Agenda

• Welcome

• Market Overview

• Performance & Risk Management

• Strategy Presentations • F-Score• TAA• EAR• Fundamental Index

• Closing Remarks2

Opening Remarks

Joseph F. Duronio, Esq., President

ASAM Class of 2014

4

• Fully invested assets

• Strong Performance in all three strategies

• Amazing Year of Events• Dinners with Ric Kayne and

Howard Marks• Co-Sponsored Dean’s

Distinguished Speaker Series: Larry Fink, David Booth and Mary Erdos

• Excellent speakers throughout the year

• Many Firm Visits• Trips to NYC, SF, Newport, and

Omaha• Meeting with Warren Buffett

Market Overview

Nedal Alqam, Chief Investment Officer

Market Themes for 2013

6

Market Performance

7

Performance & Risk Management

Alexandre Jorion and Jacob Gore, Co-Risk Managers

Performance

9

ASAM Asset Values

ASAM Performance, May 1, 2013 to April 30, 2014

Portfolio  Value

(April 30, 2014)

  Total Return

Total Portfolio $669,547 14.94%

S&P 500 Index 20.44%

TAA $217,035 5.66%

TAA Custom Benchmark 4.17%

F-Score $236,113 34.49% Russell 2000V

Index 19.61%

Fundamental Index $216,399 7.60% Russell Megacap

Index     7.28% [1] Performance for the Total Portfolio, TAA Portfolio, and the F-Score Portoflio reported from May 1, 2013 to April 30, 2014. Since inception date for the Fundamental Index Portfolio is as of October 29, 2013.

[2] The custom benchmark for the TAA strategy is comprised of 20% MSCI US REIT Index, 20% CRSP US Total Market Index, 20% FTSE All World ex-US Index, 20% Barclays US Agg Float Adjusted Index, and 20% DB Commodity Index. Prior to December 24, 2013, the benchmark was comprised of 20% MSCI US REIT Index, 20% CRSP US Total Market Index, 20% FTSE Emerging Markets Index, 20% Barclays US Agg Float Adjusted Index, and 20% DB Commodity Index.

Date TAA EAR F-ScoreFundament

al IndexTotal

Portfolio

May 1, 2013 $160,291 $145,932 $175,902 $100,402 $582,527

April 30, 2014 $217,035 $0 $236,113 $216,399 $669,547

ASAM Risk and Performance Summary, March 1, 2013 to April 30, 2014

10

  Total Portfolio TAA FSCORE Fundamental IndexBenchmark Statistics        

Benchmark S&P 500 TAA Benchmark Russell 2000 Value Russell MegacapReturn 20.44% 4.17% 19.61% 7.28%

Standard Deviation (daily) 0.70% 0.43% 0.89% 0.66%Standard Deviation

(annualized) 11.12% 6.81% 14.10% 10.43%Sharpe Ratio 1.83 0.60 1.38 0.69Treynor Ratio 0.20 0.04 0.20 0.07

Average Daily Return 0.07% 0.02% 0.07% 0.06%Number of Up Days 146 137 147 67

Number of Down Days 106 123 105 58Percentage of Down Days 40.61% 47.13% 40.23% 43.94%

Average Daily Gain 0.55% 0.32% 0.68% 0.54%Average Daily Loss -0.57% -0.32% -0.77% -0.50%

         Portfolio Statistics        

Return 14.94% 5.66% 34.49% 7.60%Excess Return (bps) (550) 149 1488 32

Standard Deviation (daily) 0.63% 0.68% 1.09% 0.61%Standard Deviation

(annualized) 9.91% 10.70% 17.24% 9.63%Beta 0.81 1.29 1.08 0.90

Correlation 0.90 0.82 0.88 0.97Sharpe Ratio 1.49 0.52 2.00 0.78Treynor Ratio 0.18 0.04 0.32 0.08

Average Daily Return 0.06% 0.02% 0.12% 0.06%Number of Up Days 150 138 147 67

Number of Down Days 100 112 103 59Percentage of Down Days 38.31% 42.91% 39.46% 44.70%

Average Daily Gain 0.46% 0.50% 0.82% 0.51%Average Daily Loss -0.54% -0.56% -0.87% -0.45%

Number of Outperforming Days 123 131 135 61

         

                

[1] Since inception date for the Fundamental Index Portfolio is as of October 29, 2013.

ASAM Risk and Performance Summary, March 1, 2013 to April 30, 2014

11

  Total Portfolio TAA FSCORE Fundamental IndexOther Portfolio Ratios        

Capture ratio -0.01% 0.01% 0.05% 0.00%Up capture indicator 83.18% 155.57% 121.68% 94.61%

Down capture indicator 94.08% 173.96% 113.76% 90.35%Up number ratio 93.15% 79.56% 85.03% 88.06%

Down number ratio 84.91% 73.98% 77.14% 87.93%Up percentage ratio 36.99% 56.93% 52.38% 37.31%

Down percentage ratio 65.09% 43.09% 55.24% 62.34%Percent gain ratio 102.74% 100.73% 100.00% 100.00%

                

[1] Since inception date for the Fundamental Index Portfolio is as of October 29, 2013.

Capture ratio is the average of the captured performance (difference between fund’s returns and benchmark’s returns)Up capture indicator is fund’s average return divided by benchmark average return, considering only periods when benchmark was upDown capture indicator is fund average/benchmark average considering only periods when benchmark was downUp number ratio is number of periods fund and benchmark were up, divided by number of periods benchmark were upDown number ratio is number of periods fund and benchmark were down, divided by number of periods benchmark was downUp percentage ratio is the percentage of periods the fund outperformed when the benchmark was upDown percentage ratio is the percentage of periods the fund outperformed when benchmark was downPercent gain ratio is number of fund up periods over number of benchmark up periods

ASAM Assets Distribution

12

Sector Weightings

13

• Implemented Sector ratings:

• Each of 9 sectors was given a rating of Overweight, Neutral or Underweight• Sector data taken from Yahoo Finance

• “Other” (16.9%) consists of AGG (iShares Barclays Aggregate Bond Fund) held by TAA

Future Work

14

• Implement VaR tracking

• Expand automated analyses

• Research using hybrid or weighted benchmarks

• Formalize updating of sector ratings

F Score

Reza Banki, Strategy Lead

16

Academic paper: Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers (2002) - by Joseph D. Piotroski from The University of Chicago

Strategy Overview

Sample selection• High BM firms• Small- and medium-market

capitalization companies

• Firms with low share turnover• Firms with no analyst following

Back-testing Results

17

Data Time Period: 4/1/2004 to 3/31/2012Data set: 1-year daily returnAsset Allocation Period: QuarterlyBenchmark: S&P 500

Methodology: Based on daily return data of 1-year period, the model calculates the optimized asset allocation which maximizes the utility for the following quarter. Based on the optimized allocation, we compare the portfolio performance to benchmark index.

Example: Using 1-year daily return data of 5 ETFs from 4/1/2004 to 3/31/2005, the model calculates the optimized asset allocation of 10% IVV, 10% IYR , 30% EEM, 10% AGG and 40% GSG. Using this asset allocation, the portfolio had 0.82% of return for the 2nd quarter of 2005, while the S&P 500 had 0.31% of return for the same time period.

18

Fund Performance

Key Metrics

• Use a table or bullets here

Key Points

• First Point• Second Point• Third Point

(7.0%)

(5.0%)

(3.0%)

(1.0%)

1.0%

3.0%

5.0%

7.0%

9.0%

5.1%

2.9%

6.4%

(0.7%)

6.4%

4.3%

6.6%

3.8%

(6.1%)

6.6%

(3.1%)

(1.2%)

3.0%

(0.4%)

6.4%

(4.4%)

5.8%

3.3% 3.9%

1.9%

(3.9%)

4.6%

1.2%

(2.6%)

FSCORE Russell 2000V

37

• Automate the alpha and risk model

• Target stock holdings of >20 (currently 14)

• Refinement of sampling criteria

• Brinson two-factor model performance attribution

• Enhancement of exit strategy

Next Steps

Tactical Asset Allocation (TAA)

Alex Revy, Strategy Lead

Strategy Overview

21

• Multi-Asset strategy that diversifies both among asset classes and within asset classes by investing in index ETFs.

• Quantitative asset allocation model uses a risk-weighted statistical framework to predict expected excess returns, measure correlations, and determine optimal portfolio weights.

• Outperformance in comparison to a static benchmark generated by over- or under- weighting asset classes comprising the equal-weighted benchmark.

Back-testing Methodology

22

Data Time Period: 5/10/2007 to 9/20/2013.Data set: Daily ETF Returns, 30-Day Implied Volatilities, and Historical Volatilities.Data Permutations: 108 different permutations of 5 portfolio parameters. Ran for 2 different models – one using VWO and one using VEU. 1,278 results per back-test.

Methodology: Based on daily return, volatility, and correlation data of various time periods, the model calculates the optimized asset allocation that maximizes the utility for the following quarter. Based on the optimized allocation, we compare the portfolio performance to benchmark index.

Back-testing Results

Ideal Parameters:

•Quarterly look-back to generate expected returns and correlations•Implied Volatility•5% Minimum Constraint•50% Maximum Constraint•2 Lambda

•Found to have the highest return and second highest Sharpe ratio of all im-plied volatility portfolios generated.

•Consistent with our tests of parameter strength in predicting future returns.

•Confirmed 2013 ASAM TAA team’s results.

24

Fund PerformanceA

pr-

13

May-1

3

Jun-1

3

Jul-

13

Aug-1

3

Sep-1

3

Oct

-13

Nov-1

3

Dec-

13

Jan-1

4

Feb-1

4

Mar-

14

Apr-

14

(5.0%)

(3.0%)

(1.0%)

1.0%

3.0%

5.0%

(1.4%)

(2.8%)

3.2%

(2.2%)

1.3%

3.7%

0.2% 0.5%

(2.9%)

4.3%

0.1%

1.8%

(1.9%)

(2.8%)

1.8%

(1.8%)

2.2%

3.0%

(1.4%)

0.5%

(1.0%)

3.9%

0.4%

1.3%

TAA Key Metrics

• 4/30/14 TTM TAA Performance was 5.25%

• 108 basis points of outperformance versus benchmark

• 10.7% Standard Deviation

Key Points

• 3 out of 4 model allocations outperformed the benchmark during their 3 month allocation.

• December switch from VWO to VEU helped performance

• Both over- and under-weights drove performance

Next Steps

25

• Improve Data in Model-Replace ETF price returns with total returns in order to add dividends to the data set-Add a long-term average return term to estimates of expected returns

• Improve Back-Testing Methodology-Current back-test very dependent on date ranges that are set. Must

increase frequency of back-test measurements from quarterly to weekly or at least monthly.

•Additional Asset Classes-Add additional asset classes – eg international fixed income-Divide existing asset classes into components – eg foreign equities

by region or domestic equities by capitalization and price-to-book.

•Consider Other Models and Optimization Methods-Black-Litterman mehodology and Bloomberg Asset Allocation

Optimizer

Earnings Announcement Return (EAR)

Debika Seth (Strategy Lead)

STRATEGY OVERVIEW

18

Academic Papers:1. Earnings Announcements are Full of Surprises by Michael Brandt, Runeet Kishore, Pedro

Santa-Clara, and Mohan Venkatachalam; June 20072. Earnings Announcements and Systemic Risk by Pavel Savor and Mungo Wilson; December

2013EAR #1:• The movement of a stock’s price around the earnings announcement window is

predictive of future price movements• If investors initially underreact to new information presented in corporate earnings

reports, it is possible to quickly purchase (sell) these stocks and generate alpha

EAR #2:• Firms that announce earnings ear-

lier in the quarter are a source of information for non-announcers

• A signal extraction problem re-sults in a high risk premium award for the announcing firms

Back-Testing Results

28

EAR #1Data Time Period: Q2 2002 to Q2 2012Data set: 3 day EAR return and 90 day holding returnAsset Allocation Period: QuarterlyBenchmark: N/A Did not find a monotonic relationshipMethodology: For simplicity, we selected two dates to trade per quarter to measure the 3 Day return around earnings announcements. This we sorted into quintiles and then further tracked these same stocks an additional 90 days out to see if there was a monotonic relationship between the two returns.

EAR #2Data Time Period: Nov 1998 to Aug 2012Data set: The paper suggests a 1 week holding period but we modified for a 12 week holding period. We excluded small and mid cap stocks which may be costlier to trade as well as mega cap stocks which we did not find to benefit from the risk premia. Finally, we limited cap size range to $5-20 billion. Asset Allocation Period: QuarterlyBenchmark: S&P 500Methodology: For simplicity, we selected a particular date every quarter and bought all the stocks that fit our criteria. We held these stocks for 12 weeks, from which we were able to assess the 12 week holding period return, standard deviation, and Sharpe ratios.

29

Back Tested Performance Ear #1

Key Metrics

Key Points

• Top Quintile outperforms all the other Quintiles, however there is no monotonic relationship

• Quintile 5 is surprisingly the 2nd best performer—perhaps due to initial overreaction of bad news

• Only 22 of the 40 quarters measured have Quintile 1 outperforming the other quintiles, therefore we could not justify trading this strategy

Quintile Return Std. Dev. Sharpe

1 7.85% 25.31% 0.31

2 3.50% 20.90% 0.38

3 3.40% 19.32% 0.41

4 -0.57% 21.11% 0.37

5 3.72% 25.67% 0.31

30

Back Tested Performance Ear #2

Key Metrics

• 12 week holding period• Market Cap $5B to $20B• 25 trades per quarter

(average)• 23.5% annualized return,

31.2% standard deviation, 0.66 Sharpe Ratio

Key Points

• Testing modified due to time and trading cost constraints• With modifications, hypothetical $100 investment Nov 1998 translates to $1,500

August 2012• Next class should continue further testing for realistic implementation.

Fundamental Index

Tommy Taw (Strategy Lead)Alexandre Jorion

Strategy Overview

32

• Initial Findings:– Re-investing each month resulted in high trading costs,

which ultimately made investing every other month the best implementation

– 4 of the 100 simulated portfolios had superior returns

33

Back-Testing Results

34

Fund Performance

Key Metrics

• Use a table or bullets here

Key Points

• First Point• Second Point• Third Point

(5.0%)

(4.0%)

(3.0%)

(2.0%)

(1.0%)

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

(0.7%)

3.5%

1.9%

(4.4%)

2.8% 3.2%

1.4%

(0.7%)

3.6%

2.4%

(4.4%)

3.5%

1.5% 1.3%

Fundamental Index

• Additional work for the 2013 BCD team:– Expand stock universe to use the entire S&P 500

– Add additional non-linear constraints to make future stock estimates coming out of the BCD model more reasonable

– Back-test over a more “normal” timeframe

– Look at additional hold durations to see if trading costs can be further lowered without impacting returns

35

Next Steps

Closing Remarks

Joseph F. Duronio, Esq., President

ASAM Class of 2014

37

• Thank you to all firms that hosted ASAM visits this year

Los Angeles/Orange County:

San Francisco: New York:

Capital Group BlackRock 3i Investments

DFA Contango Capital Advisors AIG Investments

DoubleLine Franklin Templeton Investments Bloomberg

Oaktree Capital Hall Capital Partners Guggenheim

PAAMCO Osterweis Capital Management ICAP

PIMCO   PineBridge

PineBridge Investments

 Omaha: SunAmerica Funds

PRIMECAP management

 Berkshire Hathaway

Research Affiliates  

TCW    

WAMCO    

ASAM Class of 2014

38

• Thank you to all of our guest speakers

Howard Marks, Founder and Chairman, Oaktree Capital

Grady Smith, Portfolio Manager, Dimensional Fund Advisors

John Brynjolfsson, CIO, Armored Wolf

Nicolas Amato, Partner and Director of Research & Risk Management, Dorchester Capital

Advisors

Kirk Hartman, President and CIO, Wells Capital Management

Richard Roll, Joel Fried Chair in Applied Finance, Distinguished Professor - UCLA Anderson

Avanidhar Subrahmanyam, Hearsh Chair in Money and Banking, Professor – UCLA Anderson

Jason Hsu, Assistant Adjunct Professor – UCLA Anderson & CIO and Co-Founder, Research

Affiliates

Eric Dhall, Associate, Doubleline Capital

Byron Douglass, VP Insurance Portfolio Management, AIG Investments

Hezy Shalev, Partner, Luminous Capital

Macduff Kuhnert, Portfolio Manager & Joe Gubler, Research Associate, Causeway Capital

Gary Baierl PhD, CIO, Strategic Global Advisors

Andres Berkin PhD, Director of Research, Bridgeway Capital

Patrick Kiefer, Doctoral Graduate Student - Finance, UCLA Anderson

Jiasun Li, Doctoral Graduate Student - Finance, UCLA Anderson

Congratulations

39

• ASAM Alumni Receive Multiple J. Fred Weston Awards for excellence in finance

• ASAM Students win NIBC Investment Banking Competition

• 2013: Farshid Pousartip, Felix Lorenzo, and Mahyar Kagar

• 2014: Ryan Hughes and Kesnia Yudina.

• Joseph Duronio, Alexandre Jorion, Kevin Zhang, and Zack Conroy

Welcome and Congratulations to ASAM Class of 2015

40

Andrew Holloway David Cruz, CFA Jackie Chan, CFA Razmig Der-Tavitian, CFA, CAIA

Chris Carlson David Soong James Wooten, JD Shireesh Verma, PHD

Chris Martinez, CFA

George Ku Jeff Martin Stephany Anavim

Dan Troost Han Park, PHD Jonathan Lea, CPA Zack Conroy

To showcase our strong and promising new class, here are a few highlights of their achievements.

•2 PHD degrees•5 CFA designations•1 CPA•1 Attorney

The current ASAM class of 2014 is pleased to announce the ASAM Class of 2015. Please join us in congratulating the new ASAM Fellows: