Overview of Forecasting

Steps Required to Forecast

Steps of forecast include problem definition, cash flow forecast, profit forecast, balance sheet forecast and profit determination.

Learning Objectives

Describe the process for performing a forecast

Key Takeaways

Key Points

  • It is important to note those earlier identified ‘threats’ to your business to ensure that as you forecast you can see the deviation of the best and worst models.
  • Three key forecasts include problem definition, cash flow forecast, profit forecast, and balance sheet forecast.
  • By completing these scenarios you gain an insight into the various risks that a business faces.

Key Terms

  • taxable income: Taxable income refers to the base upon which an income tax system imposes tax.

Problem definition

It is important to note those earlier identified ‘threats’ to your business to ensure that, as you forecast, you can see the deviation of the best and worst models. For example, if a business has previously identified the threat of a diminishing cheap labor force, then its forecast needs to reflect that the price of labor (or any other resource, such as power) is going to go up.

George Cowling presented the first in-vision forecast on January 11, 1954 for the BBC.

Forecast: Just like a weather forecast, businesses use different types of forecasts to analyze and prepare for their futures.

Three key forecasts

Cash flow forecast

This seeks to forecast a bank balance after a period – typically 12 months. This forecast shows the sources and application of funds.

Profit forecast

This modifies the cash flow in an attempt to calculate taxable income and, in the process, forecast a businesses income tax liability. There are two differences between a cash flow and a profit forecast. The cash flow forecast includes all expenditure in the period, whereas the profit forecast looks to match revenue with the costs associated with generating that revenue. To achieve this, one uses non-cash expenses to estimate some of the costs associated with running a business.

These two forecasts are reconciled with a forecast balance sheet.

Balance sheet forecast

While we have based this example on a smaller business and, while forecasting balance sheets demonstrates completeness and a high level of technical integrity in forecasting, we feel the process is complex and better left to a professional. We also feel that the additional benefit is outweighed by the costs for a small business.

It is always easier to forecast the future performance of a business if your business is already up and running as there are past trading results to look at. When a completely new venture is being planned, a certain amount of imagination is required. However, this is in no way a license to be overly optimistic.

Basic Steps

By completing these scenarios you gain an insight into the various risks that a business faces. Spreadsheet programs make this quite easy if they are well set up.

1. The
sales
forecast

This is the dominant influence on the performance of your business. Also, many expenses have a link to the level of activity in a business.

For existing businesses, past sales are the best predictor of future sales, for new businesses it is less simple. However, once the business is established, you will find you have a better understanding between the business’s products and its markets.

The most important thing is to keep detailed records of sales as it is these that will provide you with the growing ability to forecast income accurately.

  • Forecast the number of units you expect to sell
  • Begin with an analysis of current performance
  • Divide sales into appropriate categories
  • Consider factors that affect each category
  • Internal factors might include staffing changes (for service industries)
  • External factors might include the impact of inflation – current relevance
  • Now attempt to forecast unit sales in cash category

2. Multiply by unit price

3. Determine market price

4. Cost plus

  • Expected mark-up
  • Expected revenue per unit sold
  • Statistical review of the market
  • Determination of units sold
  • Seasonal sales pattern
  • Cash flow
  • Every business needs cash (sometimes called liquidity ) to keep going.
  • Forecasting cash flow lets us anticipate liquidity problems and helps identify solutions.

5. Profit determination

The essential difference between cash flow and profit is that cash flow includes all items of income and expense, whereas profit seeks to match income and costs related to the generation of the income in a period of time; usually 12 months.

To facilitate the calculation of profit (and hence, the income tax due) the cash flow statements were split into four sections. We now take the total of income and the operational costs into a Profit Statement. We add depreciation to the operational costs and subtract our adjusted operational costs from income. This difference will indicate a profit (where the difference is positive) or a loss (where the difference is negative).

Where there is profit, we need to then calculate income tax. This calculation depends on the legal structure adopted for the business. Where a business is registered for Goods and Services Tax, we take only the net payments and receipts into account.

It should be noted there is always a risk in new product development. Despite the time and effort put into planning the new product may not earn a significant return on investment.

Inputs

The main inputs of forecasting include time series, cross-sectional and longitudinal data, or using judgmental methods.

Learning Objectives

Describe the different forecasting methods

Key Takeaways

Key Points

  • Forecasting is the process of making statements about events whose actual outcomes (typically) have not yet been observed.
  • Time series is a sequence of data points, measured typically at successive time instants spaced at uniform time intervals.
  • Cross-sectional data refers to data collected by observing many subjects at the same point of time, or without regard to differences in time.
  • A longitudinal data involves repeated observations of the same variables over long periods of time — often many decades.
  • Judgmental forecasting methods incorporate intuitive judgements, opinions and subjective probability estimates.

Key Terms

  • probability sample: a technique of studying a population subset in which the liklihood of getting any particular subset may be calculated
  • Dow Jones index: It is an index that shows how 30 large publicly-owned companies based in the United States have traded during a standard trading session in the stock market.
  • nonprobability sample: a subset of the population in which the probability of getting any particular sample may be calculated, and therefore cannot be used to represent the whole population

Forecasting in Accounting

In corporate finance, investment banking, and the accounting profession, financial modeling is largely synonymous with cash flow forecasting.

This usually involves the preparation of detailed company specific models used for decision making purposes and financial analysis.

A financial forecast is an estimate of future financial outcomes for a company or country (for futures and currency markets). Using historical internal accounting and sales data, in addition to external market and economic indicators, a financial forecast is an economist’s best guess of what will happen to a company in financial terms over a given time period—usually one year.

Challenges

Arguably, the most difficult aspect of preparing a financial forecast is predicting revenue. Future costs can be estimated by using historical accounting data; variable costs are also a function of sales.

Forecasting vs. Financial Plans and Budgets

Unlike a financial plan or a budget, a financial forecast doesn’t have to be used as a planning document. Outside analysts can use a financial forecast to estimate a company’s success in the coming year.

Forecasting is the process of making statements about events whose actual outcomes (typically) have not yet been observed. A commonplace example might be the estimation of some variable of interest at some specified future date. Prediction is a similar, but more general term. Both might refer to formal statistical methods employing time series, cross-sectional or longitudinal data, or less formal judgmental methods.

Time Series Data

Time series is a sequence of data points, measured typically at successive time instants and spaced at uniform time intervals. Examples of time series are the daily closing value of the Dow Jones index or the annual flow volume of the Nile River at Aswan. Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values. Time series are very frequently plotted via line charts.

A picture of the front of the New York Stock Exchange.

Time Series Data: Wall Street uses time series data to monitor the stock market.

Cross-sectional data

Cross-sectional data refers to data collected by observing many subjects (such as individuals, firms or countries/regions) at the same point in time, or without regard to differences in time. Analysis of cross-sectional data usually consists of comparing the differences among the subjects.

For example, if we want to measure current obesity levels in a population, we could randomly draw a sample of 1,000 people from the population (also known as a cross section of that population), measure their weight and height, and calculate what percentage of that sample is categorized as obese. For example, 30% of our sample may be categorized as obese based on our measures. This cross-sectional sample provides us with a snapshot of that population, at that one point in time. Note that we do not know based on one cross-sectional sample if obesity is increasing or decreasing; we can only describe the current proportion. Cross-sectional data differs from time series data also known as longitudinal data, which follows one subject’s changes over the course of time. Another variant, panel data (or time-series cross-sectional (TSCS) data), combines both and looks at multiple subjects and how they change over the course of time. Panel analysis uses panel data to examine changes in variables over time and differences in variables between subjects.

Longitudinal Data

A longitudinal study is a correlational research study that involves repeated observations of the same variables over long periods of time — often many decades. It is a type of observational study. Longitudinal studies are often used in psychology to study developmental trends across the life span, and in sociology to study life events throughout lifetimes or generations. The reason for this is that unlike cross-sectional studies, in which different individuals with same characteristics are compared, longitudinal studies track the same people, and therefore the differences observed in those people are less likely to be the result of cultural differences across generations. Because of this benefit, longitudinal studies make observing changes more accurate, and they are applied in various other fields. In medicine, the design is used to uncover predictors of certain diseases. In advertising, the design is used to identify the changes that adverts have produced in the attitudes and behaviors of those within the target audience who have seen the advertising campaign.

Judgmental methods

Judgmental forecasting methods incorporate intuitive judgements, opinions and subjective probability estimates, such as Composite forecasts, Delphi method, Forecast by analogy, Scenario building, Statistical surveys and Technology forecasting.

Usage of forecasting can differ between areas of application: for example, in hydrology, the terms “forecast” and “forecasting” are sometimes reserved for estimates of values at certain specific future times, while the term “prediction” is used for more general estimates, such as the number of times floods will occur over a long period.