Do Earnings Seasons Predictably Move Markets? SP500 Earnings Strategy

Earnings seasons, the quarterly periods when companies release financial results, are known for sparking market volatility. Occurring in January, April, July, and October, these seasons are closely watched by investors aiming to capitalize on price movements.

But can these periods predictably move markets? This article explores the evidence, including academic research, practical strategies, and a specific study of the SPY ETF, which tracks the S&P 500. Our findings suggest that earnings seasons may indeed offer predictable opportunities for market gains, particularly at the aggregate level (indices).

We backtest a specific trading strategy.

Earnings Seasons: A Catalyst for Market Movements

Earnings seasons are defined by a flurry of corporate financial reports, typically concentrated in the weeks following the start of each quarter.

These announcements often lead to significant stock price swings, driven by whether companies meet, exceed, or fall short of analyst expectations. Beyond individual stocks, earnings seasons can influence broader market indices, as positive or negative surprises from major companies shape investor sentiment.

For example, strong earnings from leading firms can lift market confidence, pushing indices like the S&P 500 higher, while widespread disappointments may trigger sell-offs. This dynamic raises the question: are these movements predictable enough for investors to develop profitable strategies?

Research Evidence: Predictability at the Aggregate Level

A study titled “Seasonal patterns of earnings releases and post-earnings announcement drift” found that earnings announcements are often clustered in the busiest weeks of each season, leading to stronger post-earnings announcement drift (PEAD). PEAD occurs when stock prices continue to move in the direction of the earnings surprise (positive or negative) after the announcement, suggesting a predictable under-reaction during high-volume periods (Seasonal patterns of earnings releases and post-earnings announcement drift).

Another study, “Predictability and the earnings–returns relation”, highlights that aggregate market returns are more predictable than individual stock returns. It found that current market returns better forecast future aggregate profitability than individual stock returns do for firm-level profitability. This implies that earnings seasons may have a more consistent impact on broad indices like the S&P 500 than on specific stocks (Predictability and the earnings–returns relation).

These findings suggest that while individual stock reactions can be erratic, market-level movements during earnings seasons may follow identifiable patterns, offering opportunities for strategic investing.

A Case Study: Trading SPY During Earnings Seasons

To test the predictability of earnings seasons, we conducted a study using the SPDR S&P 500 ETF (SPY), which tracks the S&P 500.

Our strategy involved buying SPY on the first calendar day after the 20th in January, April, July, and October—key months aligning with earnings seasons—and selling on the first calendar day after the 5th in the following month (February, May, August, and November). This period captures the heart of earnings announcements and their immediate market impact.

This is the equity curve from its inception until today:

The results were striking: the average gain per trade was 0.8%, significantly higher than the average return for random periods of similar duration. This suggests that the concentrated release of earnings reports during these months creates a predictable upward bias in the S&P 500, likely driven by positive surprises, investor optimism, or market momentum following key announcements.

However, the period also covers the turn of the month effect, and this might be the main reason for the positive returns.

For context, the S&P 500’s average daily return is approximately 0.03% (based on historical annualized returns of about 7-10% over 252 trading days). A 0.8% gain over a short holding period is substantially above this baseline, underscoring the potential for earnings seasons to drive outsized market movements. While our study is specific to SPY, it aligns with research indicating stronger market predictability at the aggregate level.

Practical Strategies for Investors

Investors often use historical performance, analyst forecasts, and company guidance to anticipate market reactions during earnings seasons.

For instance, companies with a history of beating estimates may be more likely to see positive price reactions, while negative guidance can dampen enthusiasm even for strong earnings. Our SPY trading strategy offers a practical example of capitalizing on aggregate market trends, avoiding the complexity of predicting individual stock movements.

However, predictability has limits. Market sentiment, economic conditions, and external events—such as geopolitical developments or policy changes—can introduce volatility. For example, in Q1 2024, 70% of S&P 500 companies issued negative guidance, despite analyst expectations of 3.4% earnings growth, creating mixed signals for investors.

Earnings season – conclusion

Earnings seasons do predictably move markets, particularly at the aggregate level, as evidenced by academic research and our SPY trading study. The 0.8% average gain from our strategy highlights a tangible opportunity for investors to capitalize on the S&P 500’s performance during these periods.

However, individual stock reactions remain less predictable, and external factors can disrupt even well-founded strategies.

Investors should approach earnings seasons with a blend of data-driven tactics, such as our SPY trading approach, and adaptability to navigate unexpected shifts.