Stock Prognosticators? (6/11/14)
A first of its kind study by researchers at the University of Iowa suggests Yahoo’s finance message boards have a small degree of ability to predict stock price movements.
The study, “Stock Chatter: Using stock sentiment to predict price direction,” also found that more than two-thirds of the message board comments had nothing to do with finance.
The researchers analyzed 70,000 posts by more than 7,000 commenters on Yahoo’s finance message boards from April to June 2011. They determined what sentiment, if any, they expressed about 11 Fortune 500 stocks, either bullish, bearish, or neutral. The researchers then looked at the movement of those stocks’ prices the next day. Depending on the model the researchers used to classify the statements, they found that the sentiment expressed on the message boards accurately reflected the price movement anywhere between 52 and 64 percent of the time.
Michael Rechenthin, who conducted the study as a doctoral student in the Tippie College of Business, says that while the lower accuracy figures can be attributed to randomness, the 64 percent figure is statistically significant and shows a small degree of predictive ability.
The study found the predictive ability lasted only one day, though, and disappeared on follow-up days.
The study also found that only a small number of users produced the largest number of comments, and those comments seem to be responsible for whatever predictive quality the message boards had. Rechenthin says only 3 percent of the commenters wrote 50 percent of the posts during the study period, and 11 percent wrote 75 percent of the posts.
Those extreme users also tended to be the most opinionated, he says, and when they were removed from the study, what predictive ability the boards had all but disappeared. He says the study did not determine whether this was because the most forceful opinions affected stock price, or if those with stronger opinions were more seasoned market observers and able to see trends.
The study also found that, as one might expect of public message boards, many of the posts were completely irrelevant to the topic. They determined that 68 percent of posts were things like advertisements, spam, flame wars, or comments by trolls.
Politics tended to be the most common off-topic topics, he says, with President Obama and former President Bush frequent topics of criticism. The study period also included former Rep. Anthony Weiner’s resignation from Congress after texting inappropriate photos, and many comments were related to him.
Rechenthin cautions that as this is the first study of its type and follow-up studies will be needed using larger data sets. The research was published recently in the journal Algorithmic Finance and was coauthored with W. Nick Street, professor of management sciences in the Tippie College of Business, and Padmini Srinivasan, professor of computer science at the University of Iowa.
Drafty Research (5/6/14)
When general managers gather in New York for the NFL draft this week, they’ll be awash in statistics, scouting reports, interview data, and video clips as they look for a way to digest it all and make the best draft selections for their respective teams.
How should the selection be made? They can use a rule of thumb, like filling the biggest hole in their roster, or picking the best player available. But how is best measured? University of Iowa professor Jeff Ohlmann says several teams are arming their staffs with information based on analytics in efforts to gain any edge that they can.
One of Ohlmann’s research focuses is how a sports team can optimize its draft selections, and this work has led to the development of a smartphone app to help fantasy football and baseball team owners pick their teams. Ohlmann says that a sports draft is a great example of what is called a “sequential decision-making problem with uncertainty,” an area of research that attracts interest of both academics and practitioners. For example, logistics companies need to design routes to supply goods without knowing exactly how much product will be required by the retailer or precisely how long it may take to get there.
In football, the uncertainty lies in not knowing how well a player may perform in the NFL, or even if that player will be available when a team is “on the clock” because some other team may pick him first. Using probability distributions to model the uncertainty, Ohlmann and other researchers have developed models that use mathematical techniques to maximize the expected value of the players which a team drafts. At its heart, the model tries to help general managers overcome the fundamental handicap of not knowing what players will be available to draft in future rounds.
In Ohlmann’s approach, a team projects opposing teams’ selections to forecast which players will be available to the team in future rounds. He says that while there will surely be errors in guessing which players other teams will select, the errors often cancel each other out to create a sufficiently accurate forecast of the draft as a whole. The result, he says, is a model that produces a draft strategy that typically dominates alternative drafting rules-of-thumb.
“Rules-of-thumb have flaws in them and you can do better if you try to predict what will happen in the future,” he says. “A draft strategy based on analytics isn’t guaranteed to dominate, but more often than not, it will be the best strategy.”
He acknowledges data analysis is not a crystal ball and will not be 100 percent accurate in identifying which players are going to be successful. “But it may help avoid a bad decision in cases when the front office personnel are 'fooled by their eyes’ and let emotions affect their decisions.”
Ohlmann, who teaches in the department of Management Sciences in the Tippie College of Business, has used his sports research to develop and teach a first-year seminar, Sports Analytics, to introduce analytical tools to students using a topic that many students are naturally interested in.
“My goal is to show students how to formulate sports-related questions and then use data and math to try to answer them rather than just qualitatively debating them,” he says.
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