There are two different approaches to finding fault with the efficient market hypothesis. The first approach is to attack the assumptions — for example noting that there are high degrees of information asymmetry. Indeed, this should be the case because even if the asset is priced fairly, not all investors know this for a fact based on their own analysis — their knowledge is just an extension of their belief in efficient markets. In essence, rather than come to a rational conclusion about the value of the asset, they simply trust that the other market participants have already done so.
The other approach to refuting the efficient market hypothesis is to demonstrate that investors are not actually rational. A good place to start is to look at the rise of program trading. Program trading is when institutions use computer programs to trade securities. The analysts will input certain parameters into the program — for example selling a stock if it reaches a certain level, or buying. This usually begins with a proper, rational analysis. Let’s say you believe that Apple is overvalued once it reaches $600. The problem with the program is that the inputs can be outdated, but an even bigger problem occurs when programs start to move markets. At 43.1% of the NYSE last year, this is common (NYSE, 2013).
It is not hard to see that once a program begins to move markets, other investors might also engage in buying or selling of that security, based on the movements. Many investors are willing to make their decisions less on careful analysis and more on response to the movements. Technical traders will often buy or sell based on a a certain threshold. Day traders will, too — EMH depends on fairly valuing the present value of and day traders have no interest in sticking around for said cash flows. Either way, one of the things that happens with program trading in particular and with institutional trading in general is that market movements become amplified — securities increase in volatility because of the high volumes inherent in programmed institutional trading (Sias, 1996).
As far as what economic factors should be taken into consideration when investing, each security responds to different macroeconomic variables in different ways. is based on this idea, but you have to know how each variable works with each security. For some companies, the interest rate is a major variable. in particular are affected by changes in the interest rate, because prices have some degree of stickiness, and profits are based on the spread between the interest rates in the economy and the rates that can be charged to the clients.
A second macroeconomic variable that has a significant impact for many firms is the level of GDP. The GDP can be broken down into a number of constituent parts, so with some industries it just helps to understand that relevant part — health care companies need to know about health care spending; defense companies about defense spending But many companies that serve a broad range of business interests will find that the overall economic health is a key variable in their business — everyone from HP to Microsoft to SAP to FedEx is likely to see their revenues fluctuate with the overall health of the company because they are selling to business customers. A furniture retailer or a Home Depot wants to know about new housing starts, because sales spike when lots of people are moving.
Overall, there are dozens of macroeconomic variables. To determine which ones are most important, some correlations must be calculated that will help to make clear which variables matter most, and what those correlations look like. Once this is known, it is easier to base investing decisions on macroeconomic variables.
NYSE. (2013). Program trading averaged 43.1% of the NYSE during Dec 16-20. New York Stock Exchange. Retrieved May 1, 2014 from http://www.nyse.com/press/1387194756904.html
Sias, R. (1996). Volatility and the institutional investor. . Vol. 52 (2) 13-20.