Undervalued Stock Scanner: From Raw Data to Smart Insights
A collaborative project that combines financial logic with automated Excel and Power BI tools to transform raw data into user-friendly and insight-driven dashboards.
Like many aspiring investors, my husband and I were drawn to the challenge of identifying undervalued stocks because they are like hidden gems with solid fundamentals that the market hasn’t caught up with yet. What started as a weekend project in Excel became a fully automated pipeline combining Power Query, VBA, and Power BI dashboards.
Disclaimer: This project is for educational purposes only. The analysis, models, and methods used here are not financial advice. Investing carries risk. Always do your own research or consult with a financial advisor before making investment decisions.
- Project at a Glance
- Datasets
- What Makes a Stock Undervalued?
- Dashboard that Tells a Story
- Automating the Mess: Turning Chaos into Clean Data
- Final Thoughts
Project at a Glance
The idea started with my husband’s interest in value investing and my curiosity for building smarter, faster workflows. He defined the investment logic and I built the engine. Our goal? Identifying potentially undervalued stocks by analyzing key financial metrics across various sectors. The project focuses on three market cap categories (Large Cap, Mid Cap, and Small Cap) and applies statistical methods to assess stock fundamentals.
Datasets
We used 11 raw CSV files exported from Thinkorswim, each representing a different sector. These contain financial fundamentals segmented by market cap:
- Large-Cap: $10B+
- Mid-Cap: $2B–$10B
- Small-Cap: $250M–$2B
Each sector includes multiple industries. When comparing stocks, it’s important to consider market capitalization and industry because a “good” metric in one industry or market cap can be a red flag in another. We structured the pipeline to retain this nuance throughout the analysis.
What Makes a Stock Undervalued?
Key Fundamentals
Here’s a quick overview of the financial metrics we used:
Price-to-Free Cash Flow ratio (P/FCF)
Price-to-Free Cash Flow (P/FCF): A high P/FCF ratio indicates that the specific firm is trading at a high price but is not generating enough free cash flows to justify the price. Smaller price ratios are generally preferred, as they may reveal a firm generating ample cash flows that may not yet be reflected in the price.
Price-to-Book ratio (P/B)
Book Value Per Share (BVPS) shows a company’s net assets per share. If BVPS is higher than the stock price, the stock may be undervalued. The P/B ratio compares market price to book value. Thus, P/B of 1 means it is traded at exactly where it should be. Higher P/B means it might be overvalued.
Return on Equity (ROE)
This ratio is a gauge of a corporation’s profitability and how efficiently it generates those profits. The higher the ROE, the better a company is at converting its equity financing into profits.
Return on Assets (ROA)
Return on assets measures how effective a company’s management is in generating profit from the total assets on its balance sheet. A ROA that rises over time indicates that the company is doing well at increasing its profits with each investment dollar it spends.
Asset-to-equity ratio (A/E)
The asset-to-equity ratio measures a company’s financial leverage by comparing its total assets to its shareholders’ equity. A higher ratio means more debt and higher financial risk. A lower ratio signals a more conservative, equity-heavy structure.
Price-to-Earnings ratio (P/E)
This metric shows how much investors are paying for each dollar of earnings. A high P/E can signal high growth expectations or overvaluation. A low P/E might suggest undervaluation or strong recent performance.
How We Evaluate Stocks Using These Metrics
Our evaluation is based on a series of carefully selected financial criteria but here’s the key: we never compare a stock to the wrong peer group. Each stock is only evaluated relative to the industry average within its own market cap category. To make industry comparisons fair and statistically sound, we calculate the mean and standard deviation for each metric within the same industry and market cap group, excluding outliers. Outliers are identified using the interquartile range (IQR) method, and removed before calculating averages and z-scores.
Preliminary criteria:
To be considered “undervalued,” a stock must meet all of these:
- P/FCF > 0 and below the industry average
- P/B > 0 and below the industry average
- A/E > 1 and below the industry average
- ROE > 10%
- ROA > 5%
Additional criteria:
We further rank stocks based on these bonus signals:
- P/B < 70% of industry average
- ROE > industry average
- ROA > industry average
- P/E < industry average
- P/E between 1 and 25
For each criterion met, a stock gets 1 point. The highest score a stock can get is 5 points.
Dashboard that Tells a Story
To make the analysis more accessible, I created an interactive Power BI dashboard where users can explore top-scoring undervalued stocks by sector and market cap, alongside industry averages for key financial metrics.
The dashboard functions as a scanner, not a recommendation engine or curated report. While the dashboard highlights stocks that meet certain criteria and includes summary cards showing the top 3 stocks for each individual metric, it avoids naming a single “best overall” stock. What’s considered “best” can vary widely depending on which metrics an investor prioritizes. This design choice reflects an intentional effort to keep the analysis unbiased and flexible. The goal is to present clean, objective data, not advice.
Key Features & How to Use Them
This dashboard showcases stocks that meet preliminary undervaluation criteria, then scores and ranks them to help users identify top opportunities.
1. Filter by Sector and Market Cap: Use the filters (top-right) to select a sector (e.g., Communication Services) and market cap group (Large, Mid, Small).
2. Dynamic Title: Page headers update automatically based on your selections, so you always know which stock group you’re viewing.
3. Buttons: Move between pages, apply filters, or view instructions using built-in buttons.
4. Score-Based Stock Evaluation: All stocks shown have already passed the baseline filters. Each stock is then evaluated on five additional criteria. It earns 1 point for each one met, with a maximum score of 5. The score for each stock (0-5) is shown as a horizontal stacked bar with one color-coded block per point.
5. Z-Score Matrix: Shows how far a stock’s metric deviates from the industry average. This helps users compare each stock to its peers.
Undervalued metrics:
- P/FCF, P/B, A/E, P/E → negative z-score is better
- ROE, ROA → positive z-score is better
Z-scores that go in the “wrong” direction (e.g. high P/E) are grayed out. Z-scores further from 0 (stronger deviations) are shaded in darker green for easy scanning.
6. Score Distribution Pie Chart: See how many stocks earned each possible score (0–5), giving a sense of the dataset’s overall quality.
7. Summary Cards: Quick-glance cards show a count of stocks per industry and the top 3 stocks for each metric (based on z-score), ideal for spotting individual leaders in specific financial categories.
8. Metric Breakdown Table: Scrollable table with detailed financial metrics for each stock.
9. Industry Bar Charts (Page 2): Compare average fundamentals across sectors and industries to spot which industries are overhyped with lower profitability (ROE, ROA) but higher valuations (P/B, P/E, P/FCF).
10. Tooltips for Context: Hover over any visual to see context like industry name, market cap, price, score, and financials.
Automating the Mess: Turning Chaos into Clean Data
Initially, my husband manually cleaned and analyzed the raw stock data in Excel. But with 11 sector-specific datasets and dozens of formulas, the process quickly became time-consuming, repetitive, and prone to error. Especially with frequent data updates, each refresh could take hours.
I completely re-engineered the process to be fast, accurate, and user-friendly by implementing a set of tools:
- Power Query (20+ custom functions): For automatic cleaning and transformation.
- Advanced Excel formulas: For metric calculation across all datasets.
- VBA Macros: To refresh all data and recalculate results with a single click.
- Pivot tables: To calculate industry averages while filtering outliers.
- Dynamic file paths: So the project works across any machine without broken links.
- RTD function for real-time stock prices: the RTD (Real-Time Data) function integrates with the Thinkorswim platform to pull real-time stock prices directly into the workbooks.
- Conditional formatting: To surface strong candidates visually, making stock analysis easier for users.
The resulting Excel workbooks form the backbone of this project, acting as the central platform that integrates Power Query, advanced formulas, and VBA macros. These files can function independently as stock scanners, even without the dashboard. However, the dashboard plays a crucial role in bringing everything together, presenting the data in a way that’s more accessible and engaging for non-technical users.
After processing the data in Excel, I brought the results into Power BI, where I created interactive dashboards that:
- Highlight top-ranking undervalued stocks
- Compare metrics across sectors and industries
- Visualize trends in P/E, ROE, ROA, and other key indicators
- Enable non-technical users to explore insights without needing to touch the raw data
Final Thoughts
This project started with a shared curiosity and turned into a complete, repeatable workflow that saves hours and delivers clear insights. It was as much about collaboration as it was about technology. Working closely with my husband, a non-technical stakeholder, I used an informal agile approach by delivering in small chunks, gathering feedback, and adapting quickly. Clear communication and step-by-step guides I created helped my husband transition smoothly to the new process. I’m proud to have taken initiative, self-taught new skills ahead of coursework, and built a fully automated system that saves time and drives smarter investment decisions.
The real win? Building something that works for both analysts and non-technical users alike. It reminded me that the best data projects don’t just analyze — they simplify. They turn messy data into clear insights and help people make better, faster decisions. That’s what I aim to build, every time.
