The pace of mobile usage by investors continues to accelerate as smartphones and tablets become fully embedded into all aspects of daily life. In a recent study by one of the leading online retail brokerage firms, mobile trading accounted for over 20 percent of all trades for the past quarter. Even more striking was the acceleration in mobile adoption over the past five years: an increase of 344 percent in average mobile trades per day and 178 percent growth in unique mobile monthly usage.
Consider this trend in light of the increasing adoption of options trading by retail investors and the importance of a rich mobile experience for retail options traders becomes very clear. Given the vast market data challenges present in the options market, however, many firms face steep hurdles to designing and delivering the optimal mobile trading experience for options.
We all know that mobile adoption and stickiness depends on a clean and functional UX. But, is this possible to achieve for options trading, where options analytics displaying real-time volatilities and Greeks are some of the most computationally intensive market data calculations across all asset classes? Do retail options brokers have to make a tradeoff between displaying a simple, clean mobile interface and offering the analytical robustness already available on their desktop trading platforms?
The current constraints for mobile devices extend beyond the design difficulties presented by the size of the device. The most challenging issues arise due to the computational power required to calculate options analytics on the device itself. With potentially thousands of options series for any given stock symbol, processing power on a phone or tablet is quickly maxed out. The result is that many retail firms display options market data on a limited basis on mobile devices – with delayed analytics, a lack of granularity in volatility data, or other scaled back versions of the analytical tools offered on desktop. This limitation inhibits the growth of this important channel.
Consider this example: an investor is interested in trading options on one of more of the FANG stocks (Facebook FB 214.18 +1.04 +0.49%, Amazon AMZN 2,134.87 -15.00 -0.70%, Netflix NFLX 386.00 -0.19 -0.05%, or Google/Alphabet GOOGL 1,518.63 +8.57 +0.57%) and wants to screen the available options series for recent spikes in volatility. These four underlyings comprise over 10,000 options series—not a practical size for performance of real-time analytic computation on a desktop, let alone mobile. In fact, many retail trading desktop platforms only offer options analytics views for one underlying at a time. Even less information is typically provided on mobile, with approximate analytics sometimes only at the expiry level, ignoring strike detail. A savvy investor needs to be able to query, filter, and analyze the data seamlessly in order to evaluate and execute a trade. This requires a heavy computational lift that must take place outside of the app itself.
For example, newer strategies invite comparisons between names within sectors, ETFs, or active groups. Fig. 1 below illustrates comparative implied volatilities calibrated with a stochastic volatility model (SABR). Though computationally intensive this content is readily accessible to any display platform, mobile and beyond.
Figure 1 - CEV single stock volatility index FANG + AAPL
Firms that can offer options traders a seamless and unified experience across desktop, tablet, and phone will position themselves to capture new users and to increase the satisfaction of their current options customer base. One approach that has been gaining traction in the past few years is deploying “analytics as a service.” With a cloud-based analytical tool, options market data is calculated in an external datacenter and delivered via a simple data feed. Calculation and integration of the analytics feed is platform-agnostic and creates a cohesive offering across all applications.
This approach solves the compute problem inherent with mobile devices and offers a lightweight solution to retail brokerage firms who are able to extract value and efficiency through relying on third-party analytics providers like Hanweck. The value prop of options analytics as a service extends beyond broadening capabilities for mobile. With a lens focused on retaining and attracting new customers to the options market, the value of calculating, packaging and displaying usable options analytics is a straightforward way to achieve immediate results – and empower options customers with the data needed to sink their FANGs into mobile.
 “Mobile Trading Up 344 Percent Over Last 5 Years,” TD Ameritrade Press Release. http://www.amtd.com/newsroom/press-releases/press-release-details/2017/Mobile-Trading-Up-344-Percent-Over-Last-5-Years/default.aspx
 “Big Data-As-A-Service Is Next Big Thing,” Forbes. https://www.forbes.com/sites/bernardmarr/2015/04/27/big-data-as-a-service-is-next-big-thing/#4a05382e33d5
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