By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData Collective
  • Analytics
    AnalyticsShow More
    data analytics in sports industry
    Here’s How Data Analytics In Sports Is Changing The Game
    6 Min Read
    data analytics on nursing career
    Advances in Data Analytics Are Rapidly Transforming Nursing
    8 Min Read
    data analytics reveals the benefits of MBA
    Data Analytics Technology Proves Benefits of an MBA
    9 Min Read
    data-driven image seo
    Data Analytics Helps Marketers Substantially Boost Image SEO
    8 Min Read
    construction analytics
    5 Benefits of Analytics to Manage Commercial Construction
    5 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-23 SmartData Collective. All Rights Reserved.
Reading: Forecasting the Stock Market: Lessons Learned
Share
Notification Show More
Latest News
data analytics in sports industry
Here’s How Data Analytics In Sports Is Changing The Game
Big Data
data analytics on nursing career
Advances in Data Analytics Are Rapidly Transforming Nursing
Analytics
data analytics reveals the benefits of MBA
Data Analytics Technology Proves Benefits of an MBA
Analytics
anti-spoofing tips
Anti-Spoofing is Crucial for Data-Driven Businesses
Security
ai in software development
3 AI-Based Strategies to Develop Software in Uncertain Times
Software
Aa
SmartData Collective
Aa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Business Intelligence > Decision Management > Forecasting the Stock Market: Lessons Learned
AnalyticsBig DataBusiness IntelligenceDecision ManagementPredictive Analytics

Forecasting the Stock Market: Lessons Learned

mvgilliland
Last updated: 2013/03/12 at 7:34 PM
mvgilliland
5 Min Read
forecasting stock market
SHARE

forecasting stock marketThere is a well recognized phenomenon that combining forecasts, derived from different methods using different sources of information, can improve forecast accuracy. This approach, sometimes called “ensemble forecasting,” is available in SAS Forecast Server.

forecasting stock marketThere is a well recognized phenomenon that combining forecasts, derived from different methods using different sources of information, can improve forecast accuracy. This approach, sometimes called “ensemble forecasting,” is available in SAS Forecast Server.

Per Scott Armstrong’s review of 57 studies on combining forecasts, “the combined forecast can be better than the best but no worse than the average” of the forecasts being combined.* So when you have a situation where it isn’t clear what particular forecasting method is most appropriate, a simple average of several competing methods may be the way to go.

Gathering information from different perspectives is valuable in many other ways. Henry Ford preferred to have an outsider head each new division, because such a person “wasn’t already familiar with the impossible.”

More Read

Predictive Analytics

5 Applications of Predictive Analytics

Don’t Fine Tune Your Forecast!
ATM Replenishment: Forecasting and Optimization
Alfred Hitchcock and a Classic Forecasting Scam
Gaming the Forecast

An outsider provides fresh eyes on the problem. An outsider’s viewpoint is not encumbered by the dogmas that go unquestioned by the insiders. We can usually learn a thing or two by observing how the outsider thinks about our problem. So can a demand planner, concerned about filling orders and managing inventories, learn a thing or two from someone who forecasts the price of stocks?

What Demand Planners Can Learn From the Stock Market

The Fall 2012 issue of Journal of Business Forecasting provides us the stock market forecaster’s perspective in an article, “What Demand Planners Can Learn from the Stock Market,” by Charles ReCorr.

ReCorr begins with the important (but sometimes overlooked) reason why we forecast:

…because all decisions we make require some expectation about the future. Accurate forecasts improve our chances of making the right decision.

He also notes a clear distinction in the decisions to be made and the ability to react to a changing forecast:

The response time for a company to react to, for example, the hint of slowing sales is not the same as that of an institutional investor receiving the same information. A company has to work around production issues, inventory levels, human resource polices and the like before it can respond to changing markets.

The investor, on the other hand, can respond instantaneously with a buy or sell order.

ReCorr identifies seven characteristics that make a forecast useful his investor clients:

Timeframe – The date or period being forecast (e.g. closing price of the S&P500 on December 31, 2013)

Direction – Is the forecast up or down (compared to today’s price or other baseline)

Magnitude – The specific amount or “point forecast” (e.g. S&P500 will be at 1625)

Probability – The distribution of possible outcomes around the point forecast (e.g. 50% chance it will be above 1625, 75% chance it will be above 1575, etc.)

Range – The high and low value for possible outcomes (e.g. 1450 to 1750)

Confidence – A statistically based or subjective “prediction interval” (e.g. 95% confident that it will be between 1550 and 1700)

Historical Forecast Error – Accuracy and bias of previous forecasts

A forecast is useful when it provides enough information to improve decisions under conditions of uncertainty. These characteristics that make a useful stock market forecast would also improve the usefulness of demand forecasts for supply chain planning (where we usually lack anything beyond the point forecast).

Perhaps the least we demand planners can do, in bringing our forecasts to management decision makers, is to fairly present the likely range (and probablility) of possible outcomes. Point forecasts, by themselves, can lead to overconfidence and the taking-on of unnecessary risk. Before making a decision, business managers (like stock market investors) need to know whether (and how) they could live with an outcome that may be far from the point forecast.

—————

*For a thorough discussion, see Scott Armstrong’s chapter on “Combining Forecasts” in his Principles of Forecasting.

TAGGED: ensemble forecasting, forecast, forecasting
mvgilliland March 12, 2013
Share this Article
Facebook Twitter Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

data analytics in sports industry
Here’s How Data Analytics In Sports Is Changing The Game
Big Data
data analytics on nursing career
Advances in Data Analytics Are Rapidly Transforming Nursing
Analytics
data analytics reveals the benefits of MBA
Data Analytics Technology Proves Benefits of an MBA
Analytics
anti-spoofing tips
Anti-Spoofing is Crucial for Data-Driven Businesses
Security

Stay Connected

1.2k Followers Like
33.7k Followers Follow
222 Followers Pin

You Might also Like

Predictive Analytics
AnalyticsPredictive Analytics

5 Applications of Predictive Analytics

5 Min Read

Don’t Fine Tune Your Forecast!

5 Min Read

ATM Replenishment: Forecasting and Optimization

4 Min Read
Image
Predictive Analytics

Alfred Hitchcock and a Classic Forecasting Scam

5 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 chatbots
AI Chatbots Can Help Retailers Convert Live Broadcast Viewers into Sales!
Chatbots
ai in ecommerce
Artificial Intelligence for eCommerce: A Closer Look
Artificial Intelligence

Quick Link

  • About
  • Contact
  • Privacy
Follow US

© 2008-23 SmartData Collective. All Rights Reserved.

Removed from reading list

Undo
Go to mobile version
Welcome Back!

Sign in to your account

Lost your password?