• Seb Paisley

Can algorithmic trading make the efficient market hypothesis a reality?

What is the EMH?

The efficient markets hypothesis states that stock prices in the market reflect all information about the potential and the long-run value of companies. The most important implication of this theory is that because all information and activity are assumed to be known, it is impossible to consistently “beat” the market by buying at a low price and selling at an inflated one. Whilst this is viewed as a theoretical exercise rather than a reality for the markets, the introduction of the internet but also algorithmic trading has begun to remove some of the barriers to an efficient market hypothesis outcome. The two main groups of traders, retail and institutions, are often regarded as operating differently because institutions generally have more information than individuals and so this gap creates opportunities for those in the know to capitalise on their information.


What is algorithmic trading?

Algorithmic trading is a new breed of trading that is gradually being introduced by institutions as a product of artificial intelligence to react to market news and financial statements. This is a major growth area in many institutions due to the cost-cutting and increased frequency of trading possible, but it also has the impact of removing an element of speculative trading. Algorithms only operate in the way that they were programmed, and if hypothetically, all trading was done via a program then there would be no way to make speculative gains. It is estimated that only 25% of stock market trades are made by retail investors, and with flash trading on the rise, this will likely shrink in the future meaning that the influence of algorithms will grow.


Are humans necessary or a limitation?

Algorithmic trading is used to increase the speed of trading which makes it attractive in the sense that information is used instantly which will ultimately lead to more “efficient” markets. Though the main limitation of any program is always the coder’s ability and so certain elements of an algorithm may create more issues than benefits. This was seen in the 2010 flash crash when the behaviour of algorithms led to rapid shortages in liquidity and saw market prices in the US swing downward by nearly 9% and rebound within 36 minutes. However, in a world of interacting algorithms, the stock market may be thought of as one large equation to which there will be similar solutions for each institution and so the outcome predicted by the efficient market hypothesis is realised. So, the typical downfall of technological solutions is a major sticking point in the argument for full automation of trading by institutions because even with algorithmic trading, there will be scenarios unaccounted for and hence require human intervention. Therefore, humans are still necessary for market functionality because algorithms and artificial intelligence are not advanced enough to work without oversight, though this may change shortly.


Is the EMH achievable and desirable?

The efficient market hypothesis is likely achievable in the long-run as artificial intelligence develops and so improved information removes the ability to speculate and regularly outperform the market average. The long-run is a rather ambiguous concept in academia but technological advances are often sporadic so setting a date for algorithms dominating trading is difficult but likely sooner rather than later. Decreased human involvement in trading will make the efficient market hypothesis more achievable because it is related to prices reflecting available information, so full information of all aspects of a market isn’t necessary, only that each agent has the same information. The desirability of the efficient market hypothesis is highly contested because it protects companies from short-run fluctuations due to human sentiment and so having a more hypothetically accurate estimate of a companies’ value will prevent any bubbles from forming, leading to a more stable market environment. However, the downside of this lies on the retail investor side because many retail traders rely upon market volatility as a source of gains and only represent 25% of the market, they have little influence over market prices alone so are unable to stop the move to algorithmic trading by institutions. The flattening of volatile movements by letting algorithms control the market means that retail trading will gradually fall since there is less potential gain, but for the market as a whole, the removal of speculation creates a more stable environment so is positive. The bottom line is that an algorithm is only as good as the person who programmed it, but with future advances in technology, the use of algorithms will grow in trading and ultimately can bring about the efficient market hypothesis as a reality.