Abstract

In this tutorial, we apply G-Score 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 G-Score investing, and evaluate companies on seven factors that we will detail later. We specifically choose companies with Book-to-Market due to abnormal returns as a result of the Risk Premium Effect.

Method

We first sort all companies that have fundamental data by their Book-to-Market ratio, and narrow our universe to the bottom quartile. We measure the Book-to-Market ratio using

fine.financial_statements.balance_sheet.net_tangible_assets.twelve_months

divided by fine.market_cap. 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 G-Score. Thus, with seven factors, our G-Score 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 using fine.operation_ratios.ROA.one_year.

  • The Cash Flow Return on Assets (CFROA) is greater than the contemporaneous industry median.
    We measure this value using 

fine.financial_statements.cash_flow_statement.operating_cash_flow.twelve_months

divided by

fine.financial_statements.balance_sheet.total_assets.twelve_months

  • 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 of fine.operation_ratios.ROA.three_months in a RollingWindow and computing the variance of the values in the RollingWindow.

  • The Research and Development Expenditure (R&D) is higher than the contemporaneous industry median.
    We measure this using 

fine.financial_statements.income_statement.research_and_development.twelve_months
  • The Capital Expenditure (CapEx) is higher than the contemporaneous industry median.
    We measure this using 
fine.financial_statements.cash_flow_statement.cap_ex_reported.twelve_months
  • The Advertisement Expenditure (Ad) is higher than the contemporaneous industry median. We measure this using 
fine.financial_statements.income_statement.selling_general_and_administration.twelve_months

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 G-Scores for each of the securities, we long the securities with G-Scores of 5 or higher.

Results

Since we use Technology as the industry, we decided to use Nasdaq-100, 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

  1. Mohanram, Partha S., Separating Winners from Losers Among Low Book-to-Market Stocks Using Financial Statement nalysis (April 2004). Online Copy.