Can Options Data Really Predict Stock Moves?

On this IBKR Podcast episode, we explore how options market data reveals hidden signals that can help investors forecast stock movements with greater accuracy. Brian Tancock of Visual Sectors breaks down four predictive pillars, like asymmetric information and crowd wisdom, and explains how retail traders can use them to build smarter, market-neutral portfolios.

Summary – IBKR Podcasts Ep. 314

The following is a summary of a live audio recording and may contain errors in spelling or grammar. Although IBKR has edited for clarity no material changes have been made.

Andrew Wilkinson 

Welcome everybody to a new episode of IBKR Podcasts. My guest today is Brian Tancock from Visual Sectors, and today we’re going to discuss the landscape of options trading and liquidity, and how data within the options market can be used predictively to help investors build market-neutral equity portfolios. Welcome, Brian. How are you? 

Brian Tancock 

Andrew, very well. It’s good to be with you. 

Andrew Wilkinson 

It’s excellent to have you on the program. Can you explain, first of all, Brian, why you embarked on this journey and where it has delivered you to? 

Brian Tancock 

So, when we think about the meme stock craze that happened a few years ago, we saw some pretty fantastical instances of coordinated options positions really driving wild price action in the equity markets. And while this phenomenon that we know as gamma squeezing is nothing new, it was kind of a wake-up call to retail investors, if you will. There’s been a lot of academic research in this space for decades prior to the whole meme stock craze, and many sophisticated institutional buy-side firms have long used options data to build return streams that are completely uncorrelated to the broader market. What we wanted to do was take the power of options data and put it in the hands of retail investors to help them make better trading decisions. Where this has gotten us now is that we’ve been able to synthesize massive amounts of data and put it in the hands of our users to help them make better trading decisions. 

Andrew Wilkinson 

So, in terms of the landscape, how has trading changed for retail investors in the age of AI and bots? 

Brian Tancock 

There’s always going to be a danger in any trade—whether that be a short-term trade or a sophisticated asset class—of getting overcrowded. We’re starting to see this in private markets right now. What have long been considered sophisticated niche asset classes are now showing evidence of dilution, and the returns or performance are being negatively affected. This can happen in the trading space as well. For traders, especially the uninitiated, many fall into the trap of technical analysis or the trading guru. Those who move past that often enter the algorithmic space, and while they’ve been able to enjoy some edge from trading skill, that too carries the risk of becoming overcrowded and diluted. What we’d like to do is enable traders to build more of a mathematical edge and rely less on building trading skill, and more on reducing churn and building a true mathematical edge that can persist. 

Andrew Wilkinson 

So, Brian, how would you assess the changes to the overall trading environment over the last five years, say? 

Brian Tancock 

Yeah, well, I’m certainly not breaking any news to you when I say that the S&P 500 has completely changed in modern times. Right now, the 10 largest companies in the S&P 500 make up 40% of the overall capitalization. Just two years ago, that number was 27%. So, the concentration risk has become exponential. Now—a moment please—so the concentration risk is growing exponentially, and a lot of this is being driven by passive index funds. What were once thought to be safe, well-diversified investment tools are now potentially turning into completely different phenomena, with potentially a lot more risk and a lot more concentration risk. If one or two of these companies were to experience some type of black swan event at the same time, the entire risk structure we’ve come to know in broader markets could be completely redefined. Even after all the data and everything we experienced in 2008, we could be looking at a radical shift in overall market risk. That’s something we need to be cognizant of going forward. 

Another thing we’re seeing is growing evidence of speculation driving markets. If we look at two small-cap indices—the S&P 600 and the Russell 2000—the S&P 600 has a profitability screener, so the companies in it are generally of higher quality than those in the Russell 2000. If we subtract the returns of the S&P 600 from the Russell 2000, we’re able to isolate a net-zero exposure return factor that we call speculation. On the other side of speculation is what we would call quality. Looking at this return factor going back to 2000, cumulatively, quality has outperformed speculation by 26%, roughly 1% per year. But here in 2025, speculation has returned 7.5%. That’s the single largest return of that factor on record—even more than in 2020, when everybody was sitting at home day trading with their stimulus checks. 

Andrew Wilkinson 

So what is the way forward, do you think, for retail investors? 

Brian Tancock 

If we look at the financial crisis of 2008, Citadel, by some reports, lost up to 55% of their flagship fund. From everything we’ve gleaned from this very private institution, that was a defining moment for Ken Griffin. They completely changed their approach. They adopted more of a risk parity approach, and this is something their Chief Risk Officer, Joanna Welsh—an incredibly bright woman—has spoken about on many occasions. It’s also an approach that Jim Simons used at Renaissance Technologies. This is something we preach to our users: the importance of building long-term advantages from a market-neutral approach to investing and trading, especially given all the risks we’re seeing right now in the broader economy—fiscally and structurally. That’s something we’re paying a lot of attention to right now. 

Andrew Wilkinson 

The thought of new data sets is an interesting topic in itself. You wrote an article about options data use cases to predict stock moves. Can you explain how that works a little bit, please? 

Brian Tancock 

So, yeah, when we look at the academic research that’s been done in this space, there are really four pillars of options data that have demonstrated predictive capacity for the underlying stocks. And those are asymmetric information, hedging demand, embedded leverage, and finally, the mathematical principle known as crowd wisdom. So, what are these exactly? Asymmetric information refers to when a market participant—whether that be a fund manager or a trader—wants to express conviction in a single name. They’re going to prefer securities that allow them to size that exposure more effectively than simply using the equity. And this is where options come into play. How this manifests is in the presence of what we see as the option-to-stock volume ratio. So, when we see traditional option-to-stock, or OS, ratios behave differently than they have historically, this might be an indication that there’s what we call asymmetric information entering into the market. 

The second pillar, hedging demand, refers to the delta hedging requirements that dealers—who are on the other side of these trades—are forced to maintain for their own risk management purposes. As options volume has exploded, these dealers are now forced to move around more and more shares of stock to maintain their delta-neutral position. Evidence has shown that the effects of hedging demand can influence underlying stocks for up to five days moving forward. 

So, the third one we’re seeing is embedded leverage. This builds on the concept of information asymmetry. When a manager wants to build a position, a lot of times they’re going to look to take advantage of the embedded leverage that options provide. So, when we see changes in open interest along the chain—say, out-of-the-money puts or out-of-the-money calls—this can also represent that there may be information asymmetry entering into that particular stock at that time. 

And finally, the last one is the mathematical concept known as crowd wisdom. Crowd wisdom shows that the collective predictive capacity of a crowd is better than that of a random individual. And when that crowd is made up of experts, the predictive capacity is even greater. When we talk about options markets, we know that roughly 60% of trading volume is dictated by institutional investors. So, the concept of crowd wisdom has been shown to be more effective in forecasting stock prices than, say, a random selection would. In practice, this signal is enhanced by what we know as options order data types. So, if we can isolate opening buy orders and put-call ratios—with opening buy orders on puts and opening buy orders on calls, in accordance with put-call ratios—investors have been able to generate excess risk-adjusted returns by evaluating those metrics. 

Andrew Wilkinson 

So how do you actually transition from stock picking to managing portfolios then? 

Brian Tancock 

While we preach using single names versus passive indices in building portfolios, we still place an emphasis on risk parity. We want to make sure that no one position in a portfolio carries more risk than any other position, if you will. So, we focus on position sizing in order to get this done. Another thing that we’re able to do for our users is that we’ve accumulated a large cache of data, if you will. We’re constantly backtesting and using different iterations of trading signals and risk management techniques in order to find improvements on our current approach. 

Andrew Wilkinson 

And is that how you compose market-neutral portfolios? 

Brian Tancock 

Yeah. Like we touched on before, we’re big proponents of the market-neutral approach, especially given all the red flags we’re seeing in markets at the moment. We’re working to empower traders to be able to trade more like a hedge fund and less like a YouTube guru who’s drawing lines on a chart, if you will. When we look at changes in markets—especially as markets become more volatile—we’ve seen historically that asset classes meant to be risk-diversifying tend to become more correlated. Evidence has shown that during times of stress, equity market-neutral portfolios—or just market-neutral portfolios—do a better job at maintaining their diversifying characteristics, meaning their low covariance to the broader market, than, say, a traditional risk-diversifying asset like bonds. This is something we’re trying to help traders understand, especially as we see forward markets being less predictable than they currently are. If we look at the dot-com boom of the late 1990s, it was followed by a lost decade for broader markets. Are we really so arrogant to think that can’t happen again? What we want to do for retail investors is—this is going to be very difficult for them to build a market-neutral portfolio on their own—but we’re giving them the benefit of our expertise, our knowledge, and our experience to help them do this and better navigate markets going forward. 

Andrew Wilkinson 

And I think we’re going to run a webinar about this topic in December. Is that right, Brian? 

Brian Tancock 

That’s right. We’re looking forward to that. 

Andrew Wilkinson 

Okay. I’m looking forward to seeing a few of the visuals that you’ve just explained in today’s episode on screen. So, we’ll look forward to that. I think that’s December the fourth, is it? 

Brian Tancock 

Yes, sir. That’s correct. 

Andrew Wilkinson 

Brilliant. Well, thank you very much for joining me today, Brian. Alright, my guest today, Brian Tancock from Visual Sectors. And to the audience, remember to subscribe to our channel wherever you download your podcasts from. Thanks everybody. Bye for now.