
  (GARCH) model under the bootstrap methodology?

71
Reading Questions

• Why it is important to examine the profitability
  of technical trading rules after considering
  transaction costs?
• The main conclusion of this chapter is that
  technical trading strategies win the buy and
  hold strategy. Present and discuss the main
  results which confirm this argument.
• Are the reporting results consistent to the study
  of Brock, Lakonishok and LeBaron (Journal of
  Finance, 1992) and other existing empirical
  research? Why?
• Do the results of this chapter support or not the
  use of technical analysis?
• How traders would benefit reading this chapter?

72
Purpose

• This chapter investigates the profitability of
  technical trading rules in the Athens Stock
  Exchange (ASE).
• We compare various moving average trading
  strategies against the buy and hold position in
  the spirit of Brock, Lakonishok and LeBaron
  (Journal of Finance, 1992), employing traditional
  t-test and bootstrap methodology.
• The profitability of technical trading rules
  contradicts the Efficient Market Hypothesis
  (EMH).

73
Contribution

• Although the majority of the professional traders
  and investors use technical analysis, empirical
  research is still limited since most academics,
  until recently, had not recognized the validity
  of these methods.
• It focuses on a less developed and efficient
  stock market, given the existing paucity of
  research in such markets.
• In contrast to prior relevant studies, it
  examines the profitability of technical trading
  rules after transaction costs.
• Statistical and more advanced econometric methods
  can help traders to implement profitable
  technical trading strategies.

74
Literature (1)

• U.S. stock markets
• Brock, Lakonishok and LeBaron BLL (1992)
  investigate two popular trading rules (moving
  average and trading range break rule), utilizing
  the Dow Jones Index from 1897-1986.
• Kwon and Kish (2002) provide an empirical
  analysis on technical trading rules (the simple
  moving average, the momentum, and trading
  volume), utilizing the NYSE value-weighted index
  over the period 1962-1996.
• Their results indicate that the technical trading
  rules can capture profit opportunities over a
  buy-hold strategy.

75
Literature (2)

• Emerging and less developed stock markets
• Singapore Wong, Manzur and Chew (2003)
• Jordan Atmeh and Dobbs (2006)
• Greece Vasiliou, Eriotis and Papathanasiou
  (2008a, 2008b)
• Their results show that the application of
  technical trading strategies provide substantial
  profits.

76
Methodology

• t-test we compare the mean returns of the
  unconditional buy methodology with the returns of
  the buy signals given by the moving averages and
  the returns of the unconditional buy methodology
  with the returns of the buy signals minus the
  returns of the sell signals given by the moving
  averages.
• ? Null hypothesis No actual difference between
  mean returns.
• 2. Bootstrap methodology under GARCH(1,1)
  model we compare the returns conditional on buy
  (or sell) signals from the actual FTSE/ASE-20
  data with the returns from simulated comparison
  series generated by a GARCH model (500
  replications).
• ? Null hypothesis the trading rule excess return
  calculated from the original series is less than
  or equal to the average trading rule return for
  the pseudo data samples.

77
Data

• Daily closing prices (in logs) of the FTSE/ASE-20
  index from 1/1/1995 to 31/12/2008.
• The returns are calculated after transaction
  costs.
• We evaluate the performance of the following
  moving average rules against the buy and hold
  strategy 1-9, 1-15, 1-30, 1-60, 1-90, 1-120 and
  1-150.
• The first number in each pair indicates the days
  in the short period and the second number shows
  the days in the long period.

78
Empirical Results (1)

• t- test results
• Rejection of the null hypothesis.
• The technical strategies win the buy and hold
  strategy (FTSE/ASE-20).
• The buy-hold strategy gives 5.109 annually
  return, while the strategy of simple moving
  averages 24.675.

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Empirical Results (2)

• Bootstrap results
• Acceptance of the null hypothesis.
• Most of the simulated GARCH (1,1) series generate
  a mean return larger than that from the original
  FTSE/ASE-20 index.
• The technical strategies win the buy and hold
  strategy (FTSE/ASE-20).

80
Conclusions

• Making trading decisions based on moving average
  rules lead to significantly higher returns than
  the buy and hold strategy, even after transaction
  costs.
• Technical rules produce useful signals and can
  help to predict market movements.
• This contradicts the EMH since traders and
  investors can gain significant abnormal returns.
• Findings are consistent to the existing empirical
  evidence.

81
CHAPTER EIGHTTesting Technical Trading Rules as
Portfolio Selection Strategies

• Vlad Pavlov
• Stan Hurn
• Queensland University of Technology

82
Reading Questions

• What is technical analysis? Outline how
  moving-average rules operate.
• Explain what is meant by the data-snooping bias.
• What is the Reality Check?
• How is an arbitrage portfolio constructed.
• Why is it difficult to assess the profitability
  of technical trading rules?

83
Literature

• Brock, Lakonishok and LeBaron (1992) - bootstrap
  analysis of a few popular technical trading rules
• Lo, Mamaysky and Wang (2000) - wide universe of
  rules
• Sullivan, Timmermann and White (1999) data
  snooping bias in rules selection, Reality Check
  test

84
Problems

• Multitude of trading rules
• Infrequent signals
• requires large amounts of data
• Most studies use either one or a small number of
  time series
• need very long histories

85
The Main Idea

• Apply trading rules to a large dataset of stocks
• move away from using a single index or currency
• Use trading rules to form portfolios
• from an arbitrage portfolio using buy and sell
  signals

86
The Data

• AGSM dataset comprising monthly observations on
  prices and dividend payments for 6000 ASX stocks
  from 1973 to 2008.
• How to deal with missing observations?
• Mean returns become very sensitive to the
  treatment of missing returns and exits for very
  small stocks.
• Empirical exercise limits attention to top 500
  stocks.
• Additional liquidity filters used to limit the
  effect of missing observations.

87
MA Trading Rules

• Compute two MAs (long and short)
• BUY signal
• short MA (t) gt long MA (t) and short MA (t-1) lt
  long MA (t-1)
• SELL signal
• short MA (t) lt long MA (t) and short MA (t-1) gt
  long MA (t-1)
• Arbitrage portfolio
• purchase all stocks which generate buy signals
  in a particular month and finance this purchase
  by shorting the stocks for which sell signals
  are generated

88
Example of MA Rule
89
Empirical Results

• MA rules do appear to generate abnormally high
  returns
• The simple MA rule works as traditionally
  implemented
• Exponentially weighted MA rule requires
  non-traditional contrarian implementation
• Reconciliation of this result hinges on
  recognition of relationship between the two
  different types of MA rule.
• Exponentially weighted rule actually magnifies a
  small area of the possible outcomes generated by
  the simple MA rule relating to simple MA rules
  with averaging less than 12 months
• Exponentially weighted MA rules may be more
  appropriate because averaging periods greater
  than a year generate very few trading signals.

90
Bootstrap Results
91
Conclusion

• The analysis in this paper does not lead to any
  specific investment strategies based on MA
  crossover rules.
• In the Australian data over the period from 1973
  to 2008, however, contrarian interpretation of MA
  trading rules generate profits over a one month
  horizon.
• There is an obvious mode of the return
  distribution which is very difficult to explain
  by alluding to pure chance selection. Some
  parameter subset of these MA rules appear to pick
  a systematic factor in returns that is not one of
  the well-known Fama-French factors.

92
CHAPTER NINEDo Technical Trading Rules Increase
the Probability of Winning? Empirical Evidence
from the Foreign Exchange Market

• Alexandre Repkine
• Korea University

93
Reading Questions

1. What is the primary purpose of applying technical
   analysis rules to trading financial instruments
   and commodities?
2. What would be the realistic trading strategies
   applied by the practicing traders?
3. Why are stop-loss and stop-limit orders important
   for any trading strategy?
4. What would be the probability of a currency pair
   moving by b points in the favorable direction
   before moving by a points in the opposite
   direction?
5. What would be an average return on a random-entry
   strategy based on a stop-limit order at b points
   and a stop-loss order at a points?

94
Reading Questions

1. Can technical trading rules be used for entry so
   as to increase the probability of winning and to
   decrease the probability of losing?
2. How can one use pattern recognition techniques in
   order to identify charting patterns of technical
   analysis such as e.g. double bottom?
3. What are the differences between empirical and
   theoretical probabilities of winning?
4. Is there a technical trading strategy that would
   result in positive returns for all currency
   pairs?
5. What would be the reason for the persistent
   popularity of technical analysis among practicing
   traders?

95
The Value of Technical Analysis Rules

• Technical analysis rules are sometimes thought of
  as a means of realizing consistently positive
  returns
• Academic literature has recognized the ability of
  certain technical rules to increase returns, but
  it also emphasized the unstable performance of
  these rules
• Our question is not about consistent returns, it
  is about increasing the probability of winning
• Does the application of technical trading rules
  in the foreign exchange market increase the
  traders probability of beating the market?

96
Risk- and Profit-Management in Real-World
Trading Strategies

• A position is opened according to some technical
  trading rule
• Stop-limit orders specify the level of profit on
  an open position at which the latter must be
  closed (profits are earned)
• Stop-loss orders specify the level of loss on an
  open position at which the latter must be closed
  (losses are incurred)
• Stop-limit orders help traders run their profits,
  stop-loss orders help traders limit their losses

97
Efficient Market Hypothesis

1. The efficient market hypothesis implies it is
   impossible to earn positive returns on a
   random-entry strategy with whatever levels of
   stop-limit (b) and stop-loss order (a) specified
   since the expected return in that case will be
2. What if the market entry (creating an open
   position) is not random? What if one enters the
   market using signals produced by technical
   trading rules?
3. Hypothesis Technical analysis rules shift the
   probability of winning up to the level of
   , making the return due to the technical
   analysis strategy positive
4. We use two technical analysis rules for entry
   double bottom and bull flag

98
The Double Bottom Pattern
1 2 3 4 5 6 7 8 9 10
1 0.5 -1 -1 -1 0.5 0.5 -1 -1 -1 0.5
2 1 0 -1 0 1 1 0 -1 0 1
3 0.5 0 -1 0 0.5 0.5 0 -1 0 0.5
4 0 0.5 -1 0.5 0 0 0.5 -1 0.5 0
5 0 1 -1 1 0 0 1 -1 1 0
6 -1 1 0 1 -1 -1 1 0 1 -1
7 -1 0.5 0 0.5 -1 -1 0.5 0 0.5 -1
8 -1 0 0.5 0 -1 -1 0 0.5 0 -1
9 -1 0 1 0 -1 -1 0 1 0 -1
10 -1 -1 0.5 -1 -1 -1 -1 0.5 -1 -1

1. Shaded cells represent the double bottom pattern
2. Figures in each cell represent the weights that
   reflect the extent to which the actual foreign
   exchange rates are fitting the double bottom
   pattern
3. This charting template is fitting each
   individually observed exchange rate into one of
   its cells, so a fitting statistic can be
   calculated
4. If the fitting statistic is large enough, entry
   occurs

99
Calculating the Fitting Value An Example

• Suppose we want to see whether the exchange rate
  movements were fitting the double bottom pattern
  during the past 100 days
• Suppose we choose the exchange rate fluctuation
  range between the values of 1 and 2 during the
  past 100 days
• Each column in our template would correspond to a
  sub-period of ten days with the first row of the
  template corresponding to the range of 1.92,
  the second one to 1.81.9 and the tenth row
  (which is the bottom row) corresponding to the
  range of 11.1
• If hypothetically for the past 100 days the
  exchange rate were fluctuating in the range of
  1.92, then the value of the fit for each
  10-day sub-period would be simply equal to the
  template weight value, and the total fit for the
  historical window would be equal to the sum of
  the weights in the first row of the template
  table

100
Empirical Study Plan

• The sample period January 1st, 1999, through
  January 31st, 2007
• Fit the two trading patterns within the 40 days
  trading windows
• Use data on the worlds ten major currency pairs
• For each currency pair and one of the two trading
  rules, round the highest estimated fitting value
  down to the nearest integer and use this value as
  a signal to enter the market
• Computing the difference between empirical and
  theoretical probabilities of winning
• Choose the currency pair (e.g. EUR/USD)
• Let and vary between one and one thousand
  points with the increment of one point, which is
  supplying us with one million strategies for each
  currency pair and the technical analysis pattern
• Compute the number of profitable entries, i.e.
  the ones that result in the exchange rate
  deviating by points in the profit-making
  direction before deviating by points in the
  loss-making direction. Call the share of such
  profitable entries . This will be our empirical
  probability of a profitable entry.
• In case we find for a particular trading rule and
  currency pair, the average expected empirical
  return on this rule is statistically positive for
  this currency pair

101
Differences between Empirical and Theoretical
Probabilities of Realizing Positive Returns due
to Technical Analysis Application.
EURUSD GBPUSD NZDUSD USDCAD AUDUSD
Bull -1.54 -21.78 25.38 11.41 13.93
Doulbe Bottom 38.93 31.24 -14.96 11.08 1.07
USDCHF USDJPY GBPJPY GBPUSD EURCHF
Bull 23.90 25.00 10.00 17.62 -49.47
Double Bottom 26.15 50.00 2