PPT Short - shorten for press conference ...

PORTFOLIO PUMPING IN SINGAPORE: MYTH OR REALITY?

Alan Lok, FRM, MBA, CFADirector, Capital Markets Policy

2

01: Why is this a relevant theme for discussion?


3

WHAT IS THE PROJECT GENESIS?1. Gauge the after-effects of a court case in Singapore involving portfolio

pumping:• Aug 2009 – Defendants charged with priming the stock# of UET over a three-day

period from 29 December 2004 to 31 December 2004

• Sep 2010 – Defendants convicted with a civil penalty of S$250,000 on each of them

• Jul 2011 – Appeal against the verdict turned down

• Convicting a reputed firm with heavy-weight clientele conveys a strong message and is widely regarded the first publicized instance of portfolio pumping

• What was the situation prior to this case and how has it changed since then?

• How has enforcement led to (re-)enforcement of market integrity?

2. Assess the media assertion on year-end action by fund managers to artificially boost stock prices• Media points to such activities being common at year-ends to inflate stock prices

• They label it as ‘Window Dressing’ while actually discussing ‘Portfolio Pumping’

4

WHY DOES PUMPING OCCUR?1. Across markets, such activities have been researched and existence proven2. Performance-linked compensation agreements offer a natural incentive3. Literature offers three possible reasons

• Leaning for the tape Convex relationship between flows and performance

Weak persistence among best performing funds

• Clutching at straws Fear of loss of employment among poor performers

Prevent stressed sale of assets

• Beating the Benchmark Deliberate movement of key components of the benchmark

4. Empirical support largely in favour of the flow-performance argument

5

WHAT DID WE SET OUT TO DO?1. Seven set of hypotheses to evaluate facets of Pumping in Singapore

• H1: Existence of pumping at the broad market level

• H2: Concentration of pumping in the final few minutes of trade

• H3: Proportion of buyer-initiated trades on the last trading day or final few minutes

• H4: Proportion of small trades on the last trading day or final few minutes

• H5: Trader and client concentration among final set or period of trades

• H6: Distinction in potential pumping across various segments: Market cap

Listing board: Mainboard and Catalist Momentum

Domicile: S-chip versus non S-chip

• H7: Response of pumping levels to key legal and regulatory milestones

6

WHAT ARE THE PROJECT FACTS?1. Analysis over a 11 year period: 2003-20132. 189 companies in the FTSE ST All-Share Index (including delisted entities)3. Privileged data access to tick-by-tick data from SGX4. Data access included details on trader and client IDs backing quotes allowing

for granular analysis5. Analysis coverage

• Over 12 billion data points

• SQL database to store and manage voluminous data

• Extensive use of Excel VBA macros and EViews for analysis

6. A first-in-kind analysis• Evaluating cornering of trades by a select set of traders or clients

• Tracing the evolution of portfolio pumping in Singapore over time and gauging the response to legal actions and regulatory reforms

7

02: What the markets have to say?


8

#1: NO CLEAR EVIDENCE FOR PUMPING AT MARKET LEVELAverage 2-day inflation# around quarter-ends, by quarter

• Limited support for the combined legs - pumping and subsequent reversal For most years, average 2-day inflation at quarter-ends is negative Trend is more significant in the second half of the study period

Hypothesis test on 2-day inflation at quarter ends

# 2-day inflation represents deduction of day 2 return from day 1 return. For instance, as on March 31, 2 –day inflation represents difference in return of April 1 from March 31

Q12

003

Q32

003

Q12

004

Q32

004

Q12

005

Q32

005

Q12

006

Q32

006

Q12

007

Q32

007

Q12

008

Q32

008

Q12

009

Q32

009

Q12

010

Q32

010

Q12

011

Q32

011

Q12

012

Q32

012

Q12

013

Q32

013

-5

-4-3-2-10

123

Absolute Returns Excess Returns

(in %)Test for average returns being

Greater than zero

Greater than non-quarter-end days

Greater than zero

Greater than non-quarter-end days


Average -0.44 0.01

t-stat -2.17 0.19 0.08 -0.16

p-value 0.02 0.42 0.47 0.44

9

#2: A DELAYED REVERSAL ARGUMENT NOT SUPPORTIVEAverage 2-day inflation (excess returns)# at quarter-ends for various days of reversal

• A reversal on day T+2 though visually appealing, has no statistical support Only T+2 day returns carry 2-day inflation levels reasonably positive across years Statistical support is however elusive Similar tests with absolute returns also throw no support for a significant reversal

Hypothesis test on quarter-beginning (t+2) returns

# 2-day inflation represents deduction of day 2 return from day 1 return. For instance, as on March 31, 2 –day inflation represents difference in return of April 1 from March 31

Test for average returns being

Lesser than zero

Lesser than non-quarter-beginning days

Lesser than zero

Lesser than non-quarter-beginning days


Average 0.44 -0.05

t-stat 2.25 2.00 -0.41 -0.81

p-value 0.01 0.03 0.34 0.21T+1 T+2 T+3 T+4 T+5-1.0

-0.5

0.0

0.5

1.0

1.5

2003 2004 2005 2006 2007 2008 2009 2010 2011 20122013

(in %)

10

03: Does time of the year and time of the day matter?


11

#1: YEAR-ENDS PRESENT AN INTERESTING STORYExcess returns at quarter-ends over non-quarter-end days, by quarter

• Abnormal excess returns at quarter-ends exaggerated at year-ends Excess returns at year-ends consistently above the other days in the last quarter Q3 trades significantly higher while half-day trades at Q4-end explain low volumes But these are visual inspections, may not be statistically significant.

Excess std. trade volume at quarter-ends over non-quarter-end days, by quarter

Q1 Q2 Q3 Q4-4

-3

-2

-1

0

1

2

3

2003 2004 2005 2006 2007 2008 2009 2010 20112012 2013

(in %)

Q1 Q2 Q3 Q4-1.0

-0.5

0.0

0.5

1.0

2003 2004 2005 2006 2007 2008 2009 2010 20112012 2013

(x times)

12

#2: BETA-ADJUSTMENT DOES NOT CHANGE THE SCRIPTRegression of excess returns of stocks against period-defining dummies

• Beta-driven volatility not likely the driver of excess returns Significant positive excess returns at year-end; Year-beginnings though even higher Beta-adjusted returns carry a similar pattern at year-ends

Regression of beta-adjusted# excess returns against period-defining dummies

Co-eff. Std. error t-stat p-value

Q1BEG 0.14 0.09 1.55 0.06

Q1END 0.09 0.09 0.97 0.17

Q2BEG -0.15 0.09 -1.79 0.04

Q2END -0.13 0.09 -1.50 0.07

Q3BEG -0.06 0.09 -0.62 0.27

Q3END -0.03 0.09 -0.35 0.37

Q4BEG 0.78 0.08 9.20 0.00

Q4END 0.41 0.08 4.83 0.00*Constant -0.06 0.01 -11.42 0.00* significant at 5% level, ** significant at 10% level

Co-eff. Std. error t-stat p-value

Q1BEG 0.10 0.09 1.12 0.13

Q1END 0.07 0.09 0.77 0.22

Q2BEG -0.17 0.09 -1.98 0.02

Q2END -0.13 0.09 -1.50 0.07

Q3BEG -0.16 0.09 -1.78 0.04

Q3END -0.02 0.08 -0.21 0.42

Q4BEG 0.55 0.08 6.60 0.00

Q4END 0.44 0.08 5.26 0.00*Constant 0.05 0.01 9.45 0.00* significant at 5% level, ** significant at 10% level

# Beta adjusted returns of stocks computed by deducting CAPM predicted returns from actual returns. For the CAPM model, beta is based on co-movement of daily stock returns versus FTSE Straits Times Index over a 6-month time frame and Singapore government 10-year bond yield is assumed as the risk-free rate

13

#3: FINAL MINUTES OFFER MORE TRADES NOT RETURNSAverage final 30-minute returns on quarter-end days versus non-quarter-end days

• Volume higher while returns converge in the final minutes at quarter-ends Returns seem higher in the earlier quarters but non-differential and negligible recently Trade volume in the final 30 minutes clearly higher in comparison to a normal day But these are visual inspections, may not be statistically significant.

Average final 30-minute trade volume at quarter-end versus non-quarter-end days

Q12

003

Q32

003

Q12

004

Q32

004

Q12

005

Q32

005

Q12

006

Q32

006

Q12

007

Q32

007

Q12

008

Q32

008

Q12

009

Q32

009

Q12

010

Q32

010

Q12

011

Q32

011

Q12

012

Q32

012

Q12

013

Q32

013

-2.0-1.5-1.0-0.50.00.51.01.52.0

Quarter-end non-quarter-end

(in %)

Q32

003

Q12

004

Q32

004

Q12

005

Q32

005

Q12

006

Q32

006

Q12

007

Q32

007

Q12

008

Q32

008

Q12

009

Q32

009

Q12

010

Q32

010

Q12

011

Q32

011

Q12

012

Q32

012

Q12

013

Q32

013

0.00.20.40.60.81.01.21.41.61.82.0

Quarter-end non-quarter-end

(x times)

14

04: How different are trades at the end of a quarter?


15

#1: BUYER-INITIATED TRADES DOMINATEAverage buyer-initiated trades# at quarter-end days versus non-quarter-end days

• Final day and in the final minutes of that day, buyer-driven trades are higher Average levels for both cases around 0.5, implying 50% trades above ask price Based on the complete day, levels significantly higher than non-quarter-end days Final 30 minutes on quarter end days though does not carry any statistic significance

Hypothesis test on buyer-initiated trades at quarter-ends

# buyer-initiated transactions represent trades that occur at or above ask price. In effect, these represent transactions clearly initiated by the buyers

Test for average proportion of trades at quarter end being

greater than non quarter-end days

Full dayFinal 30 minutes of trade

Average 0.49 0.50

t-stat 1.81 1.19

p-value 0.04* 0.12* significant at 5% level, ** significant at 10% level

Q12

003

Q32

003

Q12

004

Q32

004

Q12

005

Q32

005

Q12

006

Q32

006

Q12

007

Q32

007

Q12

008

Q32

008

Q12

009

Q32

009

Q12

010

Q32

010

Q12

011

Q32

011

Q12

012

Q32

012

Q12

013

Q32

013

0.35

0.40

0.45

0.50

0.55

0.60

0.65

Last day Daily average 30min 30min average

(x times)

16

#2: NO CONCRETE SIGNS OF SMALL VOLUME TRADESAverage proportion of small trades# at quarter-ends versus non-quarter-end days

• Cyclical and structurally rising levels with not much variation across days Visually, no specific distinction seen on the final day or in the final minutes of trade Statistically, it is in fact significantly lower, contrary to expectations under pumping

Hypothesis test on proportion of small trades at quarter-ends

# Given that pumping involves a cost, fund managers intending to pump stocks are expected to do so with smaller orders. We therefore test for the metric small trades as a proportion of total trades, where small trades is defined as trades below 5000 shares




Average 0.44 0.47

t-stat -1.29 -2.25

p-value 0.10 0.01* significant at 5% level, ** significant at 10% levelQ

1200

3Q

3200

3Q

1200

4Q

3200

4Q

1200

5Q

3200

5Q

1200

6Q

3200

6Q

1200

7Q

3200

7Q

1200

8Q

3200

8Q

1200

9Q

3200

9Q

1201

0Q

3201

0Q

1201

1Q

3201

1Q

1201

2Q

3201

2Q

1201

3Q

3201

3

0.35

0.40

0.45

0.50

0.55

0.60

0.65


(x times)

17

#3: TRADER-CLIENT CONCENTRATION WITH MIXED VIEWSAverage trader concentration levels# at quarter-ends versus non-quarter-end days

• Significantly high concentration on the last trading day, albeit on a low base Though higher in absolute terms, concentration statistically lower in the final minutes Seems largely an year-end effect. Client concentration mimics a similar pattern

Hypothesis test on trader concentration levels at quarter-ends

# Obtained using the Herfindahl Index approach. It involves taking the square of market share of individual traders in individual stocks and then summing it across all traders to arrive at the trader concentration level in individual stocks.




Average 0.25 0.40

t-stat 1.46 -2.52

p-value 0.08** 0.01* significant at 5% level, ** significant at 10% levelQ

1200

3Q

3200

3Q

1200

4Q

3200

4Q

1200

5Q

3200

5Q

1200

6Q

3200

6Q

1200

7Q

3200

7Q

1200

8Q

3200

8Q

1200

9Q

3200

9Q

1201

0Q

3201

0Q

1201

1Q

3201

1Q

1201

2Q

3201

2Q

1201

3Q

3201

3

0.00

0.10

0.20

0.30

0.40

0.50

0.60


(x times)

18

05: How do pumping signals vary across segments?


19

#1: MID-CAPS APPEAR THE MOST VULNERABLEAverage excess returns at quarter-end and quarter-beginnings across capitalization#

• Mid-caps with strong returns at quarter-end and a mild insignificant reversal High liquidity among large caps and lack of it in small caps a possible explanation Within mid caps, stocks with S$2-S$5bn market value seem most susceptible

# Large-cap covers stocks currently included under FTSE Straits Times Index, mid-cap covers current constituents of FTSE ST mid cap index and small-cap covers current constituents of FTSE ST small cap index

Large- cap

Mid-cap

Small-cap

-0.05 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35

Quarter-end Quarter-beginning

(in %)

20

#2: CATALIST AND S-CHIP WITH ‘NO SHOW’ SURPRISE Average excess returns at quarter-end and quarter-beginnings across listing boards#

• Catalist nor S-chip stocks show signs of pumping, contrary to expectations In fact, Catalist stocks on an average carry –ve returns at quarter-ends Non S-chips carry significant +ve returns at quarter-ends. S-chips show no support

Average excess returns at quarter-ends and quarter-beginnings across domicile#

# Listing board considers listing in Mainboard and Catalist as two distinct groups. Operational domicile primarily distinguishes between China-based (S-chip) and non-China-based stocks. China group consists of constituents covered currently under FTSE ST China Index. Non-China represents the remaining stocks.

Mainboard

Catalist

-0.10 -0.05 0.00 0.05 0.10 0.15 0.20 0.25


(in %)

S-chip

Non S-chip

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40


(in %)

21

#3: RECENT WORST PERFORMERS APPEAR KEY TARGETAverage excess returns at quarter-end and quarter-beginnings of momentum groups#

• Worst performers see the maximum abnormal returns with a slight reversal Reputational and remuneration risk for fund managers plausible reason A reversal is also observed although not found to be statistically significant

Hypothesis test on average excess returns at quarter-ends across momentum groups*

# Stocks are grouped into 4 performance quartiles every quarter – 1 being the worst performers and 4 being the best performers – based on performance in the quarter until the second last day of the quarter

Test for average excess returns around a quarter being different from non-quarter

end days

1 2 3 4 1 2 3 4Last day of a quarter First day of a quarter

Average 0.32 0.09 0.19 0.07 -0.42 0.03 0.22 0.79

t-stat 4.49 1.47 1.01 -2.48 -1.06 1.06 2.21 2.95

p-value 0.00* 0.07** 0.16 0.01 0.15 0.15 0.02 0.00

* significant at 5% level, ** significant at 10% level

Group 1 (worst)

Group 2

Group 3

Group 4 (best)

-0.60 -0.40 -0.20 0.00 0.20 0.40 0.60 0.80 1.00


(in %)

22

06: How has pumping evolved over time?


23

#1: POTENTIAL PUMPING CASES ON A DECLINE

• Stocks with abnormal returns and a reversal at quarter-ends inch down Return distribution of stocks getting more closely knit, with fewer outliers Possible instances of pumping on a structural declining peaking at 2007

Proportion of possible pumped up stocks# over the study period

# Pumped-up stocks are those with a gaming proxy value of 1. Gaming proxy is assigned a ‘1’ if absolute and excess returns at quarter ends are greater than zero and the same set of returns are less than zero on first day of the subsequent quarter

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 20130

5

10

15

20

25(in %)

24

#2: LEGAL ENFORCEMENT AND PENALTY KEY DAMPENERRegression analysis of market returns against the law suit milestones

• Legal action and regulatory reforms play a critical role in managing pumping Mile 2 (first filing of suit by SGX) and Mile 3 (first indictment of the fund manager being

pronounced guilty in Sep 2010) found to be the most significant legal events Mile M2 (proposal to introduce mandatory minimum penalties for rule violations in the

securities market) found to lead to significant decline in absolute and excess returns

Regression analysis of market returns against SGX Regulatory milestones


ModelRit = b0 + b1*Mile 1 + b2*Mile 2 + b3*Mile 3 + b4*Mile 4 + b5*Mile 5

Constant 0.13* 0.06 *(0.01) (0.01)

Mile 1 -0.03 * 0.02 **(0.02) (0.01)

Mile 2 0.01 -0.03 *(0.02) (0.02)

Mile 3 -0.08 * -0.03(0.03) (0.02)

Mile 4 -0.01 0.02(0.02) (0.02)

Mile 5 0.04* 0.02(0.02) (0.02)


Model Rit = b0 + b1*Mile M1 + b2*Mile M2 + b3*Mile M3 + b4*Mile M4 + b5*Mile M5+ b6*Mile M6

Constant 0.13 0.06(0.01) (0.01)

Mile M1 0.18 0.09(0.02) (0.02)

Mile M2 -0.33 * -0.11 *(0.03) (0.03)

Mile M3 0.08 0.03(0.03) (0.02)

Mile M4 0.04 -0.03 **(0.02) (0.02)

Mile M5 -0.13 * -0.03(0.03) (0.02)

Mile M6 0.10 0.04(0.02) (0.02)

25

#3: LIMITED REWARDS EVEN FOR THE MOST PLAUSIBLEProportion of possible pumped up stocks across multiple definitions# of pumping

• Acute instances point to a decline in extent of and benefits from pumping Stricter threshold for gaming proxy also shows a trend of decline in pumping activity Average 2-day inflation at 7% (9.5%) over 2011-13 vs. 10% (13%) for the full period

Average 2-day inflation# at quarter-ends among top quartile /decile of pumped stocks

# Multiple definitions point to use of alternate thresholds of 1% and 2% in defining gaming proxy. Top-quartile represents stocks within the top 25% of potentially pumped up stocks with the highest level of 2-day inflation. Top decile represents similar stocks within the top 10% in terms of 2-day inflation.

Q12

003

Q12

003

Q12

004

Q12

004

Q12

005

Q12

005

Q12

006

Q12

006

Q12

007

Q12

007

Q12

008

Q12

008

Q12

009

Q12

009

Q12

010

Q12

010

Q12

011

Q12

011

Q12

012

Q12

012

Q12

013

Q12

013

0

10

20

30

40

50

60

Top quartile Top decile

(in %)

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 20130

5

10

15

20

25

0% 1% 2%

(in %)

26

07: What characters define potentially pumped up stocks?


27

#1: NATURE OF TRADES CARRY IMPORTANT SIGNALSProportion of buyer-initiated trades among potentially pumped /non-pumped universe#

• Proportion of buyer-initiated and small trades higher among pumped stocks Trend is strongly evident in the former while in latter it is less convincing A similar pattern seen while restricting returns at quarter-end to final 30 minutes

Proportion of small trades among potentially pumped/non-pumped universe#

# Potentially pumped up stocks represent stocks with a gaming proxy value of 1. A stock is assigned a gaming proxy value of 1 (marked ‘Yes’) for a quarter if absolute and excess returns at quarter ends is greater than zero and the same set of returns turns less than zero at first day of the subsequent quarter

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 20130.00

0.10

0.20

0.30

0.40

0.50

0.60

Yes No

(x times)

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 20130.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

Yes No

(x times)

28

#2: CAPITALISATION KEY FACTOR TO MONITORProportion of mid and small-cap universe among pumped-up stocks#

• Greater proportion of mid-cap stocks seen among the pumped universe Proportion of mid-cap universe that feature among the pumped instances higher Contribution of mid-caps in pumped group significantly higher than the alternate case

Proportion of mid-caps among potentially pumped/non-pumped universe

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 20130

5

10

15

20

25

30

Mid-caps Small-caps

(in %)

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 20130.000.050.100.150.200.250.300.350.400.45

Yes No

(x times)

# Proportion of the universe represents the extent of stocks within a particular universe featuring among the identified pumped-up universe

29

#3: TRADER CONCENTRATION AND LIQUIDITY WORTHY Average trader concentration among potentially pumped /non-pumped universe

• Mixed signals from trader concentration and liquidity Though visually appealing, trader concentration distinction not statistically significant Free-float liquidity though critical for the study period, less of a differentiator recently

Free-float liquidity# of stocks among potentially pumped/non-pumped universe#

# Average daily trading volume of a stock in a quarter divided by total number of free-float shares for the stock

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 20130.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

Yes No

(x times)

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 20130.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

Yes No

(x times)

30

#4: DOMICILE MATTERS FOR THE STRONGER CASESProportion of S-chip stocks among potentially pumped/non-pumped universe

• S-chip stocks see a larger contribution among the most pumped universe No definite pattern among the total set of pumped versus non-pumped universe Higher proportion of S-chips seen among the largest instances of pumping

Proportion of S-chips among potentially pumped universe across definitions #

# Data universe marks the complete study data set. Overall represents the complete gaming proxy universe (those with a value of 1 for gaming proxy definition), Top quartile represents the top 25% of most pumped stocks (based on 2-day inflation) and top decile represents the top 10% of mist pumped stocks

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 20130.00

0.05

0.10

0.15

0.20

0.25

Yes No

(x times)

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 20130.000.050.100.150.200.250.300.350.400.450.50

Data universe Overall Top Quartile Top Decile

(x times)

31

#5: A SIMILAR SCENARIO FOR LISTING BOARDProportion of Catalist-listed stocks among potentially pumped/non-pumped universe

• Catalist stocks show up among the most pumped-up universe Proportion lower among the overall universe of pumped up stocks Relative contribution significantly higher among the top group of pumped up universe

Proportion of Catalist stocks in potentially pumped universe across definitions #

# Data universe marks the complete study data set. Overall represents the complete gaming proxy universe (those with a value of 1 for gaming proxy definition), Top quartile represents the top 25% of most pumped stocks (based on 2-day inflation) and top decile represents the top 10% of mist pumped stocks

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 20130.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

Yes No

(x times)

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 20130.00

0.05

0.10

0.15

0.20

0.25

0.30

Data universe Overall Top Quartile Top Decile

(x times)

32

07: What this means in a nutshell?


33

OVERALL CONCLUSIONS1. No obvious evidence for pumping at the market level during the study period• Regulatory reforms put in place have done a decent job in ensuring market integrity

• Excess returns at quarter-ends present but with no subsequent reversal

• More trades in the final minutes on last trading day of a quarter; not higher returns

2. No evidence of higher proportion of small trades at quarter-ends highlights that even if pumping were to exist, it is a costly affair3. Segmental analysis point to excess quarter end return among the mid-caps, especially in the S$2bn-S$5bn market value range even though there wasn’t significant reversal there after.4. Legal enforcement and regulatory action appear to have worked to reduce market returns at quarter-ends• Legal milestone study shows positive contribution from successful identification and

conviction of market frauds

• Regulatory milestone test supports the case for stronger penalty system

5. Contrary to expectations, Catalist-listed and S-chip stocks do not see relatively higher evidence for pumping at an aggregate level

34

OVERALL CONCLUSIONS6. Gaming-proxy based logit regression analysis spell out likely characteristics of the potentially pumped universe though:• Worst performers, mid-cap stocks with low free-float liquidity

• Higher level of buyer-initiated trades and overall trade volume with a higher proportion of trades happening in the final 30 minutes of trade

7. Overall market integrity has improved over time as reflected in 2-day inflation• Among the top quartile of our gaming proxy positive population, average levels over

2011-13 dropped to 7% versus 10% for the entire period under study.

• Among the top decile of our gaming proxy positive population, average levels over 2011-13 dropped to 9.5%% versus 13.5% for the entire period under study.

35

08: Our Policy Recommendations


36

POLICY RECOMMENDATIONS1. SGX market surveillance and MAS enforcement process appears to be working well. We recommend other exchanges adopt and refine these measures:• Ensuring a fair and judicial prosecution offers critical signaling effect

• Making pumping difficult and expensive to undertake

2. Increase scrutiny on mid-cap stocks and worst performing stocks3. Market surveillance to increase focus on specific categories of stocks with observed potential to be pumping:• Mid-cap segment, especially those within the S$2 billion to S$5 billion

• Lower free-float liquidity

• Not a constituent of the SIMSCI

• Higher daily standardized trade volume

• Significantly higher degree of buyer-initiated trades

• Greater proportion of trades in the final 30 minutes of a quarter-end trading day

4. Increase the awareness and education of mainstream media about unnecessarily hyping up portfolio pumping activities at year-ends

37

THANK YOU!

PPT Short - shorten for press conference ...

Documents

Transcript of PPT Short - shorten for press conference ...