Introduction

The Efficient Market Hypothesis (EMH) states that asset prices reflect all available public information, including company earnings forecasts and risk assessments.1 Eugene Fama, a Nobel winning professor at the University of Chicago, popularized the theory in 1970.2

A highly liquid market like the stock market functions as a gigantic prediction market. It reflects the combined judgment of all buyers and sellers based on all publicly available information about each stock. For all the investors that choose to overweight a stock, other investors must have an equal amount of underweight positions.

Therefore, stocks trade at their fair value on exchanges. That isn’t to say that stocks trade at their correct price. The correct asset price is only known in hindsight. At any point in time, investors can be collectively mistaken: overly optimistic or pessimistic.

Stock valuations depend on estimates of companies earning many years into the future. Because these forecasts and the associated investment risks are likely incorrect, the discount rate is uncertain. The problem is that investors don’t know at any given time if their estimates are too high or too low.3

As Nate Silver states, you need “to think about the future in terms of a series of probabilistic beliefs or forecasts”. This follows Bayes’s theorem. What are the chances….

Trading on Fundamentals Versus Momentum (Signal Versus Noise)

In the first week of an introductory microeconomics course, college students are taught about supply, demand, and equilibrium pricing. According to microeconomic theory, trading is rational when it has the potential to make both parties better off. Financial markets may operate differently in practice.

First, investors may have different predictions about the directions of particular companies or industries. For every investor buying a stock, betting that it will go up from a particular price per share, another investor is selling the stock, making the opposite bet. But, only 10% of total trading volume in stocks is now based on underlying fundamentals.4 Quantitative and passive investing account for the remainder of daily trading volume.

Second, algorithmic trading has reshaped the financial markets. Algorithmic trading uses complex mathematical formulas to make automated buying and selling decisions. According to the Wall Street Journal, “roughly 85% of all trading is on autopilot—controlled by machines, models, or passive investing formulas, creating an unprecedented trading herd that moves in unison and is blazingly fast.”5

A subset of algorithmic trading, high-frequency trading (“HFT”), goes further, seeking to exploit miniscule pricing discrepancies that may exist for a tiny period of time.6 HFT systems may hold an individual stock for only microseconds.

Most data sources show that quantitative investing accounts for 50-60% of trading volume for U.S. equity markets.7 Credit Suisse estimates that, as of 2016, HFT resulted in 4.2 billion shares trading on a daily basis.8 To the extent that there is profit from all of this trading, it requires real-time data feeds into exotic algorithms that are running on ultra-fast computers, collocated at the stock exchanges, and communicating across exchanges over dedicated fiber optic lines and high-speed wireless links. HFT is dominated by proprietary trading firms with ultra-low latency trading capabilities that are far beyond those of the individual investor.

High-frequency trading has arguably led to improved pricing efficiency. That’s because HFT reduces the bid-ask spreads, particularly for larger-cap equities. More debatable is whether HFT firms create true market liquidity given how briefly they maintain their positions. HFT firms may create–and certainly benefit from–market volatility. And, critics question the fairness of permitting a few firms to essentially “front-run” the market trades of investors who lack access to equivalent trading platforms.

So, markets are not perfect or perfectly efficient. However, as Burton Malkiel asserts:

Markets are so good at adjusting to new information that no one can predict its future course in a superior manner. Because of the actions of the pros, the prices of the individual stocks quickly reflect all the news that is available.

In the short term, stock price changes approximate a “random walk“. Markets aren’t erratic or unresponsive to new, material information. However, news is random and stock prices quickly reflect all public information. This means that we have more skilled professionals than ever pursuing fewer underpriced assets. Communications are so rapid that any information gap is quickly closed. Regulation FD (Fair Disclosure) mandates that all publicly traded companies must disclose material information to all investors at the same time. Any proprietary trading advantage is quickly copied by others. The high-frequency traders quickly arbitrage differences in pricing, thereby reinforcing passive index price points.

Updated on December 27th, 2018