Metrics Deep Dive Continued: Pricing Profits, Reining in Risk

Yesterday we took a look at some of the basic terms and concepts needed to conduct thorough research of stocks: we’re working to look beyond useful but limited metrics and dive into the nitty-gritty of evaluating an equity.

So far we’ve focused on fundamental calculations that help us understand the balance sheet and cash flow. Today, we’re going to look at metrics focused specifically on profitability and risk.

Pricing Profitability

  1. Return on Assets (ROA): one of the most fundamental metrics for thinking about a company’s efficiency in generating returns. It consists, simply, of net income divided by total assets. Helpfully, ROA helps short-circuit some of the asset accounting games played by certain companies: account yourself a huge intangible asset and you’ll be diluting return on assets—unless it’s an intangible that’s actually profit-generating.
  2. Return on Equity (ROE): a similar metric, this one is total income divided by equity. Remember that equity, or shareholders’ value, is likely far lower than the market value of the firm. This metric is most relevant for particular industries that render asset-based ratios less useful, like Finance.

    The entire business model of banks, for instance, involves piling on new assets (loans) at relatively low-profit margins (market interest rates plus a risk premium). The ROA of banks will thus inherently be quite low. So why is the business model viable? Because banks (at least in theory) carefully manage the risk underlying these assets and lay claim to underlying collateral. There’s a lesson here:  every asset comes with some risk to its valuation, and that’s the fundamental complexity we have to deal with when evaluating ROA-type metrics. Industry-specific research is particularly essential here—some industries simply benefit from higher asset yields than others. ROE is no panacea either: it totally ignores what sort of debt is underlying income growth relative to equity.
  3. Gross Profitability: Prominent research suggests that this metric, not as ubiquitous as the last two, is quite useful. We calculate Gross Profitability by dividing Gross Profits by Assets. Gross Profits ignores expenses like overhead to focus on the profitability of core operations. This metric is all about finding high-yielding business operations that are being obscured by poor cost management or a heavy debt-load. We can generally expect that financial management failures are relatively easy to fix (perhaps with the help of a private equity firm) if the underlying business is highly profitable.

Reining in Risk

What is risk, exactly? That’s a deeply theoretical question that great financial minds still debate. For our purposes, it’s the possibility that we lose some portion of our investment. Of course, no one knows this probability exactly. We may look at risk using numbers, but (just like the long-term value of an asset) it will always be inherently subjective.

  1. Beta: you’ve almost certainly seen this metric unless you’re brand-new to investing. Beta expresses a stock’s sensitivity (or “elasticity”) relative to broader market movement. A stock with a higher beta will typically fall more than 5% if the market falls 5% on average, and vice versa for gains. People often use Beta as a stand-in for risk, but you should think carefully about that relationship. Beta is really a measure of volatility, which we would expect to be associated with risk in many (but by no means all) cases. Still, at a theoretical level, we always expect stocks with higher betas to offer greater potential returns as a reward for risk.
  2. Standard Deviation (SD, or Greek Letter σ “sigma”): Standard Deviation is a fundamental statistical concept that we don’t have the space to explicate here. It’s a commonly used risk approximator used by even non-statisticians, however. Essentially, SD expresses how dispersed, or spread out, a set of data is. In the case of stock price, this tells us whether the price over time tends to cluster around a relatively stable value or varies widely.

We’ll continue to explore some of the fundamental metrics behind savvy stock research in the coming days. In the meantime, if you’d like to learn more about data-driven investing, we recommend one of our totally free training seminars. There’s no obligation for attendees—just a look at our platform and a preview of a practical strategy for building a pipeline to the data-driven profits hedge funds and big banks have been exploiting for years. You can claim a spot in our next available session using the button below:

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