Wall Street is infamously indecipherable. Arbitrage, small firm effect, reversion to mean: these terms are thrown around all the time by finance-savvy journalists, bankers, and professors, but it’s easy to gloss over what exactly they mean. Yet, understanding these terms remains crucial to analyzing the current market and predicting future trends of the financial world. Just in time for interview season, writer Shuyang Li rounds up a few of these key concepts in an easy to understand primer.



Arbitrage underpins much of how financial markets make money. Broadly, arbitrage is deriving profit from a mispricing of an asset. In theory, the market price of an asset reflects its value, and assets are priced appropriately since a mispriced asset is expected to quickly return to its appropriate price. To do so, arbitrageurs purchase the asset if it’s valued too low and short the asset if it’s valued too high. Then, once the market drives the value up or down to the appropriate price, the arbitrageur either resells the stock if it had been purchased or buys the stock if it had been shorted. By repeatedly taking advantage of such mispricings, the arbitrageur can accumulate arbitrage profits.

Although in the financial world, arbitrage primarily occurs with fiscal assets, an illustrative example can be found in arbitrage of physical commodities such as clothing or technology. For example, an unlocked iPhone 6 (128 GB) starts at $849 when sold in the United States. However, the price of the same iPhone in China is 7,788 RMB, which converts to about $1200. Here there is an arbitrage opportunity: buying phones in the United States for the lower price and selling them back in China. After transaction costs, the arbitrageur is guaranteed a profit.

The resale value of an iPhone 6 in China is estimated to be around $1100 (resale price below Apple’s price to make it more attractive to customers), and the round trip flight ticket from Shanghai to New York and back will be roughly $940. For each iPhone 6 bought in New York and sold in Shanghai, the arbitrageur will earn roughly $250 in profit. If the arbitrageur sells more than 4 phones, the plane ticket cost is also negated.

Small-scale physical arbitrage is a relatively simple matter, but financial markets frequently use a wide variety of other forms of arbitrage. One form takes advantage of the fact that places with different currencies have different interest rates. Arbitrageurs borrow in the country with the lower interest rate and lend in the country with the higher interest rate. The rush to convert between currencies would push the exchange rate up or down until interest rates in each currency are the same. To preserve profits, arbitrageurs fix the exchange rate they will trade using a forward contract, a contract between two parties that specifies a certain exchange rate at a specified date in the future.

To demonstrate, suppose the United Kingdom interest rate is 8% and the United States interest rate is 5%, with the spot conversion rate at $1.5/pound and a forward rate of $1.4/pound, then the arbitrageur can borrow $100,000 in pounds, lend the money in the United States to earn 5% interest, convert it back to pounds using the locked-in forward rate, and then pay off the initial loan and its 8% United Kingdom interest to earn 3,000 pounds of profit without any risk at all.


Efficient Market Hypothesis

The Efficient Market Hypothesis (EMH) posits that the market can be categorized into three main forms: Weak form, Semi-Strong form, and Strong form. In fact, these categories can be depicted as nested sets in context of market information.

The smallest subset is the set of past market information and historical records. The Weak form EMH reflects this subset and states that market prices reflect all past market information (price, volume, etc.). The past information subset is contained within the larger set of all public information. The Semi-Strong form then reflects this bigger subset and indicates that market prices reflect all public information. Finally, the public information set is additionally contained within the set of all relevant information (private and public). The Strong form EMH then reflects this largest subset and indicates that market prices reflect all public and private information.

In short, the Efficient Market Hypothesis states that equal access to information governs the efficiency of markets and the prices of assets in those markets. If market prices perfectly reflect historical data, public information, and/or private information, then methods of wealth management and money management including technical analysis, fundamental analysis, and valuation methods, will be unable to generate abnormal returns. Since EMH explains the relationship between market prices and different sets of information, we see that market efficiency is affected by the availability of information to the markets themselves.

Weak-Form EMH is relatively unaffected by this, since the information it assumes is historical data from the markets. Of course, in certain markets like developing or emerging markets, information storage is inadequate and historical data can be spotty. Semi-Strong and Strong Form EMH, however, are highly affected by market information availability – in established markets like the USA, London and Hong Kong, market analysis is constantly being generated by large quantities of firms, and thus a great deal of information is public. As such, mispricings and arbitrage are identified and cleared quickly in established markets, while such abnormalities can persist for much longer periods in emerging markets.

The speed of information propagation is also a clear difference in markets. Information propagates through communication but also through market trends. In markets in which trades are conducted on a high frequency, pricing shifts extremely quickly, and thus information is constantly updated so EMH is more likely to hold. In slow or less liquid markets, trades occur with low frequency and information is propagated slowly, which can violate EMH and cause mispricings to occur.

Finally, legal barriers including regulation of the markets can prevent individuals from utilizing certain information available to them (most easily seen in the prohibition on insider trading). As such, regulation reduces availability of information to actions that would affect the market (e.g., trades). However, highly regulated markets also can call for greater amounts of information released to the public, which facilitates EMH.


Small Firm Effect

The Small Firm Effect is one of the most well-known market anomalies, and has been extensively studied and put into practice by many wealth management services. In the basic sense, anomalies in the market are events or phenomena that contradict established theory. In this context, market anomalies refer to phenomena that violate the Efficient Market Hypothesis (EMH). As a result, anomalies can help explain abnormal performance in the market.

The Small Firm Effect is indeed an anomaly: market capitalization (existing shares that could be easily sold multiplied by the stock price) is a statistic reflected in all forms of market information, and thus under EMH, the market price of stocks should take into account the market capitalization. Different-sized companies should, in theory, perform as expected relative to each other given their different market caps.

Of course, nothing ever goes the way it should. The Small Firm Effect says that when firms are categorized based on market capitalization, the smaller companies tend to outperform the large companies, even when adjusted for risk. The reasoning makes sense: small companies tend to have just started out, and many are formed by industry veterans or researchers who sit at the bleeding edge of their field and are constantly looking for new applications.

Thus, it is rational to think that small companies are more likely to make large leaps and bounds in their business, resulting in sales and profit booms, and the associated high returns for shareholders.

This is in fact well-noted by hedge funds and mutual funds, which led to the construction of many small-cap funds such as the Vanguard Small-Cap Value Index, Fidelity Small Cap Discovery Fund, and Schwab Fundamental US Small Company Index ETF.


Reversion to the Mean

Reversion to the mean refers to the property of an asset’s price to return to the average market price of an asset over a long period of time. This mathematical property of asset pricing that can inform investment strategies. In short, the rolling average of the market price of an asset is assumed to be its “true” value. The asset price “regresses to the mean” by falling until it reaches the average if the price is above average, and rising until it meets the average if the price is below average.

Using mean regression, investors can identify underpriced assets that they can buy, with the expectation that their price will rise to match the mean. Similarly, investors can short sell overpriced assets, expecting that their price will fall. This suggests that various analytical techniques have been built to calculate the moving averages required to find the mean.

Blindly following mean reversion, however, can lead to dangerous investments. Large-scale events that boost or reduce performance can often permanently change the mean. An advance in silicon wafer-processing technology may result in a production advance that permanently boosts a hardware company’s stock price. In this case, an investor might short the stock in expectation of a large reversion to the mean, while ignorant of the fact that the mean has been boosted.

Written by thefinancier

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