Houston research: Can predictive tools help forecast the stock market?

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The stock market has always been hard, if not impossible, to forecast. Image via Getty Images

What do you think the Standard & Poor’s 500 index will do over the next year?

When Rice Business finance professor Kevin Crotty asks his MBA students this question, the answers are all over the map. Some students expect the overall return on the stock market to be 10 percent, while others predict a loss of 20 percent.

This guessing game is closer to real life than many people realize. Experienced investors, people who have watched the stock market ebb and flow for many years, know that making predictions is a risky business. “Many money managers are more confident choosing individual stocks than trying to time the market,” says finance professor Kevin Crotty.

For most of the past century, academics have applied their power of analysis to understanding and predicting the stock market. Recently, some finance researchers have taken a closer look at option prices—the price paid for the right to buy or sell a security (like a stock or bond) at a specified price in the future. Combining economic theory with high-frequency options price data, they argued that they could estimate the expected return on the market in real-time, which would represent a tremendous development for finance practitioners and academics alike.

Crotty teamed up with Kerry Back, a fellow Rice Business professor, and Seyed Mohammad Kazempour, a finance Ph.D. student at the Jones Graduate School of Business, to evaluate whether the new predictors based on option prices really are a valuable forecasting tool. “Options are essentially a forward-looking contract, so it’s possible that they could be used to create a forward-looking measure of expected returns,” says Kazempour.

Economic theory suggests that the new predictors might systematically underestimate expected returns. The team set out to test if this may be the case, and if so, whether the predictors are useful as a forecasting tool. In their paper, “Validity, Tightness, and Forecasting Power of Risk Premium Bounds,” the Rice Business researchers ran the predictors through a more rigorous set of statistical tests that provide more power to detect whether the predictors systematically underestimate expected returns. The statistical tests used in previous research on the topic were less stringent, leading to conclusions that the predictors do not underestimate expected returns.

In short, the new predictors didn’t pass the more stringent tests. The researchers found that forecasts built on stock options consistently underestimated market returns. Moreover, the predictors are enough of an underestimate that they are not very useful as forecasts of market returns.

The results were somewhat anticlimatic, the researchers admit. If the option-based predictors had panned out, it could have become an innovative new tool for thinking about market timing for asset managers as well as investment decision-making for corporate finance projects. “Trying to estimate expected market returns is closely related to whether corporations decide to invest in projects,” notes Crotty. “The expected market return is an input in estimating the cost of capital when evaluating projects, and I explain in my MBA courses that we don’t have very precise estimates for this input. During this research project, I kept thinking about how cool it would be if we really had a better estimate,” he says.

Their research doesn’t end here. Crotty and Back have already begun brainstorming ways to potentially improve the option-based forecasting tool so that it can become more accurate.

At best, though, using option prices as a forecasting tool will only be one ingredient out of many that investors use to make decisions. “This tool may inform money management, but it will never drive it,” says Back.

For now, at least, the Rice researchers believe that trying to predict the stock market is still a very risky game.

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This article originally ran on Rice Business Wisdom and was based on research from Rice Professors Kerry Back and Kevin Crotty.

Investors might be drawn to active fund investing, but index funds might be less risky, according to Rice University researchers. Getty Images

Rice University research finds how index funds can be a good investment opportunity for the risk adverse

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It's easy to assume that investing, like cooking, requires skill to get the right mix of ingredients. But that's not the case with index funds. Effort goes into building them, but these ready-made investments need minimal intervention. Yet the outcomes are appetizing indeed.

In the past few decades, use of index funds has exploded. So have media coverage and advertisements questioning if they can truly compete with active funds. A recent study by Alan Crane and Kevin Crotty, professors at the business school, provides a resounding "yes." These humble investment recipes, it turns out, are richer than they might seem.

Index funds track benchmark stock indexes, from the familiar Dow Jones Industrial Average to the widely followed Standard & Poor's 500. Like viewers following a cooking show, index fund managers buy stocks in the same companies and same proportions as those listed in a stock index. The best-known indices are traditionally based on the size of the companies.

The idea is that the index fund's returns will match those of its model. An S&P 500 index fund, for example, includes stocks in the same 500 major companies included in the Standard & Poor index, ranging from Apple to Whole Foods.

Index funds are part of the broad range of investment products called mutual funds. Like cooks making a stew, mutual fund managers add shares of various stocks into one single concoction, inviting investors to buy portions of the whole mixture.

While some mutual funds are active, meaning professional managers regularly buy and sell their assets, index funds are passive. Their managers theoretically just need to keep an eye on any changes in the index they're copying. Not surprisingly, active index funds tend to charge more than passive ones.

Curiously, not all index funds perform at the same level. So what should that mean for investors? To study these variations and their implications, Crane and Crotty expanded on past research about skill and index fund management, analyzing the full cross section of funds.

This wasn't possible to do until fairly recently: there simply weren't enough index funds to study. The first index fund, which tracked the S&P 500, was developed by Vanguard in the 1970s. To do their research, the Rice Business scholars looked at performance information for both index and active funds, starting their sample in 1995 with 29 index funds. The sample expanded to include a total of 240 index funds, all at least two years old with at least $5 million in assets, mostly invested in common stocks. They also analyzed 1,913 actively managed funds.

Using several statistical models, Crane and Cotty found that outperformance in index-fund returns was greater than it would be by chance. The discovery suggests that passive funds, although they require little skill to run, have almost as much upside as active funds.

In fact, the professors found, the best index funds perform surprisingly closely to the best active funds, but at a lower cost to the investor. The worst active funds perform far worse than the worst index funds–even before management fees.

The findings topple the conventional wisdom that only actively managed funds stand a chance of beating the market. While active-fund managers often measure their success against that of passive funds, the data show investors who are risk averse would do better to choose passive funds over more expensive active ones.

More adventurous investors, of course, will always be tempted by what's cooking in actively managed funds. But overall, investing in plain index funds is as good a meal at a lower price.

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This story originally ran on Rice Business Wisdom.

Alan D. Crane and Kevin Crotty are associate professors of finance at the Jones Graduate School of Business at Rice University.

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Rice University MBA programs rank among top 5 in prestigious annual report

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Rice University’s Jones Graduate School of Business MBA programs have been ranked among the top five in the country again in The Princeton Review’s 2025 Best Business Schools rankings.

The university's MBA program in finance earned a No. 3 ranking, climbing up two spots from its 2024 ranking. Finance MBA programs at the University of Virginia's Darden Graduate School of Business and New York University's Leonard N. Stern School of Business were the only ones to outrank Rice, claiming No. 2 and No. 1 spots, respectively.

Rice's online MBA program was ranked No. 5, compared to No. 4 last year. Indiana University's Bloomington Kelley School of Business' online program claimed the top spot.

“These rankings reflect the commitment of our faculty and staff, the drive and talent of our students and the strong support of our alumni and partners,” Peter Rodriguez, dean of Rice Business, said in a news release. “They are exceptional honors but also reminders — not just of our top-tier programs and world-class faculty and students but of our broader impact on the future of business education.”

Rice also ranked at No. 6 for “greatest resources for minority students."

The Princeton Review’s 2025 business school rankings are based on data from surveys of administrators at 244 business schools as well as surveys of 22,800 students enrolled in the schools’ MBA programs during the previous three academic years.

"The schools that made our lists for 2025 share four characteristics that inform our criteria for designating them as 'best': excellent academics, robust experiential learning components, outstanding career services, and positive feedback about them from enrolled students we surveyed," Rob Franek, The Princeton Review's editor-in-chief, said in a press release. "No b-school is best overall or best for all students, but to all students considering earning an MBA, we highly recommend these b-schools and salute them for their impressive programs."

Rice's finance program has ranked in the top 10 for eight consecutive years, and its online MBA has ranked in the top five for four years.

Rice and the University of Houston also claimed top marks on the Princeton Review's entrepreneurship rankings. Rice ranks as No. 1 on the Top 50 Entrepreneurship: Grad list, and the University of Houston ranked No. 1 on Top 50 Entrepreneurship: Ugrad. Read more here.

Houston named ‘star’ metro for artificial intelligence in new report

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A new report declares Houston one of the country’s 28 “star” hubs for artificial intelligence.

The Houston metro area appears at No. 16 in the Brookings Institution’s ranking of metros that are AI “stars.” The metro areas earned star status based on data from three AI buckets: talent, innovation and adoption. Only two places, the San Francisco Bay Area and Silicon Valley, made Brookings’ “superstar” list.

According to Brookings, the Houston area had 11,369 job postings in 2024 that sought candidates with AI skills, 210 AI startups (based on Crunchbase data from 2014 to 2024), and 113 venture capital deals for AI startups (based on PitchBook data from 2023 to 2024).

A number of developments are boosting Houston’s AI profile, such as:

Brookings also named Texas’s three other major metros as AI stars:

  • No. 11 Austin
  • No. 13 Dallas-Fort Worth
  • No. 40 San Antonio

Brookings said star metros like Houston “are bridging the gap” between the two superstar regions and the rest of the country. In 2025, the 28 star metros made up 46 percent of the country’s metro-area employment but 54 percent of AI job postings. Across the 28 metros, the number of AI job postings soared 139 percent between 2018 and 2025, according to Brookings.

Around the country, dozens of metros fell into three other categories on Brookings’ AI list: “emerging centers” (14 metros), “focused movers” (29 metros) and “nascent adopters” (79 metros).

Forecasting retail sales just got smarter, thanks to Google searches

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Imagine being able to predict the future of retail sales — not with months-old economic reports, but with real-time digital breadcrumbs left behind by consumers. Thanks to new research from K. Ramesh (Rice Business) and Gary Lind (University of Pittsburgh), that future is already here.

Key findings

  • Search trends predict future retail revenue — up to three quarters ahead.
  • Markets don’t fully price in search-based insights, especially before earnings announcements.
  • Trading strategies using search data outperform traditional asset-pricing models by 2–3%.
  • Analysts often underuse this data, resulting in predictable forecast errors.
  • At the macro level, search trends closely track — and often lead — official retail sales and GDP.

Their study shows that Google search trends are powerful predictors of consumer demand, capable of forecasting U.S. retail sales up to three quarters in advance. The implications span industries and audiences — from Wall Street traders to Main Street retailers, and from data-driven CFOs to curious business students.

A smarter, faster crystal ball for retail demand

Retail sales drive nearly 70% of U.S. gross domestic product (GDP), making them one of the most important indicators of economic health. Yet traditional forecasting relies heavily on backward-looking data and broad economic indicators. Ramesh and Lind’s work offers a leap forward: search data from Google Trends — publicly available and easy to use — can offer a faster, more precise signal of where sales are headed.

“If you want to understand consumer demand,” says Ramesh, the Herbert S. Autrey Professor of Accounting at Rice Business, “look at what people are searching for — not just what companies report after the fact. These digital footprints can tell us where revenue is going, often before the market catches up.”

This low-cost approach stands in stark contrast to expensive alternatives, such as satellite images of parking lots, in-store foot traffic from mobile data, or retail scanner feeds — tools often reserved for hedge funds and large institutional investors. Google Trends offers a scalable, accessible option for anyone seeking insight into consumer behavior.

Why this matters for investors and analysts

The researchers analyzed search patterns for nearly 200 publicly traded U.S. retail companies between 2004 and 2019. They found that firms with rising search volumes tend to report higher revenues — often well before analysts or investors react.

In fact, investment strategies built around these trends earned 2–3% higher returns than traditional models would predict. And most of these gains were concentrated around earnings announcements — a sign that markets were slow to recognize what search data had already revealed.

The advantage is even more striking for companies that fly under Wall Street’s radar. For firms with limited analyst coverage, the predictive power of search data on stock performance was more than 80% stronger.

A new tool for analysts — if they’ll use it

Despite the clear signal in the data, the study finds that analysts routinely fail to incorporate search-based insights into their forecasts. That gap translates into missed revenue predictions and market inefficiencies — which sophisticated investors can exploit.

“The search data are out there,” says Lind, a Rice Business Ph.D. ’19 graduate now on faculty at Pitt Business. “They’re public, powerful, and free. Analysts who ignore them are missing an opportunity.”

The impact of this research extends well beyond equity markets. By aggregating Google search data across the retail sector, the authors found strong alignment with U.S. Census retail sales figures and GDP trends. In other words, these digital signals reflect not just firm-level interest, but macroeconomic momentum.

A data wake-up call

This makes search trends a potentially valuable early-warning system for policymakers, central banks, and anyone responsible for interpreting the economic landscape. During volatile or uncertain periods, such tools could offer precious foresight into shifting consumer behavior.

In a digital era where consumers research before they shop — whether online or in-store — it’s no surprise that their Google queries leave valuable clues. What’s surprising is how often businesses, investors, and even economists still overlook them, Ramesh says. “Search data is a goldmine. And it's just sitting there, waiting to be used.”

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This article originally appeared on Rice Business Wisdom.

Lind and Ramesh, “Using Internet Search Data to Predict Aggregate Retail Sales and Enhance Firm-Level Revenue Expectations,” Contemporary Accounting Research (April 2025): 1-32. https://doi.org/10.1111/1911-3846.13043.

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