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Getting an Edge on

Market Efficiency

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There are many cycles driving U.S. markets ... quarterly earning cycles, triple witching cycles, Federal Reserve meetings, weekly cycles, political election year cycles, the annual end-of-year stock dumping cycle, sunspot cycles (which affect agricultural productivity), and the slow Kitchin (3-5 years), Juglar (7-11 years), Kuznet (15-25 years) and Kondratieff (45-60 years) cycles, and more. So it should be easy to figure out when to trade ... right?

Do you remember late night commercials promoting how "easy" it is for anyone (including truck drivers and busy housewives) to simply pick up the phone and make huge profits trading commodities or securities? Predictable market cycles was supposed to make it easy for any dummy, um... prospective client, to buy and sell. Yet, over 90% of all individual traders wipe out their account in under 5 years. Why?

The flaw here is that you are convinced to buy from
someone equally convinced that it is better to sell.
And he knows all about those cycles too.

Although cycles are predictable, market players readily discount their presence as far ahead in time as is reasonable. There is not much left in these cycles for players to exploit. And so, with respect to fundamental cycles, the markets are said to be "efficient". By that, we mean prices reflect all available cycle information.

In 1900, Louis Bachelier made an important contribution to the formalism of classical economics with a theory that says trading strategies based only on observed price changes cannot succeed. Markets are moved by news and since, by definition, news cannot be predicted (or it would not be news), price movement cannot be anticipated. Consequently, price data are not linked and price series follow a geometric Brownian random walk, whereby market prices are log-normal distributed, i.e. the differences of the logarithms of prices are Gaussian distributed.

Bachelier also proposed there are roughly 3 degrees of market efficiency:

  • WEAK FORM EFFICIENCY: No investor can earn excess returns by developing trading rules based solely on historical price or return information.

  • SEMI-STRONG-FORM EFFICIENCY: No investor can earn excess returns from using trading rules based on any publicly available information.

  • STRONG FORM EFFICIENCY: No investor can earn excess returns using any information whether publicly available or not.

The problems with Market Efficiency and Brownian price motion are:

  • The tails of measured distributions are fatter than theory predicts.
  • The second moment of price changes fluctuates with time.
  • Various aspects of market behavior are predictable.
  • Certain strategies can earn excess returns, year after year.

How is this possible?

"I'd be a bum in the street with a tin cup if the markets were efficient." --- Warren Buffett, Fortune, 1995

There is more to market behavior than efficiently discounting cycles. In fact, the fundamental assumption that all players are rational, fully informed and have optimal trading strategies is flawed. Recent work in behavioral economics and finance research offers new insight into why markets are inefficient. Here are just a few observations:

PLAYER BEHAVIOR:

  • Informed traders are constrained by risk aversion and the limits of arbitrage. Consequently their influence on the markets are limited.
  • In bull phases, futures trading is less based on news and more on realizing paper profits and portfolio rebalancing.
  • Players tend to hold losing investments too long and sell winning investments too soon.
  • They engage in tax-loss selling activity, mostly in December, which is not optimal.
  • Overconfidence in the quality of one's analysis delays critical decision making, induces excess "noise" trading, and reduces diversity.
  • Changing a fund's name yields an average cumulative abnormal flow of 28%, even with no improvement in performance, suggesting investors are irrationally influenced by irrelevant events when searching for stocks to buy.
  • Individual traders tend to chase trends, trade to reduce regret and have limited attention.
  • Historical price information (e.g. being at a monthly high or low) is also news, and traders respond to it as well as to other forms of news. For example, they are attracted to abnormal trading volume and extreme price moves. Collectively, traders forge weak links between past price data and current price movement, making price motion not Brownian.
  • The most consistent pattern across most markets is the well-known building of positions by index traders about 75 days prior to expiration and the rapid liquidation of these positions around 25 days to expiration.

GROUP DYNAMICS:

  • When traders respond to market events, they are closing a feedback loop. The actions of individual traders collect to produce changes in the market, and those actions prompt a collective response.
  • If one trader needs to sell, others can engage in predatory trading whereby they also sell and subsequently buy back the asset. This leads to price overshooting and a reduced liquidation value for the distressed trader. Hence, the market is illiquid when liquidity is most needed. Further, a trader profits from triggering another trader's crisis, and the crisis can spill over across traders and across markets.
  • If arbitrageurs are not well capitalized, capital constraints make their trades predictable. Other market players can exploit this by trading against them. This behavior may produce significant price distortions, increase price manipulation, and trigger forced liquidations of large traders.
  • The behavior of irrational players creates volatility in price that deters rational arbitrageurs from aggressively betting against them. This is exacerbated when arbitrageurs are temporarily unable to coordinate their counter-strategies. Combined with everyone's individual incentive to time the market, irrational players can make prices diverge significantly from fundamental values and persist over a substantial period.

MARKET BEHAVIOR:

  • IPO offerings cluster at market peaks, and can be used to predict market downturns.
  • In most commodities, prices had a tendency to trend upwards during the final days of a contract’s life, suggesting that prices were not consistently inflated above fundamental value.
  • When there is large disagreement between analysts on a stock's value, the stock's price continues to fall until the analysts are in close agreement.
  • Stocks that have had unusually high past returns or good earnings news continue to deliver relatively strong returns over the subsequent 6 - 12 months (and visa-versa for stocks with low past returns or bad earnings news). In addition, this trend reverses into the opposite direction over the subsequent 1-3 years.
  • Stocks heavily bought on news by individual investors in one week tend to outperform for 2 weeks, then underperform for several months. Stocks heavily bought for one year tend to underperform the next year.
  • Strategies involving a long position in high performance stocks (on the basis of performance over the previous 3-12 months) in any developed market (US, European, Asian) combined with a short position in low performance stocks in the same regional market tend to produce significantly positive returns of about 1% a month for the following year.

These observations run counter to
the notion of an unpredicatable market,
moving with a random walk.

Collectively, strategies operate across a wide range of time frames, from minutes to years. The cumulative effect is a different kind of price motion. As early as 1963, using data from cotton prices, Benoit Mandelbrot analyzed the statistical properties of market pricing and concluded that price changes in financial markets are discontinuous and follow a scaling law. This ushered in the new field of "Econophysics", whose discoveries include a very reliable scaling law formula. (Right-click to download PDF file)

Today there is no doubt that historical market data does not support the theoretical model of classical economics. The markets exhibit forms of predictability and thus, inefficiency. The question now becomes ... how does one exploit it for profit?

Two aspects of market motion
make exploitation difficult:
NOISE and LAG.

The prevalence of computers among players has changed everything. In comparing today's price motion to that of 20 years ago, we see increased volatility with respect to trend duration, a phenomena we refer to as NOISE. This slows down technical analysis, creating LAG in the decision making process. Noise and the consequent lag have made the timely detection of trends, reversals and simple price congestion more difficult. It is in this sense that markets appear efficient.

Yet not all is lost because this apparent efficiency affects all players. And this time around there's lower trading costs, improved execution speed, easier access to data, and we all have computers. In this more level playing field, victory goes to those having better analysis and faster timing. In other words, you need an edge.

This implies, of course, that you have ...

  • a trading system with profitable expectation,
  • sound money management principles (e.g. asset allocation),
  • the psychological fortitude to trade consistently, and
  • adequate capitalization.

"Profitable expectation" involves knowing not only when to enter/exit, but also how much to place at risk. Timing and risk management require being able to see through market NOISE with more clarity and less LAG than ever before. And that requires analytical tools displaying low noise and low lag. Yes, it is possible to get an edge. Just look at all the TESTIMONIALS we received.















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