CASTrader Blog

Quant Turmoil and Volatility Harvesting

Some quant funds have had heavy losses (WSJ) just in the past few weeks.  Some so-called market neutral funds have been hit hard according to this MarketWatch article (emphasis mine):

Black Mesa Capital, a hedge-fund firm that uses computer models to track down investment ideas, said that at least one large hedge fund or investment bank is liquidating "massive" trading portfolios, according to a letter the Santa Fe, N.M.-based firm sent to investors Wednesday.

The warning is causing disruptions and triggering big losses among other so-called market-neutral hedge funds, Black Mesa said in its letter, a copy of which was obtained Thursday by MarketWatch. "Clearly, something is amiss in the markets that few in our strategy, if anyone, have experienced before," Black Mesa's managers, Dave DeMers and Jonathan Spring, wrote. DeMers declined to comment Thursday.

The firm's hedge fund, which has about $1.9 billion in long positions and $1.9 billion in short positions, was down roughly 7.5% this month through Aug. 7. Those losses could grow to as much as 10% for August so far,

The article continues:

Two hedge-fund investors who didn't want to be identified said Thursday that the current turmoil is reminiscent of the collapse of Long-Term Capital Management in 1998.

Update: More via Calculated Risk.  More on Goldman Alpha.

Update II: More on the quant fund pain with an in-depth commentary on what it means, and this quote Bear Stearns-esque quote from David Faber/CNBC: "This is the worst 5 days that [quant funds have] seen in the last 20 years.”  Tykhe capital reportedly loses 20% in August alone.

Market neutral strategies are often long a massive pile of stocks and nearly equally short a massive pile of stocks as Black Mesa is (was?), hoping their strategies pick good longs and shorts.  In a downturn like the past couple of weeks, these funds should in theory not even notice it, yet it would appear they are clocking losses equal to or exceeding the market.  Alpha has suddenly turned to magnified beta.

Update III:  A Lehman analyst has a theory what's going on with the quants.  Update IV: the first week with a 2% up and 2% down day since March 2003.

Volatility Harvesting.  Volatility harvesting methods like the Shannon Method that I currently follow for my own investments typically profit from the normal ups and downs of the market, as long as you aren't trying to catch any falling knives.  These methods buy on the way down and sell on the way up.  Since the market peak yesterday afternoon through close today, some very unusual events have occured.  While the approximately 3-4% drop in the markets over that period would normally trigger 4-5 trades, likely all buys, I've seen 18 trades, with sells nearly equalling buys and my portfolio tracking the market.   What this means is that there is a huge amount of turmoil in many of the individual stocks I track, but it's not all down.  This is consistent with a long/short market neutral unwinding described above, and it's the most unusual period I've seen since running this method beginning Summer, 2003.  Perhaps my portfolio is just unusally concentrated in stocks the quants play with.

A New Indicator.  It's a phenomenon that needs it's own indicator, in my opinion.  Call it the "unwinding indicator" or something similar, but there isn't a good indicator to track this phenomenon that I'm aware of.  Certainly volatility measures it indirectly, but it doesn't fully quantify what's going on under the surface and is somewhat tainted by market directional moves.  Perhaps the ratio of the number of 1% days for a market's components vs. the market would capture the turmoil.  High ratios would indicate unwinding, low ratios would indicate levering.  If possible, volume would be incorporated as well.

Update:  Credit Unwinding, and the carry trade unwinding that happened before.

Hey Senator, got any stock tips?

I stumbled across an older paper from 2004 (PDF):

(emphasis mine) "The actions of the federal government can have a profound impact on financial markets. As prominent participants in the government decision making process, U.S. Senators are likely to have knowledge of forthcoming government actions before the information becomes public. This could provide them with an informational advantage over other investors. We test for abnormal returns from the common stock investments of members of the U.S. Senate during the period 1993--1998. We document that a portfolio that mimics the purchases of U.S. Senators beats the market by 85 basis points per month, while a portfolio that mimics the sales of Senators lags the market by 12 basis points per month. The large difference in the returns of stocks bought and sold (nearly one percentage point per month) is economically large and reliably positive."

With the top 25 Senators worth between $2.6M to $234M, that's probably some fairly hefty gains.  If the top 25 Senators pooled their 2005 assets, they could open a hedge fund worth between $760 million to $1.4 billion - not too shabby.  With 12% + market outperformance, they would no doubt attract additional capital and hefty management fees.  The study compares the results of their study to other studies of common groups of shareholders:

• Average US household underperform the market by 12 basis points a month
• Corporate insiders outperform by 50 basis points per month
• Senators outperform by nearly 100 basis points per month

The Senators seem to not just be good stockpickers, but good timers as well:

"Cumulative abnormal returns for the portfolio of stocks bought by Senators are near zero for the calendar year prior to the date of purchase. After acquisition,the cumulative abnormal return rises over 25% within one calendar year after the purchase date. The cumulative abnormal returns for the portfolio of stocks sold by the Senators are near zero for the calendar year after the date of sale. However,these same stocks saw a cumulative abnormal positive return of 25% during the year immediately preceding the event date. These results suggest that Senators knew appropriate times to both buy and sell their common stocks."

Lest you think you might put together a lazy portfolio of Senate stockpicks, the data only appears to come out once per year and appears to be pretty stale by then, but the next report for 2006 is due May 15.   Opensecrets carries the Senator's stock picks.

Northern lights and the stock market

This perhaps wins the award for one of the most bizarre yet fascinating academic finance papers (PDF) regarding environmental effects on the stock market I have seen yet (the paper is also at SSRN).  What's more, it provides a direct link between the field of behavioral finance and econophysics, while at the same time potentially providing several stabs in the "death by 1000 cuts" dismantling of the Efficient Market Hypothesis (EMH).  It's an older paper (2003), but searching indicates it hasn't really been reported on in the blogging or mainstream financial world, so I thought I'd give it a shot.

The paper by the husband and wife team of Krivelyova and Robotti proposes that the same Geomagnetic Storms (GMS) from the sun that cause the spectacular northern lights or Aurora Borealis (image courtesy US Air force via Wikipedia  Update: video) also impacts the stockmarket in a negative way.  It turns out that there is a rational explanation here: GMS are well-documented to have all sorts of impacts on human mood and behavior.  For instance, one study found that hospital admissions for depression are 36.2% higher when GMS activity is high.  The authors cite another study that found that the average number of hospital patients with mental and cardiovascular increases two times over quiet periods.  The authors logically make the connection that with all this going on, people would be down on the market during GMS.  So how much of an effect did they find, and is it real or just butter in Bangladesh style datasnooping?  The authors say:

"This effect is statistically and economically significant, and seems to generate some trading gains. For the US, the GMS effect is similar across indices, ranging from -0.84 to -2.51 percent of average annual returns. We also document a more pronounced GMS effect in the pricing of smaller capitalization stocks. We rationalize this finding by noticing that institutional ownership is higher for large cap stocks, while small cap stocks are being held mostly by individuals."

They say GMS impacted about 10% of the study period.  GMS cycles aren't perfectly correlated with sunspot cycles, but they appear to be related.  The authors go on to say (emphasis mine):

"The initial phase is associated with compression of the magnetosphere, resulting in an increase in local intensity. This lasts for 2-8 hours. The main phase is associated with erratic but general decreases in background field intensities. This phase lasts for 12-24 hours and is followed by a recovery period that may require tens of hours to a week. Geomagnetic storms are predictable and persist for periods of two to four days. On average, we have 35 stormy days a year with a higher concentration of stormy days in March-April and September-October."

Related Reading:

Sun Downer: More about the authors and their findings.

GMS Data, even predictions of GMS.

Winter Blues: A SAD Stock Market Cycle:  "Patterns at different latitudes and in both hemispheres provide compelling evidence of a link between seasonal depression and seasonal variation in stock returns: Higher latitude markets show more pronounced SAD effects and results in the Southern Hemisphere are six months out of phase, as are the seasons. Overall, the economic magnitude of the SAD effect is large."

Stock Market Returns: A note on the temperature anomaly (PDF): "Research in psychology has shown that temperature significantly affects mood, and mood changes in turn cause behavioral changes....We examine many stock markets world-wide and find a statistically significant, negative correlation between temperature and returns across the whole range of temperature."

Would you believe Lunar Cycle effects in stock returns?

Stock returns and the weather: "Sunshine is strongly positively correlated with daily stock returns. After controlling for sunshine, other weather conditions such as rain and snow are unrelated to returns. If transactions costs are assumed to be minor, it is possible to trade profitably on the weather. These results are difficult to reconcile with fully rational price-setting."

Spaceweather.com

NASA's Solor and Heliospheric Observatory with this movie of the sun (MPEG)

Suicide and GMS

Cardiac Arrhythmia and Geomagnetic Activity

Sunspots and clouds

Geomagnetic Storms and El Nino (PDF)?  "The 3 to 6 year SML cycles also show a surprising agreement with the naturally occurring climate cycles known as El Nino/Southern Oscillations (ENSO). On the basis of SSC and El Ni˜no records from 1968 to 2003, it is found that a warm ENSO phase (El Ni˜no) takes place at about the middle of each of seven identified SML cycles."

Sunspots and global warming

Trading Patterns linkfest - supersized!

A Quantitative Approach to Tactical Asset Allocation (via Covel) "The purpose of this paper is to present a simple quantitative method that improves the risk-adjusted returns across various asset classes. A moving-average timing model is tested in-sample on the United States equity market and out-of-sample on more than twenty additional domestic and foreign markets...The empirical results are equity-like returns with bond-like volatility and drawdown, and over thirty consecutive years of positive performance."

Guru Lazy Portfolios:  "...virtually all the Guru portfolios have about the same characteristics, notably not acting much differently than a simple 60/40 stock bond allocation. The endowment method produces returns that are equity like, with bond like risk and drawdown measures. As is often missed in the asset allocation debate, the golden key is finding truly uncorrelated asset classes."

Spamming Stocks is Profitable.

Global Economic Investment Cycle Clock.

Profit with Split-offs.

Beating the Dow with Dogs, Flyers, Bonds, and Darlings

Mean reversion strategies: "What happens to an asset class after after it experienced down years both 2 and 3 years ago? Since 1972, across the six asset classes, that has occurred 24 times. 24 out of 24 times the return has been positive. 100% accuracy. Average return? A whopping 24%."

When Congress goes to work it's time to sell  "This “Congressional Effect” can be quite large - more than 90% of the capital gains over the life of the DJIA have come on days when Congress is out of session."

The relative valuations of small vs. large caps

NYSE margin hits all-time record "Margin debt, as the borrowing is called, in January broke the prior high set at the peak of the so-called Internet bubble."

Sector Performance Leading Up to the Start of Bear Markets:  "As the major equity indices have made significant declines in recent weeks, some have speculated that we are in the early stages of a bear market. Historical precedent however, would refute that view."

The Little Stock that beats the Market: "They take the top 25 stocks where the hedge funds own the highest % of shares outstanding. ...they found the strategy significantly outperformed the indices over the 1999-2006 time period. The results are a bit fantastic (500% total return for the strategy vs. ~ 20% for the S&P over the same time period)..."

Gold/Dow Ratio

Trading a platinum-gold seasonal spread:  "This spread earned an average of $6,600 every year since 1995. We earned $15,400 in the best year, while in the worst year we lose only $3,810. With a margin requirement of only $743 for trading this spread at NYMEX...What is the fundamental reason this seasonal spread works? Amusingly, it has to do with the end of the Chinese New Year."

If the Number of Net/Nets is a Contrary Indicator, We're in Trouble!  "It's slim pickings among the ranks, and we have not seen it this "bad" since our research began in this area several years ago."

Book Value Per Share - More Useful than Earnings Per Share?  "...companies may resort to short-term decision-making to boost quarterly EPS. ... But book value is a long-term thing and cannot be easily hidden."

T2108 Indicator and the December Low Indicator:  "When the Dow closes below its December closing low in the first quarter, it is frequently an excellent warning sign." Hulbert is not worried.

NYSE TICK Volume: Tracking The Sentiment Of Large Traders:  "One of the strengths of TICK Volume as a measure is that it captures short-term breakouts in sentiment quite nicely.."

What historic low volatility means for the stock market:  "the notion that range-bound, non-volatile markets are "due" for a breakout just doesn't hold water. ...non-volatile periods tend to be followed by non-volatile periods, not dramatic breakouts....no bear market--including those of 1987, 1990, 1998, and 2000--has begun during a period of ultra-low volatility."

Index arbitrage with XLE

Normalized P/E Ratios

The Bearish Equity Put-Call Ratio: What To Make Of Extreme Options Sentiment:  "Going back to 2001, such fear has had bullish implications. When we've had a single put/call reading above 1.0 (N = 44), the next 20 days in the S&P 500 Index (SPY) have averaged a gain of 2.09% (33 up, 11 down). That is quite a bit stronger than the average 20-day gain of .16% (899 up, 628 down) for the entire sample."

Trading Pattern Links

Note: this is an old post from last November that I never got around to finalizing.  The first link below is interesting, given recent events.

In 3 signs that a stock crash is coming, Michael Brush looks at:

• the SPY/TLT ratio (stocks to 20+ year bonds): The current ratio has moved up to 1.58, compared with an average of 1.5 over the past 90 days...In the three months after such an extreme reading, the performance of the S&P 500 has ranged from a loss of 8.7% to a gain of just 1.7%.
• mutual fund cash levels: Since 1950, whenever cash shortfalls hit these lows, the S&P 500 has fallen 69% of the time with an average decline of 4%. Ominously, the last two times cash levels were this low, bad things happened to stocks. Cash levels hit these lows in early 2000 just ahead of the last big bear market. Cash also hit current levels in early 1981 just before a two-year market slump.
• "Smart" money:  Right now, commercial traders have a $30 billion net short position in futures on the S&P 500, the Dow Jones Industrial Average and the Nasdaq Composite Index...This is only the third time in recent history that this short position has been so large. The other two times were early 2001, just before the S&P 500 tumbled 38%, and November 2004, after which the market rose some more and then corrected in early 2005.

The best economic indicator you've never heard of (the Baltic Dry Index)  via Kirk Report.

From CXO Advisory Group: Which financial and economic variables best predict stock returns over the short, intermediate and long terms? Is "best" good enough for market timing?  Conclusion:  In summary, over the last 80 years, a few price-normalized variables (price-dividend, price-earnings, price-output and price-consumption ratios) and the approximate consumption-aggregate wealth ratio have been the best stock market indicators.

Thomas Cover's Universal Portfolio - Part II, Further Reading

In Part I, I briefly discussed Thomas Cover and the Universal Portfolio.  There's lots of detailed reading material on the Universal Portfolio Method, including in Cover's publication list.  Cover has a book on Information Theory that contains a chapter on Information Theory and the stock market (that chapter from the first edition appears to be online here).  Ernest Chan provides an easy to read description.  Further reading:

• Universal Portfolios With and Without Transaction Costs (1997).  For the universal algorithm of Cover (Cover, 1991), we provide a simple analysis which naturally extends to the case of a fixed percentage transaction cost (commission), answering a question raised in (Cover, 1991; Helmbold et al., 1996; Cover and Ordentlich, 1996a; Cover and Ordentlich, 1996b; Ordentlich and Cover, 1996; Cover, 1996). In addition, we present a simple randomized implementation that is significantly faster in practice.
• Can we learn to Beat the Best Stock? (2003).  A novel algorithm for actively trading stocks is presented. While traditional universal algorithms (and technical trading heuristics) attempt to predict winners or trends, our approach relies on predictable statistical relations between all pairs of stocks in the market. Our empirical results on historical markets provide strong evidence that this type of technical trading can “beat the market” and moreover, can beat the best stock in the market. In doing so we utilize a new idea for smoothing critical parameters in the context of expert learning. 
• Universal Portfolios and the Hindsight Bias Issue: A New Perspective?    As this paper involves the application of the Universal Portfolio concepts of Cover ([1][2]) to the Hindsight Bias issue, we begin by introducing each of these individually, beginning with the latter. The Hindsight Bias problem, often also referred to as the ’Data Mining’ problem, occurs when first, many possible models to describe an aspect of the past are posited, and then the bestfitting of these is selected a posteriori (i.e., after the fact). When a model thus selected is then applied as a model for the future, the results tend to be disappointing...
• Efficient Algorithms for Universal Portfolios (2002).  There has been much work on Cover's Universal algorithm, which is competitive with the best constant rebalanced portfolio de- termined in hindsight (Cover, 1991, Helmbold et al, 1998, Blum and Kalai, 1999, Foster and Vohra, 1999, Vovk, 1998, Cover and Ordentlich, 1996a, Cover, 1996c). While this algorithm has good performance guarantees, all known implementations are exponential in the number of stocks, restricting the number of stocks used in experiments (Helmbold et al, 1998, Cover and Ordentlich, 1996a, Ordentlich and Cover, 1996b, Cover, 1996c, Blum and Kalai, 1999). We present an effcient implementation of the Universal algorithm that is based on non-uniform random walks that are rapidly mixing (Applegate and Kannan, 1991, Lovasz and Simonovits, 1992, Frieze and Kannan, 1999). This same implementation also works for nonfinancial applications of the Universal algorithm, such as data compression (Cover, 1996c) and language modeling (Chen et al, 1999).
• A statistical view of universal portfolios (2005)  In this thesis, we provide a statistical view of universal portfolios in order to develop a clearer understanding of their performance on actual financial data sequences. By recasting the analysis of a universal portfolio in statistical terms;with a special emphasis on statistical estimation;we are able to resolve a long standing and false perception of a disconnect between information theory and empirical finance.

There's even a method that claims to outperform the Universal Portfolio:

• Switching Portfolios.  Recently, there has been work on on-line portfolio selection algorithms which are competitive with the best constant rebalanced portfolio determined in hindsight [2, 6, 3]. By their nature, these algorithms employ the assumption that high yield returns can be achieved using a fixed asset allocation strategy. However, stock markets are far from being stationary and in many cases the return of a constant rebalanced portfolio is much smaller than the return of an ad-hoc investment strategy that adapts to changes in the market. In this paper we present an efficient portfolio selection algorithm that is able to track a changing market. We also describe a simple extension of the algorithm for the case of a general transaction cost, including a fixed percentage transaction cost which was recently investigated [1]. We provide a simple analysis of the competitiveness of the algorithm and check its performance on real stock data from the New York Stock Exchange accumulated during a 22-year period.  On this data, our algorithm outperforms all other algorithms mentioned above both with and without transaction costs.
• Nonparametric Kernel-Based Sequential Investment Strategies (2006). The purpose of this paper is to introduce sequential investment strategies that guarantee an optimal rate of growth of the capital, under minimal assumptions on the behavior of the market. The new strategies are analyzed both theoretically and empirically. The theoretical results show that the asymptotic rate of growth matches the optimal one that one could achieve with a full knowledge of the statistical properties of the underlying process generating the market, under the only assumption that the market is stationary and ergodic. The empirical results show that the performance of the proposed investment strategies measured on past NYSE and currency exchange data is solid, and sometimes even spectacular. (See also a related slideshow)

A general slideshow on information theory and investing.

Thomas Cover's Universal Portfolio - Part I, Introduction

Another trading methodology similar to Shannon's Method (aka Shannon's Demon) is the Universal Portfolio method, advocated by Thomas Cover, a professor at Stanford.  Interestingly, he was a Shannon Award winner (the highest award in information theory) along with Elwyn Berlekamp.  Cover's methodology is implemented by Mountain View Analytics, which he is involved with.  The Cover method is probably best described in an old (2000) Stanford news article:

To create a universal portfolio, the investor buys very small amounts of every stock in a market -- no small task in itself. The New York Stock Exchange, for example, lists 3,025 companies. In essence, the universal investor mimics the buy order of a sea of investors using all possible "constant rebalanced" strategies, in which the amount of money invested in each stock is adjusted each day to achieve a fixed proportion.

...

"Imagine we have, for a simple example, two stocks," Cover explains. "A good constant rebalanced portfolio might invest, say, one-fourth in one stock and three-fourths in the other. At the end of the day, the wealth you have in each stock would not be exactly one-fourth, three-fourths because the prices of the stocks change, so you would do the necessary buying and selling to restore it to one-fourth, three-fourths."

Cover's universal portfolio algorithm invests uniformly in all constant rebalanced portfolio strategies. The result is a strategy that is nearly optimal. Cover has shown, for any sequence of stock market outcomes, that this mixture of investments has as high a compound growth rate in the long run as the best constant rebalanced portfolio. Over time, the best strategy (that is, the best constant rebalanced portfolio) fights its way to the top of the fiscal food chain.

...

One aspect of information theory is data compression. "The beauty of it is, the mathematics of growth-rate-optimal investment turns out to be parallel to the mathematics for optimal data compression," Cover says. Thus universal investment algorithms are a counterpart to the universal data compression algorithms used to compress voice, fax and computer files.

...

"This is an automatic investment algorithm in the stock market," Cover says. "The portfolio rides the stocks and lives off the fluctuations. It essentially puts a little bit of money on every possible rebalanced investment algorithm, and the surviving algorithms -- the ones that made most of the money -- make enough so that your money grows at the same rate as if you had used the best algorithm to start with."

The algorithm is "somewhat ponderous," Cover says. "The performance of the algorithm, although good relative to the best portfolio in hindsight, is still slow in responding in an absolute sense. It sometimes requires hundreds of days before the initial conditions wash out, leaving the 'fittest' rebalanced portfolio dominating the performance. It's guiding thinking, but no one's making money off it yet."

The Universal Portfolio has quite a bit in common with the Shannon Method:

• It's a Constantly Rebalanced Portfolio (CRP), meaning it it is an extension of Kelly's optimal long term growth rate formula. 
• The CRP thrives on volatility.  In fact, Thomas Cover coined the term "volatility pumping" to describe the way it makes money.  I think "volatility harvesting" is a good description as well.  In a crude sense, it's turning beta into alpha.

The Universal Portfolio is a lot more complicated than the Shannon Method, but I gather by what Cover says, it is proven to outperform it.

In Part II, I'll list some additional detailed reading on the Universal Portfolio.

Traders: Are you a predator or prey?

Is your trading system acting like a predator or prey in the markets?  This is an exploration of two hypothetical trading system types.  This isn't about who wins and who loses, necessarily.  In this thought exercise, both predator and prey can "win."  I'm guessing that chances are, even if your system is a winning one, it is a "prey-type" system.  CASTrader as it stands now, is a prey-type system.

What is a prey-type trading system?  A prey-type system is a system that is developed purely based on historical market patterns and perfected assuming market conditions will not be altered by your actions.  You are reacting to market history, hopefully profiting from the patterns the market hands you.  If you are working with a small amount of capital, you can happily graze on the profits you find.  However, if you overgraze with a bigger bankroll, you may find the very patterns you hoped to profit from are gone.  Either they never existed in the first place (due to datasnooping), or you've eliminated them.  The pitfalls of backtesting are many, but it is the only logical way to test out a prey-type system.  Most testing software I am aware of calculates profits assuming market conditions will not be altered.  A prey-type system generates profits from existing unclaimed price patterns.

Ok, what is a predatory trading system.  A predatory trading system would be a system that aims to profit from market changes that it induces in the market.   On the Great plains of North America, indigenous peoples once guided buffalo herds towards what's known as a buffalo jump.  (At left is the appropriately-named "Head-Smashed-In" buffalo jump - image courtesy of Wikipedia).  In other words, the buffalo were induced off the cliff by the actions of their predators preying on the behavioral biases of the buffalo.  Sane buffalo obviously do not jump off cliffs on their own.  A predatory trading system profits from induced price patterns.  In other words, it makes a market-moving trade that induces sufficient favorable liquidity to get out with a profit.  If the market is a chaotic system, this might be the butterfly effect that you can predict, and thus profit from(Update: PRD has more on Chaos and the financial markets) .  Unlike a prey-type system, a predatory system cannot be backtested, at least in the standard way.  Conventional backtesting systems that I am aware of do not attempt to measure how your system alters the market.  Prey-type backtests are based on the assumption of infinite liquidity, which isn't a bad assumption for small traders.  In fact, a perfect backtest could not possibly be performed on a predator system because you cannot possibly know apriori the exact consequences of your actions.

Who are the predators and who do they prey on, exactly?  Well, to wring out the last drops of cliche from the predator/prey analogy: the weak, oblivious, or simply unlucky prey.  Prey-type systems are numerous and diverse - predators wouldn't capture them all, so some survive, even thrive.  Others simply see their profits vanish, greatly reduced, or worse: go negative.  It's certainly happened before on a grand scale in other markets with disastrous results for the predators.  Naked shorting is predatory, in my opinion.  Predator systems at the day-in/day-out level however, if they exist at all, are probably rare, but well-funded.  It's pure speculation on my part that such systems exist and are profitable in the stock market, as I am not aware of any.   Then again, I don't hang out on Wall Street much.  Perhaps market-makers know, since they have the ability to alter the bid-ask, and I'm pretty sure they are well-aware of the many nuances of what happens when they do while they provide liquidity for profit.  Maybe the Super Quants have used high frequency predatory algorithms.  When a quantitative fund can account for 10% of all the transactions on the NASDAQ in a single day, would you guess they are using prey-type or predator algorithms?  Maybe some traders simply do it in their heads by looking at the order book.  Are you predator or prey?  If you are prey, are you being hunted and are you nimble enough to escape?

Update: Move the Markets offers more perspective in You actually do move the markets.

Elections, politics and the markets weekend linkfest

A Merrill Lynch Analysis finds gridlock good for the market and examines it's impact on sectors (via The Kirk Report).  Even more to the point, CXO Advisory examines a paper that finds, among other things, that "stock index volatility is lower when Congress is not in session," and "annualized returns are therefore 2%-6% higher when Congress is out of session."

Financial Sense has some detailed statistics on elections and market performance.

Barry Ritholtz examines some studies and data.

Automated political risk analysis - "Researchers at Harvard and Ohio State University have been working for the past 6 or so years on an automated Political Crisis Early Warning System that anticipates the worst case scenario --internal armed conflict."

Econoblog looks at how election shakeups affect the economy.

Election prediction markets and how they react to political blunders.

What really wins elections: "An uncanny correlation exists between social mood trends and the success or failure of incumbent politicians, and this relationship doesn't end at The White House. Social mood affects everything from the elections for U.S. Congress and governors' offices to your city council and school board. "

Quant Investor does a pre-election analysis.

CXO Advisory asks "is there a significant positive correlation between company campaign contributions and stock returns?"  Among their conclusions: "The number of candidates a company supports is a significant positive indicator of the company's future stock returns. A one standard deviation increase in the number of candidates supported indicates a 0.5% increase in monthly returns. This effect is larger than the value premium and the size effect, and is about as large as the momentum effect."  Their bottom line conclusion: "In summary, investors might want lean toward companies that contribute to lots of political candidates (especially Democratic candidates for the House of Representatives)."

The Presidential Year Cycle: "In summary, there appears to be some connection between the presidential term cycle and stock market performance, with Year 3 the best and Year 1 the worst."

The Halloween Effect Linkfest: Real or not?

Is it time to buy the Halloween Effect?

The Halloween Effect in US Sectors:

All US stock market sectors and industries perform better during winter than during summer in our sample from 1926-2005. In more than two-third of all sectors and industries this difference in summer and winter returns, known as the Halloween effect, is statistically significant and in half of all sectors and industries risk premia are negative during summer. However, while all sectors and industries show this effect, there are large differences across sectors and industries. The effect is almost absent in sectors related to consumer consumption but strong in production sectors. We illustrate how these differences between sectors might be used to improve the risk return trade off using sector rotation based on Fidelity sector funds and show how an investor might have benefited from such a trading strategy.

The Halloween Effect and Japanese Equity Prices: Myth or Exploitable Anomaly?

Bouman and Jacobsen (2002) examine monthly stock returns for 37 world stock markets for the period January 1970-August 1998. They report that returns are significantly higher during the November-April periods versus the May-October periods in 36 of 37 markets examined and label this phenomenon the Halloween puzzle. These results conflict with those predicted by the efficient market paradigm. Using United States monthly return date, Maberly and Pierce (2004) report that, in terms of statistical significance, the Halloween puzzle is not robust to alternative model specifications. In particular, after adjusting for the October 1987 Crash and the August 1998 failure of Long-Term Capital Management, the Halloween puzzle becomes statistically insignificant at a meaningful level.

A seasonal timing strategy:

We discovered that those best days on average are October 16 for the entry into the market for its favorable seasonal period, and April 20 for the exit from the market’s favorable season. However, those are just the best days as averaged over a very long time period. Obviously the market does not begin a rally on the same day each year, or begin to decline from a top on the same day each year. So we then concentrated on determining a means by which the entries and exits could be more accurately pinpointed as they change each year.

The strategy uses MACD to time entries and exits. 

This paper claims the Halloween Effect is simply due to a few "outliers," and Efficient Markets are safe:

On re-examination, the documentation of a Halloween effect in the U.S. disappears after an adjustment is made for the impact of outliers, in particular the largemonthly declines for October 1987 and August 1998 associated with the stock market crash and collapse of the hedge fund Long-Term Capital Management, respectively. For the U.S., the empirical evidence indicates that the Halloween effect is not an exploitable anomaly, and this is true for both spot and futures prices. However, in bear market years there exists anecdotal evidence that most of the negative decline in equity prices occurs during the May-October periods.

From SafeHaven:

According to the Stock Trader's Almanac, if you invested in the Dow from November through April, and switched to fixed-income investments from May to October, over a 54-year period (1950-2004), your returns would have been quite dramatic. Using this strategy, a $10,000 investment in the Dow compounded from 1950-2004 produced a $492,060 gain in the November - April period, versus a loss of $318 for the May-October period.

Hmmm, you decide, but have a Happy Halloween!