# The Wisdom of Crowds: What Will Replace Financial Analytics?

Quite often a consolidated opinion of homemakers, blue collars, and students proves to be more precise than forecasts by professional analysts. How could the collective intelligence be applied in business?

#### When the mobs are right

In 1906 renowned British Francis Galton came across a competition at a local fair in which the people were asked to guess the weight of a displayed ox and put this figure down to a special ticket. The show hosts promised prizes for correct answers. As a result, the voting embraced some 800 persons — both avid farmers and the people bluntly ignorant of stock-raising. Upon having collected all tickets after the fair for analysis, Galton determined the arithmetic average for the sampling; that stood at 1197 pounds. The ox’s real weight proved to be 1198 pounds. In a mysterious way, the disparate public gave the answer that was closest to the actual number. Galton, who had previously firmly believed in selection and superiority of some people over others, had to change the vector of his research.

Statistics of the Who Wants To Be a Millionaire TV quiz indicate that in the case of a call to an erudite friend the right option was only selected in 65% of occasions, and when a player favored audience assistance, the reply of the majority was correct in 91% of cases.

When the study of group dynamics was booming (1920–1960s), similar research of the wisdom of the mobs was plentiful. For instance, social scientist Hagel Knight asked her students to evaluate the room temperature. The arithmetic average for the whole group suggested that the room temperature should have been 22.44°C degrees, whereas the actual temperature was 22.2°C.

Research with varied complexity profile, in various groups and diverse fields of knowledge (sports, politics, finance, technology, showbiz), proved that the crowd is not as stupid as people think. The major challenge is how to collect promptly and analyze a huge sampling of opinions. Now that nearly every person around the globe has a smartphone the technological obstacle has been erased.

#### Analysis tool

I have been dealing in hi-tech Internet startups since 2009. In early 2015, an idea struck my partners and me to establish a new format of mobile news media. Having invested \$20,000 of our own money, we set to developing the Vote mobile gaming application.

There’s a multitude of news aggregators, but nobody gives readers a chance to interact, generally only to comment and “like.” So in Vote, we offered our users to foretell the news outcome, compete in their forecasts with other users. People use the application as a platform for showing their analytical skills to the public at large.

Concurrently we began assessing the options of entering the global market, primarily in the U.S. — the English-speaking audience is incomparable by the numbers with those who speak Russian. At the time of releasing the app’s first version we received an offer from Starta Accelerator, and I bought a one-way ticket to New York.

It dawned on me at some point of time that Vote was not a mere toy, and users provide very accurate overall weighted average forecasts in a variety of domains. We set to scrutinizing the research on the topic. The scientists managed to reveal a trend suggesting that, provided adherence to some preconditions (decentralization, diversity and knowledge minimum), the average weighted opinion of the crowds will in 80% of cases in the longer term be more exact than an outlook by a most competent expert in the field. A study taken by Estimize corroborates the hypothesis, saying that over the past five years the collective intelligence predicts by 69% more accurately than analysts.

It was when we began examining various business areas to find the fields wherein the weighted average opinion data were the most valuable. The reply proved to be simple — the finance and economics.

#### Opinion market

The financial market is the daily fortune-telling per se. At what price and when will the shares of Facebook, Brent oil or US dollar be the best buy? Traders and analysts make forecasts on the issues every other minute. We chatted to Wall Street traders and learned that their desks are buried under analytical reports from various firms, but with the same data, although the wordings could differ. The pace of information retrieval has been accelerating year by year, and the value of similar reports keeps tumbling — fewer and fewer professional traders read and take them seriously.

The point is that the major mass of current analytics, for instance, linked to financial outlooks, have been originated by a pack of professionals who use nearly the same information. We held 20 interviews with Wall Street analysts and traders, and they confirmed our conjectures: 95% of the financial analytics are made in reliance upon the same insiders.

However, the world spends huge money on various financial analytics. In 2015 alone, professional and amateur exchange traders spent more than \$50 bn to purchase financial market data, of which \$4 bn went to professional analytical services and systems (Predictive Analytics). By 2020 this figure is expected to rise approximately sixfold. This goes beyond professional analytical systems. The B2C financial data market for amateurs is immense — for instance, 54% of US residents at least once in their life have purchased stock, whereas in China some 30% of the population is engaged in stock trading.

Having realized that the world of money desperately needs high-quality forecasting, we have upgraded our application to a separate analytical service - Cindicator, which uses the data from the Vote app. By using it, an analyst may access the crowd opinion index on the events that are crucial for the news agenda, ask his/her own questions and study the public opinion online.

Why should people answer questions? First and foremost, it contains an element of gamification — you could excel your friends in armchair analytics on the topics wherein you have zero expertise but always wanted to have your own say. Secondly, we decided to give the users financial incentives: on a monthly basis, we share the prize money between two percents of the most accurate forecasters (we call them superforecasters), thus motivating them to make most precise forecasts daily.

Finance and economics are way too serious and risky matters to make decisions in reliance upon views even from the best professional experts. We created the service that offers to lend an ear to people from various social groups, with diverse professional background and uniquely personal experience. I am confident such tools will change the future.