Newfound Research LLC White Paper Seeking Timeless Momentum In this paper we explore our unique approach to momentum investing, recognizing the timeless nature of momentum, using our proprietary Relative Exposure model to dynamically exploit opportunities along the momentum term-structure. We compare our model to the results of a six-month relative strength model. To compare the models, momentum investment strategies are generated for each component of the S&P 500. The strategies dynamically choose between investing in the component or the index based on the driving model s momentum signals. We demonstrate that our process preserves strategy convexity, known as the momentum smile, while reducing whipsaw behavior and costs from trade friction. Introduction Momentum, the tendency for securities to demonstrate persistence in their relative performance to one another, is a well-documented and researched effect in financial markets. The tendency for positive serial-correlation in recent relative-performance has been mainly attributed to psychological effects that drive asymmetric responses, such as herding or anchoring, as well as the varying vdiffusion rate of new information into the market place. This paper does not seek to summarize the existing momentum literature or provide a new explanation for the momentum phenomenon, as there are numerous sources available already that explore this market anomaly. Furthermore, this paper does not seek to provide new evidence for the addition of a momentum factor, either as part of the investment process or as an allocation in an overall portfolio, as it has also been well demonstrated already (Faber (2010) and Berger, Israel, Moskowitz (2009)). This paper seeks to demonstrate our unique approach to momentum investing that is capable of preserving the convex nature of the strategy, known as the momentum smile, while reducing the potentially hazardous costs of whipsaw.
Exploring Timeless Momentum Traditional momentum models use a fixed-size rolling window of time to determine relative performance between securities. A common method is known as the relative strength strategy, which compares total-returns over the previous N months to determine asset allocations for the following month. This strategy is often used in two different ways: 1. Use (risk-adjusted) returns over the previous N months to sort the investment universe, selecting from only top-ranked investment opportunities 2. Compare the previous N-month (risk adjusted) returns of each investment to the previous N-month return of the benchmark, limiting the selectable investment universe only to those that have outperformed the benchmark. This strategy has several positive aspects to it. First, it is a simple heuristic, which we believe are more robust than complex decision methods in uncertain environments. Second, the comparison of performance metrics implicitly includes realized changes to co-volatility, meaning that we do not need to incorporate unnecessary sources of model risk and uncertainty by estimating volatility and correlation. The issue we take with this methodology is that it can only capture momentum opportunities that fit the N-month time-scale. Consider the following hypothetical situation, demonstrating the risks of using a six-month relative strength strategy when positive trends are not on six-month cycles: While Security #1 gains slowly and steadily, Security #2 s growth is more volatile. The Relative Strength score is the ratio of the securities trailing 6-month return. When the score is greater that 1, the strategy invests in Security #1; when the score is less than 1, the strategy invests in Security #2. In this manufactured scenario, the growth cycles in Security #2 are timed such that when the relative strength score tells us to invest in Security #2, its performance will turn negative. By chasing performance, this scenario demonstrates the dangers known as whipsaw that are inherent in all momentum strategies: buying high and selling low. By anchoring the definition of momentum to a fixed time period, not only is the ability to capture short-term, large magnitude relative gains restricted, but also the ability to normalize to expected levels of volatility. While contrived, this example demonstrates the risks of time-based measures. Momentum, however, is not a time-based anomaly; information flow is what drives market prices. Information flow is heteroscedastic, more likely to arrive in coarse lumps rather than at a smooth and fixed rate. Therefore, to take advantage of the time-independent nature of momentum, we must remove time from our momentum signal. Practitioners are not unaware of this limitation in time-based momentum measures. Often, simple models will be extended to combine relative strength signals constructed over various look-back periods, incorporating short, medium, and long-term signals to try to capture the full termstructure of momentum. This methodology, however, is still flawed in the same way, as the weights chosen determine the importance of each part of the momentum termstructure in your decision. The methodology is still fixed measuring momentum in a time-based dimension. 2
By removing the fixed window of six-months from the definition, and instead computing the ratio of total returns over time, a very different picture of relative performance of Security #1 and Security #2 emerges: To establish the effectiveness of our Relative Exposure approach, we seek to demonstrate two essential components of improved momentum models: 1. Our approach retains, or improves, the momentum smile. The smile emerges when plotting strategy returns against benchmark returns where strategy underperformance is fixed and low, but outperformance is variable and high. The smile is an important feature because it allows a strategy to be correct in the bets it places less than 50% of the time but still create excess return because the payoff structure is convex; To derive a signal from this ratio, we utilize our proprietary Absolute Exposure (A.E.) technology. Our A.E. technology provides a positive, neutral, or negative exposure recommendation for a given time-series, using volatility to filter out noise and drive a dynamic look-back window in an attempt to re-index the series to an information dimension. In other words, instead of price change being measured in a consistent unit of time, it is measured over a consistent unit of information flow. For example, if the same amount of information were to flow into the market in a single day as it did over the previous five, instead of the return being weighed at only 1/6th (1 of 6 trading days), it is weighed at ½ (1 of 2 information units ). Our measure of information flow is proprietary, but is based on rate of change of the time-series, volatility, and the volatility-of-volatility. At Newfound, we call the ratio signal in combination with our Absolute Exposure technology our Relative Exposure model, and is the basis for how we capture timeless momentum. In practice, we find that when the Relative Exposure model is parameterized to target six-month momentum, it has the capacity to dynamically adjust and capture opportunities from three to nine months on the momentum term-structure. Capturing Timeless Momentum with the Relative Exposure Model 2. Our model reduces overall whipsaw costs To test these aspects, we evaluate our Relative Exposure model versus a six-month Relative Strength model on the current components of the S&P 500 versus an S&P 500 proxy, the SPDR S&P 500 ETF ( SPY ). Relative Strength Model Strategies For each component, trailing 6-month linear returns are divided by the trailing 6-month linear returns of SPY to create the relative strength score. When the relative strength score is greater than 1, the strategy invests fully in the component. When the relative strength score is less than 1, the strategy invests in SPY. Relative Exposure Model Strategies For each component, trailing linear total-returns are divided by the trailing linear total-return for SPY to create the ratio score. The Relative Exposure model is applied to the ratio score. The model provides three recommendation outputs: positive (1), neutral (0), and negative (-1). On a 1, the strategy invests fully in the component; on a 0, the strategy invests 50% in the component and 50% in SPY; for a -1, the strategy invests fully in SPY. Together, these two methods create 500 paired strategies: a Relative Strength strategy and a Relative Exposure strategy for each S&P 500 component. 3
The Benchmarks To determine whether the tactical momentum decisions create excess return, the benchmarks should be designed to represent full uncertainty in our models. In other words, with no outside information, and if our models provided no information, how would we invest? An equal weight investment scheme was chosen, with a 50% allocation in the component and a 50% allocation in SPY. Backtest Assumptions Since signals from the strategies are generated using closing data, re-allocations occurred at the next available market open. Both zero transaction costs and zero slippage costs are initially assumed and all strategies are rebalanced daily so that signals could be immediately acted upon. Results Visualization is often the fastest way to explore results. Below, annualized returns from the Relative Strength model strategy are plotted against benchmark returns: The smile characteristics are retained. In fact, they are somewhat improved: the relative exposure model s returns beat that of the relative strength model 54.29% of the time. Below is a table of summary statistics for the annualized strategy returns divided by the benchmark returns. While, on average, both strategies returned less than their benchmark counterpart, the skewness feature identifies both payoff strategies as convex bets: Relative Stregth Relative Exposure Average 98.97% 99.23% Median 98.33% 98.61% Std Dev 4.79% 4.16% Skewness 0.20 1.00 Kurtosis 0.06 2.90 The smile characteristics emerge, demonstrating the convexity of the momentum bet: outperformance gains are far greater than underperformance costs. The Relative Exposure model can be visualized the same way: If we are reluctant to believe the convexity of the data, we can pessimistically fit a linear model to the scatter plots. For the Relative Strength model, the best-fit line is: y = 1.0552x 0.0176; for the Relative Exposure model, the best-fit line is y = 1.1654x 0.0271. These relationships capture the fact that while both models have a fixed cost associated 4
Newfound Research, LLC with running them of 176bp and 271bp annualized a year (we can call this the cost of whipsaw ), they also scale annualized returns by 5.52% and 16.54%. These lines are very sensitive to outliers, however, and should only be looked at as a general heuristic to understand the implications of a momentum model and not as governing law of how they are guaranteed to behave. While total-returns are important, so is the risk taken to achieve the returns. A relevant measure of risk, in this case, is not volatility, which will penalize a strategy for upside returns, but maximum drawdown. seems that we took on commensurate risk for the excess returns we achieved over the benchmark, it is important to remember that these are individual momentum strategy bets. Portfolio theory tells us to expect that by combining these bets into a portfolio, diversification will help play a role in reducing absolute drawdown. Relative Stregth Relative Exposure Average 98.97% 99.23% Median 98.33% 98.61% Std Dev 4.79% 4.16% Skewness 0.20 1.00 Kurtosis 0.06 2.90 Playing The Reality Blues Unfortunately, the backtesting methodology made a major assumption that can dramatically change performance results: zero turnover costs. Momentum strategies often exhibit high turnover and therefore compounding fees can destroy the attractive convexity of the strategy. In the following analysis, a 10 basis-point fee is assumed for a 100% turnover. While less pronounced, the table of summary statistics on the following column confirms that even when accounting for risk, both strategies exhibit skewness. While the convexity may not be as compelling as when simply comparing annualized returns, as it 5 Newfound Reseach LLC 2013
The results of these plots can be summarized with a single statistic: before fees, the Relative Exposure model outperformed the Relative Strength model 54.29% of the time; after fees, it outperforms 77.89% of the time. How is this possible? Consider the ratio of trades made for each strategy: There is dramatic shift down in the level of the Relative Strength smile, but not in Relative Exposure smile. The shift is the effect of fees and high turnover rates. Before fees, 34.52% of the Relative Strength strategies outperformed the benchmark; after fees, only 19.94% outperformed the benchmark. Before fees, 33.51% of the Relative Exposure strategies outperformed the benchmark; after fees, the percentage only reduced to 29.08%. To consider the impact of fees, the returns for each strategy before and after fees are plotted against each other and a linear model is fit. In this histogram, values above 100% would represent times the Relative Exposure strategies traded more frequently than their Relative Strength counter-parts. Values below 100% represent times the Relative Exposure strategies traded less frequently. The Relative Exposure method for capturing the timeless nature of momentum is not only able to retain the momentum smile, but is able to dramatically reduce the number of trades executed. In 99.8% of tests, the Relative Exposure strategy traded less frequently than the Relative Strength strategy, trading on average only 40% as often. 6
Plotting the Relative Exposure strategy returns against the Relative Strength strategy returns (with the removal of a single outlier), a smile emerges, capturing the compounding costs of fees: Conclusion Using the current components of the S&P 500, we constructed investment strategies using a standard Relative Strength model and our proprietary Relative Exposure model. Comparing the results of these strategies to their respective benchmarks, we find that not only does our dynamic model effectively capture the momentum smile, but also dramatically reduces trading frictions. In 99.8% of tests, the Relative Exposure model traded less frequently than the Relative Strength model, and on average the Relative Exposure model executed 60% less trades. The purpose of this paper was to introduce our methodology: a new and unique approach that attempts to capture the timeless nature of momentum. We believe this approach is unique within the industry and coupled with prudent risk management techniques, we believe we are able to exploit momentum in a more efficient manner. In this paper we explored our unique approach to capturing momentum, one of the most well researched and persistent financial market anomalies. The weaknesses of fixed-time methods were demonstrated and we introduced our proprietary method for capturing the timeless nature of momentum, by constructing a measure relative to information flow rather than time.. 7
Appendix: Equities Used A total of 397 securities were used in this analysis. Companies with no were excluded from this study because the Relative Exposure model did not calibrate until at least 1/1/2006. Starred companies were removed due to missing data. Yahoo! Finance was utilized as the provider for open, high, low, close, and adjusted close pricing information. 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(Hldg. Co.) 2/7/94 CA CA, Inc. 4/13/94 NFLX NetFlix Inc. 10/2/03 CAG ConAgra Foods Inc. 11/15/93 NFX Newfield Exploration Co 1/13/95 CAH Cardinal Health Inc. 9/19/94 NI NiSource Inc. 1/13/94 8
CAM Cameron International Corp. 9/4/96 NKE NIKE Inc. 3/21/94 CAT Caterpillar Inc. 4/12/94 NOC* Northrop Grumman Corp. 11/23/93 CB* Chubb Corp. 1/6/94 NOV National Oilwell Varco Inc. 11/19/97 CBG CBRE Group 8/26/05 NRG NRG Energy 4/18/05 CBS CBS Corp. NSC Norfolk Southern Corp. 3/24/94 CCE Coca-Cola Enterprises 4/21/94 NTAP NetApp 2/13/97 CCI Crown Castle International Corp. 2/15/00 NTRS Northern Trust Corp. 2/18/94 CCL Carnival Corp. 5/19/94 NU Northeast Utilities 5/18/94 CELG Celgene Corp. 3/14/94 NUE Nucor Corp. 1/19/94 CERN Cerner 4/6/95 NVDA Nvidia Corporation 3/2/00 CF CF Industries Holdings Inc 12/20/06 NWL Newell Rubbermaid Co. 4/22/94 CFN Carefusion NWSA News Corporation 2/25/97 CHK* Chesapeake Energy 5/11/94 NYX NYSE Euronext 3/10/06 CHRW C. H. Robinson Worldwide 9/9/98 OI Owens-Illinois Inc 2/11/94 CI CIGNA Corp. 3/8/94 OKE* ONEOK 5/18/94 CINF* Cincinnati Financial 3/31/94 OMC Omnicom Group 5/17/94 CL Colgate-Palmolive 7/22/94 ORCL Oracle Corp. 1/11/94 CLF Cliffs Natural Resources 12/13/93 ORLY O'Reilly Automotive 4/28/94 CLX The Clorox 7/20/94 OXY Occidental Petroleum 4/15/94 CMA* Comerica Inc. 6/9/94 PAYX Paychex Inc. 5/18/94 CMCSA Comcast Corp. 1/27/94 PBCT* People's United Bank 10/17/94 CME CME Group Inc. 1/16/04 PBI Pitney-Bowes 2/22/94 CMG Chipotle Mexican Grill PCAR PACCAR Inc. 12/7/93 CMI Cummins Inc. 3/11/94 PCG* PG&E Corp. 3/4/94 CMS CMS Energy 6/16/94 PCL Plum Creek Timber Co. 1/12/94 CNP* CenterPoint Energy 3/15/94 PCLN Priceline.com Inc 4/3/00 CNX* CONSOL Energy Inc. 5/12/00 PCP* Precision Castparts 2/24/94 COF* Capital One Financial 11/21/95 PCS MetroPCS Communications Inc. COG* Cabot Oil & Gas 6/16/94 PDCO* Patterson Companies 6/15/94 COH Coach Inc. 1/29/02 PEG Public Serv. Enterprise Inc. COL Rockwell Collins 1/15/03 PEP PepsiCo Inc. 3/17/94 COP ConocoPhillips 11/15/93 PETM PetSmart, Inc. 9/6/94 COST Costco Co. 1/28/94 PFE Pfizer Inc. 8/10/94 COV Covidien plc PFG Principal Financial Group 4/9/03 CPB Campbell Soup 3/7/94 PG Procter & Gamble 4/14/94 CRM Salesforce.com 12/1/05 PGR Progressive Corp. 4/20/94 CSC Computer Sciences Corp. 1/12/94 PH Parker-Hannifin 3/18/94 CSCO Cisco Systems 5/10/94 PHM* Pulte Homes Inc. 2/28/94 CSX CSX Corp. 5/3/94 PKI* PerkinElmer 3/25/94 CTAS* Cintas Corporation 9/6/94 PLD ProLogis 1/21/99 CTL* CenturyLink Inc 3/9/94 PLL Pall Corp. 5/24/94 CTSH* Cognizant Technology Solutions 12/7/99 PM Philip Morris International CTXS* Citrix Systems 9/5/97 PNC PNC Financial Services 4/13/94 CVC Cablevision Systems Corp. 3/11/94 PNR Pentair Ltd. 4/6/94 CVH* Coventry Health Care Inc. 3/29/94 PNW* Pinnacle West Capital 11/9/93 CVS CVS Caremark Corp. 1/7/94 POM Pepco Holdings Inc. 2/28/94 CVX Chevron Corp. 5/4/94 PPG PPG Industries 4/15/94 D* Dominion Resources 1/17/94 PPL PPL Corp. 11/4/93 DD Du Pont (E.I.) 4/8/94 PRGO* Perrigo 3/1/94 DE Deere & Co. 4/29/94 PRU Prudential Financial 1/15/03 DELL Dell Inc. 6/16/94 PSA* Public Storage 5/31/94 DF Dean Foods 7/14/03 PSX Phillips 66 DFS Discover Financial Services PWR Quanta Services Inc. 4/26/99 DG Dollar General PX Praxair Inc. 2/22/94 DGX Quest Diagnostics 10/30/97 PXD* Pioneer Natural Resources 11/11/98 DHI D. R. Horton 8/3/94 QCOM QUALCOMM Inc. 7/14/94 DHR Danaher Corp. 4/15/94 QEP QEP Resources DIS The Walt Disney 7/14/94 R Ryder System 1/13/94 DISCA Discovery Communications RAI Reynolds American Inc. 7/28/00 DLPH Delphi Automotive RDC Rowan Cos. 2/2/94 DLTR* Dollar Tree 6/6/96 RF Regions Financial Corp. 4/11/94 DNB Dun & Bradstreet 3/15/94 RHI Robert Half International 6/6/94 DNR* Denbury Resources Inc. 7/14/97 RHT* Red Hat Inc. 11/7/00 DO* Diamond Offshore Drilling 1/9/97 RL Polo Ralph Lauren Corp. 10/15/98 DOV Dover Corp. 10/15/93 ROK* Rockwell Automation Inc. 1/17/94 DOW Dow Chemical 3/15/94 ROP Roper Industries 1/24/95 DPS Dr Pepper Snapple Group ROST Ross Stores Inc. 4/21/94 DRI Darden Restaurants 8/2/96 RRC Range Resources Corp. 5/9/94 DTE* DTE Energy Co. 11/16/93 RSG* Republic Services Inc 8/13/99 DTV DirecTV 9/9/05 RTN Raytheon Co. 3/10/94 DUK Duke Energy 7/28/94 S Sprint Nextel Corp. 1/19/94 DVA* DaVita Inc. 1/10/97 SAI SAIC DVN Devon Energy Corp. 7/7/94 SBUX Starbucks Corp. 1/13/94 EA* Electronic Arts 4/25/94 SCG SCANA Corp 3/30/94 EBAY ebay Inc. 2/28/00 SCHW Charles Schwab 5/26/94 ECL* Ecolab Inc. 3/28/94 SE Spectra Energy Corp. ED Consolidated Edison 3/30/94 SEE Sealed Air Corp.(New) 1/28/94 EFX* Equifax Inc. 3/4/94 SHW* Sherwin-Williams 3/22/94 EIX Edison Int'l 1/17/94 SIAL Sigma-Aldrich 4/18/94 EL Estee Lauder Cos. 12/30/96 SJM Smucker (J.M.) 11/28/01 EMC EMC Corp. 7/13/94 SLB Schlumberger Ltd. 5/31/94 EMN Eastman Chemical 3/1/95 SLM SLM Corporation 5/20/94 EMR* Emerson Electric 3/24/94 SNA Snap-On Inc. 3/25/94 EOG EOG Resources 3/17/94 SNDK SanDisk Corporation 1/9/97 EQR Equity Residential 10/14/94 SNI Scripps Networks Interactive Inc. EQT* EQT Corporation 4/18/94 SO* Southern Co. 4/13/94 9
ESRX Express Scripts 3/30/94 SPG Simon Property Group Inc 10/12/95 ESV Ensco plc 9/21/94 SPLS Staples Inc. 7/13/94 ETFC E-Trade 11/17/97 SRCL* Stericycle Inc 8/7/97 ETN Eaton Corp. 4/22/94 SRE Sempra Energy 10/7/99 ETR* Entergy Corp. 5/6/94 STI SunTrust Banks 3/23/94 EW Edwards Lifesciences 2/6/02 STJ St Jude Medical 6/17/94 EXC Exelon Corp. 1/14/94 STT State Street Corp. 7/19/94 EXPD* Expeditors Int'l 3/28/94 STX Seagate Technology 3/8/04 EXPE Expedia Inc. STZ Constellation Brands 6/21/94 F Ford Motor 4/25/94 SWK Stanley Black & Decker 4/21/94 FAST Fastenal Co 9/8/94 SWN Southwestern Energy 5/10/94 FCX Freeport-McMoran Cp & Gld 5/24/96 SWY Safeway Inc. 3/22/94 FDO Family Dollar Stores 1/31/94 SYK Stryker Corp. 5/18/94 FDX FedEx Corporation 3/14/94 SYMC Symantec Corp. 6/23/94 FE FirstEnergy Corp 9/30/98 SYY Sysco Corp. 6/13/94 FFIV F5 Networks 11/17/00 T AT&T Inc 2/24/94 FHN First Horizon National 6/9/94 TAP Molson Coors Brewing 6/29/94 FIS Fidelity National Information 8/12/02 TDC Teradata Corp. Services FISV* Fiserv Inc 3/29/94 TE TECO Energy 5/26/94 FITB* Fifth Third Bancorp 7/13/94 TEG Integrys Energy Group Inc. 10/14/94 FLIR FLIR Systems 3/29/95 TEL TE Connectivity Ltd. FLR Fluor Corp. 2/21/02 TER Teradyne Inc. 6/21/94 FLS Flowserve Corporation 5/11/94 TGT Target Corp. 3/30/94 FMC* FMC Corporation 4/20/94 THC Tenet Healthcare Corp. 9/28/94 FOSL Fossil, Inc. 12/22/94 TIF Tiffany & Co. 5/16/94 FRX Forest Laboratories 5/13/94 TJX TJX Companies Inc. 4/20/94 FSLR First Solar Inc TMK Torchmark Corp. 5/27/94 FTI FMC Technologies Inc. 9/10/02 TMO* Thermo Fisher Scientific 3/10/94 FTR Frontier Communications 5/17/94 TRIP TripAdvisor GAS* AGL Resources Inc. 3/17/94 TROW* T. Rowe Price Group 7/11/94 GCI Gannett Co. 7/18/94 TRV* The Travelers Companies Inc. 1/20/94 GD* General Dynamics 8/23/94 TSN Tyson Foods 3/22/94 GE General Electric 3/30/94 TSO Tesoro Petroleum Co. 8/3/94 GILD* Gilead Sciences 12/28/93 TSS* Total System Services 3/28/94 GIS General Mills 3/29/94 TWC Time Warner Cable Inc. GLW Corning Inc. 6/29/94 TWX Time Warner Inc. 4/20/94 GME GameStop Corp. 2/18/03 TXN Texas Instruments 1/12/94 GNW Genworth Financial Inc. 6/24/05 TXT Textron Inc. 6/3/94 GOOG Google Inc. 4/10/06 TYC Tyco International 4/21/94 GPC Genuine Parts 12/14/93 UNH United Health Group Inc. 4/26/94 GPS Gap (The) 4/25/94 UNM Unum Group 3/7/94 GRMN Garmin Ltd. 4/3/02 UNP Union Pacific 6/29/94 GS Goldman Sachs Group 4/10/00 UPS United Parcel Service 10/10/00 GT Goodyear Tire & Rubber 4/19/94 URBN Urban Outfitters 2/9/95 GWW Grainger (W.W.) Inc. 3/24/94 USB U.S. Bancorp 4/8/94 HAL* Halliburton Co. 4/12/94 UTX United Technologies 11/23/93 HAR Harman Int'l Industries 3/4/94 V Visa Inc. HAS Hasbro Inc. 5/23/94 VAR* Varian Medical Systems 1/10/94 HBAN Huntington Bancshares 2/3/94 VFC V.F. Corp. 2/8/94 HCBK Hudson City Bancorp 6/7/00 VIAB Viacom Inc. HCN Health Care REIT 7/20/94 VLO* Valero Energy 1/28/94 HCP HCP Inc. 5/31/94 VMC Vulcan Materials 2/4/94 HD Home Depot 4/5/94 VNO Vornado Realty Trust HES* Hess Corporation 1/3/94 VRSN* Verisign Inc. 4/27/99 HIG* Hartford Financial Svc.Gp. 2/10/97 VTR Ventas Inc 1/19/99 HNZ Heinz (H.J.) 2/1/94 VZ Verizon Communications 12/2/93 HOG Harley-Davidson 6/17/94 WAG Walgreen Co. 6/28/94 HON Honeywell Int'l Inc. 12/17/93 WAT* Waters Corporation 2/24/97 HOT* Starwood Hotels & Resorts 3/15/94 WDC* Western Digital 4/20/94 HP* Helmerich & Payne 3/25/94 WEC* Wisconsin Energy Corporation 1/10/94 HPQ Hewlett-Packard 3/29/94 WFC Wells Fargo 12/1/94 HRB Block H&R 7/5/94 WFM Whole Foods Market 3/30/94 HRL Hormel Foods Corp. 2/28/94 WHR Whirlpool Corp. 4/14/94 HRS Harris Corporation 4/8/94 WIN Windstream Corporation 7/12/06 HSP Hospira Inc. 1/25/06 WLP WellPoint Inc. 12/4/02 HST Host Hotels & Resorts 12/3/93 WM* Waste Management Inc. 10/10/94 HSY The Hershey 3/4/94 WMB* Williams Cos. 11/3/93 HUM Humana Inc. 9/8/94 WMT Wal-Mart Stores 6/8/94 IBM International Bus. Machines 3/25/94 WPO Washington Post Co B 12/6/93 ICE IntercontinentalExchange Inc. WPX WPX Energy, Inc. IFF International Flav/Frag 1/19/94 WU Western Union Co IGT International Game Technology 5/23/94 WY Weyerhaeuser Corp. 1/5/94 INTC Intel Corp. 8/23/94 WYN Wyndham Worldwide INTU Intuit Inc. 7/13/94 WYNN Wynn Resorts Ltd 2/9/04 IP International Paper 1/5/94 X United States Steel Corp. 3/11/94 IPG Interpublic Group 5/20/94 XEL Xcel Energy Inc 3/24/94 IR* Ingersoll-Rand PLC 4/18/94 XL XL Capital 3/14/94 IRM Iron Mountain Incorporated 5/30/97 XLNX* Xilinx Inc 4/28/94 ISRG Intuitive Surgical Inc. 7/27/01 XOM Exxon Mobil Corp. 3/29/94 ITW Illinois Tool Works 5/4/94 XRAY Dentsply International 8/2/94 IVZ* Invesco Ltd. 11/11/96 XRX Xerox Corp. 6/22/94 JBL Jabil Circuit 10/26/94 XYL Xylem Inc. JCI Johnson Controls 3/7/94 YHOO Yahoo Inc. 7/8/97 JCP Penney (J.C.) 4/12/94 YUM Yum! Brands Inc 11/27/98 ZION Zions Bancorp 5/23/94 ZMH Zimmer Holdings 10/11/02 10
References Berger, Adam L., Israel, Ronen, Moskowitz, Tobias J. The Case for Momentum Investing. (2009) Available at AQR Capital Management: http://www.aqrindex.com/resources/docs/pdf/news/news_case_for_momentum.pdf Faber, Mebane T., Relative Strength Strategies for Investing (April 1, 2010). Available at SSRN: http://ssrn.com/abstract=1585517 or http://dx.doi.org/10.2139/ssrn.1585517 11
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