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◈ BOOK REVIEW · INVESTING

FINDING #1 STOCKS

Name: FINDING #1 STOCKS
Author: Kevin Matras

by Kevin Matras

Who this is for

For retail investors who prefer a systematic, rule-based approach to stock selection and are willing to use Zacks platform tools to execute the screens. Investors who want to build fully independent, platform-agnostic quantitative strategies will find the book too tied to proprietary Zacks data.

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◈ KEY TAKEAWAYS

What this book actually teaches

01Earnings estimate revisions by sell-side analysts carry information the market underreacts to — stocks with accelerating upward revisions tend to outperform over the following months.
02The Zacks Rank quantifies revision direction and magnitude into a five-tier system that has shown persistent performance differentiation between the top and bottom tiers across historical backtests.
03Combining a revision-based rank with complementary screens (valuation, price momentum, return on equity) reduces false positives and improves the signal quality of any individual factor.
04Backtesting requires explicit controls for look-ahead bias — using data that would not have been available at the time of the decision is the most common error that makes paper strategies fail in practice.
05High-turnover screening strategies have real transaction costs and tax consequences that historical backtests typically exclude — implementation friction is a material drag on live returns.

◈ THE SUMMARY

What's in this book

Scored against ClearValue's published methodology · Jun 28, 2026

Finding #1 Stocks by Kevin Matras is a Zacks Investment Research methodology book that makes a specific argument: systematic screening using earnings estimate revisions, combined with a disciplined backtesting framework, can identify stocks likely to outperform the market over a three-to-twelve-month horizon. Matras is a long-time Zacks analyst, and the book is in large part a detailed explanation of the Zacks Rank system — a quantitative ranking model built around the direction and magnitude of earnings estimate revisions by sell-side analysts.

The core thesis draws on the academic research around earnings surprise and post-earnings announcement drift: stocks with upward earnings estimate revisions tend to outperform, partly because the market underreacts to earnings revisions and partly because upward revisions correlate with improving fundamental momentum. Matras translates this into a five-tier ranking system (Rank 1 = Strong Buy through Rank 5 = Strong Sell) and then spends the majority of the book showing how to combine Zacks Rank with additional screening variables — valuation, price momentum, earnings growth rate, return on equity — to build more refined strategies.

The backtesting chapters are the book's distinguishing contribution for a retail audience. Matras walks through how to construct a backtest, what to watch for in terms of look-ahead bias and overfitting, and how to evaluate whether a strategy's historical returns are driven by a persistent edge or by noise in a limited time window. For a book aimed at retail investors, the backtesting literacy content is more substantive than most competitors offer.

Weaknesses are significant. The Zacks Rank is a proprietary system available through Zacks' subscription products, so the reader cannot fully replicate the screens without a Zacks account — the book functions partly as an extended advertisement for those products. The backtesting methodology, while usefully described, does not engage with the transaction costs, tax friction, or capacity constraints that affect real-money implementation of high-turnover screens. And earnings estimate revision momentum, while supported by evidence, is one factor among many that the academic literature has explored — the book does not situate the Zacks approach within the broader factor research landscape.

For retail investors who want a systematic, screen-based approach to stock selection and are willing to subscribe to Zacks tools, this is a practical guide to one coherent methodology. It is not a comprehensive quantitative investing text, and the factor it emphasizes — earnings revision momentum — should be understood as one lens, not a complete investment system.

Summary as of June 28, 2026AI-assisted summary.