The Big Picture - Macro Perspective on the Capital Markets, Economy, Technology, and Digital Media

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Apple is disproportionately impacting indices and earnings data, skewing the picture of what is actually occurring.

WSJ:

“While most U.S. companies have struggled to meet earnings expectations, the Cupertino, Calif.-based maker of iPads and iPhones has surpassed even the most bullish of expectations, reporting $13.1 billion in profits during the fiscal 2012 first quarter that ended Dec. 31, more than double that of a year earlier. Revenue soared 73% to $46.3 billion. Those earnings account for about 6% of the S&P 500′s fourth-quarter earnings, according to S&P Indices, making Apple the biggest earnings contributor to the S&P 500.”

I have jokingly told people recently that there are 4 asset classes: Stocks, Bonds, Commodities & Apple. This article is more evidence supporting that . . .

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Source:
Apple’s Size Clouds Market
JONATHAN CHENG And BRENDAN INTINDOLA
WSJ, FEBRUARY 15, 2012
http://online.wsj.com/article/SB10001424052970204062704577223513581427728.html

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I am the keynote speaker today at the Dow Jones event: Correlation Nation: What happens when all markets and asset classes are in correlation?

As markets trade on headline risk versus pure fundamentals, finding a winner is more challenging than ever before.  Kelly Evans hosts a panel discussion afterwards, with a reception to follow.

NOTE: You need to register for the event to attend.

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To register, sign up here:

Over the years, I have become friendly with Mebane Faber, co-founder and the Chief Investment Officer of Cambria Investment Management. He manages an ETF called the Cambria Global Tactical ETF (GTAA).

Back in 2007, Meb authored an excellent paper titled “A Quantitative Approach to Tactical Asset Allocation.” It was published in the Journal of Wealth Management, Spring 2007.

That analysis tested and reviewed simple timing models, using a 10-month moving average on various asset classes as a signal to enter and exit asset holdings. Compared with traditional “Buy & Hold” investing, the performance improvements across all asset classes were quite significant.  The methodology has the advantage of being objective, unemotional and mechanical (fee free to insert “first wife” joke here).

The timing strategy works as an effective risk management tool that enabled investors to miss most of the fall in major bear markets. It also captured a significant portion of the subsequent rally. Signals were infrequent, not prone to false positives, and avoided “whiplash” – the many false buy and sell signals that typically plague timing systems.

As an example, look at the 2007-09 bear market. It peaked in October 2007 at 14,100, and bottomed in March 2009 at 6,500. Using the 10 Month Moving Average, traders would have exited the Dow Jones Industrials ~12,650 (1/31/08), and avoided the next 6,000 points down. The re-entry was July 31, 2009, at 9100.

Improving the methodology

I describe this quantitative method as symmetrical: The identical signal that triggers exits (markets breaking below their 10-month MA) also triggers entries (markets breaking above their 10-month MA).

However, in my experience, market tops and bottoms, are asymmetrical. They have very different characteristics in terms of timing, investor psychology, trading activity, market breadth, economic factors, and internal metrics. Tops are much more of a process, as buyers slowly lose their ardor for equities. Bottoms are more of an event, as sellers capitulate and dump holdings. Hence, tops develop over longer periods of time, while bottoms tend towards a faster more climactic event.

Thus, a ripe place to explore for possibly improving the original quantitative methodology might be on the entry side. Is it possible to identify a better entry than the 10 month MA?

I have been discussing this with Meb, and we plan on exploring a variety of factors related to (in alphabetical order): Earnings, economic data, Fed funds, insider buying, market price, reversions, sentiment, trend, valuation, volatility, and yield curve. If anything comes of this, we will publish our findings at SSRN or some other suitable journal.

Question: What other metrics are worthy of market timing exploration? If Traders use a 10  month MA as their exit, what might enhance their entry price?

The goal is an entry determined by a systematic, objective, data driven, metric, with modest drawdowns and limited false signals.

Feel free to add any ideas, suggestions, research, metrics . . .

I was pleasantly surprised this morning to see a WSJ article that suggests the SEC is beginning to use the tools of Quantitative Research in its enforcement: SEC Ups Its Game to Identify Rogue Firms. This is a positive step for enforcing the laws governing markets.

Recall 3 years ago, we asked if the SEC did Quantitative Research?

“I would suggest to the incoming head of the SEC to put together a blue ribbon of math professors, quant scientists and algo specialists to develop a few basic programs that ferrets thru market, options, and performance data looking for aberrational data series, and leading to criminals and fraud artists.”

Then again two years ago, during the option backdating scandal, we noted the advantages of using quant tools for law enforcement:

“It also points out the need for the SEC to develop a Department of Quantitative Analysis filled with math geeks and computers, doing nothing but sifting through data looking for investor fraud. I’d bet they would get more convictions than the rest of the SEC combined. (If someone in the SEC would call me, I’ll help you set it up).”

Mathematics provides an ability to sift through mountains of data to find anomalous results — whether you are looking for Alpha or Felons,  it matters not.  I am pleased to see that the SEC is adopting useful, cost-effective techniques. The bottom line is that the prosecutors whoa re charged with enforcing the rules have not been using the most current tools of the trade.

If this process continues to change — prosecutors actually pursuing criminals — perhaps we might begin to see investor confidence return to markets. Yes, this is only a small step — real improvement remains a long way off. But the camel’s nose is now in the tent, with more enforcement tools to follow.

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click for larger graphic

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Previously:
Does the SEC Do Quantitative Research ? (December 13th, 2008)

Mathematical Proof: Companies Manage Earnings (February 13th, 2010)

SEC Budget vs Wall Street Spending (March 9th, 2011)

Source:
SEC Ups Its Game to Identify Rogue Firms
WSJ, DECEMBER 27, 2011
JEAN EAGLESHAM And STEVE EDER  
http://online.wsj.com/article/SB10001424052970203686204577116752943871934.html

FusionIQ Investor
https://www.fusioniqinvestor.com/

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All of the Big Picture conference videos are now available.

Here is the latest video posted: Markets in Turmoil: A Macro, Quant & Technical View

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Watch all of the Big Picture Conference for $39.95 or choose just the speakers you want to see on FORA.tv

James O’Shaughnessy is a well known “value quant” for his book What Works on Wall Street (4th Ed). He has a new column in Marketwatch discussing what he calls  “the top stock-market strategy of the past 50 years.”

According to Jim, using a combination of value and momentum strategies — “Trending Value” — is the best performing strategy since 1963. To capture this, he ranks stocks based on:

• Price-to-Sales
• Price-to-Earnings
• Price-to-Book
• Price-to-Cash Flow
• EBITDA/Enterprise Value
• Shareholder yield (dividend yield + rate of share repurchases)

O’Shaughnessy ranks all of these on a 1-100 basis for his Trending Value portfolio. He works with the top 10% of those ranked stocks with the best composite score. He selects a concentrated portfolio of 25 stocks based on trailing six-month momentum, creating an extremely cheap group of stocks that are on the mend.

Its an interesting ideas, one worth exploring . . .

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Source:
The top stock-market strategy of the past 50 years
James O’Shaughnessy and Patrick O’Shaughnessy
Market Watch, December 16, 2011
http://www.marketwatch.com/story/the-top-stock-market-strategy-of-the-past-50-years-2011-12-16

Dow Jones Industrial Average 1900- present (log scale, monthly)

Click for ginormous chart

Source: Monthly Chart Portfolio, Merrill Lynch Market Analysis, November 4, 2011

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I mentioned yesterday I had a long term chart of secular bear markets that was informative; the above chart (via Merrill Lynch) is what I was referring to.

There are three issues worth noting here plus one important caveat:

1. The long 10-20 year secular bear moves seem to have lots of major rallies and sell offs; the ups and downs are intense, but make little in the way of net progress. After 15 years, the average secular bear is essentially unchanged.

2. The roller coaster ride leaves investors psychologically exhausted. They come to forget the good times of so long ago, and believe there is no way out of the morass. Naturally, they are reluctant to believe in the new bull market once it begins.

3. The major bottom seems to occur about halfway through; this implies that the March 2009 lows will not be revisited (note I only wrote IMPLY and not guarantee or forecast!)  If we look at the current Bear versus the ’66-’82 (with lows like ’73-’74), it suggest that 8500-9000 on the Dow is possible, but barring another crisis 6500 is much less likely. And it also suggests that the next secular bull might begin around 2016-18.

Now for the caveat: We have but one century of data, and within that 100 year span, only four examples of long term secular bear markets. We really need 500-1000 years of data, 20-40 secular bears during the era of modern capital markets. That would allow us greater confidence that these four patterns aren’t merely coincidences.

See you around 2900 to validate the data . . .

What does it say about the state of our exchanges that trader on proprietary and execution desks now can buy a software program to alert them to the activities of Co-Located Algo Servers?

“HFT Alert, the first real time software designed to detect high frequency and algorithmic trading systems. HFT Alert identifies when these trading systems are running and what stocks are being affected. HFT Alert can detect several types of algorithms as well as stocks experiencing elevated quote rates associated with algorithmic trading.”

We are now apparently in a silicon based arms race to learn when quotes are real and when they are spoofed faux quotes driven by HFT algos designed to increase volatility.

The exchanges once operated fro the greater good of the investing public, akin to nonprofit utilities. They are now hellbent on chasing away private investors who will eventually learn that this is a zero sum game, and co-located HFTs are a tax on saving and investments . . .

Video here; Descriptions here, press release here.

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An NYU Poly Department of Finance and Risk Engineering professor has a forthcoming paper in Algorithmic Finance that claims that “Markets are efficient if and only if P = NP.”

Why is this important? Most economists think markets are at least weakly efficient (I disagree).

Computer scientists think that P != NP — that current prices fully reflect all information available in past prices.

This paper claims they both cannot be correct; one must be incorrect. The author’s proof is that they both cannot be correct at the same time, and therefore one must be wrong.

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Abstract:

I prove that if markets are efficient, meaning current prices fully reflect all information available in past prices, then P = NP, meaning every computational problem whose solution can be verified in polynomial time can also be solved in polynomial time. I also prove the converse by showing how we can “program” the market to solve NP-complete problems. Since P probably does not equal NP, markets are probably not efficient. Specifically, markets become increasingly inefficient as the time series lengthens or becomes more frequent. An illustration by way of partitioning the excess returns to momentum strategies based on data availability confirms this prediction.