Abstract
In this tutorial, we apply GScore Investing to choose a Universe of stocks to invest in.
Introduction
Analyzing a company’s fundamentals is a method of trading that doesn’t rely purely on price and volume data. We will apply the use of computers to automate the analysis of this data, and we will do so using a method of Factor Investing, the process of using different attributes, in this case, fundamental data, to choose stocks to purchase. More specifically, we will use GScore investing, and evaluate companies on seven factors that we will detail later. We specifically choose companies with BooktoMarket due to abnormal returns as a result of the Risk Premium Effect.
Method
We first sort all companies that have fundamental data by their BooktoMarket ratio, and narrow our universe to the bottom quartile. We measure the BooktoMarket ratio using
fine.FinancialStatements.BalanceSheet.NetTangibleAssets.TwelveMonths
divided by
fine.MarketCap
. In this strategy, we will use Technology as the industry of choice, thus, we further
narrow this universe to Technology stocks only.
For each of the conditions that are described below, if met, one point will be added to the GScore. Thus, with seven factors, our GScore can range from 0 to 7. We evaluate a company based on the following:

The Return on Assets (ROA) is greater than the contemporaneous industry median. In other words, the ROA for the analyzed company is greater than the median of the ROAs of all companies in the same industry.
We measure this value usingfine.OperationRatios.ROA.OneYear
. 
The Cash Flow Return on Assets (CFROA) is greater than the contemporaneous industry median.
We measure this value using
fine.FinancialStatements.CashFlowStatement.OperatingCashFlow.TwelveMonths
divided by
fine.FinancialStatements.BalanceSheet.TotalAssets.TwelveMonths

The CFROA is greater than the ROA.

The Variance of the ROA (VARROA) is lower than the contemporaneous industry median.
We measure this by storing the past twelve values offine.OperationRatios.ROA.ThreeMonths
in a RollingWindow and computing the variance of the values in theRollingWindow
. 
The Research and Development Expenditure (R&D) is higher than the contemporaneous industry median.
We measure this using
fine.FinancialStatements.IncomeStatement.ResearchAndDevelopment.TwelveMonths
 The Capital Expenditure (CapEx) is higher than the contemporaneous industry median.
We measure this using
fine.FinancialStatements.CashFlowStatement.CapExReported.TwelveMonths
 The Advertisement Expenditure (Ad) is higher than the contemporaneous industry median. We measure this using
fine.FinancialStatements.IncomeStatement.SellingGeneralAndAdministration.TwelveMonths
The fundamental data used in our algorithms is sourced from MorningStar, and to read more about our fundamental data, see US Fundamental Data.
Once we have computed the GScores for each of the securities, we long the securities with GScores of 5 or higher.
Results
Since we use Technology as the industry, we decided to use Nasdaq100, or ^NDX, as the benchmark, which we track using the QQQ ETF. Our algorithm achieves a Sharpe Ratio of 0.609 from April 2016 to September 2020, and so it is outperformed by simply holding QQQ, which yielded a Sharpe Ratio of 1.002 over the same period.
Reference
 Mohanram, Partha S., Separating Winners from Losers Among Low BooktoMarket Stocks Using Financial Statement nalysis (April 2004). Online Copy.