I have read so many trading books that say you need to understand your edge in trading. They all go on to describe how to measure the size of your edge by looking at the expectancy of your system, but so few of these books fail to describe how to find an edge or where the edge for their recommended trading strategies actually comes from. In this posting, I will try to define where my edge in trading comes from. I have read so many trading books that say you need to understand your edge in trading. They all go on to describe how to measure the size of your edge by looking at the expectancy of your system, but so few of these books fail to describe how to find an edge or where the edge for their recommended trading strategies actually comes from. In this posting, I will try to define where my edge in trading comes from.In trying to identify where a trader’s edge comes from, there are two key questions you need to answer:
Is the market too random to make money?
Is the market too efficient to make money?
If your answer is “yes” to either of these questions, then you have no business trading. If you can explain why the markets are neither random nor efficient, then you may have the basis for a tradeable edge.
Randomness: Is the random walk hypothesis a crock?
“Luck is what happens when preparation meets opportunity” – Seneca
The random walk hypothesis is a financial theory stating that market prices evolve according to a random walk and thus markets cannot be profitably traded. The random walk hypothesis implies that you could construct a price series by flipping a coin to figure out if the next bar on the chart is likely to be higher or lower than the previous bar (or some other statistically richer techique), and there is no way to tell the difference between a synthetically constructued random walk and a real price series.
From a pureley statistical perspective it is very difficult to tell the difference between a random walk and a real price series. This is why academia clings so strongly to the random walk hypothesis (or perhaps it is of a case of sour grapes over hedge fund pay scales versus academic pay scales). For example, both will demonstrate very similiar properties, such as run lengths and if you construct a random walk appropriately, you can even get a random walk to simulate the skew and kurtosis (or fat tails as it is commonly known) of real price series. However, there are a number of difficulties with the random walk hypothesis. By their very nature, random walks are constructed out of a series of independent random events that determine whether the price moves up or down. On the other hand, price movement in real markets do not fit this pattern as events driving the market are rarely independent. For example:
Different markets frequently display correlated behaviour as they move in unison to the flow of large investors and speculators moving their money around the globe (and if they don’t someone will leverage an arbitrage opportunity and make money so that the markets will correlate again). Multiple random walks just do not exhibit this behaviour. If you ever wondered why hedge funds are so obsessed with high end statistical packages that have advanced correlation testing tools, it is because a portion of their edge is built around analysing correlating behaviours between different markets;
An individual market displays “path dependent behaviour” where price action will frequently test and retest established support and resistance levels or it will display price spikes as it punches its way through the level. This happens because support and resistance levels are price levels where traders have placed large numbers of conditional orders. Again random walks just don’t display this kind of behaviour. Many traders exploit the path dependence in the market in their trading strategies, either for break out trading, catching reversals, range trading and for stop hunting. In online forums for trading you will often hear some members saying “I have given up on indicators, I just use support and resistance levels”. It is because they are exploiting this behaviour;
In a real market the volatility of the price moves in cycles. Price action will become congested when the market has reached an agreed price and is awaiting new orders based on new information before setting off again. A random walk does not display this behaviour as it does not sit and wait for more information before heading off again. Many traders exploit these volatility cycles as part of their break out trading strategies;
Real markets are seasonal. In some futures markets, such as corn, soy beans, etc there are marked seasonal effects on price around summer and winter and similiarly around el nino and la nina. In other markets seasonal effects like presidential cycles, tax seasons and hedge fund manager bonus calculation times also impact upon the markets. Again a randomly walking price series does not demonstrate these. Some major hedge funds exploit this cyclic relationship in the portfolios they build;
Structural and regulatory differences in markets and financial products also create non-random effects which are tradeable and don’t occur in randomly walking price series. For example, some hedge funds seek to exploit lax tax rulings around options (where the underlying stock is goverened by different tax regulations) in order to gain an edge.
Efficiency: Is the efficient market hypothesis a crock?
The efficient market hypothesis (EMH) states that it is not possible to consistently outperform the market by using any information that the market already knows, except through luck. The EMH typically comes in three forms:
Strong EMH: The strong EMH contends that it is impossible to beat the market as all information, including insider and public information, is already embedded in the price.
Semi-Strong EMH: The semi-strong EMH contends that it is impossible to beat the market using fundamental and/or technical analysis as all public accessible information is already embedded in the price.
Weak EMH: The weak EMH contends that it is impossible to beat the market using technical analysis alone as information available from the price history is already embedded in the price.
In any of its forms, the efficient market hypothesis implies that as new information enters the market, the market will almost immediately find a new price which represents a rational valuation of the underlying instrument. Under the EMH, it is assumed that market players will act rationally, and if they don’t act rationally then either the combination of investor errors will neutralise each other out or an arbitrage opportunity will appear and the market will work quickly to correct itself.
If the markets were truly efficient, then a price series should look like step function as the market instantly adjusts to new information. However, the reality is the depth of arbitrageur’s pockets is limited and they can only focus on a key opportunities and crowds of humans display quite irrational behaviours. Therefore, while real price series do often demonstrate periods of efficiency and have a step like shape, but they also act in ways that don’t fit the EHR theory:
Many markets will trend as the market slowly reacts to new information. This slow reaction occurs because each market participant has different investment goals, they trade in different time frames, they have different portfolio makeups and they have different tolerances for drawdown. As a result, the readjustment process can take some time before all participants react and the information is fully captured in the market. The slow absorption of information can create a stable and potentially tradeable trends;
Many market participants will react to short term momentum in the market, believing it could be the start of a new trend. In markets with a history of trending, this reaction to short term momentum sometimes leads to even more momentum being created as new participants join in. Momentum traders try to capitalise on this and focus on identifying short term increases in momentum in markets that have a predisposition to trending;
Some markets will over-react to new information, sometimes to the point of irrational over-exuberance. This reaction will give the trend trader unexpected windfalls at times and will provide traders who specialise in trading the short side the opportunity to make money on market corrections;
Sometimes markets will under-react or react in totally contrary directions to what the new information entering the market would imply. This is because the market had a number of expectations built into the price and when the facts became available the market participants had to renegotiate a new price. This behaviour opens the possibility to “buying the rumour and selling the fact” style trading.
At the end of the day, an innefficient market is necessary for the sound functioning of the economy. If the market was too efficient traders would not participate and hedgers would not have any one to transfer their risk to.
Honing your trading edge
“Markets are not models. Those who wish to render the market rational and susceptible to mathematical quantification misunderstand its true nature. The application of probability, statistical regression, and diversification only gives the sense and appearance of control. Yet in their search for certitude, they reflect a pessimistic confusion about the economic future and its limits. We cannot drive absolute certitude about the market because it has its roots within ourselves. The market is not illogical per se, but its mathematical exactitude is a trap. The market is governed by laws of the mind. Insight, imagination, and faith—and not just economics and rationality—are the sentiment that mobilize the investor to risk his capital. After all the research is done, the wisdom of his actions will not be apparent until well after the investor commits himself to it. An investor must trust his intuition enough to pursue his vision and act upon it. And in doing so, he carries the insecurity that comes from standing alone and not with others. In most cases, a reasoned calculation of gain or loss would impel an individual to the sidelines in search of security. Investors who never act until all the market statistics are available or wait until all speculation becomes fact are doomed to mediocrity by their dependence on an illusory rationality.” F.J. Chu
As a word of caution, most of the available “edges” described in the section on randomness and market efficiency, are in general fairly weak edges. For example, under certain market conditions, trend traders who try to buy their way into the start of every short term increase in momentum, will be dissappointed as about only one third of their entries will be successful and they will have a pretty unimpressive return. However, under different market conditions, trend traders using exactly the same system can expect 80%+ of their trades to be successful and can attract a much more handsome return. Almost all trend traders fear being whipsawed to death by a ranging market.
The key to honing your edge is therefore blindlingly obvious: trend trade a trending market, counter trend trade a correction and range trade a ranging market. The hard thing is telling the difference. It is often easy to spot a trending market, a correction or a ranging market after the fact. However, spotting a regime shift between the different kinds of markets while it is underway is incredibly difficult.
If anything is the “search for the holy grail” in trading, then the search for a reliable and lag free approach to spotting regime shifts is it. Most automated trading systems fail in a back test. Of the few that stand up to simulated trading in a back test (and aren’t cheating by peeking into the future or are over optimised for one heavily data mined price series), many of them perform abysmally in real markets. This is because they lack a decent set of filters that can spot if a certain regime is in place or if a regime has shifted before applying trading rules. Adding this logic is non-trivial because technical analysis by itself lacks the tools to identify the underlying regime in a lag free way. It is my conjecture that only by applying fundamental analysis and sentiment analysis together with technical analysis together that you will have any hope of doing this.