Earlier this month, a blog by the Chartered Alternative Investment Analyst (CAIA) Association, All About Alpha, wrote a post about Data and its association with Alpha. Although the post was written about algorithmic and high-frequency trading, many of the points were attributable to most investing strategies.
If thereâ€™s one thing firms must have a strong grasp on in the financial markets, itâ€™s data. These days, data comes from every direction possible, and it comes quickly. But to take full advantage of the ocean of information rushing toward you requires getting a handle on data and then finding meaning within the data to capitalize on opportunities.
We live in what I call the new normal, defined by higher regulatory scrutiny, increasing competition, tighter spreads, thinner margins and a lower risk appetite. To find alpha in this atmosphere, firms must broaden their data analysis to create smarter algorithms…
Again, they are referencing many algorithmic trading strategies that look to correlations and cross asset classes, but the underlying principle holds that, “defining a new normal does not warrant an emotional capitulation.” Creating unemotional sets of trading rules will protect the principles of an investment philosophy. Many fine stewards of capital run adrift when the market goes directionally uninterupted. Theories are questioned. Traditional beliefs are doubted. Things look “different this time.” New and untested chances are taken. Sticking to what works, wins in the end. Data can stongly assist in keep the implementation of a strategy both stable and focused.
Proper data sources, and systems to process vast amounts of data, allow one to go faster, or at least do more without getting slower. Although high frequency trading (HFT) rests on the speed of data to the hands of the user, processing speed is an important factor to any type of professional investor. The greater the amount of data, the more equipped an investor is to use that data to his or her advantage. However, vast amounts of data take vast processing time and power.
The â€śHoly Grailâ€ť is actually to outsmart the crowd, which does not imply relying solely on speed, but also on being smarter than the competition. To this end, quants must demand access to a deeper pool of global historical data and use observations from the past to characterize relationships, understand behaviors and explain future developments.
Market inefficiencies, the life blood of alpha generating strategies, are manifested by many things, including but not limited to human behavior, geo-political events and complex market structure. Quants must apply an empirically-tested and rules-based approach to exploit these inefficiencies if they hope to outsmart the competition.
Every investor is essentially in competition with one another, and market efficiency is such that forward predictions are a tough business. However, an understanding of what works, and what doesn’t, and crafting an investment strategy that sets and expectation of both return and risk can be the greatest building block.
Historical analysis of high quality and comprehensive data can lead to the recognition of similar market conditions in the past, which can shed light on their consequences. Back-testing your models against past market conditions enables you to fine-tune algorithms, manage inherent risk and reveal alpha.
This new normal that we live in today is defined by diminishing volumes, wild rallies and uncertain regulatory policy. But it does not signal an end to profitability and the discovery of alpha hidden within the depths of our markets â€“ on the contrary, when equipped with data and the tools to tame it, this is where the quest to tap profitability and alpha begins. [my emphasis]
They are singing our tune. Bloodhound users have access to a unique 26-year point-in-time database to develop and validate proprietary investing and trading models ranging from simple to highly complex. The Bloodhound System brings the intellectual rigor of high performance computing and advanced simulation to the problem of how to acheive differentiation. Highly intricate models with multiple fundamental and technical parameters can be computed in seconds. Additionally, models are updated overnight to provide users with the information they need to start the day. You don’t need to be a high-frequency trader to use alogrithmic data processing to your advantage, you just need a desire to maintain investment performance.
Thanks to broker/dealer Jones Trading, we have a recap of changes in holdings at major hedge funds. As we have noted in previous posts, it can be difficult to replicate investor activities with 13F because of the timing of the filing. Note that this list is as of the end of March – or almost two months old. 13D filings are more informative due to the filing requirements, but are fewer and far between. Also take note that many of the same securities are just changing hands.
Jones Trading Recap of 13F Filings as of 3/31/2013
â€˘ Appaloosa Management –
Took Stakes in CMCSA, PRU, HES, CHKP, KBR;
Boosted Stakes in RIG, MET, QCOM, HIG, SNDK;
Cut Stakes in AAPL, JPM, AIG, VLO, CIM;
Exited Stakes in ORCL, NE, ESV.
â€˘ Basswood –
Took Stakes – TAYC, COF, PHH, MET, PNC;
Boosted Stakes in MS, CIT, BK, C, STT;
Cut Stakes in RF, EME, KEY, VCBI, FFCH;
Exited Stakes FULT, WSFS, WCBO, BBT, TCB.
â€˘ Baupost Group –
Took Stakes in ELN, DTV;
Boosted Stakes in BP, AIG, ROVI, IDIX;
Exited Stakes in NWSA, GNW, ANV;
Cut Stakes in ORCL, NWS, ENZN, ITRN, AOI.
â€˘ Berkshire Hathaway -
Took Stakes in LMCA, CBI;
Boosted Stakes in WFC, IBM, WMT, DTV, VRSN, USB, NOV;
Cut Stakes in MDLZ, KRFT, BK;
Exited Stakes in GD, ADM.
â€˘ BP Capital -
Took Stakes in APA, TSO, MPC, GPOR, PSX;
Boosted Stakes in GDP, PXD, CNX, ACI, OXY;
Cut Stakes in VLO, HAL, FCX, APC, DVN;
Exited Stakes in SWN, RRC, NOV, NFX, MRO.
â€˘ Brahman Capital -
Took Stakes in ENDP, AIZ, AZO, WAG, OIS;
Boosted Stakes in LBTYA, RLGY, LINTA, ORCL;
Cut Stakes in SIX, OCR, KAR, AIG, VIAB, VRX, SYMC, CHTR;
Exited Stakes in CIT, WCG, UAM, DLPH.
â€˘ Bridgewater -
Took Stakes in CTL, GE, DIS, EXPD, BXP;
Boosted Stakes in VWO, EEM, LMT, ORCL, EMC;
Cut Stakes in EWZ, SPLC, PG, AGN, NEM;
Exited Stakes in APOL, HPQ, DELL, SWY, VMED.
â€˘ Capital Growth -
Took Stakes in GS, HTZ, NVR, FTI, BLK;
Boosted Stakes in MHK, WHR, RKT, EQR;
Cut Stakes in PHM, PSA, SSS, JPM, EXR;
Exited Stakes in BAC, HLF, F, FL, TCO.
â€˘ Coatue Management -
Took Stakes in AIG, YNDX, BRCM, GMCR, RAX, MS, MCP;
Boosted Stakes in AKAM, AAPL, EBAY, CHTR, CBS, BBRY, MLNX, NWSA, NFLX;
Cut Stakes in GRPN, ATML, PCLN, INFA, PBI, LOGI;
Exited Stakes in YELP, BIDU.
â€˘ Discovery Capital -
Took Stakes AXLL, DRI, DNDN, DG, FL, GMCR, HFC, HUN, IFT, IR, LVS, LBTYA, PCLN, TIBX, WYNN, YOKU;
Boosted Stakes APC, CIS, COF, CF, CIE, DFS, EQIX, GOOG, HCA, MPC, MTG, NKE, MDLZ, NAV, NKE, QCOM, TSO, DIS; Cut Stakes in ALL, C, DTV, EBAY, HTZ, MANU, SNDK, S, RIG, YNDX;
Exited Stakes in AAPL, BBD, BSBR, BAC, COH, CS, HUM, NIHD, ORCL, PNC, QIHU, SLXP.
â€˘ Eton Park -
Took a stake in YNDX, BIDU, CHTR, LNG, INTC, MPC, MJN;
Boosted Stakes in LBTYK, PCLN, CMCSA, DLTR, CMG;
Cut Stakes in S, YPF, LBTYA, CXW;
Exited Stakes in T, ADSK, BBY, HII, MR, RL, VZ, VIAB, WMT, RL, MAS.
â€˘ Fairholme Capital -
Took Stakes in CHK, CNQ, GNW;
Boosted Stakes in AIG, SHLD;
Cut Stakes in JOE, OSH, MBI;
Exited Stake in CIT.
â€˘ Highfields Capital -
Took MHFI, DELL, HES, BEN, DISH;
Boosted UPS, THI, APC, FDO, ICE ;
Cut Stakes in IVZ, JPM, IR, GNW, GOOG;
Exited BLK, STX, CAH, AAP, INTC.
â€˘ Icahn Associates -
Took HLF, CVRR, DELL, NUAN;
Boosts Stakes in RIG, VLTC.
â€˘ JAT Capital -
Took Stakes in TWX, CMCSA, RCL, LBTYA, MHFI;
Boosted Stakes in INTU, LVS, CBS, LNKD;
Cut Stakes in EQIX, CTRP, SBAC, N, GRPN;
Exited Stakes in DIS, GOOG, EBAY, CTXS, EXPE.
â€˘ Greenlight Capital -
Took Stakes in OIS, HES, SPR, IACI;
Boosted Stakes in AAPL;
Cut Stakes in MSFT, STX, DLPH, CBS;
Exits Stakes in ESV, XRX, YHOO, NVR.
â€˘ Jana Partners -
Took Stakes in AGU, ASH, BMC, BA, GRPN, ZNGA;
Boosted Stakes in BIG, VRSN, AET, LVNTA, FNP;
Cut Stakes in CCE, QEP, SE, CVG, ROC;
Exited Stakes in AIG, ADT, MHFI, TRIP.
â€˘ Lansdowne Partners -
Took Stakes in LNKD, MS, GS, CG, MTG;
Boosted Stakes in CMCSA, ACN, BAC, NKE, CL;
Cut Stakes in WFC, AIG, C, KKR, WAC;
Exited Stakes in KO, VMED, CIT, FB, CYMI.
â€˘ Lone Pine –
Took Stakes in VRX, VMED, TMO, CME, LMCA, HRB;
Boosted Stakes in MJN, NWSA, ISRG, MON, QCOM;
Cut Stakes in CTSH, EQIX, RL, DIS, OII
â€˘ Moore Capital –
Took stakes in MS, TWX, LBTYA;
Boosted Stakes in AGO, IBM, LVS, NWSA, BLK;
Cut stakes in JPM, STI, EEM, FXI;
Exited Stakes in AIG, WFC, EMB.
â€˘ Omega Advisors –
Took Stakes in LYB, OXY, COV, EVEP, SVU;
Boosted Stakes in LINE, CIM, S, QCOM, C;
Cut Stakes in EXXI, GCI, GOOG, DISH, KKR;
Exited Stakes in HUM, ACT, WLP, WU, PAY.
â€˘ Paulson -
Took Stakes in FDO, HES, IOC, MTG, VOD;
Boosted Stakes in S, LIFE, PXD, BPOP, AET;
Cut Stakes in MYL, DLPH, HIG, HCA, RHP;
Exited Stakes in MUR, NRG, ACAS, ABX, XL.
â€˘ Pershing Square –
Boosted Stakes in BKW;
Cut Stakes in MATX.
â€˘ Relational Investors –
Took Stakes in MDLZ, TYC, JOSB;
Boosted Stake in TKR;
Cut Stakes in CVS, SPY, ITW, IWS, DGX;
Exited Stakes in PEP, MTW.
â€˘ SAC Capital –
Took Stakes in DISCA, LBTYA, LMCA, NCLH, CBST;
Boosted Stakes in SU, EQT, AMZN, V, GNC;
Cut Stakes in NWSA, FB, SHW, SYMC, AVGO;
Exited Stakes in COH, DOV, VMED, EWJ.
â€˘ Soros Fund Management –
Took Stakes in MWV, BRCD, RHT, LBTYK;
Boosted Stakes in GOOG, LBTYA, EQT, LCC;
Cut Stakes in C, AIG, AAPL, IVZ, MS;
Exited Stakes in JPM, GE, CF, MS, COF.
â€˘ Third Point –
Took Stakes in VMED, TIF, BEAV, APC, TMO;
Boosted Stakes in IP, ABBV, TDG, STZ, DG;
Cut Stakes in DLPH, MUR, AIG, LYB, LBTYA;
Exited Stakes in HLF, MS, SYMC, TSO, ILMN.
â€˘ Tiger Global -
Took Stakes in JCP, DLTR, LULU, NFLX, VRA, GMCR, CCI, ZTS;
Boosted Stakes NWSA;
Cut Stakes in AAPL, AMZN, MA, PCLN;
Exited Stakes in FSLR, YHOO, Z, P.
â€˘ Trian Funds -
Boosts Stakes in MDLZ, PEP, FDO;
Cuts Stakes in TIF, STT;
Exited Stakes in MWV.
â€˘ Tudor Investment -
Took Stakes in PFE, A, BIIB, ZTS, HYG; XLF, WLP, MRK, COV, ABBV; MSFT, CA, TMUS, WHR, MRVL;
Exited AAPL, EEM, VOD, ANF, VOD
â€˘ Viking Global -
Took Stakes in BA, CX, VLO, MPC, ADBE;
Boosted Stakes in TWX, ISRG, ALXN, LYB, HRB, CMCSA, KORS;
Cut Stakes in WMB, ACE, EL, DVA, LYB, COF;
Exited Stakes in NWSA, SLB, LVS, HUM, AMT
As we have noted in previous blog posts, Apple may have lost its shine, but it still remains the largest capitalization stock on the U.S. exchanges â€“ retaking the lead from Exxon earlier this month. It also didn’t prevent it from grabbing the top spot in last week’s 15th annual Barron’s 500 – a ranking of the 500 largest publicly traded companies in the U.S. and Canada. It was 2nd last year when the stock was near $580, so I’m not too sure what it says about the predictive power of the list (although it diid make a run to $700 shortly thereafter). Laggard J.C. Penny took 491st last year, and nabbed place 500 this year – so it might say somethin.’
“The Barron’s 500 is a unique ranking of the largest publicly traded companies in the U.S. and Canada, as measured by total sales in the latest fiscal year. It is prepared by HOLT, a unit of Credit Suisse, which compares companies on the basis of three equally weighted metrics: median three-year cash-flow-based return on investment, the one-year change in that measure relative to the three-year median, and adjusted sales growth in the latest fiscal year.”
The entire current list can be found on their site.
Although nice to be named a “good company,” we are focused on the predictive power. The top 10 last year were:
with the bottom:
Considering three year cash flow is one third of the grade, I’m a little surprised the list bounces around as much as it does. Western Digital ranked 447th last year, only to rebound to third this year.
Barronâ€™s utilized the Credit Suisse HOLT system of CFROI (Cash Flow Return on Investment) as is basis of rank. CFROI is a modified measure of ROA (Return on Assets). Return on assets can be mathematically split into profit margin times asset turnover. The HOLT system substitutes accounting profit with operating cash flow, and gross assets with gross investment. However, a modest flaw in the HOLT system is it uses a forward looking estimates and a number of assumptions. Most notably, HOLT factors in an â€śobjectiveâ€ť mean reversion into what it calls a companyâ€™s corporate life cycle. Therefore, one needs to accurately predict future cash flows and objectively acknowledge where a company sits of the life cycle chart (growth, fade, matureâ€¦). Results are comparable intra-industry, but become more difficult to compare across industries.
My favorite Barron’s editor, Jackie Doherty, takes a look if there is an ability to find the best bargain stocks among those 500. They rank the stocks based on P/E and track the performance of the cheapest 30. “The results were so impressive that we’re repeating the exercise this year. On average, the 30 stocks rose by 42% in the 12 months ended April 26, far outpacing the Standard & Poor’s 500, which returned 15.6%.” However, that’s where I get a little confused. On one hand, they laud themselves for placing J.C. Penny in the bottom 1% last year (and dead last this), yet on the other hand, create a strategy in which “companies with strong financial performance” no matter where they are on the list. Three of the five cheapest stocks are in the bottom third of the list, and one of the names on the list, Continental United (UAL) is 23 places out of last. By their definition, UAL isn’t a strong financial performer, in fact it got grades of “D” and “F”s for financial performance. Rather, it’s just a big, “cheap” company. Thirteen names on this yearâ€™s list (just shy of half) were on last year’s list too – meaning they stayed cheap.
We took a look at it a different way. First we used Bloodhoundâ€™s screening tool to identify the cut off level for the 500 largest capitalization names listed on the NYSE, NASDAQ and OTC exchanges. The current Mendoza line is approximately $6.3 billion, equal to where is was in 2007. However, the in-between years, and the years prior to 2007, the cut off was significantly lower. As such, our current candidate pool will be larger than 500 names as we will determine our view with a little cushion by identifying companies with market caps greater than $5bn. As of today, 593 companies fit that bill. In comparison, there were 468 such names in 2005.
We ranked the universe on our own calculation of cash flow return, “CFROA.” We looked at EBITDA less interest, thereby ignoring changes in working capital which can work against a business when itâ€™s growing and improve the cash flow situation when business is actually deteriorating. To get a return figure, we divide the cash flows by total assets. Since Bloodhound has the ability to screen historically, we set the screen date to closely match that of the Barronâ€™s 500 – May 1, 2012. Of the largest 518 market caps, Lorillad (LO) topped the list with a 56% CFROA. Barronâ€™s 500 top contender at that time, CF Industries, ranked 18th. Apple placed 25th. Interestingly, all four of the top ranked companies lost money in the subsequent year.
It immediately begs the question, is cash flow return on investment a predictor of â€śgood companies?â€ť On first glance, the most obvious missing piece is valuation. All factors are accounting-oriented; there is no measurement of market value or price. Fortunately, The Bloodhound System allows users to formulate a strategy and simulate it over 26 years of real-life data, and allows us to study the question.
The average returns of the top 10 and top 50 holdings over the last 26 years is approximately 13%. However, it should be noted that prior to 2004, our buy rule of $5 billion market cap reduces our candidates, capturing a bigger percentage of the largest cap names. When one compares the top 100 names over the last 10 years, the results are not materially different from that of the S&P 500.
The top 10 names have underperformed in each of the last four years, although substantially outperformed the 2008 downturn.
Looking at valuation, as one would expect, P/E ratios are all over the place. P/Es range from 3.4x to 79x. On the other hand, CFROA ranges from 56% by Lorillad to negative by Tesla (TSLA). Rather than ranking the whole universe by P/E as the article does, we looked at only the top CFROA companies, and ranked them by valuation. The median CFROA of the group is 14%. Therefore we ranked the universe by P/E, but created a buy rule that set a hurdle of 15%, to capture the best.
In the last four and a half years, the strategy of picking the ten lowest P/E names among the highest CFROA companies did significantly better than just picking the highest CFROA companies alone. That shouldnâ€™t be too shocking. Amidst a four year bull run, low P/E names are almost by definition poised to outperform. However, that strategy got destroyed in 2008 – down 61.5%. Outside of 2008, the strategy performs quite admirably, but thatâ€™s like saying, â€śbesides hitting the iceberg, the Titanic passengers quite enjoyed themselves.â€ť
A strategy of 50-names performs in-line, but also underperforms the S&P in the last three years, and realistically underperforms the strategy ranked solely by CFROA.
I looked at reversing the process: taking the highest CFROA names that had a hurdle of a certain P/E, but none performed materially different. As such, itâ€™s probably nice to be mentioned in the Barronâ€™s 500, but it likely means little for future performance.
We continue to monitor the fixed income markets for signs of trouble, but none appear to emerge. Spreads, or the difference between yield and the benchmarked treasury, continue to grind tighter. On an absolute basis, the yield on the Merrill Lynch Master High Yield Index is at a low. Since Treasuries are near lows, the spread is not as tight as its ever been, but more interestingly is a chart published by Bespoke Investment Group showing that the yield on junk bonds are now below levels you could have owned Treasuries – not 30 years ago when Treasuries sported double digits, but no less than 6 years ago! The Barclays US High Yield (eh hmm) Index dropped below 5% (4.97%) for the first time ever, down from 6% just three months ago.
General Motors (GM) did a dual offering of 5- and 10-year notes priced at 3.25% and 4.25%, respectively, and the book was more than 10x oversubscribed. Sonic Automotive (SAH), rated B- by S&P, sold $300 million subordinated 10-year notes with a 5% coupon. BB-rated Sirius XM (SIRI) completed a 7-year senior $500 million at 4.25% with the use of proceeds to buyback equity. The demand was so strong, they added a $500 million 10-year tranche priced at 4.625%.
On Friday, Reuters headline said it all, “The US junk bond market is back in full swing, with investors embracing riskier assets in the hunt for yield and issuers getting away with historically tight pricing – and increasingly aggressive structures.”
The demand for yield isn’t just here in the U.S. Note last week’s Wall Street Journal:
Not only are investors seeing more lower-rated companies, but less investor-friendly structures are also creeping into the market, such as debt sales in the form of payment-in-kind notes, or PIKsâ€”bonds on which interest is paid in the form of new debt rather than cash, meaning the total debt mounts up, and is then settled in full at the end of the term. PIKs pay very high rates of interest but rank below all other forms of debt should the borrower default.
A total of $2 billion of PIKs have been sold in Europe this year, close to the full-year record of $2.14 billion in 2007, according to Dealogic.
That last line is most telling. Bankers have sold as many PIK bonds through early May as they sold through ALL of 2007.
In a speech last week, Federal Reserve Chairman Ben Bernanke left the audience with this thought, “we are watching particularly closely for instances of â€śreaching for yieldâ€ť and other forms of excessive risk-taking, which may affect asset prices and their relationships with fundamentals. It is worth emphasizing that looking for historically unusual patterns or relationships in asset prices can be useful even if you believe that asset markets are generally efficient in setting prices. For the purpose of safeguarding financial stability, we are less concerned about whether a given asset price is justified in some average sense than in the possibility of a sharp move. Asset prices that are far from historically normal levels would seem to be more susceptible to such destabilizing moves.”
Meanwhile, two days earlier, Fed economists Fernando Duarte and Carlo Rosa, published a piece that Equity Premium models are forecasting excess returns for the next five years, and provide evidence that such forecasts have predictive capability. The equity risk premium is the expected future return of stocks minus the risk-free rate over an investment horizon. Market expectations of future returns are not observable, they need to be inferred through indirect calculation. There is a good tutorial of Equity Risk Premia in Antti Ilmanen’s book, Expected Returns. We should note, as do the authors, that the expected return is highly influenced by extremely low Treasury rates – so the tho topics are not unrelated. However, the comparison of the two is why I believe the Fixed Income market is a losers game right now.
We have written about the Robin Hood Foundation before on the Bloodhound Exchange. I have been both to the Gala events with “the 1% of the 1%” as Seth Myers notes (as an extra, not a resident of that list), as well as the on-the-ground implementation of its good works. I can attest to its ability to draw dollars, and its execution of good works. 60 Minutes ran a piece about Paul Tudor Jones and his baby, The Robin Hood Foundation, last weekend. Today I head to New York City to meet with some of Bloodhound’s clients as well as some new prospects. Therefore, I find it fitting to run this segment about an organization that does great work for that city. Click the image below to be taken to the 60 Minutes Video.