The Morningstar Approach to Options

The Morningstar Approach to Options presents a framework for understanding and analyzing options that is based on fundamental analysis.  It contains several key concepts that pertain to any type of investing, not just options.  You can download a 40 page manual for options investing by Philip Guziec, Morningstar derivatives strategist and editor of Morningstar OptionInvestor.

 

Guziec includes a definition that distinguishes between investing and gambling;  If your expected return is positive, it's investing.  If it's negative, it's gambling.  

Think of this in terms of Las Vegas. The house always has an edge, or it doesn't allow the bet. Buying stock in a casino that owns all the games, and has that edge, is investing. Going to Las Vegas and playing the games is gambling. Some options investments may be very risky, but we'll make those investments only if we have an edge and understand it--we'll be doing options investing, not options gambling.

Guziec's approach is tied to fundamental analysis.  The Black-Sholes model, on the other hand, does not take into account any of the fundamentals of a stock, only its volatility.  While fundamentals have a greater impact on a stocks price the longer the time horizon, they also impact shorter term movements as well. 

Interpretations:

The key is to invest in options only when you have an edge—an insight that gives the investment a positive expected return. The old adage, buy low, sell high is easier said than done.  According to Morningstar:

"An option buyer thinks the volatility implied by the option price is lower than the actual volatility of the underlying stock.  He wants to buy an option because he thinks its value is based on an assumption for future volatility that is too low. In other words, he buys low.  Conversely, the seller thinks the option price implies too high a level of volatility—that future volatility will actually be lower. As such, he becomes a seller.  He sells high."

Having a good probability distribution for a stock enables you to know when you have a positive expected return -- or better yet, an investment with an expected return larger than other investment opportunities.

By deriving the probability distribution from judgments of a diverse, independent, decentralized group of people, both fundamental and technical influences are present to the extent that each individual's judgments are influenced by their research, reason, or intuition about the stock.  We believe that this is a much more straightforward and accurate way to evaluate stock and option prices, rather than to try to tie everything back to volatility and the Black-Sholes model.  For example, Morningstar asks:

"What is the Right Volatility Level"?

 and responds by saying:

 

"To estimate the future volatility of a stock, it’s reasonable to assume that we can look at how volatile it has been in the past. The degree to which the stock has moved around in the past is called the “historical” or “realized” or “statistical” volatility. ...  But as we all know, past performance is not indicative of future results. So rather than using historical volatility directly, people valuing options use a hybrid approach. Buyers and sellers look at the price of an option and back out the volatility level that will make the option trade at this price (this is the volatility “implied” by the market price or “implied volatility”). Then, they might compare the implied volatility with the historical volatility of the stock, taking into account their view of what will likely happen to the firm in the future, and ask themselves “Is this a reasonable amount of volatility to expect in the future? 

 

 

We believe that rather than asking for judgments about the future volatility of a stock based on its volatility in the past and using a hybrid approach to back out the volatility level that will make the stock trade at a specific price, we think it is much more direct and we hope to show more accurate to ask for judgments about the likelihood of the stock trading at different prices (it's probability distribution) at a given time in the future.

 

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