Adam Drake

Thoughts on the Efficient Market Hypothesis

The Efficient Market Hypothesis (EMH) is both important and often misunderstood. For our purposes, the term efficient is related to informational efficiency, or how quickly information affects prices. In a way, the level of informational efficiency in a market tells you what level of randomness to expect and consequently if it is possible to outperform the market. In its simplest form, the EMH states that market prices reflect decisions made by participants who have acted rationally based on information they possess. It is not required that all participants act rationally at all times, but only that at any given time most participants are acting rationally given the information they possess. In order to clarify the impact of varying levels of information, the EMH has been split into sub-types depending on what kind of information is incorporated into the asset price. There are currently three forms of the EMH: the Weak EMH, the Semi-strong EMH, and the Strong EMH.

The Weak-form EMH stipulates that all past publicly-available information has been accounted for in the price of an asset. It is often said that such information is priced in. In this version of the EMH you can see that if someone had new information that was not public then they could use that information to buy or sell the stock before everyone else does and obtain a profit. If this version of the EMH were true then there would be numerous opportunities to outperform the market for any person who had more information than other market participants. However, any insider information you may obtain cannot legally be used for investment purposes. The few people who choose not to abide by the law are typically not enough to move the price and therefore the information anomaly is not much of a concern. The key idea with this form of the EMH is that past rates of return do not affect future rates of return. In other words, rates of return for one time period are independent of rates of return for another time period. This is an important implication because it invalidates all forms of technical analysis outright. Technical analysis is a method of identifying patterns in pricing data in order to draw future conclusions about what will happen next, but since the weak-form EMH says there are no patterns technical analysis cannot provide any reliable information. There is a vast amount of empirical support for this form of the EMH.

The Semi-strong EMH takes the Weak EMH one step further and stipulates that not only is it the case that all past publicly-available information is priced in to an asset, but the price of the asset immediately corrects based on any new publicly-available information. Because of this additional caveat, it is impossible for investors to obtain above-market returns by acting on new information. So on top of the concept of independent returns from the Weak-form EMH which prevents past returns from having any effect on future returns, we now have the additional restriction that any new public information is effectively useless as the asset price adjust instantaneously. The key difference between the Semi-strong EMH and the Weak EMH is that the Semi-Strong-Form EMH also prevents someone from consistently outperforming the market based on Fundamental Analysis in addition to Technical Analysis.

In the Strong-form EMH all public and private information is reflected in prices and it is impossible for anyone to outperform the market. Since it is illegal to trade on insider information, Strong-Form Efficiency is impossible to achieve. So what does all this mean for performance? In a 100% efficient market, it is impossible to outperform the market on purpose, but that doesn’t mean that it is impossible to outperform the market. Consider an example from the Clustering Illusion, wherein it can be calculated that you can expect 2,876 advisors in the US to beat the market for 5 consecutive years on nothing but chance alone. They may advertise it as skill, and they may genuinely believe it to be, but simply outperforming the market for 5 consecutive years is something you expect 3.125% of advisors to always do by chance. Now an important thing to consider is that with the rise of algorithmic and high-frequency trading (HFT), markets become increasingly efficient since machines can trade based on information faster than humans. In this way, new information is processed and reflected in the price of an asset faster than ever before. There have been statements that the average holding period of securities is down to 11 seconds at some firms and while that number is an estimate, holding periods are certainly decreasing. As holding periods decrease and transactions in the markets increase the price action becomes more like pure noise, i.e., totally unpredictable. That is precisely the purpose of our market forecasts. We know that it is not possible to say with certainty which way a stock price will move and therefore we produce ranges wherein we believe the stock will close. This simply functions as a noise filter of sorts. We don’t claim to know what a stock will do, only that we have a fairly good idea of what a stock is unlikely to do. We know based on empirical data that a stock has approximately an 85% chance of closing within our range.

The markets are already fairly efficient/random and are becoming more efficient as HFT becomes more prevalent. We predict that noise filtering in markets to become increasingly important and for this reason we strive to provide noise filtering algorithms that are as accurate as possible.