Strategy Library

G-Score Investing

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.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 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.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 of fine.OperationRatios.ROA.ThreeMonths 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.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, please visit the Fundamentals section of our documentation.

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

Algorithm

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 0.778 from April 2016 to September 2020, and so it is outperformed by simply holding QQQ, which yielded a Sharpe Ratio of 1.22 over the same period.

References

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

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