Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    data analytics and truck accident claims
    How Data Analytics Reduces Truck Accidents and Speeds Up Claims
    7 Min Read
    predictive analytics for interior designers
    Interior Designers Boost Profits with Predictive Analytics
    8 Min Read
    image fx (67)
    Improving LinkedIn Ad Strategies with Data Analytics
    9 Min Read
    big data and remote work
    Data Helps Speech-Language Pathologists Deliver Better Results
    6 Min Read
    data driven insights
    How Data-Driven Insights Are Addressing Gaps in Patient Communication and Equity
    8 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Why Predicting the Future is So Darn Difficult
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Data Management > Culture/Leadership > Why Predicting the Future is So Darn Difficult
AnalyticsCulture/LeadershipExclusiveModelingPredictive AnalyticsRisk Management

Why Predicting the Future is So Darn Difficult

paulbarsch
paulbarsch
5 Min Read
SHARE

Predicting the future is difficult—just ask George Soros.  While Soros is often celebrated for the $1 billion profit he made in 1992 on a bet that the pound sterling would collapse in valuation, other trades ended up costing him almost as much money as he made.

Predicting the future is difficult—just ask George Soros.  While Soros is often celebrated for the $1 billion profit he made in 1992 on a bet that the pound sterling would collapse in valuation, other trades ended up costing him almost as much money as he made.

As detailed in Sebastian Mallaby’s “More Money than God”, leading up to October 19, 1987, Soros’ Quantum fund had been up 60%.  However, when “Black Monday” hit and the Dow Jones lost 22.6% of its value, Soros was in the middle of the mess deciding whether to sell or buy. While he held his positions through Wednesday of that week, on Thursday he abruptly changed his mind and sold positions worth $1 billion.  Soros’ decision to unload his massive portfolio sparked other traders to also sell stocks and bonds, thus causing a downward spiral in markets. At the end of the day, Soros was out of the market; however his Quantum fund lost $840 million!

More Read

What is the Future of Social Media Analysis?
Conducting Research on Social Networks
How Assistive AI Decreases Damage During Natural Disasters
PMML and Open Source Data Mining – Predictive Analytics on the go!
From “The Farm” to FarmVille

Alas, that’s the problem with gut decision making, you say. Soros should have used quantitative analysis, right?  Even quantitative analysis can produce the wrong outcome.

Editor’s note: Paul Barsch is an employee of Teradata. Teradata is a sponsor of The Smart Data Collective.

According to Roger Lowenstein’s “When Genius Failed”, hedge fund Long Term Capital Management (LTCM) was chock full of the best minds in finance.  Assembling PhDs in finance, mathematics, economics and more, LTCM partners built sophisticated trading models based on the assumption that while investors sometimes panic or get too optimistic, eventually markets settle towards equilibrium. And in moments of panic or too much optimism, LTCM’s partners believed there was money to be made.

Unfortunately, LTCM is a case study in over reliance on analytical models for decision making.  Lowenstein writes, “LTCM Partners believed that all else being equal, the future would look like the past” and this—of course—turned out to be a calamitous assumption. LTCM bet heavily on models, often doubling down on investments that they believed had an infinitesimal probability of failing. The assumption underpinning these models was that markets are efficient and rational. And when markets proved otherwise, Lowenstein notes, “The fund with the highest IQs lost 77% of its capital, while the ordinary stock investor doubled his money during the same period.”

It is apparent in studying Soros and LTCM, that even the most experienced minds supplemented by analytical tools and techniques can make extremely poor decisions about the future.  So why is predicting the future so difficult?

In Scientific American, author Michael Shermer has an answer. He says that the world is a “messy, complex and contingent place with countless intervening variables and confounding factors which our brains are not equipped to evaluate.”  He says we should stick to short term predictions rather than those longer term trends which we so often get wrong.

Does this mean that any attempt to predict the future is for naught? Of course not, as there are definitely limited applications for prediction models in preventing fraud, recommending products, discerning customer defections, and more. Even three day weather forecasts are more right than wrong!

The real lesson is that predicting the future is hard, especially when we’re confronted with millions of potential variables. Deciding which variables to pay attention to, and weighting the importance of those variables is especially difficult.

And maybe then, the solution is not so much to spend countless hours predicting the future (especially for strategic decisions), but instead to expend energy preparing for it. 

Question:

  • Do you agree with Michael Shermer that our brains are not equipped to evaluate our “messy world”?
  • What other case studies have you encountered where either gut or analytical decision making was taken to the extreme?
TAGGED:Decision Makingforecastingrisk management
Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

data analytics and truck accident claims
How Data Analytics Reduces Truck Accidents and Speeds Up Claims
Analytics Big Data Exclusive
predictive analytics for interior designers
Interior Designers Boost Profits with Predictive Analytics
Analytics Exclusive Predictive Analytics
big data and cybercrime
Stopping Lateral Movement in a Data-Heavy, Edge-First World
Big Data Exclusive
AI and data mining
What the Rise of AI Web Scrapers Means for Data Teams
Artificial Intelligence Big Data Exclusive

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

Could You Be POTUS?

4 Min Read

Is Cloud Computing Hurtling Towards Disaster?

4 Min Read

Gaming the Forecast

4 Min Read

Are Public Clouds Complex Environments?

4 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

AI and chatbots
Chatbots and SEO: How Can Chatbots Improve Your SEO Ranking?
Artificial Intelligence Chatbots Exclusive
ai chatbot
The Art of Conversation: Enhancing Chatbots with Advanced AI Prompts
Chatbots

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?