What It Is, How It’s Used in Business and Investing


Forecasting is a technique that uses historical data to make informed decisions about future events or conditions. It isn’t simply guessing. A tool for businesses and investors alike, forecasting takes expert analysis and applies complex models to allocate portfolios and budgets.

But just how reliable are these crystal ball-like predictions? After all, economists, investors, and financial planners frequently display a striking talent for mordant humor about the art of economic prediction. “The only function of economic forecasting,” the famed 20th-century economist John Kenneth Galbraith said, “is to make astrology look respectable.” Or, as an equally influential economist, Paul A. Samuelson, put it, Wall Street models “predicted nine of the last five recessions.”

Table of Contents

Key Takeaways

  • Forecasting involves making predictions.
  • In finance, companies use forecasting to estimate earnings or other data for later periods.
  • Traders and analysts use forecasts in valuation models, to time trades, and to identify trends.
  • Standard forecasting techniques include qualitative methods like expert opinion and quantitative methods like statistical models and trend analysis.
  • The limits of forecasting include the uncertainty of future events and the potential for errors in data or assumptions used in the forecasting process.

Yet, forecasting is central to modern investing and business practices. Businesses hire and expand based on predicting sales figures, market demand, or economic indicators. Investors trade stocks, invest in funds, or rashly exit the market based on predictions about stock prices, interest rates, or broader market moves. However, the work of forecasting applies far beyond boardrooms and trading floors. Consumer spending patterns, job market trends, and even geopolitical events all fall under the purview of forecasters.

Keep in mind economist John Maynard Keynes’s dictum that “the inevitable never happens. It is the unexpected always.” We detail below the different forecasting methods and how they share a common goal: To reduce uncertainty and provide a basis for the planning we can do today. We also provide 12 principles you can apply when forecasting to get better results.

Theresa Chiechi / Investopedia

How Forecasting Works

Today, forecasting blends data analysis, machine learning, statistical modeling, and expert judgment. Forecasting provides benchmarks for firms, which need a long-term perspective of operations. For example, much of the derivatives market in options and futures trading is an outgrowth of business and investor forecasting, all to hedge or insure businesses against adverse market changes that could hurt their firms.

Forecasting in Investing

Equity analysts use forecasting to predict how trends, such as gross domestic product (GDP) or unemployment, will change in the coming quarter or year. Statisticians employ forecasting to analyze the potential impact of a change in business operations. Analysts then derive earnings estimates that are often aggregated into a consensus number. If actual earnings announcements miss the estimates, it can have a large impact on a company’s stock price.

Forecasting in Business

In business management, forecasting serves as a cornerstone of strategic decisions, influencing almost every aspect of an organization’s operations. By attempting to predict trends and conditions through qualitative and quantitative measures discussed below, companies aim to position themselves advantageously in the marketplace.

These predictions guide critical choices ranging from market entry strategies and product development to supply chain management and workforce planning, and so the task is often to move from forecasts to planning.

Putting Forecasts Into Action

The consequences of getting a forecast wrong can be far-reaching. Correct predictions allow businesses to improve how they divide their resources, whether they can capitalize on emerging prospects, and mitigate risks. Conversely, inaccurate forecasts can lead to misaligned strategies, inefficient use of resources, missed opportunities, and risks that weren’t managed or insured for. Here are the ripple effects of forecasting on various business functions:

  • Market strategy: Accurate projections of consumer demand and market trends inform which segments to target and how to pitch products and services.
  • Production planning: Forecasts drive decisions on production volumes, helping to balance inventory costs with the ability to meet customer demand.
  • Supply chain management: Predicting resource availability, supplier dependability, and the constraints on both is crucial for maintaining smooth operations and controlling costs.
  • Human resources: Workforce planning relies heavily on forecasts for future business needs and labor conditions.
  • Financial planning: Projections of revenue, costs, and market conditions underpin budgeting and investment decisions.

The consequences of poor forecasting are often severe. Companies may find themselves overextended in declining markets, struggling with excess inventory, or unable to meet unexpected surges in demand.

This is a good point to pause and review, with this table below, the differences between the two:

Forecasting vs. Planning in Business and Investing
Feature Forecasting Planning
Purpose To predict future outcomes based on historical data and trends To outline specific actions and strategies to achieve specific goals
Focus Primarily based on estimating what might happen Primarily based on determining what should happen and how to make it happen
Time Horizon Can be short-term, medium-term, or long-term, depending on what’s being forecast Typically focused on the near future, but may include longer-term objectives, as in financial planning for retirement goals
Methods Statistical analysis, trend extrapolation, expert judgment, simulations, etc. Goal setting, resource allocation, budgeting, scheduling, contingency planning, etc.
Output Quantitative estimates (e.g., sales figures, stock prices, market share) and qualitative methods Actionable plans, budgets, timelines, and performance measures
Use in Investing Predict stock prices, market trends, and economic indicators, informing investment decisions Create investment portfolios, set financial goals, and develop strategies to attain those goals
Use in Business Used to anticipate demand, estimate financial performance, assess market potential, and identify risks and prospects Used to develop business strategies, allocate resources, manage operations, and review performance

Forecasting Techniques

We can now explore the main methods used in forecasting, each with specific strengths and times when they’re best applied:

Quantitative methods in forecasting

Quantitative forecasting techniques rely on numerical data and statistical models to predict future outcomes. These methods are particularly useful for short- to medium-term forecasts where historical data is available and patterns can be discerned.

The further out the forecast, the greater the likelihood that the forecast will be wrong.

Time Series Analysis

This method analyzes historical data points, such as sales figures or stock prices, to identify patterns or trends over time. These statistical relationships are then extrapolated into the future to generate forecasts with confidence intervals to understand the likelihood of specific outcomes. As with all forecasting methods, success is not guaranteed.

Techniques like moving averages and exponential smoothing help level out fluctuations to highlight underlying trends, making it easier to predict future values. This is at the heart of technical analysis in investing. In addition, time series forecasts often involve trend and cyclical fluctuation analysis.

Regression Analysis

Regression models assess the relationship between a dependent variable and one or more independent variables. For example, a company might use regression analysis to understand how their spending on marketing or economic conditions affects their sales. By establishing these relationships, businesses can know how to plan when marketing budgets are needed or as economic conditions change.

Econometric Models

Econometrics is a specialized field that bridges economics, mathematics, and statistics. It focuses on using statistical methods to analyze economic data and test economic theories. Econometricians develop models that quantify relationships between economic variables, such as how changes in interest rates affect investment or how government spending impacts economic growth.

Analysts use these models to predict GDP growth, inflation rates, and unemployment levels. Econometric models are particularly valuable for long-term planning and policymaking.

Quantitative models tend to share these aspects:

  • Model building: Quantitative analysts create mathematical models based on economic and financial theory, incorporating variables that are believed to influence what’s being studied, whether GDP or the price of a stock over time.
  • Data analysis: Real-world data is collected and examined to estimate the relationships between variables.
  • Hypothesis testing: Statistical tests are used to assess the validity of the model and its assumptions, determining whether the relationships identified are statistically significant. Simulations are often used at this point, whether in investing, business, or macroeconomic studies.
  • Forecasting: Quantitative models can then be used to generate predictions about future economic outcomes, so decision-makers can begin planning.
Strengths and Weaknesses of Quantitative Methods in Forecasting
Strengths Weaknesses
Objective: Based on numerical data and statistical models, cutting the potential impact of personal bias Rigid: May struggle to adapt to sudden changes or events not captured in historical data
Consistent: Provides standardized and repeatable results, facilitating comparisons across time periods Can miss non-quantitative information: Doesn’t readily incorporate qualitative factors or expert opinions
Quantifies relationships: Can uncover correlations and patterns within large datasets Requires sufficient data: Relies on having enough historical data to build reliable models
Scalable: Can be applied to large datasets and complex forecasting problems Can be overly complex: Sophisticated models may be difficult to interpret or explain to stakeholders

Qualitative Techniques in Forecasting

Qualitative forecasting methods rely on expert opinions and market insights rather than purely numerical data. Researchers also call this area “judgmental forecasting.” Examples of qualitative forecasting models include interviews, on-site visits, market research, polls, and surveys that apply the Delphi method (which relies on aggregated expert opinions).

Gathering data for qualitative analysis can sometimes be difficult or time-consuming. The CEOs of large companies aren’t going to take a phone call from a retail investor or show them around a facility. However, you can still sift through news reports and the text included in companies’ filings to get a sense of managers’ records, strategies, and philosophies. These techniques are especially valuable in situations where historical data is limited or in a period where previous data is unreliable since the market is changing.

Delphi Method

This structured technique involves a panel of experts who provide their forecasts and assumptions anonymously. Their responses are aggregated and shared with the group, followed by rounds of discussion and revision until a consensus is reached. The Delphi method is often used in all kinds of studies where expertise is needed but it’s especially worthwhile for long-term strategic planning and for forecasting in uncertain or rapidly changing environments.

Market Research

Surveys, focus groups, and interviews are common tools used to gather qualitative data from customers, industry experts, and other stakeholders. This information can reveal emerging trends, shifts in consumer preferences, and other factors that might not yet be seen in numerical data.

Scenario Analysis

This technique involves developing multiple, plausible scenarios based on different assumptions about future conditions. Businesses can then assess how each scenario might impact their operations and plan accordingly. This is a crucial tool in risk management.

Strengths and Weaknesses of Qualitative Methods in Forecasting
Strengths Weaknesses
Responsive: Can adapt to changing conditions or new information Subjective: Relies heavily on human judgment, which is subject to bias and inconsistency
Incorporates insider information: Can take up experience and knowledge that might not be captured in numerical data Limited backward view: Human forecasters may overlook or underemphasize certain factors because of cognitive limits or behavioral biases
Can handle one-off or unusual events: Quantitative methods are highly reliant on previous data, which may leave them unprepared for unusual situations in a way that isn’t the case with qualitative approaches Short-termism: Forecasters may rely too heavily on recent events and neglect longer-term patterns or those they didn’t experience first hand
Encourages ownership: The process itself can foster buy-in from stakeholders involved Could encourage too much ownership: While quantitative approaches can also foster a bias to defend one’s conclusions, those can be checked by data as it comes in, which might not occur with qualitative forecasting

Hybrid or Combined Approaches to Forecasting

Researchers have long been interested in which kind of methods work best and in which circumstances. Of course, different parts of finance and other disciplines tend to side toward quantitative or qualitative methods (e.g., quants are unlikely to yield studies on the gains of market research or other judgmental methods).

But researchers have often found that determining which method to use depends on a variety of factors. For instance, a company might use time series analysis to identify historical trends and supplement this with insights from market research to account for recent changes in consumer behavior.

Given this, it’s no surprise that researchers have demonstrated empirically that combined or hybrid approaches often do best by pulling in the best of both worlds for more informed predictions. Neither human judgment nor quantitative methods are universally superior. Instead, their strengths are often complementary.

Flawed forecasts are commonly a central part of the story when businesses fail.

Quantitative methods do best in processing large data sets and identifying patterns in them, especially when augmented with AI and machine learning, while human judgment is best in interpreting ambiguous situations and incorporating contextual knowledge that may not be captured by data alone. Reviewing recent work in this area, we culled these key points about combining qualitative and quantitative approaches:

  • Combine at the end, not the beginning: Each forecasting approach should be conducted on its own. This independence ensures that each method provides its own perspective on future outcomes, free from the influence of other forecasting techniques.
  • Don’t use qualitative judgments to correct for data: The last point goes hand in hand with the errors often found by using qualitative expertise to correct for quantitative data. This is the most popular way to combine approaches, but it’s often the least accurate.
  • Use diverse sources of information: An effective hybrid approach relies on methods that draw from distinct pools of data. For instance, statistical forecasts typically use historical data and quantitative metrics, while judgmental forecasts often incorporate qualitative insights, market sentiment, and expert intuition. This diversity in information sources enhances how robust the conclusions can be.
  • Expertise matters: When incorporating judgmental forecasts, it’s crucial that they derive from individuals with relevant domain expertise. This criterion acknowledges that effective judgmental forecasting requires more than general intuitions about a field. It requires a deep understanding of the specific field or industry for which the forecast is being done. In short, while it might seem fine to include experts with more general knowledge, the more granular the expertise, the better.

Choosing the Right Forecasting Method

The correct forecasting method depends on the type and scope of the forecast. Qualitative methods are more time-consuming and costly but can make very accurate forecasts given a limited scope. For instance, they might be used to predict how well a company’s new product launch might be received by the public.

For quicker analyses that can encompass a larger scope, quantitative methods are often more useful. Looking at big data sets, statistical software packages today can crunch the numbers in a matter of minutes or seconds. However, the larger the data set and the more complex the analysis, the pricier it can be.

Thus, forecasters often perform a quick cost-benefit analysis—a mini-forecast—to determine which method will increase their chances of accurate predictions for the least cost in time and money.

Budgeting and Forecasting: What’s The Difference?

While often mentioned in the same breath, budgeting and forecasting serve separate yet complementary roles in financial planning, investing, and business management. Budgeting is primarily a planning tool, so it has some of the elements seen above when we covered planning as opposed to forecasting.

Budgeting is a detailed estimate of future income and expenses so you have a road-map for allocating resources and setting performance targets. Budgets are often more static documents, representing management’s commitments and expectations for the period. Here are the main characteristics of budgeting:

  1. Goal-oriented: Budgets reflect specific financial targets.
  2. Expense monitoring: They provide a basis for monitoring and controlling expenses.
  3. Fixed time frame: Usually covers a specific fiscal period, often one year.
  4. Internal focus: Primarily used for internal management and accounting.

Meanwhile, forecasting is about predicting financial outcomes based on present and historical data. So, when you forecast, you aren’t setting targets. Instead, you anticipate what will happen in the future and why you do it, helping organizations and investors adjust strategies and respond to changing conditions. Here are the main characteristics of forecasting:

  1. Predictive: Aims to project likely outcomes rather than set targets.
  2. Adaptable: Regularly updated to incorporate new information.
  3. Various time horizons: Can cover short- or long-term periods.
  4. Considers external factors: Takes into account market conditions and trends, not just the internal conditions of a portfolio, business, or economic sector.

Budgeting vs. Forecasting


  • Purpose and focus: Budgeting sets financial targets and allocates resources

  • Time horizon: Typically covers a fixed period (usually one year)

  • Specificity: Highly detailed with specific line items

  • Application: Used for setting goals, controlling costs, and measuring performance

  • Frequency of review and adjustment: Reviewed and adjusted annually—relatively static


  • Purpose and focus: Forecasting predicts future financial outcomes and trends

  • Time horizon: Can be short-term (monthly/quarterly) or long-term

  • Specificity: Less detailed, focusing on broader financial trends

  • Application: Used for strategic planning and decisions

  • Frequency of review and adjustment: Reviewed and adjusted regularly (monthly/quarterly)

12 Principles of Effective Forecasting

Effective forecasting is a critical skill in business and finance, providing a basis for the decisions that can make or break a business or portfolio. While no forecast is perfect, companies and investors with dependable forecasts are better equipped to navigate uncertainties, seize prospects, and maintain a competitive edge. The following principles, drawn from expert insights and those with practical experience, form the core of effective forecasting:

  1. Be methodical: The best outcomes derive from forecasting that adheres to a systematic, well-defined process. Using a systematic, repeatable approach ensures consistency, allows for continuous improvement, and improves the reliability of your predictions.
  2. Look back to look forward: A rule some researchers cite is looking back at least twice as far into the past as you are forecasting into the future. This simply means looking back at history. Forecasters can identify patterns that can help predict the future if they examine trends over a long period. The recent past is unreliable, so forecasters should look back at least twice as far into the past as they forecast into the future. The unexpected happens, and history doesn’t always repeat itself. Be mindful of history, not a prisoner of it.
  3. Embrace uncertainty: Perfect forecasts are for the gods, not for you. All predictions carry a degree of uncertainty, reflecting the complex and dynamic nature of business environments.
  4. Quantify your uncertainty where possible: Forecasts that can provide a “distribution” of possibilities—an airline predicting the price of jet fuel between minimum and maximum likely prices, for example—help the most in planning since that doesn’t make the prediction an all-or-nothing affair.
  5. Be aware of wild cards: These are low-probability events with high potential impact at the edges of the possible range. Acknowledging outliers and unexpected events is crucial for comprehensive forecasting and, especially, for risk management.
  6. Greater accuracy is found in the aggregate: Forecasts are more precise when applied to broader categories or groups rather than individual items. This principle, known as the law of large numbers, is at the heart of statistics and means that forecasts yield more reliable predictions for aggregate data.
  7. Take heed of the “S curve”: In data science, the S-shaped curve occurs when some process starts slowly, speeds up quickly, and then levels off. According to Silicon Valley-based forecaster Paul Saffo, recognizing this pattern early can help you anticipate different stages of development in various fields and plan accordingly. Forecasters should identify the S-curve pattern as it begins to emerge, and they can then look for precursors to an inflection point rather than the inflection point itself (when changes made might be too late).
  8. The longer the time, the more that can go wrong: The accuracy of forecasts typically diminishes as the time horizon extends. Near-term predictions generally offer greater precision than long-range projections. Unforeseen variables can simply compound their effects over time.
  9. Look for the oddballs: Embrace things that don’t fit. New ideas often emerge as weak signals that can be hard to recognize because they seem strange or don’t fit into existing categories. These “oddball curiosities” sometimes are indicators of future trends. For example, the sale of virtual goods in online games in the late 1990s foreshadowed the rise of virtual world commerce like Second Life. Forecasters should be tuned to these indicators, which often appear as mere curiosities or failures but can actually be forerunners of significant change.
  10. Hold strong views weakly: Don’t fall in love with the oddballs, though. The golden rule of forecasting, as one researcher puts it, is to “be conservative” and rely only on knowledge and methods consistent with the problem at hand. This means also that forecasters should be open to new information that contradicts their initial assumptions. Sometimes, substantial evidence can be misleading, while seemingly weak evidence can signify a future trend. Forecasters should always be willing to revise or discard forecasts when contradictory evidence surfaces. If forecasters adopt a process of strong opinions, weakly held, their range of uncertainty will be refined over time toward more accurate predictions. This rule encourages continuous refining rather than relying on a single, unyielding forecast.
  11. Combine methods independently: When using multiple forecasting methods, ensure they are generated separately, based on different information sources, and incorporate domain expertise where relevant. Researchers have found that this makes for more accurate predictions.
  12. Know when not to forecast: Sometimes, the future is too uncertain to make any predictions. During periods of dramatic, rapid transformation, a good forecaster will refrain from making definitive predictions and instead look for emerging indicators that can provide clues about the future. This is just a reflection of the reality that even in times of significant change, there are often more elements that remain constant than new elements that arise. Forecasters should be aware of the limits of their knowledge and avoid making pronouncements when there’s too much uncertainty.

In addition to these rules, you’ll want to practice the number one principle for what to do after you put a forecast into action through planning and budgets: Regularly reassess the forecast accuracy using appropriate measures and adjust your models as needed to improve performance over time.

What Are Some Limits of Forecasting?

A major constraint on forecasting is that it involves the future, which is fundamentally unknowable. As a result, forecasts can only be educated conjectures. While there are several methods of improving the reliability of forecasts, the assumptions or data that go into the models have to be correct. Otherwise, the result will be “garbage in, garbage out.” Even if the data is good, forecasting often relies on historical data, which is not guaranteed to be valid in the future, as things can and do change over time. It’s also impossible to correctly factor in unusual or one-off events, like a crisis or disaster.

Can Forecasting Be Used To Predict The Stock Market?

Perfectly predicting the market’s ups and downs is impossible. However, investors can use forecasts to analyze company valuations, identify growth sectors, and manage risk within their portfolios. That said, unforeseeable events always impact the market, so forecasts should be just one piece of the investment puzzle.

What’s a Major Economic Prediction That Went Wrong?

The 2007-08 financial crisis stands out as a major event that seemed, to most, to occur out of nowhere. More recently, one of the most notable economic predictions that went wrong was the underestimated impact of the COVID-19 pandemic on the global economy. In early 2020, many experts and financial institutions, including the International Monetary Fund (IMF) and various central banks, initially predicted a relatively swift economic recovery following short-term disruptions. However, the prolonged nature of the pandemic, with multiple waves of infection and varying responses by different countries, led to far more severe and long-lasting economic consequences than anticipated. Then the IMF failed to predict the surge in inflation that arrived afterward.

The Bottom Line

Forecasts help managers, analysts, and investors make informed decisions about the future. Without good forecasts, many of us would be in the dark and would resort to guesses or speculation. By using qualitative and quantitative data analysis, forecasters can better understand what lies ahead.

Businesses use forecasts and projections to inform managerial decisions and capital allocations. Analysts use forecasts to estimate corporate earnings for subsequent periods. Economists may make more macro-level forecasts as well, such as predicting GDP growth or changes to employment. However, since we cannot definitively know the future, and since forecasts often rely on historical data, their accuracy will always come with some room for error—and, in some cases, may end up being way off.

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