How to Improve Your Earnings Forecast
Earnings forecast is a critical aspect of stock analysis because it helps investors make an informed decision on whether to buy or sell stocks. This is because the price of a company's shares can move depending on how closely a company matches or fails to meet Wall Street analysts' earnings estimates for the year.
A Company's earnings are calculated by adding revenue to costs, and then dividing the total by the number of outstanding shares. This is known as earnings per share or EPS, and it's the number that most investors use to evaluate a company's performance.
Analysts often build models to predict earnings for a particular product line or business segment. These forecasts are based on a number of different factors, including sales volume, cost structures, and capital structure.
For example, analysts might assume that a car dealer will increase the number of cars sold for each quarter as consumers continue to demand them. However, the price of fuel, economic conditions, pollution control laws, and other factors can affect car sales.
These factors can vary widely from company to company, making it difficult for analysts to make accurate earnings predictions.
Many analysts publish their earnings forecasts in reports prepared for investors, and they usually explain why they think the company will report the numbers they've predicted. This information is then published in the financial press, and it can help investors determine whether a company is likely to perform well.
Consensus estimates are often used by investors to assess a company's valuation and its future growth prospects. They are a common benchmark for investors, and are watched carefully by the financial press and the market.
There is a growing interest in improving analysts' earnings forecasts using quantitative methods. One method is mixed data sampling regression, which combines various high-frequency time-series data to improve the accuracy of the model. This model has been shown to outperform raw analysts' forecasts in some cases, and can also be combined with analysts' forecasts for more accurate and less biased forecasts.
Quantile regression, a method that uses the tails of the earnings distribution to predict a firm's earnings, has also been applied in recent research on forecasting earnings. Tian et al65 and Hendriock66 find that this method improves earnings forecasts when the firm's distribution has heavier tails. They also find that these models are more accurate than out-of-sample OLS methods.
Researchers have also examined the effect of company-varying coefficients on earnings forecasting. Vorst and Yohn137 examine whether these coefficients can improve out-of-sample profitability and growth forecasts. Fairfield et al136, on the other hand, find that industry models are more accurate when forecasting profitability but not growth.
The most important thing to remember when watching a company's earnings is that it's not always a good idea to get too excited if a company misses an estimate. This is because it's important to consider why the company missed its forecast, and whether it's a sign of weaker earnings or an analyst's miscalculation.
