In recent years, personality tests have entered the spotlight. Tests like the Myers–Briggs Type Indicator have become a cultural phenomenon, offering individuals a better perception of themselves and others. We are fascinated by human personalities and behavioral differences and have developed techniques to classify and understand those peculiarities.Â
Knowing your Myers-Briggs personality type is not a prerequisite for successful trading strategies. However, understanding your assets' typical behavior is essential for a comprehensive trading system. Do you know what the bid-ask spread of the specific underlyings you trade should resemble?Â
This article explores that question, defining what characterizes an asset’s personality, offering five case study examples.
Importance of the Typical
In a previous Research Insights blog, we examined the bid-ask spread and identified unique events impacting the spread of a TLT iron condor.
Understanding the basic personality of underlying assets is an essential step before a deeper study of bid-ask spread anomalies.
Options traders tend to agree that liquidity is an essential piece of a successful trading system. Liquidity is simply the ability to efficiently sell an asset at a reasonable price.
However, many wrongly assume that volume and open interest are the only relevant determinants of liquidity. There are other factors; liquidity is largely subjective.
Our goal in studying bid-ask spread anomalies is to objectively show the importance of a broad view of liquidity, and that goal starts with an understanding of asset behavior on a typical day.
In this blog, we consider multiple underlying symbols on a trading day with minimal volatility, sharing graphs and comments on the behavior of these assets.
Selection ProcessÂ
The case studies required a “calm” trading session to observe typical bid-ask spread behavior, with calmness defined as minimized volatility in the bid-ask spread of contracts and in the underlying itself.
There are not traditional statistical tools applicable to the variety of underlyings studied. Approximation through a heuristic was an effective solution, as we were not testing for statistical significance, only identifying a day with sustained low volatility in the bid-ask spread. This heuristic was applied to the SPY tick-by-tick data for each date in our testing period (June 1 - July 1, 2021).
We created a 500 tick rolling window and computed the average bid-ask spread value. If the next tick’s spread was more than twice the computed average and greater than 10% of the current ask price, we recorded the tick as an anomaly. June 2 had the lowest anomaly count for SPY within our testing period.
Then, we compared the day’s trading range to the average true range (14-day lookback period). On June 2, SPY traded in a $1.94 range with an ATR of $4.00. With a trading range less than half of the average range over the last 14 days, June 2 met our second criteria for a calm trading day.
Five securities were studied based on their diversification and popularity in the auto-trading platform. We selected SPY, TLT, EEM, TSLA, and NVDA.
We chose the five closest out-of-the-money, whole-dollar strike price calls and puts based on the closing price of each security on June 2, 2021. We selected the nearest monthly expiration shared by all five underlyings, June 18, 2021. These contracts had high trading volume and were representative of each symbol’s personality.
Typical Personality Visualizations
Key bid-ask spread personality variables include the tick update frequency, average spread width, and spread variability. Tick update frequency is determined by market participants. Whenever the bid or ask prices for an asset change, a tick update is triggered. Spread variability is how much the bid-ask spread moves up and down throughout the day.
The chart of a highly variable bid-ask spread looks “jumpy.” An appropriate metric to quantify this variability is the coefficient of variation, which measures the dispersion of a dataset by dividing the standard deviation by the mean. This standardization by the data’s mean allows for direct comparison between different assets.Â
The average value for each personality variable is provided below. This case study does not propose an explanation for the behavior of each security; instead, it provides a visual representation of each underlying’s typical bid-ask spread behavior.Â
Each chart below represents one options contract on a specific ticker symbol. Contracts were chosen based on the criteria specified above. The line in each plot is the bid-ask spread, calculated by subtracting the bid price from the ask price for each collected tick update. The density of the line relates to the number of tick updates for the contract.Â
SPY
Yes, that is a line plot in those mini-charts. The incredibly high update frequency created this odd chart shape for SPY contracts. On June 2, the 421 strike call option had 19,780 ticks, 0.92 ticks per second. Out-of-the-money puts showed a higher average spread and more variability, especially near the end of the trading day.
TLT
Despite the chart’s appearance, TLT shows very similar variability to SPY across contracts.
TLT contracts had an average CoV of 0.35, tied with SPY for the highest in our study.Â
EEM
There was a stark difference in EEM’s update frequency for different strike prices. The 55 strike put had an update frequency of 0.13 ticks per second, while the 51 strike (further from the money) had a frequency of 0.09 ticks per second. A similar update frequency occurred on the call side. Variability also decreases further out-of-the-money.Â
TSLA
TSLA’s out-of-the-money contracts had an update frequency similar to SPY, with 0.95 ticks per second. Spread width remained range-bound, with the lowest CoV in our study. Volatility was more prevalent in the morning and fell throughout the day.Â
NVDA
NVDA had a similar personality to TSLA but showed a more stable volatility pattern. NVDA had the widest spreads in our study, with an overall average bid-ask spread of $0.25.
Summary
Securities have unique personality traits that create their normal bid-ask spread. Update frequency. Volume. Variability. These factors, and countless others, combine to create the spreads you see on a typical trading day.
This blog asked if you understand the typical bid-ask spread behavior of the securities in your portfolio. After reading this case study, does your answer change?
Armed with this foundational understanding of asset personality, we will investigate specific anomalies in our next Research Insights blog.
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