Value is one of the most common equity factors in existence and represents stocks that appear cheap relative to their fundamentals. The fundamentals of a stock are the underlying balance sheet, income statement and cashflow statements, which contain all the up to date financial data for the company. This data gives investors, both retail and institutional, the information they need to decide a fair market price for the company. This market price is the price on the stock exchange where buyers and sellers interact and an equilibrium price is reached.

There are free financial data websites that provide data for thousands of listed companies. Some DIY investors would load this data into large cumbersome excel spreadsheets and build their own investing strategies. Nowadays there is an easier way to handle and visualize this large set of data for DIY investors. Using computer code such as python allows users to simply call an API to access the data directly from the free provider.

This free data comes with a few caveats:

• Can contain more errors than costly data packages and goes through less due diligence. This is not always the case but users should be aware of the risks.
• Will most likely not get an accurate set of "historical" data, rather just the most recently released data.
• May only get data for a subset of the stocks you are interested in rather than the whole universe of stocks.

The alternative to free data is a costly subscription to a professional data provider. This is not economically justifiable for an individual investor. However for a company or investment team the cost is worth it to get fully accurate, historically correct, financial data for hundreds of company metrics for every stock in your universe. This type of high-quality financial data is what we have at STRATxAI via our data partner Factset. We use this premium data under the hood to generate our pre-packaged quantitative strategies and deliver those to our customers.

We have described how to get financial data, however we now need to deep dive into this data to obtain different underlying company metrics that allow us to classify a stock as a growth stock or a value stock. We will explain specific value metrics and what they represent.

Two of the most popular and widely used value metrics used by retail investors are

• P/B, price-to-book: this represents the ratio of the price of the stock per share, as traded on the stock exchange, to the book value per share. It represents on a per share basis what the market price is compared to the theoretical book price. The book price is essentially the value of the company on a per share basis. A lower number typically will represent a value stock and a high number a growth stock.
• B/M, book-to-market: the inverse of price-to-book. A higher number will typically represent a value stock. This ratio can work better for finding value stocks as it handles negative book values by definition, i.e negative values correspond to companies of little value as our goal is to find high B/M stocks.

P/B for a basket of tech and utilities stocks.

These P/B and B/M metrics have an explicit price impact built into their ratios either via the numerator or the denominator. They have the advantage that they are simple to understand and explain, however it has been queried if they represent true value on a forward horizon basis. There have been numerous academic papers that show that measuring companies using a book-to-market ratio on its own, exposes investors to concentration risk. By this we mean that forming a portfolio based purely on book-to-market, and nothing else, means the investor historically has had to rely on a small number of these stocks to drive their portfolio performance. Most of the portfolio was in fact made up of deteriorating companies, whose price was declining and wrongly indicating value when in fact their businesses were flailing.

Another very popular and widely reported value metric to illustrate the difference between value stocks and growth stocks is the P/E, price-to-earnings ratio. This measures on a per share basis the ratio of the price per share to the earnings per share. In a similar manner to price-to-book, a higher P/E ratio is typically associated with a growth stock due to the high prices and low P/E stocks linked to more value stocks. It can be a useful way to find over or undervalued companies within a given sector.

P/E for a basket of tech and utilities stocks.

To account for this price bias or risk when using P/B, academic research started to focus on more nuanced methods to find value stocks. This new research focused more on accounting value metrics. Accounting metrics make no use of price explicitly in their formulation, by definition, and they are constructed based on raw company balance sheet, cashflow and income statement data. Some examples of value metrics that can be used to form value portfolios

• ROA, return-on-assets:
• ROE, return-on-equity:
• CFO, cashflow-from-operations:
• Gross margin:
• Leverage, debt-over-assets:
• Turnover, sales-over-assets:

Each of these ratios represents some piece of the value pie and there are many more ratios we have not mentioned here. No universal and consensus value strategy exists in reality. Rather it is up to each hedge fund, institutional fund or retail investor to build their own quantitative value model themselves, backtest it and if happy, implement the strategy and invest real capital. This is what we are providing to our STRATxAI customers using our Custom Strategy tool, we are lifting the lid on the black box and allowing our users to control and test their own investment theses.

Examples of four metrics that can be used to identify value stocks, for three stocks (Apple, Walmart and GM)

We touched on this briefly before when talking about P/B, one very important item to consider is to avoid the value trap. The value trap as it has been coined is the tendency for stocks that appear to be good value, to in fact represent poor companies on a downward trajectory. These companies tend to never outperform and stay cheap (indicating value) for long, long periods of time.