Value is one of the most common equity factors in existence and represents stocks that appear 

 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 

. This market price is the price on the stock exchange where buyers and sellers interact and an equilibrium price is reached. 

Where do we get financial data for all stocks

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. 

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 

 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 

. We will explain specific value metrics and what they represent.

, 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 

, book-to-market: the inverse of price-to-book. A higher number will typically represent a 

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.

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 

 when in fact their businesses were flailing. 

Another very popular and widely reported value metric to illustrate the difference between 

, 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.   

To account for this price bias or risk when using P/B, academic research started to focus on more nuanced methods to find 

. 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 

 metrics that can be used to form value portfolios

 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. 

We touched on this briefly before when talking about P/B, one very important item to consider is to 

. 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. 

How do we identify value stocks using our fundamental company data sets.




1. Value Stocks

Quality is another one of the most common equity factors in existence and perfectly reflects its name. The goal is to search for companies that are of the highest quality to investors. Usually this is represented as stocks that have 

Even though it is popular as an overall concept and factor, the definition of what exactly constitutes a quality company is not easily agreed upon. We have also touched on this idea of the 

 in a previous topic, that was focused more on factor performance. This post is more about the metrics themselves that our users can investigate to find their own quality companies. Some investors may wish to focus on one facet of quality in particular (for example earnings growth) whereas others may want to focus more on the governance and safety aspect.

Why can't we just find high-quality companies and buy them ? 

If only it was that easy! It turns out lots of investors are fighting to find these companies and therefore the highest quality companies tend to have the highest prices and this can lead to lower future returns. The goal is to find the sweet spot of companies that are deemed to be of good quality but also at good prices. There is a famous quote from Benjamin Graham, a famous economist and investing godfather: 



The chief losses to investors come from the purchase of low-quality securities at times of good business conditions.


Essentially in order to avoid the greatest losses, it is still better to purchase the highest quality companies at high prices rather than buy cheap companies of really low quality - with the ideal scenario being to find high quality companies at attractive prices. 

To help filter and find quality stocks, we are first going to construct three broad categories to represent the different traits of quality companies. Then we will give some explicit examples of various metrics from each of the broad quality categories, which we define as:

focuses on a stocks gross margin, return-on-equity, return-on-assets and also cashflow metrics. Higher values of each of these indicate higher quality companies. This makes sense, companies that have a competitive advantage in a sector tend to be able to charge more and increase margins and returns on equity/assets and have strong cashflow. 

: identifies companies that are illustrating strong 

 in their underlying profitability metrics. For example, companies with a large increase in return-on-equity from last year to this year. Likewise, companies who have managed to increase gross margins since last year would again be ranked as higher quality companies rather than companies who have flat or have lowered their gross margins. 

measures the volatility, leverage, bankruptcy risk and variability of earnings for example. For this category, we would like to find companies with lower levels of leverage, volatility (outright and earnings) and bankruptcy risk as these would indicate safer companies to invest in and therefore higher quality. 

By using some or all of these categories, investors hope to create a systematic way to identify high-quality companies. The end choice as to how you want to weight each category is up to the individual investor as there is no fixed rule. Clearly, an equal-weight on each individual metric inside another equal-weighting of those three categories is arguably the most consistent. Hopefully this short introductory article describing the quality factor has given you the base to start exploring the data yourself and seeing how to incorporate the quality factor into your investing portfolio. 

How do we identify quality stocks using our fundamental company data sets.




2. Quality Stocks

We discussed in previous posts both the value factor and quality factor and what fundamental data we use to construct metrics that help us find the stocks with those properties. Now we move to another popular factor that is called the 

What does an example of a low-volatility factor represent ?

The low-volatility factor identifies the stocks that have the lowest historical volatility and forms portfolios based on those stocks

 but we will briefly mention it for this article. Volatility is the variation of the underlying data around its average value over some historical period. It measures the 

 of the data around the average value. For stocks, volatility is usually referenced when we are discussing the stocks returns and higher dispersion means higher volatility, which typically means higher risk. The important concept now for this topic is to discuss and dive deep to explain exactly how we construct volatility from our data. 

A question that is often asked is, what is the volatility of the S+P 500 ? To answer this question fully, we need just a little bit of extra information, which relates to the historical period we want to use to analyze the S+P. Essentially we have two options, we could:

compute the annualized volatility using daily returns over a large multi-year historical window. This will effectively show that over that large window, the S+P had a volatility of x% annualized. The answer we get will be one single volatility % number.

compute a rolling 1y volatility of the S+P 500 based on daily returns of the last 252 trading days, over the large historical window. This calculation outputs a time-series of 1y volatilities, which naturally change through time as the rolling 1y window of data moves with time. 

The benefit of method 1 is that it gives an investor a really clear cut, single number to summarize the volatility. This number for our S+P 500 example was 15.6%. The downside of method 1 is that it shows no 

 of the volatility so you cannot plot or graph the volatility to see how it changes. For example, you cannot answer the question "what was the maximum volatility during 2020-2022", all you have to go on is the 15.6%.

The benefit of method 2, as we can see from the graph, is that we can see how the 1y rolling volatility changes and we can identify spikes and troughs in volatility. The downside is that we need to agree on some method to simplify this data down to either a single number or a small set of numbers that are easily interpretable. Obviously, combining the two methods is a viable option and used by many in this way.

We do not use any balance sheet or fundamental data for the construction of the low-volatility factor. Instead we use purely the price data for all stocks and use this price data to then construct the stock returns. If we take all the stocks in our universe and calculate their return volatilities, then a very simple low-volatility factor would be to rank the stocks in volatility ascending order and invest in the first 15-20 stocks (i.e these are the stocks with the lowest volatilities).

This is just one method to simply rank stocks, we can of course start to add complexity and start to have multiple volatility metrics over different historical timeframes. Then we can average our volatility scores to construct a final 

Compute and rank the lowest 1y historical volatility portfolios 

Compute and rank the lowest 6 month historical volatility portfolios 

Compute and rank the lowest 3 month historical volatility portfolios 

A portfolio can then be formed based on an equal weight of the three volatility metrics, where the model is trying to for example pick up a trend and rely on more recent information by using 6 month and 3 month data.

Alternatively, a more sophisticated investor may not want to just focus on 

. Similar to the previous method, a final low-volatility portfolio could then be formed by weighting equally stocks returned from the usual historical volatility method and the stocks returned from the new forecasted volatility model. This type of method is beyond the scope of this article but we can devote a follow-up article to volatility forecasting if users prefer, please contact us. 

How do we identify low volatility stocks using our data sets.




3. Low-Volatility Stocks

The momentum style of trading is one of the most natural investing styles around and yet it lacks a lot of the things that make value and quality investing popular. First and foremost, 

 is based on the popular philosophy that 

 If certain stocks are having a good run and increasing significantly in price, there must be a reason for it and all else being equal they will most likely keep increasing in value. 

Secondly, momentum investing is not based on company financials explicitly - unlike value and quality factors which tend to take into account multiple balance sheet or cashflow statement items. Instead momentum is purely a price-based signal where the underlying hypothesis from momentum believers is that 

the price of the stock contains all the relevant financial balance sheet items and other market sensitive information

What data goes into a momentum strategy ?

Given its reliance on price alone, it is therefore logical that all we need to construct a momentum signal is stock price data. Some investors may augment this price data with the underlying volume data and as a result, may weight certain price moves that occurred with large volume as stronger signals. 

For someone who wants to start investigating momentum investing strategies themselves, they can easily use python to start downloading yahoo stock price data and start constructing their own strategies (and we give some sample code at the end of this article). However, at STRATxAI we make all this easily available to you so you don't have to, our platform handles it all for you and you can run instant backtests for your strategies. 

 in the factor investing world are mainly based on recent stock returns and constructing portfolios that are rebalanced at a frequency that . One well-known academic paper on this subject is "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency" by Jegadeesh and Titman. 

Their paper showed that stocks that have outperformed over the previous year, have then outperformed again over the subsequent 3m and 6m periods. Since the release of that paper, quantitative hedge funds and asset managers have embraced the concept of momentum and it is an active component of most portfolios. Each implementation is different with some firms focusing on 

sector-neutral momentum, each stocks momentum controls for the sector momentum that occurred.  

residual momentum, where the returns used are the returns minus the benchmark market return

implemented varying speeds of momentum (i.e swapped out 12 month look-back window for 3 month or 6 month or a blended combination) to match their portfolio turnover    

For our purposes and for this article it is ideal to focus on the simplest form of momentum, that is using the trailing 12 month return to rank our stocks to identify the stocks most likely to keep outperforming for the next 3 - 6 months. 

What indicators to use if my stock universe is small ?

If someone is not interested in the large universe of stocks (i.e thousands) but instead interested in designing and investing in a momentum strategy that focuses on one particular sector - what can you do ? 

You can certainly still design an overall strategy based on the method of trailing returns we discussed in the previous section. However given the smaller universe size, potentially just 10-15 stocks, an investor could now start to add other momentum indicators to their strategy builder. 

This would most likely be in the form of technical indicators. By utilising the extra information from these additional indicators, it could potentially lead to extra outperformance for your strategy, especially for fast moving sectors. The reason extra indicators can be used is mainly because of the smaller universe. This universe size is critical, especially if an investor wants to visually inspect the technical indicators. Some examples of some indicators may be 

, relative strength index: measures the speed of price changes to indicate if stocks may be overbought or oversold and potential to revert in the short-term.

, moving average convergence divergence: classic trend-following signal. It utilises a moving average of two other moving averages of different look-back windows. The two windows will be based on one shorter window and one longer window. If the moving average of the difference between these two is positive it indicates positive momentum. 

, stochastic oscillator: a momentum indicator that calculates how far the current price is above the low price, in relation to the high-low range over a given historical window. It is beneficial to think of this metric as being like a percentile and if the price is at the highest point it will be at 100. In the short-term, this could potentially mean that some selling could occur as investors take profit.   

, rate-of-change: a simplistic momentum indicator that measures the percentage increase or decrease in price based on the price at the beginning of your chosen look-back window.

We will be providing all these technical indicators and tools to our users in upcoming product releases.

Python code for calculating trailing returns

In case anyone wants to start downloading and building their own toy strategies, here is some sample python code to try. The code uses yahoo finance to download historical stock price data for the ticker of our choice, in this case Apple. For our pre-packaged strategies at STRATxAI, to make things really easy for our users, we calculate our momentum strategies over thousands of stocks, using our premium data provider Factset. 

How do we identify momentum stocks using our data sets.




Value Stocks

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