How You Can Capitalize on Long-Term Growth That the Market Misses

Long-term growth expectations form the backbone of virtually every investment decision and corporate valuation model. Whether using discounted cash flow analysis or comparing price/earnings ratios, assumptions about future growth rates dramatically influence conclusions about a stock’s fair value. Yet despite this central importance, the investment world has lacked concrete guidance on how to accurately estimate these crucial growth rates.

Angel Tengulov, Josef Zechner, and Jeffrey Zwiebel, authors of the study “Valuation and Long-Term Growth Expectations,” published in the August 2025 issue of the Journal of Financial and Quantitative Analysis, set out to answer a fundamental question that has puzzled investors and academics alike:

How should long-term growth rates be determined, given their dominant effect on firm value yet little guidance from academic literature and practitioner conventions?

To address this challenge, the authors developed a framework that examines the relationship between long-term growth expectations and an extensive array of company, industry, and market characteristics.

Using machine learning techniques, alongside traditional econometric methods, to uncover patterns that might be invisible to conventional analysis, the authors analyzed thousands of companies over multiple decades, examining everything from financial metrics and competitive positioning to macroeconomic conditions and industry dynamics. The dataset includes US companies, and the longest period covered was 1963-2018.

Key Findings

The study’s most striking finding was that market prices do not seem to fully capture long-term growth information—cross-sectional tests yielded substantial positive abnormal returns for firms with high expected long-term growth. This suggests that the market collectively fails to properly price long-term growth prospects into stock valuations.

Why? The authors offered these explanatory variables:

• As should be expected (because markets incorporate growth expectations into prices), book/market exhibits a negative and statistically significant relation to future long-term growth in sales.
• There was a positive and statistically significant relation between analyst earnings forecasts and long-term growth in sales—analysts’ long-term forecasts are informative for future realizations of long-term growth.
• There was a positive relation between barriers to entry, a variable representing firms’ competitive positioning, and subsequent long-term growth rates.
• Companies that sustain high profitability and high profit retention enjoy higher long-term sales growth rates in the future.
• The propensity of firms exiting an industry was correlated with lower future long-term growth rates of remaining firms in that industry.
• Companies with more leverage were associated with lower long-term growth—a potential explanation is that increased usage of debt financing is indicative of higher bankruptcy likelihood and costs, which leads to lower future growth.
• They found negative firm size and age effects, indicating that as firms grow larger and older, they grow at lower rates.
• There was a positive relation between variables representing current investment opportunities (such as external financing and capital expenditures) and subsequent long-term growth rates.
• Equity analysts’ long-term earnings forecasts were positively related to long-term growth.
• Capital markets appear to price growth expectations more efficiently for large stocks, whereas long-term growth expectations for small stocks contain valuable information when predicting their future stock returns.

Predictable Growth Patterns

Long-term growth rates aren’t totally random or unpredictable. Instead, the authors found meaningful relationships between future growth and observable company characteristics—their empirical models explained up to 22% of the variation of long-term growth rates. Their models also achieved adjusted R-squared values of 14.7% for sales growth and 7.2% for EBITDA growth over five-year periods, indicating predictive power that exceeds what would be expected by chance alone.

Investment Strategy Implications

The author’s findings of a positive and significant association between expectations for long-term growth and subsequent stock returns—that persist after controlling for major known return predictors—provide evidence that these growth predictions can be translated into profitable investment strategies. Firms identified as having high expected long-term growth but relatively modest current valuations generated significant abnormal returns.

The Methodology: Beyond Traditional Analysis

What sets this research apart is its comprehensive approach to identifying growth predictors. Rather than focusing on a single metric or using simple linear relationships, the authors cast a wide net:

Company-Level Factors: Financial ratios, profitability metrics, balance sheet strength, cash flow characteristics, and historical growth patterns all contributed to their models.

Industry Dynamics: Competitive positioning, market concentration, regulatory environment, and sector-specific trends helped contextualize individual company prospects.

Market Conditions: Broader economic factors, interest rate environments, and market sentiment indicators provided additional predictive power.

Advanced Analytics: Machine learning algorithms identified complex, nonlinear relationships that traditional statistical methods might miss, while still maintaining interpretability for practical application. As a note of caution, machine learning findings could be the result of overfitting—the model learns the details and noise of its training data too well, memorizing patterns that don’t actually generalize to new, unseen data. As a result, the model may produce excellent predictions on the training set but fail to make reliable predictions on out-of-sample (new) data.

Key Takeaways for Investors

This research offers several actionable insights that can improve investment decision-making:

1. Look Beyond Current Valuations

Traditional value investing focuses heavily on companies trading at low multiples relative to current earnings or book value. While these metrics remain important, the study suggests that investors should place equal emphasis on identifying companies with superior long-term growth prospects that aren’t fully reflected in current prices.

2. Systematic Approach to Growth Assessment

Investors should develop systematic frameworks for evaluating growth potential. The research demonstrates that multiple factors contribute to long-term growth. Considering them collectively provides more-reliable predictions than focusing on any single indicator.

3. Long-Term Perspective Pays Off

The study shows abnormal returns tend to go to investors who hold their positions until the market sees the growth potential, reinforcing the importance of patient capital and conviction-based investing.

4. Quantitative Tools Matter

The success of machine learning techniques in predicting growth outcomes suggests that there are benefits from more-sophisticated analytical tools.

Market Inefficiency

The finding that markets don’t fully capture growth information suggests that markets are not perfectly efficient. Andrew Lo, developer of the adaptive markets hypothesis, explained that, while financial markets are not always perfectly efficient, they do evolve and adapt over time as market participants learn, compete, and respond to changing conditions—explaining why abnormal earnings of investment strategies tend to rapidly dissipate after publication of the findings. The conclusion we should draw is that sophisticated investment firms will rapidly incorporate findings of abnormal returns into their strategies.

The work by Tengulov, Zechner, and Zwiebel provides compelling evidence that systematic approaches to identifying long-term growth prospects have been able to generate improved investment returns. Their research offers both theoretical insights into how markets price growth expectations and practical tools for exploiting the resulting inefficiencies. However, as investment markets continue to evolve and incorporate their findings, the ability to discover and capitalize on long-term growth opportunities are likely to become harder to identify.