The Sharpe ratio, originally devised in the 1960s, essentially tells you if the potential return expected from an investment justifies the risks involved. It compares the probability of a large loss against the likelihood of a substantial profit. While the ratio is commonly used and can help make sense of available alternatives, it has important limitations.
Definition and Calculation
The Sharpe ratio quantifies how much excess return you get for each unit of risk you are willing to take. Excess return equals profits higher than those offered by risk-free investment instruments. Since the federal government can print money to honor payment obligations, its bonds are risk-free.
By subtracting the return expected from holding an asset, such as a stock, from the risk-free rate, you arrive at its excess return. Dividing excess return by the standard deviation of the asset equals its Sharpe ratio. Standard deviation, a measure of investment risk, indicates how wildly the asset price fluctuates.
Benefits of the Sharpe Ratio
An advantage of the Sharpe ratio is that it's easy for investors to grasp and calculate. Checking the rates of government bonds reveals the risk-free rate. You then merely plug return and standard deviation figures into the formula.
The Sharpe ratio also standardizes the relationship between risk and return and therefore can be used to compare different asset classes. Not only can you use it to compare different stocks, but you can also use the Sharpe ratio to weigh a corporate bond against an investment in gold, for example. The higher the Sharpe ratio, the better an investment option you have.
Limitations of Past Data
While an accurate risk-free rate is easy to access, finding the right expected return and standard deviation for an investment is a real challenge. As always, you can use past data or try to predict the future.
In a highly stable environment, past data may work. If macroeconomic factors and the competitive conditions and product mix of a business haven't changed much in recent years, the return and standard deviation over that period may be a good predictor of expected numbers for the next year. However, in today's dynamic markets, the future rarely replicates the past.
Limitations of Standard Deviation
Another challenge when using the Sharpe ratio is that the risk may be hard to quantify by using just the standard deviation. If potential gains and losses fall on a bell curve and have a normal distribution, standard deviation properly quantifies investment risk. If, however, the risk distribution is unusual, standard deviation can be misleading.
A business may be bidding for a government contract, which guarantees a large profit if earned. If it doesn't win the bid, however, a dozen options will be put on the table, resulting in significant uncertainty. The risk distribution of such a company does not resemble a bell curve, so the Sharpe ratio proves less useful.
Hunkar Ozyasar is the former high-yield bond strategist for Deutsche Bank. He has been quoted in publications including "Financial Times" and the "Wall Street Journal." His book, "When Time Management Fails," is published in 12 countries while Ozyasar’s finance articles are featured on Nikkei, Japan’s premier financial news service. He holds a Master of Business Administration from Kellogg Graduate School.