What is a prediction?
Forecasting is a technique that uses historical data as input to make informed estimates that become predictive in determining the direction of future trends.
Businesses use forecasts to decide how to allocate budgets and to plan expected expenses for future periods. This is usually based on predicted demand for the goods or services offered.
Important points
- Forecasting involves making predictions about the future.
- In finance, forecasts are used by businesses to estimate revenues and other data for subsequent periods.
- Traders and analysts use predictions in valuation models to time trades and identify trends.
- Forecasts are often based on historical data.
- Because the future is uncertain, forecasts must be revised frequently and actual results may differ materially.
Teresa Chiech/Investopedia
How prediction works
Investors use forecasts to determine whether events that affect a company, such as sales forecasts, will cause the company's stock price to rise or fall. Forecasts also serve as important benchmarks for companies that require a long-term view of their management.
Stock analysts use forecasts to estimate how trends such as gross domestic product (GDP) and unemployment rates will change over the next quarter or year. Finally, statisticians can use forecasting to analyze the potential impact of business changes. For example, data may be collected about the impact of changes in business hours on customer satisfaction or employee productivity due to changes in certain working conditions. These analysts make earnings forecasts, which are often aggregated into a consensus number. If the actual results announced are lower than expected, it could have a significant impact on the company's stock price.
Predictions address a problem or set of data. Economists make assumptions about the situation they are analyzing, but they must be established before determining the variables for the prediction. Based on the determined items, an appropriate dataset is selected and used for information manipulation. Data is analyzed and predictions are determined. Finally, a validation period occurs during which predictions are compared with actual results to establish a more accurate model for future predictions.
The further away the predictions are, the more likely the estimates are to be inaccurate.
Prediction method
In general, forecasting can be approached using qualitative or quantitative methods. Quantitative forecasting methods eliminate expert opinion and utilize statistical data based on quantitative information. Quantitative forecasting models include time series techniques, discounting, leading or lagging indicator analysis, and econometric modeling that attempts to ascertain cause-and-effect relationships.
Qualitative method
Qualitative predictive models are useful for making predictions within a limited scope. These models rely heavily on expert opinion and are most beneficial in the short term. Examples of qualitative predictive models include interviews, site visits, market research, opinion polls, and surveys that may apply Delphi techniques (based on aggregation of expert opinions).
Collecting data for qualitative analysis can sometimes be difficult or time-consuming. CEOs of large companies are often too busy to take calls from individual investors or show them around their facilities. However, he can scrutinize the text contained in news reports and company filings to learn about a management's record, strategy, and philosophy.
Time series analysis
Time series analysis examines historical data and how different variables have interacted in the past. Extrapolate these statistical relationships into the future to generate predictions with confidence intervals to understand the likelihood that the actual outcome will fall within that range. As with all prediction methods, success is not guaranteed.
The Box-Jenkins model is a technique designed to predict data ranges based on input from a given time series. Predict data using three principles: autoregression, difference, and moving average. Another method known as rescaling range analysis can be used to detect and evaluate the amount of persistence, randomness, or mean reversion in time series data. You can use the rescaled range to estimate future values or averages for your data and see if trends are stable or potentially reversing.
Time series forecasting most often involves trend analysis, periodic variation analysis, and seasonality issues.
econometric inference
Another quantitative approach is to examine cross-sectional data to identify associations between variables. However, determining causal relationships is difficult and often incorrect. This is known as econometric analysis and often uses regression models. When available, techniques such as the use of instrumental variables can help make stronger causal claims.
For example, an analyst might focus on earnings and compare it to economic indicators such as inflation or unemployment. Changes in financial or statistical data are observed to determine relationships between multiple variables. Therefore, sales forecasts can be based on several inputs such as total demand, interest rates, market share, and advertising budget.
Choosing the right forecasting method
The appropriate forecasting method depends on the type and scope of the forecast. Qualitative methods are time consuming and costly, but can produce very accurate predictions over a limited range. For example, it may be used to predict how well a company's new product launch will be received by the public.
Quantitative methods are often more convenient for rapid analysis that can cover a larger area. When you look at big data sets, today's statistical software packages allow you to crunch numbers in minutes or seconds. However, as the dataset becomes larger and the analysis becomes more complex, the costs can increase.
Therefore, forecasters often perform a type of cost-benefit analysis to determine which method is most efficient and maximizes the likelihood of accurate predictions. Moreover, combining techniques can provide synergistic effects and improve the reliability of predictions.
What is business forecasting?
Business forecasting attempts to make informed guesses or predictions about the future state of certain business metrics, such as sales growth or economy-wide forecasts such as next quarter's gross domestic product (GDP) growth. Business forecasting relies on both quantitative and qualitative techniques to improve accuracy. Managers use forecasts for internal purposes to make capital allocation decisions and decide whether to acquire, expand, or sell. We also prepare forward-looking statements for public disclosure, such as earnings forecasts.
What are the limitations of predictions?
The biggest limitation of prediction is that it involves a future that is basically impossible today. As a result, predictions can only be best guesses. There are several ways to improve the reliability of predictions, but the assumptions built into the model, or the data input into the model, must be accurate. Otherwise, the result will be garbage in, garbage out. Even when the data is good, forecasts often rely on historical data that can change over time and is not guaranteed to be valid into the future. It is also impossible to properly account for unusual or one-off events such as crises or disasters.
What kinds of predictive technologies are available?
There are several prediction methods, which can be broadly categorized as qualitative or quantitative. Within each category, there are several techniques at your disposal.
- under qualitative Methods may include interviews, site visits, the Delphi method of gathering expert opinion, focus groups, and textual analysis of financial documents and news items.
- under quantitative Methods and techniques generally use statistical models that examine time-series or cross-sectional data, such as econometric regression analysis and causal inference (where available).
conclusion
Forecasts help managers, analysts, and investors make informed decisions about the future. Without proper forecasting, many of us will be left guessing and guessing in the dark. Using qualitative and quantitative data analysis, forecasters can better understand what's coming.
Businesses use forecasts and forecasts to inform operational decisions and capital allocation. Analysts use forecasts to estimate a company's earnings for subsequent periods. Economists may also make more macro-level predictions, such as GDP growth or changes in employment. However, since we cannot know the future with certainty and predictions often rely on past data, their accuracy will always be subject to some degree of error, and in some cases may be far off.