In comparison, here is how other hedge categories stacked up. Note these are 3-year returns for 2010-2012, and as a result, do not include results for 2013.

Despite the strong equity market – or likely because of it – note the returns of equity-based strategies. Equity long/short, in particular, has had a tough go of it for the last few years. Separating “winners” from “losers” can be a tough process in a solidly bull market.

If there’s one thing firms must have a strong grasp on in the financial markets, it’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 “Holy Grail” 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 – 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.

Jones Trading Recap of 13F Filings as of 3/31/2013

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• Fairholme Capital -
Took Stakes in CHK, CNQ, GNW;
Boosted Stakes in AIG, SHLD;
Cut Stakes in JOE, OSH, MBI;
Exited Stake in CIT.
• 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.
• Icahn Associates -
Took HLF, CVRR, DELL, NUAN;
Boosts Stakes in RIG, VLTC.
• 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.
• 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.
• 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.
• 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.
• 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
• 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.
• 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.
• 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.
• Pershing Square –
Boosted Stakes in BKW;
Cut Stakes in MATX.
• 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.
• 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.
• 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.
• 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.
• 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.
• Trian Funds -
Boosts Stakes in MDLZ, PEP, FDO;
Cuts Stakes in TIF, STT;
Exited Stakes in MWV.
• 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
• 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

“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.

Barron’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 “objective” mean reversion into what it calls a company’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, mature…). 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 year’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 Bloodhound’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 it’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 Barron’s 500 – May 1, 2012. Of the largest 518 market caps, Lorillad (LO) topped the list with a 56% CFROA. Barron’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 “good companies?” 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 shouldn’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 that’s like saying, “besides hitting the iceberg, the Titanic passengers quite enjoyed themselves.”

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, it’s probably nice to be mentioned in the Barron’s 500, but it likely means little for future performance.

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 PIKs—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 “reaching for yield” 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.

There are a number of basic caveats to back-testing that you need to be aware of and some I have come a real cropper on:

First, and potentially most dangerous, is allowing your system ’see’ future data – by this I mean you must not let your ‘tests’ access any data that could be potentially in the future. This can be really subtle and difficult to debug and not especially obvious. The best way to get around this problem is to be really disciplined in your coding and to isolate data based on age. I do this by creating ’subsets’ of data with Linq using the ‘current’ date and the earliest date in your back-testing model. As the ’subset’ is a Linq table object and contains all the price data I need: High, Low, Opening, Close and Volume, all I subsequently need to do is separate out the bits I need such as a date array and a closing price array.

Second, and a potential danger to us working with ‘free’ data, is Survivor Bias. This is where stocks have dropped out of the indices over the years for performance reasons and therefore your initial dataset is already biased in favour of ’survivors’. There is no easy way round this, unless you want to fork-out for a ‘clean’ dataset – I believe that if your testing is thorough enough and your sample sizes are large enough, then this will not necessarily be a problem.

Third, and most important, avoid ‘curve fitting’. By this I mean that if you add countless parameters to your model, you should not be surprised if you get excellent returns in back-testing. The art of model development is definitely ‘less is more’ – you should aim to reduce your parameters to an absolute minimum, that way your model will perform in the widest ranges and types of market. The sign of a good model is how few, and how simple the parameters are. You should aim to continually test and reduce your parameters until you see no observable change in your results. This is a hard point to make, but crucial, especially for us amateurs. I suggest you read Ernie Chan’s opinions on simplifying trading models either in his blog or in his book.

Fourth, compounding. I made this mistake for a while, my back-testing model would use the returns of previous trades to fund future ones. This looks great and does help you to see the effects of compounding, however it does not help in testing or verifying the success or otherwise of your model. You need to strip-out such effects from your initial model testing so that you are testing only the veracity or otherwise of your parameters rather than the vagaries of market timing.

I don’t know what happened to Pete. His Blogger posts stopped almost as quickly as they started. Based on his comments, it’s too bad that Bloodhound was around for him. We adhere to many of the same principles, and execute much of the hurdles away for the investor.

Our point-in-time database and on-line business model solves the caveats that Pete addresses for both the professional and amateur strategy builder. Our simulator only utilizes information that was available on the date being analyzed. Our data is free of survivorship bias. We advocate simplicity in factor testing. And finally, our performance measurement presents data multiple ways including eliminating or computing the compounding effect.

Best yet… like me, you don’t have to be a programmer!

These returns are in light of an S&P 500 and S&P 100 that generated 1.9% and 2.1% total returns in April, respectively. Tech stocks remains the weakest of the mix. Although there was some reversal in April, year-to-date, the larger up the capitalization wheel you went the worse your returns look. The 10-largest tech names are under-performing the NASDAQ 100 by almost 400 basis points.

Hedge Funds continue to lag the stock surge. According to FINAlternatives, Hedge Fund Research’s HFRX Global Hedge Fund Index rose 0.62% in April, a month that saw the Standard & Poor’s 500 Index add a further 1.81% to its double-digit returns for the year. By contrast, the HFRX benchmark is up only 3.77% in the first third of 2013.

Today, we review some of our Model Strategies. To show the strength of our system and the benefits of portfolio concentration, we create a number of model portfolios. The Bloodhound System includes a library of multi-factor Model Strategies developed by experts that range in style from conservative to super aggressive. The strategies reflect traditional investing strategies followed by a wide variety of investors, or an investment approach by a notable finance figure. Strategies range from Fundamental Growth to the Warren Buffet Diversified Yield to a number of our strategies culled from our one million deep research library.

We reviewed both simple average returns as well as compounded returns over 24 years from 1989 to 2012. In no case did Sell in May and go away benefit you as an investor. However, it should be noted that in no case did that period reflect more than 50% of the returns for the year even though we are segmenting the year into two six month halves.

The Lynch GARP Strategy, based on the famous efforts of former Fidelity fund manager Peter Lynch, was the closest candidate to sell in May. The compounded returns for just investing during the six months between May and October yielded an annual return of 1.8%. That represented only 14.5% of the whole year’s return, the lowest percent of any of the model strategies by a wide margin. In one in every four years, the period excluding May-Oct was positive while the period of just May through October was negative. But it’s not the month of May that does the strategy in! The compounded for average for just the month of May is 2.2%, the fourth highest of any of the strategies listed. It’s poor period is actually June through September which compound return is -1.3%.

The strategy with the least skew of returns is the High Dividend Performance Strategy taken from our Research Library. The compounded returns split between the two periods was almost identical (rounding put the May-Oct period is a slightly trailing position). The Strategy which was identified for its relative outperformance of the S&P 500, paid a dividend yield of more than 6% for 25 out of the last 26 years, and its total return has exceeded the S&P 500 index in 16 out of 26 years. May, as a month, represents more than its fair share of return at 12.1% of the annual performance (as opposed to 8.3%, or 1/12th, if it were equal).

May has been a good month for Earnings Growth Leaders. This growth strategy looks to buy companies from the major US exchanges where their growth in earnings per share exceeds that of their sales growth. In addition, it looks for stocks generally no more volatile than the market and selects those that ranked highest in net margin, but lowest in standard deviations of earnings per share. It is sector neutral, and is generally of low risk. The compounded return for just the month of May is 2.4%, or roughly twice of the average month.

The two highest outright compounded return over the upcoming six months is the Moderate Risk Growth Strategy, and the Historical Outperformance strategy. The growth strategy looks to buy mid- to large-cap growth stocks that have a moderate level of volatility. Its portfolios are sector neutral and built with stocks drawn from the Value Line 1700 index. It has one of the fewest instances where the May-Oct period is negative whilst the rest of the year is positive. On a compounded basis, investing in just the months of May through October would yield a 8.3% return. The Outperformance strategy from our research database has exceeded the return of the S&P 500 index for 25 of the last 26 years. Its return in May at 3.0% is equal to its monthly fair share, and the May through October performance was unmatched by any other. Then again, its full year performance was unequaled as well. While past performance may not be repeated, there must be something to a strategy that has created outperformance over so many cycles and economic situations.

Sell in May? No way. In the third and final look at the May through October time period suggest that anyone who wishes to take the Summer and Fall off, do so at their own risk. So I guess we’ll see you tomorrow…

May isn’t the month to avoid. In fact, May has better average returns than January, despite its own “effect.” Rather, August and September have the worst records on average. None of the 16 indicies we track have a positive average return in September.

I’ll take May, thank you very much. However, clearly a few month can have a significant impact of the average as a whole. As such, we looked at the standard deviation of those monthly returns.

As one would expect, August and September stand out. May remains one of the least volatile. In fact, the stretch of March through June represents the lowest third of the year in terms of volatility of monthly returns. In the S&P 500, June has the second lowest standard deviation of monthly results behind only December. When we look at each months best and worst returns over the 26 years a few things stand out.

October 2008 weighs heavily on the S&P 500. However, outside of October, the worst months are fairly consistent. A month that scares many investors, even October has had its fair share of gains.

One item we found interesting was the low volatility in returns in the tech-centric NASDAQ indicies. We highlighted the May, June and July periods for the NASDAQ 100 and NASDAQ Composite. The “worst” months in that quarter of the year, are among the “best of the worst,” and the “best” months are in the bottom half of other months.

There was a Tech variation of “Sell in May.” The AEA was the American Electronics Association big trade show and took place in October. Companies rolled out new gadgets and lobbied for new contracts. Hambrecht & Quist Technology Conference used to take place at the end of April in San Francisco. H&Q was an investment bank noted for its focus on the technology and internet sectors participating in the IPOs of Apple, Genentech, Netscape and Amazon. It was acquired by Chase in 1999 just ahead of the bubble pop. The H&Q Tech Conference was the place to be for Silicon Valley companies and New York investors alike. Companies made big annoucements ahead of the conference, and got everyone lathered for the future despite the fact that an ultimately boring Summer product release was closer. Buy at AEA, Sell at H&Q was the hot saying in 1999. Since neither one is still around, that saying has gone out of style for reasons beyond the fact that it didn’t work.