Why Windmill Capital Does Not Release Company-Specific Reports

When a stock enters or exits a smallcase, the first question is always – why?

It’s a fair one. Traditional investing is built around company stories: understanding management, competitive edges, and market opportunities.

But we build different smallcases differently.

Thematic & tracker smallcases: These are narrative-led. For example, we talked about Electric Mobility smallcase in May 2025, discussed turnaround opportunities, and wrote articles explaining why we hold or drop high P/E stocks. However, we share these insights mainly to highlight broader themes or industry trends.

Model smallcases: These are rule-led. Stocks enter or exit based on data filters and factor signals, not analyst opinions or business stories. The framework is built and overseen by our research team, but the model itself drives decisions.

That’s why our communication for model smallcases focuses on the strategy, its design, factors, and performance, not individual company updates.

Let’s explore why we take that approach.

Understanding Windmill Capital’s Model Portfolios

To understand this better, it helps to look at what Windmill Capital’s (WCPL) model portfolios are and the thinking behind them. Each portfolio is designed to give exposure to specific factors through a smart beta investing approach.

Factor investing focuses on measurable traits or “factors” that explain differences in stock returns over time. For instance, the value factor is based on the idea that cheaper stocks (those with lower P/E ratios) tend to outperform expensive ones in the long run.

Two Ways to Pick Stocks: Story vs. System

Most investors are familiar with traditional stock investing. It usually begins with research reports or market ideas. The analyst then studies the industry, reviews financials, assesses competitive positioning, and evaluates management quality, business potential, and valuation. The decision to invest is ultimately based on these insights and personal interpretation.

This discretionary, research-driven approach is also what we use while building thematic or tracker smallcases.

Quantitative investing, on the other hand, starts differently. Ideas come from academic studies, data-backed research and our own market experience of more than 10 years that help us understand market behaviour, risk, and performance drivers, not from company stories or sector trends.

Once the strategy framework is set, we define the investment universe (say, the top 750 listed companies) and apply data filters to identify those meeting specific criteria.

For example, the Growth Multicap – Quant smallcase filters companies that demonstrate revenue and EBITDA growth, maintain low interest costs, and show positive momentum in their stock prices.

There’s no narrative or “stock story” in this process. Instead of focusing on brand strength, management quality, or competitive advantage, the model relies purely on measurable data. The idea is that strong fundamentals eventually show up in the numbers in financial results, price trends, and other indicators. 

Once potential candidates are identified, we run additional checks such as liquidity filters, promoter pledge reviews, and exclusion of companies flagged by regulators or linked to negative news. Only those passing every filter enter the final portfolio.

Comparing the Two Approaches

In short, traditional investing starts with a story. Quantitative investing starts with data.

What This Means for Investors

Because our model smallcases are built entirely on predefined factor filters, a company’s inclusion or removal depends only on whether it meets those quantitative rules, not on subjective opinions about management, brand, or strategy.

In other words, there’s no individual “story” to tell for each company. A stock enters when it fits the model and exits when it doesn’t.

That’s why we don’t publish company-specific reports for our model smallcases. The insight lies in understanding the overall strategy, its design, factors, and rules rather than tracking each stock.

Our aim is to shift the conversation from “Which stock?” to “Which strategy?” This focus on data and discipline helps investors stay objective and long-term oriented, the essence of how our model smallcases are built.

Frequently asked follow-up questions

1. If models are so objective, why do results differ across quant portfolios?

Quant portfolios differ in performance because each one is based on a specific strategy. For instance, the Value & Momentum Model aims to shortlist undervalued stocks with strong momentum; it often self-selects mid and small-cap stocks, which leads to higher volatility. On the other hand, the Safe Haven Model prioritises low-beta, stable stocks to protect capital, providing steadier returns. As a result, variations in factors, stock selection, and risk targets create different outcomes that align with specific investor goals.

2. Even if decisions are rule-based, shouldn’t investors know why certain stocks qualify, at least in plain language?

Since decisions follow rules, investors already understand why stocks qualify. They meet the model’s set criteria! For instance, in the GEM-Q Model smallcase, companies are chosen only if they show profit growth, use capital efficiently, and have strong price momentum. In simple terms, the model selects businesses that are doing well fundamentally and gaining market traction.

3. Do analysts play any role in rebalancing model portfolios?

Yes, analysts are involved in running the models and reviewing the shortlisted stocks during each rebalance. However, the process is systematic and based on rules, so there is usually no need for discretionary intervention. The analyst’s role is mainly to ensure the model runs correctly and that data and screening inputs are accurate. They do not override the model’s decisions.

4. How do we ensure the data used in the model is reliable?

We ensure data reliability by sourcing all model inputs from trusted providers like Refinitiv Eikon. These platforms have a strong reputation in the global financial industry. They perform strict data quality checks, helping to maintain accuracy and consistency in the information used in the models.

5. Do you override the model in extreme market conditions?

No. We do not change the model just because of market conditions. Rebalances are systematic and based on rules, even during volatile times. The only exceptions are company-specific issues, such as corporate governance concerns, major corporate actions, or regulatory developments. In those cases, we may need to remove or avoid a stock. Otherwise, the model does not change in response to short-term market movements.

6. Why do some rebalances feel counterintuitive compared to the news cycle?

Rebalances may sometimes seem disconnected from the news cycle as they rely on quantitative rules and data, not market chatter or headlines. The model adjusts according to changes in earnings, valuations, momentum, and other metrics, whereas news often reflects sentiment or expectations that may not be reflected in the numbers yet. Markets tend to react emotionally to short-term events. In contrast, systematic portfolios remain disciplined and steer clear of such noise. So even when it seems counterintuitive, model-driven rebalances are driven by data, not drama.

7. Why don’t we have an ad hoc rebalance when a stock hits upper or lower circuits between rebalances?

Markets often respond emotionally to short-term events. That’s why setting rebalance schedules makes sure the model stays focused on data rather than drama. We don’t make changes on the fly just because a stock hits upper or lower limits. A stock is added or removed only when it meets or fails the model’s criteria, not due to temporary price changes or market feelings. The only exceptions are specific company issues like governance concerns, major corporate actions, or regulatory changes, where an early exit may be needed. Staying systematic helps maintain discipline and prevents emotional decisions.

Disclaimer: Investment in securities market are subject to market risks. Read all the related documents carefully before investing. Registration granted by SEBI, membership of a SEBI recognized supervisory body (if any) and certification from NISM in no way guarantee performance of the intermediary or provide any assurance of returns to investors.

The content in these posts/articles is for informational and educational purposes only and should not be construed as professional financial advice and nor to be construed as an offer to buy /sell or the solicitation of an offer to buy/sell any security or financial products.Users must make their own investment decisions based on their specific investment objective and financial position and using such independent advisors as they believe necessary. Windmill Capital Team: Windmill Capital Private Limited is a SEBI registered research analyst (Regn. No. INH200007645) based in Bengaluru at No 51 Le Parc Richmonde, Richmond Road, Shanthala Nagar, Bangalore, Karnataka – 560025 creating Thematic & Quantamental curated stock/ETF portfolios. Data analysis is the heart and soul behind our portfolio construction & with 50+ offerings, we have something for everyone. CIN of the company is U74999KA2020PTC132398. For more information and disclosures, visit our disclosures page here.