Earnings Forecasting and Analyst Forecast Bias

The critical insight is distributional: consensus earnings forecasts are already incorporated into stock prices by the time an analyst reads them. Earning an excess return from earnings analysis requires one of three things — being more accurate than the market consensus, forecasting changes in the consensus before they occur, or identifying firms where the consensus is systematically biased in a predictable direction.

Two empirical regularities frame this lesson: (1) past earnings growth predicts future growth barely better than chance — the "growth stock" label is mostly a projection bias; (2) analyst consensus long-term earnings growth forecasts are systematically upward-biased, and stocks priced to reflect that bias subsequently underperform.

• state the evidence on what perfect earnings knowledge is worth and the three ranked sources of earnings alpha;
• summarise the economy-industry-firm decomposition of earnings variance;
• describe the two competing models of the earnings process (mean reversion vs random walk) and the empirical evidence;
• explain analyst forecast bias (optimism and inefficiency) and its connection to the value/growth anomaly;
• describe the forecast error decomposition and its practical implications.

1. 1. Why earnings matter for stock prices
2. 1. Economy, industry, and firm influences on earnings
3. 1. Time-series properties of earnings
4. 1. Analyst forecast bias
5. 1. Connecting earnings forecasting to the value/growth anomaly