As the broader market continues to show signs of weakness, financial technology (fintech) is a powerhouse SoFi Technologies Inc (NASDAQ:SOFI) He had a choppy session on Wednesday. In particular, investors are eagerly awaiting the Federal Reserve’s stance on the benchmark interest rate. Combined with risk aversion that is negatively impacting growth-oriented companies, SOFI shares have tumbled over the past week. Still, the red ink could hide an intriguing contrarian opportunity.
One of the overlooked problems in assessing market behavior – especially in options trading – is the constantly kinetic nature of the stock arena. Almost every shopping-friendly chart or graph shows price as a function of time. Even in fundamental analysis, financial measures (such as earnings per share or revenue) follow the same format.
It is truly a product of humanity’s social evolution. Early humans observed changing seasons, animal migration patterns, and day-night cycles, among countless other events. In other words, humans have evolved to understand the world through temporal sequences. That’s why we, investors and traders, like to think in terms of profit cycles and seasonal trends; these are time-based functions.
At the same time, we are very bad at understanding probability because the concept is abstract. It doesn’t help that probability is non-linear and non-sequential. We, on the other hand, gravitate toward price charts because they tell a story.
However, the problem with time-based systems such as fundamental and technical analysis is that while a time measurement is fixed, the market (when it is open) is constantly evolving. It’s like trying to measure a tidal wave with a ruler. Time-based systems are useful for measuring fixed objects such as buildings. To measure a kinetic substance we need an entirely new framework.
Deciphering the fluid geometry behind SOFI Stock
Quantitative analysis differs from the fundamental and technical approaches by attempting to measure the probability or statistical structure of future movement. This is categorically different from technical analysis, which measures movement itself as a property of time.
Furthermore, under the specific system I use – a Kolmogorov-Markov framework layered with kernel density estimation (KM-KDE) – price is analyzed as a function of probability density. Mathematically, this means that I do not view the prize as a unique journey through time. Instead, I divide price data into hundreds (sometimes thousands) of rolling series at a given interval.
Fundamentally, the idea here is that after enough trials, certain behaviors become apparent, with prices clustering more strongly at certain points than at others. This is the peak cluster effect you see on a distribution curve. We also know from GARCH (Generalized Autoregressive Conditional Heteroskedasticity) studies that the diffusion properties of volatility depend on previous volatile events.
As such, specific signals – such as longer sessions of buying or selling trades – can create different behavior. These kinetic differences can be measured via custom algorithmic programs running KDEs, revealing the hidden geometry behind every publicly traded security.
In the past Options Corner articles I discussed the concept of ‘informational arbitration’. I use this term because the financial publishing industry currently has no concept of probability density as a function of price. We could very well be 15 to 20 years ahead of the industry – that’s no joke.
Returning to SOFI stock, using the KM-KDE approach referenced above, the probabilistic distribution of outcomes over the next 10 weeks would likely be between $24.20 and $27.60 (assuming an anchor price of $25.30). Furthermore, price clustering would likely prevail at just under $26.
The above review compiles all data since SoFi’s debut on the public market. However, we are interested in the market’s reaction to the current signal, which is a 3-7-D formation; that is, over the next ten weeks, SOFI stock showed three weeks of increase and seven weeks of decrease, with an overall downward slope.
Under this order, probabilistic expectations shift positively, with outcomes likely to range between $25.50 and $28.80. Moreover, price clustering would likely dominate at $27.20. And that’s right there: the hidden geometry or information arbitrage, where the probability density can be 4.62% higher than what would normally be expected.
Taking the smart bet
Using the above market information, the smartest idea might be to consider the 26/27 bull call spread expiring on January 16th. This transaction involves two simultaneous trades: buy the $26 call and sell the $27 call, for a net write-off of $52 (the most that can be lost). Both transactions must take place on the same combined ticket.
If SOFI stock rises through the second strike ($27) at expiration, the maximum profit would be $48, which translates to a payout of over 92%. Breakeven would be $26.52, making this trade contextually realistic – assuming, of course, that the quantitative premise is correct.
A more aggressive idea is to consider the 26/28 bull spread, which also expires on January 16. This trade has a breakeven price of $26.92, which appears to be a contextually realistic target. Moreover, the payout is attractive at over 117%. The problem obviously comes down to activating the higher strike, which is quite ambitious as it is at the far end of the distribution. Still, the improved reward may be worth it for some traders.
The opinions and views expressed in this content are those of the individual author and do not necessarily reflect the views of Benzinga. Benzinga is not responsible for the accuracy or reliability of the information provided herein. This content is for informational purposes only and should not be misconstrued as investment advice or a recommendation to buy or sell any security. Readers are asked not to rely on the opinions or information contained herein and are encouraged to do their own due diligence before making any investment decisions.
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