Does activity in equity derivatives play a role in the price discovery process of the underlying stocks? And does the flow of information about the stock drive the dynamics of its option volume distribution and the associated return predictability? Given the current economic environment, more than ever traders are looking to the options market to try to glean information. New research offers interesting insights on what this data might reveal and how it can be used.
Options trading appears to play an important informational role to help investors understand underlying markets. For years researchers have debated aspects of this premise.
But, as investors, what can we really learn from prices and volume in security trading in stocks and options?
With stocks, as a single security, if the investor buys he/she is bullish; if they sell, they are bearish. However, with options many other parameters can impact options prices and make it difficult to assess traders’ intent solely from observing trades. If traders buy call options, it could mean they are bullish on stock price, or it could mean that they want to take volatility exposure, for instance.
While past research has focused on factors such as option put and call volumes, other aspects, such as order flow, have the potential to offer a lot of information. However, analyzing order flow can be difficult, if not impossible, for the average investor or trader, as trade speed and the amount of tick data grows exponentially. Even for large institutions that might afford high frequency data, this information is costly and difficult to process.
How can traders more easily and readily assess call volume to guide investments? That is the question researchers Gennaro Bernile, University of Miami; Fei Gao, Singapore Institute of Technology, and Jianfeng Hu, Singapore Management University explored In their recently circulated paper, “Center of Volume Mass: Does Options Trading Predict Stock Returns?”
Researchers propose a styled effect that can help simplify the problem that also boils down to how options are traded. They examine whether the shape of the volume distribution, along variable option contracts (with different moneyness), contains information about future stock price dynamics. They also assess whether the flow of information about the stock drives its option volume distribution and the associated return predictability.
The research is motivated by past research from Hu. Hu says in that research “We find, typically, customers are net buyers of out of the money options, but they like to sell in the money options. As a result, trades of puts and calls at the same strike price would typically reflect the demand for similar risk exposure to underlying price movements (delta exposure).”
This is true for both calls and puts. “Because calls and puts give you different exposure to delta to the underlying price at the same strike level, we find we can just use the strike price to tease out delta information,” says Hu. “Basically, at a particular strike price, calls will be out of the money (OTM) and puts will be in the money (ITM), or the other way around. And because investors are customers, they typically have different net demand for in the money and out of the money. At the same strike price, their net demands for calls and puts will be the opposite. If the net demand for calls is long, then the net demand for puts will be short at the same strike level.”
To standardize this information in their most recent research, the researchers determine where the mass of volumes concentrates with a term they coined ‘volume weighted strike’ to spot price ratio or VWKS. VWKS determines the central location of the distribution of trades along the moneyness of available option contracts (or set of strike prices of option contracts) on the same stock to help provide information about underlying price discovery. It can be used to tease out delta to capture directional bet, and the price going up or down.
“We think there is a pattern that certain types of trading, let’s call it informed, with certain investors having advanced information, will produce,” says Hu. “We show that you can use low frequency, after market data—like OptionMetrics, as the standard in options research—to largely proxy for this information to arrive at some very useful insights.”
“We borrow a concept from physics, called the center of mass. We look at the whole volume of distribution along strike prices and identify hot spots, where the customer focus is, and come up with the volume weighted strike to spot price ratio. This simple, non-parametric measure describes the distribution of volume on options strike prices to make it easier to look at a whole cross section of stocks with options,” adds Hu.
The ratio of the contract’s strike price (K) and the underlying stock price (S) measures the option moneyness, where call (put) options are out of the money when K/S is above (below) one. VWKS reflects higher values for the center of mass in option volume distribution along strike prices of available contracts when trading volume leans toward OTM calls and ITM puts. It reflects lower values when they are more focused on ITM calls and OTM puts. It indicates option traders’ demand for directional exposure to underlying stocks.
Researchers verify their premise by examining VWKS and option traders’ demand for options with data from OptionMetrics (including daily options trading volumes, strike prices, expiration dates, option delta, as well as call and put indicators starting from 1996), Center for Research in Security Prices, Compustat, The New York Stock Exchange and International Securities Exchange. They find return predictability is greatest, but not limited to, stocks in active markets.
They also use VWKS to assess customers’ net demands in the options market as an ability to reflect some private information. Researchers suggest that if the correlation is positive there should be a positive initial price impact with no reversal to permanently predict future stock price changes. Researchers find the evidence consistent with their thesis that customers’ net demand in the options market can reflect private information.
Researchers conclude that the options trading by informed investors, and in particular, moneyness, can have an impact on distribution of option volume across contracts, and assessing this information can provide valuable insights.
While VWKS can be useful to guide traders in assessing delta information, it is not applicable for other greeks, such as gamma (jump) and vega (volatility). This is because buying calls and selling puts at the same strike price, such as when betting on volatility with vega, can lead to opposite exposure, with some exposure canceling out. VWKS, conversely, only captures delta/directional bets of options going up or down.
“If you look at options volume, it’s quite murky; we don’t know what investors are going to do, and what their intentions are. The beauty of this model is that it is a very flexible, efficient way for traders to extract underlying strike price information from options volume without having to deal with every transaction and options tick data. Using VWKS we can tease out delta to gain informative insights that may help in predicting returns,” says Hu.
The views represented in this commentary are those of its author and do not reflect the opinion of Traders Magazine, Markets Media Group or its staff. Traders Magazine welcomes reader feedback on this column and on all issues relevant to the institutional trading community.