Stock analysts need to forecast revenue and growth to project what expected earnings will be. Forecasted revenue and growth projections are important components of security analysis, often leading to a stock’s future worth. For example, if a company shows a high rate of growth over several periods, it will command multiples that exceed the current market multiple. When its forward multiple increases, its stock price should consequently increase, resulting in a higher return for investors. Making forward projections requires numerous inputs; some come from quantitative data and others are more subjective. The reliability and accuracy of the data drive the forecasts.
Forecasting Revenue
Modeled revenue and growth will be mostreliable if inputs used to determine them are as close to accurate as possible. To forecast revenue, analysts gather data from the company, the industry, and consumers. Typically, both companies and industry trade groups publish data related to the potential size of the market, the number of competitors, and current market shares. This information can be found in annual reportsand through industry groups. Consumer data ascertained from buyer surveys, UPC bar coding, and similar outlets paint a picture of current and future expected demand.
Further inputs are needed to specifically model a company’s revenue forecasts. Financial statements, such as the balance sheet, inform analysts of a company’s current inventory and changes in inventory levels from one period to another. Often companies will also provide updates on inventory, shipments, and expected number of unit sales in the current period.
Average price-per-unit can be calculated using the revenue provided in the income statement divided by the change in inventory (or number of units sold). For past transactions, these data can found in a US company’s Securities and Exchange Commission (SEC) reports, but for future transactions, assumptions are required—like the impact of competition on pricing power and expected demand versus supply.
In competitive markets, prices usually fall, either directly through price cuts or indirectly in the form of rebates. Competition comes in the form of similar products by different manufacturers, or new products entering and cannibalizing old ones. When supply exceeds demand, companies usually push products to the consumer, typically resulting in lower price points. Forecasted revenue is calculated by taking the average selling price (ASP) for future periods and multiplying that by the number of expected units sold. These calculated forecasts can be “confirmed” by company management, who may discuss revenue and its expectations for growth on conference calls, usually scheduled around the release of the latest annual or quarterly report. Additionally, company management may participate in intra-period events, such as industry conferences, where theyrelease new information on inventory, market competitiveness, or pricing to confirm or assist in building revenue models.
Forecasting Growth
Once revenue is determined, future growth can be modeled. Applying a growth rate on revenue can help determine the future earnings growth. Setting the appropriate growth rate will be based on expectations about product price and future unit sales. Penetration into new and existing markets and the ability to steal market share will impact future unit sales. Industry outlook, analyzing the key product features, and demand are integral components to forecasting growth rates.
Let’s look at anexample.Company ABC starts with $100 in revenue. They are expected to grow in-line with the market.ABC is forecasting its ability to increase market share and set prices. Here is their forecast:
Growth Rate Calculation
Year | Market Growth | Incremental Market Share Gains | Pricing Power | Calculated Growth Rate | Revenue |
$100.00 | |||||
1 | 10% | 5% | 0% | 15.00% | $115.00 |
2 | 9% | 5% | 0% | 14.00% | $131.10 |
3 | 9% | 1% | -10% | 0.00% | $131.10 |
4 | 9% | 1% | -5% | 5.00% | $137.66 |
In Years 3 and 4, both incremental market share and pricing power decrease, which directly impacts growth rates.
Impact of Forecasts on Valuation
Analysts’ ultimate goal when forecasting revenue and growth is to determine the appropriate value for a stock. After modeling expected revenue, and concluding that costs will continue to be the same fixed percentage of revenues, analysts can calculate expected earnings for each future period.
The following table shows expected earnings for Company ABC:
Year | Revenue | Expenses (% of Revenue) | Earnings |
$ 100.00 | 85.0% | $ 15.00 | |
1 | $ 115.00 | 84.9% | $ 17.37 |
2 | $ 131.10 | 84.6% | $ 20.19 |
3 | $ 131.10 | 84.4% | $ 20.45 |
4 | $ 137.66 | 84.7% | $ 21.06 |
From these models, analysts can then compare earnings growth to revenue growth to see how well the company is able to manage costs and bring revenue growth to the bottom line.
Year | Earnings | Earnings Growth | Revenue Growth | Variance (Earnings-Revenue Growth) |
$ 15.00 | ||||
1 | $ 17.37 | 15.77% | 15.00% | 0.77% |
2 | $ 20.19 | 16.26% | 14.00% | 2.26% |
3 | $ 20.45 | 1.30% | 0.00% | 1.30% |
4 | $ 21.06 | 2.98% | 5.00% | -2.02% |
In each of Years 1, 2, and 3, ABC’s earnings growth exceeds its revenue growth. The change in growth rates will be reflected in the valuation multiple the market is willing to pay for this stock. Stocks that have sustainable or increasing growth rates will be assigned higher multiples, and stocks with negative growth will receive lower multiples. For ABC, increased growth from Year 1 to Year 2 will result in a high multiplewhilethe low growth in Year 4 (actually negative earnings growth compared to revenue growth) will be reflected in a lower multiple.
The Bottom Line
Analysts’ forecasts are crucial to setting expected stock prices, which in turn, lead torecommendations. Without the ability to make accurate forecasts, the determination to buy or sell a stock cannot be made. Although stock forecasts require the compilation of many quantitative data points from a variety of sources, as well as subjective determinations, analysts should be able to create a fairly accurate model to make recommendations.
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