3 Investment Research Methods that Drive Successful Decisions
VC Realm
3 Investment Research Methods that Drive Successful Decisions
In the ever-evolving world of investment, harnessing expert insights is key to staying ahead of the curve. This article demystifies complex investment research methods that have been endorsed by leading analysts in the field. Discover the strategies that empower investors to make decisions with confidence, from mosaic theory to industry trend tracking.
- Mosaic Theory: Connecting Data for Intrinsic Value
- DCF Analysis Uncovers Undervalued Renewable Energy Stock
- Fundamental Analysis Meets Industry Trend Tracking
Mosaic Theory: Connecting Data for Intrinsic Value
There are many investment research methods, but one investment research method or framework that I have found particularly helpful in evaluating potential investments is the Mosaic Theory.
This method focuses on creating disparate, non-material data points (financial statements, industry trends, management behavior, regulatory shifts, sociopolitical factors) into an interconnected narrative to measure a company's intrinsic value. Joel Greenblatt and Seth Klarman popularized the Mosaic Theory. This method is applicable in today's dynamic markets where traditional methods don't work.
Mosaic Theory consists of three principles:
Non-physical data:
First, it gathers no physical information to analyze. It uses private but legally available information.
Non-physical data include supplier trends, customer sentiment surveys & patent filings.
Pattern Identification:
It refers to finding out repeated themes from qualitative and quantitative inputs.
For example, a CEO's capital allocation history paired with rising R&D spend.
Stochastic thinking:
Stochastic thinking means assigning confidence levels to each data point to avoid overreliance on any single factor.
Let's see an example of how I evaluate a consumer goods company. I triangulate data from retailer foot traffic analytics, social media sentiment around new product launches, and shifts in raw material pricing to predict revenue trends before they appear in financial statements.
I implement this theory in the following four simple phases:
Hypothesis development:
First, I formulate a hypothesis. My thesis could be that a company is undervalued due to miscalculated ESG risks.
Data Sourcing:
I collect primary data by taking industry experts' interviews, attending earnings calls for tone analysis, and reviewing SEC filings for nuanced disclosures such as changes in risk factors.
I leverage alternative datasets for secondary data, for example, geospatial satellite imagery like tracking warehouse activity for e-commerce firms or job postings such as a biotech firm hiring oncology experts may signal pipeline developments.
Pattern Justification:
I cross-reference findings with traditional metrics.
If I find supplier data production bottlenecks, then I validate against inventory turnover ratios and accounts payable trends.
Risk-Adjusted Assessment:
I assign probabilities to scenarios, for example, a 60% chance of regulatory approval for a drug, and adjust discounted cash flow or comparable-based valuations accordingly.
DCF Analysis Uncovers Undervalued Renewable Energy Stock
One investment analysis I'm particularly proud of involved identifying an undervalued stock in the renewable energy sector. During a routine screening, I noticed a mid-cap solar company with solid fundamentals—consistent revenue growth and increasing market share—but its stock price had dropped significantly due to short-term supply chain issues. Digging deeper, I found that management had a robust plan to mitigate these challenges and was expanding into new markets with high growth potential.
I ran a discounted cash flow (DCF) analysis to confirm the intrinsic value and compared it to peer companies in the sector. The stock appeared undervalued by nearly 30%. After buying, the company's quarterly earnings beat expectations, and investor sentiment improved. Within six months, the stock price rose by over 40%, validating the analysis. The experience underscored the value of looking beyond market noise to find long-term opportunities.
Fundamental Analysis Meets Industry Trend Tracking
The investment method I rely on is a mix of fundamental analysis and tracking industry trends. It's like buying a car: you look beyond the exterior and focus on how it runs. I start with a company's financial health: revenue, profit margins, and debt, but also pay attention to global shifts. For example, when remote work surged during the pandemic, companies with remote-first tools thrived, while traditional office suppliers struggled. Understanding these big picture trends can help us predict how companies will perform in the future. It's not just about looking at past numbers but seeing where the world is heading. By connecting financials with broader industry changes, you can make smarter, more informed investment choices. This simply means that a bit of forward thinking can give us an edge in spotting profitable opportunities.
