dynamic factor rotation
TRANSCRIPT
Dynamic Factor Rotation
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Return and Drawdown Measurements (10 yrs)
Annualized Returns 16%
Volatility 8%
Inf Ratio 2.0
DD -9.7%
-10.0%
-9.0%
-8.0%
-7.0%
-6.0%
-5.0%
-4.0%
-3.0%
-2.0%
-1.0%
0.0%
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Annualized Returns 10.3%
Volatility 3.6%
Inf Ratio 2.9
DD -1.8%
Risk and Return Measurements (3 years)
-2.0%
-1.5%
-1.0%
-0.5%
0.0%
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Drawdown DFR Mkt Neutral
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Performance Stats
Return (%) Model SPDR S&P 500 ETF Trust
Total 993.32 114.9
Annualized 25.5 7.54
Year To Date 9.22 -0.02
Month To Date -2.44 -2.59
4 Week -0.74 -0.36
13 Week -0.16 -2.78
1 Year 10.77 1.2
3 Year 109.28 56.22
Monthly Performance (Last 12 Months)
Return (%) Dec-14 Jan-15 Feb-15 Mar-15 Apr-15 May-15 Jun-15 Jul-15 Aug-15 Sep-15 Oct-15 Nov-15
Model -0.31 -1.25 7.08 -0.73 1.32 2.08 -1.03 1.83 -4.16 -1.13 7.97 -2.44
Benchmark -0.25 -2.96 5.62 -1.57 0.98 1.29 -2.03 2.26 -6.1 -2.55 8.51 -2.59
Excess -0.06 1.71 1.46 0.84 0.34 0.79 1 -0.42 1.93 1.42 -0.54 0.15
Annualized Performance by Calendar Year
Return (%) 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015**
Model 28.66 33.62 18.59 0.01 58.75 24.18 19.4 25.41 51.57 19.23 10.77
Benchmark 12.53 15.85 5.15 -36.79 26.35 15.06 1.89 15.99 32.31 13.46 -0.02
Excess 16.13 17.77 13.44 36.81 32.39 9.12 17.51 9.42 19.26 5.77 10.79
Performance Stats
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* Before Fees. See Disclosure and Investment Risks
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34
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0
59
24
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1413
16
5
-37
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-30
-20
-10
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2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 YTD Ann.
DFR Long Only S&P Short DFR Mkt Neutral
Annual Returns (%)
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Monthly Returns (Avg 1.28%)
-2
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5/3/2005 5/3/2006 5/3/2007 5/3/2008 5/3/2009 5/3/2010 5/3/2011 5/3/2012 5/3/2013 5/3/2014 5/3/2015
Scorecard
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3/1/2011 3/1/2012 3/1/2013 3/1/2014 3/1/2015
Returns
0.00
1.00
2.00
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4.00
5.00
6.00
7.00
8.00
1/3/2011 1/3/2012 1/3/2013 1/3/2014 1/3/2015
Rolling Sharpe
0.00
0.01
0.01
0.02
0.02
0.03
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1/3/2011 1/3/2012 1/3/2013 1/3/2014 1/3/2015
Annualized Vol
-3.00%
-2.50%
-2.00%
-1.50%
-1.00%
-0.50%
0.00%
1/3/2011 1/3/2012 1/3/2013 1/3/2014 1/3/2015
Drawdown
-0.08
-0.06
-0.04
-0.02
0
0.02
0.04
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0.08
0.1
1/3/2011 1/3/2012 1/3/2013 1/3/2014 1/3/2015
Beta
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0
1/3/2011 1/3/2012 1/3/2013 1/3/2014 1/3/2015
Drawdown/StDev
Investment Objective
The investment philosophy is founded on the premise that the systematic application of quantitativetechniques has the potential to deliver risk-adjusted performance, regardless of profit cycle oreconomic environment. In addition to that, it is my view that macro and political events often causetemporary dislocations in the market, but ultimately, stocks follow fundamentals.
The investment objective of the Strategy is to earn long-term absolute (positive) returns in all marketenvironments by going long signals that have shown a positive Sharpe Ratio, while attempting toneutralize general risks associated with unintended exposures. In addition, part of the Strategy is torotate periodically between signals, utilizing proprietary market timing techniques.
Although market conditions constantly change, I believe that attempting to control risk while adaptingto the current environment changes in a dynamic way and hedging against an Index, has a highlikelihood of being profitable over the medium and long terms in almost all market conditions.
Competitive Edge
We believe that the way we process and analyze the vast amounts of financial data is a keydifferentiating factor that gives us an edge over our competitors.
Our competitive advantage is our quantitative process and in-depth experience in designing andoperating equity market neutral strategies. Utilizing a world-class investment research program,state-of-the-art technology, and proprietary multi-factor models, our strategies have performedwell over the most challenging market environments.
Over the years, I have developed proprietary risk control and optimization processes, expressed inmy portfolio construction, trading algorithms and risk on/off signals.
Evolution of strategy
The Strategy was first traded in mid-2006 with a long equity model, hedged to be approximately betaneutral using a combination of ETFs. By the end of 2006, a short model was developed allowingequities to be shorted directly instead of via ETFs. At that point, the strategy became approximatelysector and industry neutral as well.
The Strategy was not traded during June to September 2008, at which time Mr. Gleiser joinedMillennium Partners, which began trading the Strategy but with the portfolio hedged against style andindustry risk factors and with other proprietary risk reduction mechanisms. An optimizer was added tothe process to enable the risk controls and to account for modeled transaction cost.
Between 2011 and 2013 the Strategy was further developed to include the Dynamic Factor Rotationframework and was traded in Brazil as a Long only and a Long Short Fund.
In 2014 the Strategy evolved into a Multi Asset Class Dynamic Factor Rotation to include Fixed IncomeAssets via ETFs.
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Investment Process
• The overall Strategy (DFR) is currently a combination of 12 alpha signals. All 12 signals are
quantitative and rules based
• By combining signals of uncorrelated sources of alpha, the synergies add up to a higher Sharpe
Ratio than each individual signal alone
• Given the correlation of the signals and their volatilities, their weights are calculated by two risk
models in order to minimize risk in the short term while maximizing return over the long term.
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Equity Investment Process
• Attempts to exploit intermediate-run (1-6 months) inefficiencies in equity markets across the board by
looking at factors like: Operating Profit, Net Working Capital, Net Fixed Assets, Enterprise Value,
Debt/Equity Ratio.
• Our return model forecasts rational valuations for all securities in the investment universe based on
their exposures to stock characteristics (e.g., earnings yield, leverage, volatility, size, etc) that have a
well-known impact on security returns. The factors fall into a broad range of categories including both
the value and growth spectra, and are uncorrelated with each other and the market. Momentum and
Mean Reversion signals are also taken into account.
• Given the valuations as inputs, a long/short market neutral portfolio is constructed for each signal and
turned on or off depending on its rolling sharpe ratio, momentum and performance. Once a signal is in
the portfolio, its weights are calculated by two risk models, with the goal of maximizing expected
return (alpha) subject to risk constraints.
Investment Process
Alpha Factory
• Alpha Factorselection
•Ex: Value, Growth, Momentum, High Dividends, Reversion, Sentiment
• Ranking System –Asset selection
Ex- Ante RiskManagement
•Timing ofAlpha Signals
Portfolio Construction
• CalculateEfficientFrontier for RiskBudgeting andweights ofsignals
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Fundamental
Momentum
Sentiment
Seasonality
Reversal
Liquidity
Rebalancing/
Trade Execution
Investment Process
Signal Timing
OptimizationLarge Cap Defensives
Mid Cap GARP
Short Term Momentum
Mean Reversion
Large Cap Momentum
Blue Chips Value
Minimum Volatility
High Dividend
Yields
Russell 3000 Growth
Earnings Revision
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Rules Based Approach to Quant Investing
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Alpha Factory
Ranking System –Mean Reversion, Value and Momentum
Ticker Name100% Stock
Rank
60% Valuation
20% RelStrength
Mean Reversion
Yield5YAvg
Pr2BookQ
PEInclXorTTM
Pr2SalesTTM
ROE%TTM
Sales10YCGr%
3MoPctRet
3MoRet3MoAgo
3MoRet6MoAgo
3MoRet9MoAgo
VLO Valero Energy Corp 99.98 99.21 80.74 66.18 95.7 91.17 90.27 78.22 85.07 42.04 84.84 69.71 75.53 78.78
CEA China Eastern Airlines Corp Ltd 99.97 99.08 69.86 67.18 95.81 83.82 91.05 91.67 81.91 12.64 82.81 96.23 91.72 80.22
HFC HollyFrontier Corp 99.95 96.02 81.7 61.7 76.69 82.61 68.78 94.83 82.21 65.8 89.9 59.05 30.31 87.14
LAKE Lakeland Industries Inc 99.94 92.56 69.86 66.02 94.36 72.47 92.4 53.16 95.27 95.65 79.15 83.25 10.88 83.85
MFLX Multi-Fineline Electronix Inc 99.92 96.14 69.86 68.9 90.56 78.92 79.7 79.33 88.44 73.22 19.97 96.93 96.02 79.55
XIN Xinyuan Real Estate Co Ltd 99.91 99.92 94.87 97.44 97.51 92.45 57.66 97.82 83.48 92.75 32.34 94.63 28.02 73.08
KONEKingtone Wirelessinfo Solution Holding Ltd 99.89 97.42 69.86 97.76 99.28 85.89 83.68 41.14 69.4 10.88 78.71 78.94 96.81 93
CALVF Caledonia Mining Corp 99.88 95.55 69.86 85.22 80.14 74.16 53.26 99.94 84.52 88.27 42.71 76.05 46.38 83.04
ZNH China Southern Airlines Co Ltd 99.86 99.79 86.1 73.25 95.32 86.97 86.52 92.34 80.26 16.11 72.79 98.03 95.29 73.92
IEHC IEH Corp 99.85 96.92 69.86 66.38 94.42 70.12 87.74 83.65 92.82 92.21 60.08 93.33 21.34 65.95
WSTG Wayside Technology Group 99.83 98.58 94.95 50.76 82.46 93 83.3 84.72 69.92 45.35 74.45 65.02 58.34 79.97
TTLO Torotel Inc 99.82 96.86 69.86 91.38 77.36 92.89 56.74 83.62 59.96 51.24 70.18 70.68 25.9 93.96
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Trading System
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Risk Management - Portfolio Construction Process
• Takes into account volatilities and correlation between sub-strategies to come up with weights.
• Utilizes 2 risk models. A Short Term for minimizing risk and a long term to maximizing returns
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Allocation (as of 2/11/2015)
Exchange Mkt Cap
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Dynamic Nature of the Process
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0.00
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M SR ES LCM LCD IEF MV HD
Strategy Weights Number of Strategies
Signal Performance
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Risk Attribution– Sources of Risk
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Risk Attribution– Sources of Risk
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Risk Attribution–Marginal Excess Returns given Factor rises by 1 Std Dev
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Risk Attribution– Sector Weight Distribution
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Risk Attribution– Style Report
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FAQ
Explain trading cost assumptions in simulation.We have a proprietary market impact model that assumes executions at slightly worst then VWAP, including transactioncosts.
Explain universe selection processWe only trade very liquid stocks from the S&P1500 and Russell 3000 Indices.
What infrastructure is required for trade executionNo special infrastructure is required
How scalable is the strategyWe believe, U$2B in the US only
What maximum percent of adv is allowed in tradesDon’t trade more then 7% of daily volume
What maximum percent of adv is allowed in holding positionsNo position bigger then 10% of daily volume or 1% of Market Capitalization.
Is the strategy international –Yes, however the strategy has only been traded live in the US and Brazil.
What language is the strategy written inMatlab and Java
Any special data requirementWe use Compustat Capital IQ. For the international strategy we also use Factset. I also use data from specific Fintech data providers to gain competitive advantage
Time to get the strategy operationalAbout 1 week
Is strategy industry and factor neutralNo
Is there a discretionary component to the strategyThe whole process is automated. However, we are constantly researching new factors that may increase the performance.
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Integrity of Modeling Process
Several measures were taken to ensure that there is minimal look-ahead or survivorship bias in database
Several measures were taken to ensure that:
• There is minimal look-ahead or survivorship bias in back-tested results.
• The fundamental research database includes companies which no longer exist due to mergers, bankruptcy, etc. This reducessurvivorship bias. It also uses point-in-time balance sheet / income statement data, to guard against look-ahead bias.
• Only information which would have been available at the time was used in forecasting and in simulatedtrading.
• Proprietary methods were used to reduce the risk of over-fitting.
Biography
Ilan Gleiser:
BS (Economic Sciences) Federal University of Rio de Janeiro, Brazil;
MS (Mathematical Economics) Federal University of Rio de Janeiro, Brazil;
Banco Pactual, Rio de Janeiro, Brazil. 1994 –1996, Trader;
Morgan Stanley & Co, New York, NY. 1996 – 1997, Equity Derivatives Sales Trader;
Morgan Stanley & Co, Sao Paulo, Brazil. 1997 – 2000, Equity Derivatives Sales and Structured Products;
Morgan Stanley & Co, San Francisco, CA 2000 – 2005, Head of West Coast Electronic Trading;
Millennium Partners, Tiburon, CA 05/2005 – 08/2009, Quantitative Portfolio Manager;
MSCIBarra, Berkeley, CA 08/2009 – 08/2011, Head of Portfolio Construction
Plural Bank, San Francisco, CA, 08/2011 – 04/2014, Chief Risk Officer
Leste Global Investments, San Francisco , CA, 01/2014 – Present, Quant PM
Published a book called “Chaos and Complexity in Economics and Finance”. Brazil, 2002;