Quality Investing: Defining the Best Companies

Ozkan Ozkaynak

September 21, 2023

This research explores the concept of Quality Investing in U.S. large capitalisation stocks, focusing on generating abnormal returns. By utilising fundamental metrics related to Profitability, Growth, and Safety, a quality score is calculated to construct portfolios of high-quality stocks. The study defines quality across three dimensions: Profitability, Growth, and Safety, and evaluates the effectiveness of various investing metrics and ratios within these dimensions. The research process involves creating 24 portfolios based on the defined quality criteria, with portfolio constituents recalculated annually. The performance of these portfolios is compared to the S&P 100 index, a benchmark for large capitalisation stocks. The study emphasises the importance of metrics like Return on Equity (ROE), Operating Income Growth, and Cash Flow to Debt ratios in constructing high-quality portfolios. The results highlight that all quality-defined portfolios consistently outperform the benchmark index, showing higher risk-adjusted returns and Sharpe Ratios. Notably, the portfolio defined using a combination of ROE, operating profit growth, and cash flow to debt ratio demonstrates particularly successful results, surpassing other portfolios and the broader market. In conclusion, the research findings indicate that focusing on quality, specifically through profitability, growth, and safety metrics, can lead to statistically significant annual returns (alpha) of 5.88%. This study is valuable for investors interested in long-term investments in U.S. large capitalisation stocks, offering insights into utilising quantitative analysis of fundamental ratios to achieve outperformance.

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1. Introduction

Over the past decade, there has been a significant shift in the investment style from actively managed funds to passively managed funds and Exchange-Traded Funds (ETFs). This trend is evident through the substantial inflows of more than $2 trillion into passive equity funds and ETFs, while active funds have experienced outflows of over $1.5 trillion (EPFR, 2023). However, these numbers might underestimate the prevalence of passive investing, as many large institutional investors like pension funds and sovereign wealth funds manage their own index-tracking passive strategies internally (FT, 2023).

Despite the popularity of passive investing, the current market environment presents particular challenges. Stock market valuations are relatively high compared to historical averages, making it harder to find undervalued opportunities. Additionally, central banks around the world, including the Bank of England (BOE), Federal Reserve (Fed), and European Central Bank (ECB), have begun tightening their monetary policies to address rising inflation concerns. These changing economic conditions create opportunities for active portfolio management. While passive funds provide broad market exposure, active managers change their portfolios proactively with changing market conditions and potentially generate alpha by capitalising on mispriced assets or undervalued stocks.

Quality investing is an active investment strategy that focuses on selecting stocks of high-quality companies based on specific fundamental characteristics, aiming to achieve better long-term returns. These characteristics include quantifiable metrics like profitability, growth potential, safety, dividend payouts, and efficiency, and non-quantifiable ones such as management quality, corporate governance, Environmental, Social, and Governance (ESG) factors, and competitive advantages (Gonzalez et al., 2021).

While quality investing has gained popularity in recent years, there are varying interpretations of what constitutes quality, leading to various metrics and factors researchers and investors use. Despite this complexity, the strategy aims to identify companies that exhibit resilience, stability, and potential for sustainable growth, allowing investors to achieve better risk-adjusted returns. The underlying assumption is that high-quality companies with robust financial health, consistent earnings, and solid business fundamentals generally carry lower risk and exhibit more sustainable growth than their counterparts.

Investors have considered quality characteristics like strong, healthy financial standing, promising growth prospects, and low financial leverage while making investment decisions. Despite this, the concept of Quality took time to be embraced in asset pricing research, only recently gaining prominence and lagging factors like value, size, and momentum in the number of studies.

Studies and practical experience suggest that quality investing can yield better returns than overall market indices in the long run. Inspired by these diverse definitions of quality, this research aims to examine Quality Investing using quantitative analysis of financial ratios of companies. This study explores the concept of Quality investing within the context of U.S. large capitalisation stocks. This strategy was initiated in 1930s when Benjamin Graham published his research on the contrast between investing in undervalued stocks and quality stocks (Graham, Dodd, 1934). However, defining quality investing precisely remains a challenge, as researchers sometimes define quality stocks using financial metrics, while others connect quality to non-financial aspects of business practices.

This study investigates the potential for quality investing to generate abnormal returns from large capitalisation companies registered on U.S. stock exchanges. Quality scores are determined using profitability, growth, and safety criteria. Using these scores, portfolios of the highest-quality stocks are formed, and their performances are analysed. Fama-French five-factor model (2015) tests the best performer quality portfolio to determine whether the constructed portfolios can yield abnormal returns.

1.1 Purpose and Structure of the Study

While it appears to have gained popularity in recent years, the concept and application of Quality Investing remain complex, mainly due to varying interpretations of what constitutes quality. This fundamental discrepancy, both in the perception of quality and its purpose, sets the groundwork for the research objectives of this research.

This research explores the application of quality investing as a strategy in U.S. large capitalisation stocks. We define quality based on Profitability, Growth, and Safety criteria. The quality scores of companies were calculated by adopting the methodology used by Asness, Frazzini, and Pedersen (2019). Multiple portfolios are formed, and their performances are analysed. To determine whether quality investing can yield significant abnormal returns, we perform a regression test using the Ordinary Least Square (OLS) method and compute alpha, a measure of the portfolio's risk-adjusted excess return from the five-factor model by Fama and French (2015).

The study's methodology involves using a sample of stocks from the large capitalisation U.S. companies that are the most liquid stocks across the World. The stocks are selected based on quality rankings formed from their fundamental analysis. Portfolios are then created using these rankings and compared against the broader market, the S&P100 Index. The S&P 100 Index is designed to track the performance of major large capitalisation companies in the U.S. The S&P 100 Index is often used as a benchmark to evaluate large capitalisation equity investments' performance and gauge the overall stock market. These companies are considered prominent and well-established blue-chip companies representing various sectors and industries within the U.S. economy.

The research aims to answer three main questions:

·       What are the primary fundamental ratios that can be used for defining Quality and calculating Quality scores of companies?

·       Which one of the defined Quality portfolios provides the highest risk-adjusted returns?

·       Does Quality Investing provide better returns and outperformance than the large capitalization benchmark index, SPX 100 and overall, is Quality Investing a successful investment strategy in the long term?

This research aims to deepen our understanding of Quality Investing, considering both academic and practical perspectives, and present a more comprehensive and universally accepted definition that can help improve investment decision-making processes. The second purpose of this study is to identify which of the defined quality definitions provides better results. Third, to investigate whether a portfolio formed from top-quality stocks by scoring and selecting stocks based on fundamental quality indicators generates statistically significant alpha in the long term. These indicators include profitability ratios, growth, and financial safety. The process involves collecting historical financial data, formulating a strategy, implementing the strategy in a real-world investment portfolio, backtesting it on historical data, evaluating performance, and conducting statistical analysis for significance. Continuing the methodology of previous research (Asness et al., 2018), we create portfolios in the US large capitalisation stock market. These portfolios are based on stock quality indicators and spanning 2013 to 2023. Our analysis compares the performance of portfolios containing high-quality stocks with the large capitalisation overall market index, S&P 100. The analysis is based on a Quantitative research approach to fundamental financial data. Time series data of company fundamentals and stock price data for the last ten years are used.

This research is structured according to the research onion model (Saunders et al., 2018), incorporating key elements: a positivist philosophical stance, inductive reasoning, a quantitative research approach, an experimental strategy, a longitudinal time horizon, and the analysis of financial data.

It is significant to note that investing always carries risks. While quantitative and fundamental analysis can offer insights, no strategy guarantees success due to the complex and unpredictable nature of markets.

The study begins by setting the context for understanding factor investing. The Quality Investing literature review and an overview of the Quality Investing sector are in the next section. The empirical part of the research starts with the methodology in Chapter 3 and continues with research findings and discussion in the following sections. The last chapter concludes the research and summarises the primary results.

2. Literature Review

2.1 Introduction

Factor investing has been becoming a major strategy in the investment world. It utilizes broad, persistent, and measurable factors that help explain risk and return across asset classes.

By understanding these factors and their individual and combined impacts, investors can better predict risk and return and make more informed investment decisions. However, how each factor is defined and measured can significantly impact the outcomes, underscoring the importance of carefully constructing and managing factor portfolios.

The concept of factor production discussed by Harvey and Liu (2015) highlights the challenge of distinguishing meaningful investment factors from statistical noise or data mining outcomes in academic research. With an abundance of over 400 factors published in journals, it becomes essential to identify factors that genuinely contribute to investment performance. Hou, Xue, and Zhang (2020) emphasise the importance of subjecting proposed factors to rigorous statistical testing, as many may not hold up under conventional significance levels. This highlights the need for a critical and cautious approach to factor selection. Furthermore, the complexity of core factors like value, size, momentum, quality, and volatility must be acknowledged. The challenge of factor production underscores the need to thoroughly evaluate and test factors to determine their true impact on investment strategies. The complex nature of factors necessitates a comprehensive understanding of their construction and potential implications for investment decisions.

There are the six core factors often discussed in academic literature and mainly used in practice:

Value: The value factor is one of the key elements in factor investing, representing the propensity for underpriced stocks relative to their intrinsic value to outperform overpriced stocks. The value factor reflects the inclination of cheaper stocks with low market prices relative to their fundamentals and expects them to outperform pricier ones over time. Stocks considered undervalued are often characterised by lower market multiples like price-to-book or price-to-earnings ratios.

Blitz and Hanauer (2020) suggest that a robust value premium can still be realised with adjustments to traditional valuation methods. They propose the inclusion of intangible assets in the company valuation and the consideration of earnings and cash-flow-related metrics, which are more universally applicable to firms across various sectors.

Size Factor: The size factor, also known as the small capitalisation premium, is the tendency for small companies, those with smaller market capitalisation, to outperform larger companies over the long term. It is an inverse relationship where smaller companies often offer greater returns relative to their risk. Asness et al. (2018) suggested that the size premium appears when controlling for small stocks' quality characteristics. Blitz and Hanauer (2020) countered this by stating that this result does not hold for international markets and that the perceived size alpha in the United States may be elusive for investors.

Momentum: The essence of momentum investing is the idea that companies that showed better performance recently would continue their performance and vice versa. However, it has often been left out because this concept doesn't fit into traditional risk-based models like Fama and French's.

The rationale is that during periods of high volatility, the risk of a momentum crash is greater; hence, reducing exposure to the momentum factor (Barroso, Clara, 2015).

The momentum factor can potentially be enhanced by considering other sources of information beyond stock prices. For instance, earnings momentum, changes in analyst recommendations and targets, and sentiment analysis of investors can be used in momentum investing practice.

Volatility Factor: Contrary to traditional finance theories, which indicate that higher risk should yield higher returns, the low volatility factor suggests that less volatile stocks have been found to outperform more volatile stocks over time. It is often measured using stock returns' historical standard deviation or beta.

Dividend Yield: The dividend yield factor highlights the tendency of stocks with higher dividend yields to outperform those with lower yields. This factor can be particularly attractive to stable, income-focused, risk-averse investors.

Quality: The quality factor pertains to the excess returns of companies that exhibit robust financial health. Key indicators include high profitability, growth, efficient use of assets and financial safety. However, defining and quantifying quality can be challenging, and different approaches exist to identify quality stocks.

In addition, non-quantifiable quality investing metrics are essential considerations for assessing the overall health and potential of a company, even though they cannot be easily expressed in numerical terms. These qualitative factors provide insights into the company's management, corporate governance, reputation, and innovation. While they may not have specific values, they take a major role in determining a company's quality and long-term prospects. Evaluating these non-quantifiable metrics requires careful research, analysis, and judgment, often drawing from sources like annual reports, industry news, and expert opinions. In quality investing, understanding, and incorporating these qualitative aspects alongside quantifiable financial metrics may contribute to an assessment of a company's potential. In this study, quantitative analysis of companies' fundamental ratios is used.

2.2 Quality Investing Literature Review

Quality investing is a type of active investing that mainly focuses on buying and holding a portfolio of high-quality companies. As opposed to other strategies like value, growth, and momentum, there is no well-agreed definition of Quality Investing in practice. The origin of the quality approach can be traced back to Benjamin Graham, considered the pioneer of value investing. He classified stocks as high or low quality and recognized the advantages of investing in quality stocks over cheap ones. He emphasized low debt ratios, stable earnings, and reasonable valuation metrics as quality indicators (Graham, Dodd, 1934).

V Various definitions of Quality Investing have been presented in the literature. Investors have the flexibility to consider a range of factors to assess a company's quality. This diversity in definitions underscores the complexity and subjectivity of the quality concept in stock market analysis. Many researchers conclude that investing in high-quality stocks would provide significant outperformance over investing in low-quality stocks.

Novy-Marx (2014) emphasizes the distinction between quality and value investing, suggesting that quality investing involves acquiring high-quality assets at reasonable prices, while value investing focuses on acquiring average-quality assets at discounted prices. He introduced a simple quality metric called gross profitability-to-assets. This metric has demonstrated predictive power like traditional value measures like book-to-price in forecasting the relative performance of different stocks. He concluded that high-profitability stocks outperform the market.

Asness, Frazzini, and Pedersen (2019) found that companies with high-quality characteristics, including profitability, growth, and safety, tend to outperform companies with lower-quality characteristics. They introduced the Quality Minus Junk (QMJ) strategy, which involves buying high-quality stocks and shorting low-quality stocks, demonstrating its effectiveness in generating higher returns. Their findings highlighted the significance of considering quality metrics when making investment decisions. The QMJ strategy's success across different market capitalisations emphasised that quality investing can be relevant for both large and small companies. By defining a high-quality firm that exhibits profitability, growth, and safety, the researchers established a framework for investors to identify potential winners in their portfolios.

Hsu, Kalesnik, and Kose (2019) explained Quality Investment criteria defining good financial quality companies.

Hanson & Dhanuka (2015) defined quality companies as those incorporating sustainable competitive advantages that can be maintained or increased in the future. Companies with durable franchises are more likely to maintain their competitive advantages in the long term. They proposed combining non-financial indicators with quality financial metrics.

Quality investing is rooted in bottom-up investing, assuming that pricing inefficiencies exist in capital markets. Investors following this approach analyse a wide range of stocks based on financial metrics rather than focusing on broader economic factors. Various studies have pointed out that market anomalies exist, contradicting the Efficient Market Hypothesis proposed by Kendall (1953), which may not function as expected, given the existence of certain market anomalies that can be exploited for abnormal returns. These anomalies can be leveraged to gain abnormal returns by investing in value stocks or buying high-performing stocks and selling low-performing ones. More recently, the Quality of stock issuers has been examined as an anomaly to obtain risk-adjusted excess returns. An increasing number of studies have shown that investments in high-quality stocks can generate significantly positive returns. In contrast, investments in low-quality stocks often result in significant negative excess returns.

Kozlov and Petajisto (2013) suggested that going long on stocks with high-earning quality and short on those with low-earning quality might offer a high Sharpe ratio. Gallagher et al. (2013) found that high-quality stocks yield abnormal returns compared to lower-quality counterparts.

Introduced by Greenblatt (2010), The Little Book That Beats the Market, Magic Formula Metric uses Return on Invested Capital (ROIC) and Earnings Yield to rank companies. He found that Return on Invested Capital can provide excess returns.

Piotroski (2000) highlights the differences between value and quality strategies. He argues that the performance of the high book-to-market strategy primarily stems from a small number of firms, raising questions about whether other fundamental indicators can differentiate between strong and weak companies within such a strategy. Quality investing attempts to address these questions. He developed the F-Score, which involves summing nine binary variables that indicate financial strength or weakness. These variables encompass profitability, liquidity, and operating efficiency. A higher F-Score from 0 to 9 indicates stronger financial performance or quality.

Grantham (2004) defined quality companies as those with low earnings volatility, low leverage, and high profitability. Grantham's Quality Score is calculated by averaging rankings based on three measures: profitability, earnings volatility and leverage. The aim is to identify companies with high profitability, low leverage, and stable earnings.

These measures offer different perspectives on evaluating the quality of stocks. The Magic Formula considers ROIC and EY, Piotroski's F-Score combines various financial strength indicators, Gross Profitability focuses on profitability-to-assets, and Grantham's Quality Score considers factors like ROE stability, asset-to-book equity, and inverse ROE volatility. Each measure aims to help investors identify stocks with desirable quality characteristics that could lead to better long-term performance.

Even though these definitions of quality vary and carry different levels of complexity, they all essentially measure a company's Profitability, growth and safety. Profitability has been consistently shown to attract investors and contribute to higher stock prices. Various studies, including those by Fama and French (2015), Novy-Marx (2014), Hou et al. (2020) have provided evidence of a positive relationship between Profitability and stock premiums. Profitable companies tend to outperform their less profitable counterparts in the market. Growth is another key factor in assessing quality. It reflects a company's ability to maintain and increase Profitability over time. Mohanram (2005) highlighted that companies with higher growth rates tend to achieve better performance than those with lower growth rates. This emphasizes the importance of sustainable growth as a quality indicator. Safety, often assessed through metrics like low leverage, also plays a role in quality investing. Studies conducted by George and Hwang (2010) and Nissim and Penman (2001) have found that companies with lower leverage levels tend to outperform those with higher leverage levels. This suggests that companies with more stable financial structures are perceived as higher-quality investments.

The first aim of this research is to present a more comprehensive and universally accepted definition that can help improve investment decision-making processes. The table below summarises the Quality definition used by researchers and by the Quality Investment Industry. Quality is generally viewed as a concept with multiple dimensions despite significant variations in its measurement. Several indexes have been launched, like the MSCI World Quality Index, which considers three main variables: Return on Equity (ROE), stable earnings growth (Earnings Variability) and low leverage (Debt to Equity). Investors and researchers have continuously researched and applied methodologies to outperform their benchmarks and earn a premium return.

This research expands the investigation of Quality Investing to large capitalisation companies and aims to compare the results with the benchmark large capitalisation index, S&P100. To achieve this, a Quality Score is calculated by aggregating three different fundamental metrics from a company's Profitability, Growth and Safety dimensions. A specific investment strategy is employed, involving the selection of stocks from the highest Quality scores.

This study evaluates the effectiveness of various Quality indicators for stock selection. It considers metrics Return on Invested Capital (ROIC), Return on Assets (ROA), and Return on Equity (ROE) under Profitability. The Growth dimension includes Revenue Growth, EBIT, EBITDA Growth, and Operating Cash Flow to Sales Ratio. Safety is assessed using Debt to Equity and Cash Flow to Debt Ratios. Each dimension provides a unique lens to gauge a company's performance, financial health, and risk profile.

The second purpose of this study is to identify which of the defined quality definitions provides better results.

Novy-Marx (2014) introduced a simple quality metric called gross profitability-to-assets, which demonstrated predictive power like traditional value measures like book-to-price in forecasting the relative performance of different stocks. He concluded that high-profitability stocks outperform the market. Asness, Frazzini, and Pedersen (2019) found that companies with high-quality characteristics, including profitability, growth, and safety, tend to outperform companies with lower-quality characteristics. They introduced the Quality Minus Junk (QMJ) strategy, which involves buying high-quality stocks and shorting low-quality stocks, demonstrating its effectiveness in generating higher returns. Introduced by Greenblatt (2010) defined the Magic Formula Metric, which ranked companies based on Return on Invested Capital (ROIC) and Earnings Yield metrics. He found that Return on Invested Capital (ROIC) can deliver excess returns, another angle of quality investing.

Even though these definitions of quality vary and carry different levels of complexity, they all essentially measure a company's Profitability, growth and safety. Profitability has been consistently shown to attract investors and contribute to higher stock prices. Various studies, including those by Fama and French (2015), Novy-Marx (2014), Hou, Xue, and Zhang (2020) have provided evidence of a positive relationship between Profitability and stock premiums. Profitable companies tend to outperform their less profitable counterparts in the market. Growth is another key factor in assessing quality. It reflects a company's ability to maintain and increase Profitability over time. Mohanram (2005) highlighted that companies with higher growth rates tend to achieve better performance than those with lower growth rates. This emphasizes the importance of sustainable growth as a quality indicator. Safety, often assessed through metrics like low leverage, also plays a role in quality investing. Studies conducted by George and Hwang (2010) and Nissim and Penman (2001) have found that companies with lower leverage levels tend to outperform those with higher leverage levels. This suggests that companies with more stable financial structures are perceived as higher-quality investments.

24 Quality portfolios were constructed from the three dimensions of Quality: Profitability, Growth, and Safety. The first set of constituents is calculated for 31 July 2014 using the fundamental data of the previous year. Every year, at the last trading date of July, the Quality scores are calculated, and with the updated scores, all the portfolio constituents are reallocated.

The third aim of this research is to investigate whether a portfolio formed from top-quality stocks by scoring and selecting stocks based on fundamental quality indicators generates statistically significant alpha in the long term.

Carhart’s four-factor model (Carhart, 1997) and Fama-French Five-Factor (Fama, French, 2015) were used in previous studies. Hezbi and Salahi (2016) compared the explanatory power of the Fama-French five-factor model and the Carhart four-factor model, and their results showed that the Fama-French five-factor model has more explanatory power than Carhart four-factor model. Carhart's Four Factor Model did not provide a better explanatory power compared to the Fama-French’ previous Three Factor Model (1992) as well (Tazi et al., 2021).

The Fama-French Five-Factor Model is an asset pricing model developed by Eugene Fama and Kenneth French (2015). It extends the original Fama-French Three-Factor Model (1992) by adding two additional factors to explain better the stock returns cross-section. The model aims to provide a more comprehensive framework for understanding the sources of stock returns beyond the traditional Capital Asset Pricing Model (CAPM).

The Fama-French Five-Factor Model is designed to provide a more accurate explanation of stock returns by accounting for market return, size, value, profitability, investment factors, and market risk. Researchers and investors use this model to understand the sources of risk better and return in the equity market and assess the performance of investment strategies.

In this analysis, the performance of portfolios constructed based on Quality strategies is compared against a benchmark, which is a large capitalisation overall market index, S&P100. To investigate whether quality investing strategies yield excess returns, the Fama-French Five-Factor Model is used to uncover whether quality-based investing provides additional value beyond these core factors.

2.3 Research Gaps and Limitations

In this research, the Quality definition is structured along three dimensions: Profitability, Growth, and Safety. In the context of stock selection based on Quality indicators, this study addresses the question of which investing metrics and ratios are most effective. Return on Invested Capital (ROIC), Return on Assets (ROA), and Return on Equity (ROE) metrics are used from the Profitability dimension. For Growth, Revenue Growth, Operating Profit Growth (EBIT), EBITDA Growth and Operating Cash Flow to Sales Ratio were used. Debt to Equity and Cash Flow to Debt Ratio is used for the Safety dimension of Quality.

The combined Quality scores are calculated by creating equally weighted portfolios of Quality ratios. There were no previous studies regarding the quality aspects of large capitalisation stocks in the U.S. market. In addition, focusing only on large capitalisation stocks has not received special attention. This research is the first study to test the quality dimension in a large capitalisation stocks universe in the World`s largest stock market. The results from this research may have significant implications for investing decision-makers, as this study provides evidence on the performance of some fundamental value and quality indicators that can help improve long-term investment performance.

There are some limitations regarding this study. Firstly, this research only focuses on U.S. large capitalisation stocks in the U.S. market and compared with the largest companies in the S&P100 Index. As with the same research methodology, it can easily be applied to all stocks in the U.S. as well as global stocks. U.S. markets` total capitalisation is 58.4% (Statista, 2023) of the World’s total market capitalisation and the largest Index, S&P100’s market capitalisation, was 78% of total U.S. companies as of July 2023. For the aim of the study, analysing the largest U.S. companies’ universe provides results that may help investing decision-makers in the long term. The market focus of this study is restricted to the U.S., one of the largest markets in the World, but the methodology of the research can be applied to other developed country markets as well.

Secondly, banks and other financial companies were excluded from the sample to ensure that only firms for which all required fundamental data were available were compared. Quality scores for each company are calculated at the end of July each year, and performances are calculated monthly. To calculate the company’s profitability and safety scores, the prior year`s fundamental data are required. In contrast, the previous three-year data were used in each year`s score calculation for growth scores. If fewer observations are available, those companies are excluded. After completing the data cleaning, 74 companies with at least $100bn market capitalisation are included in this research.

Thirdly, this research does not include transaction costs or liquidity constraints, as it focuses on the largest companies in the U.S. market. A transaction commission is a cost that occurs while buying or selling the positions. Since one of the aims of the research is to compare the differences in portfolio performances, and each portfolio was rebalanced once on the last trading day of July every year, the differences between transaction costs of portfolios are small enough to affect the findings of this study. In addition, the investment universe includes the largest market capitalisation companies, there are no liquidity constraints, and many investment brokers have provided commission-free investment opportunities to these companies` shares in recent years. 

Additionally, the Quality Investment analysis in this research does not incorporate sectoral differences while scoring and creating portfolios. Even though this approach aligns with other relevant research, considering sector-level differences may improve the results. In addition, this research can be extended to invest in smaller capitalisation stocks, which are less liquid and more costly to invest in. Still, these companies` stocks may provide higher return potential with high-risk levels. 

Finally, in recent years, the Environmental, Social, and Governance (ESG) concept has gained significant popularity, particularly in the context of investment decision-making. ESG may be considered as one of the key Quality dimensions (Otero, 2021). ESG is a non-financial metric that assesses a company's environmental, social, and governance performance. Even though this research does not include the ESG concept, investment decision-makers have increasingly considered ESG while evaluating the Quality of companies. Overall, by incorporating non-quantifiable quality indicators like management quality, corporate governance, ESG factors, and competitive advantage into their investment decisions, investors can enhance their ability to select stocks more likely to yield better returns.

2.4 Conclusion

The chapter explores six core investment factors extensively studied in academic literature and used in practical investing: value, size, momentum, volatility, dividend yield, and quality. However, the quality factor stands out due to its diverse interpretations in research, reflecting its complex and subjective nature in stock market analysis. This diversity is also challenging for practitioners seeking a consistent approach to quality investing. Despite these variations, researchers generally agree that investing in high-quality stocks yields better performance than lower-quality stocks, emphasising the importance of solid fundamentals and positive business practices for long-term success.

A clear consensus on its definition is lacking compared to other approaches, such as value or growth investing. Originating from Benjamin Graham's classification of stocks based on stability, valuation, and debt ratios, various definitions of quality investing have emerged in literature, highlighting its complexity. Novy-Marx (2014) introduced a quality metric based on gross profitability-to-assets, showing its predictive power in stock performance. Asness, Frazzini, and Pedersen (2019) established a Quality Minus Junk (QMJ) strategy, indicating the outperformance of high-quality stocks. Hanson and Dhanuka (2015) emphasised sustainable competitive advantage as a core quality criterion. Quality investing leverages market inefficiencies, as evident from studies by Kozlov and Petajisto (2013), Gallagher et al. (2013), and Greenblatt (2010). Metrics like Piotroski's F-Score and Grantham's Quality Score provide different facets of quality, all emphasising profitability, growth, and safety.

3. Research Methodology

3.1 Research Data

This research focuses on the large capitalisation companies listed on U.S. stock exchanges. The S&P100 index is a comprehensive representation of around 78% with a market capitalisation of $28.9 trillion compared with $37.1 trillion of the S&P 500 index, covering 11 sectors as of 31 July 2023. To enhance data consistency, non-financial companies' fiscal year fundamental data were considered, aligning with the portfolio creation on the last trading day of July each year. The study selects stocks from non-financial companies with a market capitalisation of over $100 billion within the S&P Index to ensure liquidity. A list of the companies included in the analysis is provided in Figure 8.1 in the Appendix section. The data used for the research, including index constituents, dividend-adjusted stock prices, and financial statements, is sourced from the Refinitiv Eikon database. In addition, the dataset for the Fama-French model was obtained from the Fama-French time series database (Fama, French 2015). All price and fundamental data are measured in U.S. dollars. The study's time frame spans from July 2013 to July 2023. Each year, on the last day of July, portfolios are constructed based on the discussed quality metrics using the most recent annual financial data. Non-financial companies with a minimum of 100bn USD market capitalisation are considered and compared with the overall large capitalisation market Index, S&P100.

For three quality indicators, Profitability, Growth and Safety measures, portfolios are created, selecting the top 10 stocks based on their quality ranking. The portfolios are formed in a long-only fashion, meaning they only contain stocks for buying, not short-selling. The research aims to provide retail and professional investors insight into their long-term investing decisions.

The portfolios are equally weighted and rebalanced annually on the last trading day of July every year using the new quality rankings from the most recent financial data analysis. This approach results in 24 annual portfolios for each quality strategy over the last ten years, providing a comprehensive view of their performance over the study period.

Quality Score Calculation

The limited research on quality investing can be attributed to the absence of a unified univariate definition of quality. Many studies provided in the literature review section establish a connection between a firm's quality metrics and stock returns, or they create portfolio strategies grounded in quality measures. However, researchers often differ in their interpretations of what defines quality, or their investigations are confined to a restricted range of factors linked to quality. In many cases, these quality proxies are explored individually without being integrated into a composite score, thus contributing to the complexity and diversity in the field of quality investing research.

One of the pioneering and most influential studies that provided a definitive scoring for a company's quality was conducted by Asness et al. (2019). In their research, the authors formed a quality score with various metrics within the dimensions of Profitability, Growth, and Safety, all of which they associated with quality.

This study expands the investigation of Quality Investing to large capitalisation companies and aims to compare the results with the benchmark large capitalisation index, S&P100. To achieve this, a Quality Score is calculated by aggregating three different fundamental metrics from a company's Profitability, Growth and Safety dimensions. A specific investment strategy is employed, involving the selection of stocks from the highest Quality scores.

This study evaluates the effectiveness of various Quality indicators for stock selection. It considers metrics like ROIC, ROA, and ROE under Profitability. The Growth dimension includes Revenue Growth, EBIT, EBITDA Growth, and Operating Cash Flow to Sales Ratio. Safety is assessed using Debt to Equity and Cash Flow to Debt Ratios. Each dimension provides a unique lens to gauge a company's performance, financial health, and risk profile.

The average portfolio return difference shows whether the high-Quality portfolio's performance surpasses the overall market benchmark index. This test holds practical importance for investors, as achieving statistical significance indicates that the portfolio constructed using this definition will likely outperform the benchmark on its own merits. Additionally, it implies that the Sharpe Ratio associated with this factor is likely to be relatively high, indicating a potentially strong risk-adjusted return from the investment strategy.

The methodology employed to obtain the Quality score follows the same approach as that of Asness, Frazzini, and Pedersen (2019).

Three composite proxies for Quality are used: Profitability, Growth, and Safety.

The Quality z-score is calculated using the following equation:

Quality = z-score of (Profitability + Growth + Safety)

Here, the quality z-score calculated in the equation is the average z-score of the three categories (profitability, growth, and safety). The z-score methodology is used to combine these different ratios appropriately. To standardise these variables and combine them, z-scores are calculated. By converting each variable into ranks, a uniform scale is created, which allows for comparison and combination of these different variables.

The z-score is calculated as follows:

z = (x- μ)/σ

In the equation, z represents the z-score, x represents the observed variable, μ represents the mean of the observed variable, and σ represents the standard deviation of the observed variable. The z-score shows how many standard deviations a value lies from the mean, which helps to quantify the relative position of a particular stock's characteristic.

After calculating the quality z-score for all stocks, the top 10 stocks with the highest quality score are considered Quality Stocks for each portfolio formation.

The methodology to quantify the quality scores of stocks is based on three dimensions of a quality stock:

Profitability: Measured by Return on Invested Capital (ROIC), ROE or ROA

Growth: Revenue Growth, EBITDA Growth, Operating Profit Growth, Cash Flow to Operating Profit

Safety: Debt to Equity, Cash Flow to Debt

Scores are calculated for each dimension.                      

Profitability Score = z-score of Measured by Return on Invested Capital (ROIC), ROE, ROA

Growth Score = Revenue Growth, EBITDA Growth, Operating Profit Growth, Cash Flow to Operating Profit

Safety Score = z-score of Debt to Equity, Cash Flow to Debt

Finally, the Quality Score is computed as the average of these three-dimension scores:

Quality Score = (Profitability Score + Growth Score + Safety Score) / 3

This approach provides a quantitative measure of stock quality that can be compared across various stocks. Note that this approach's effectiveness depends on the selected dimensions' relevance, accuracy, and associated measures. The methodology also assumes equal importance for each dimension. To determine the quality score associated with this Quality definition, the same methodology as Asness et al. (2018) has been used.

Portfolio Weighting

Choosing how to weigh the components of an investment portfolio is an important decision that can significantly affect portfolio returns. One commonly used method is equal weighting, where each stock contributes equally to the portfolio. This method doesn't require knowledge of the company's market capitalisation or other characteristics. However, it tends to overweight smaller companies and underweight larger ones compared to market capitalisation-based weighting. Another common method is market capitalisation-based weighting, in which each security's weight in the portfolio corresponds to its market capitalisation. This is how most indices, such as the broader market index S&P 500, are structured. The benefit of this approach is that it automatically adjusts for the size of the companies. Still, it can lead to concentration in a few large-cap stocks, potentially increasing the portfolio's risk.

This research focuses on large capitalisation companies, and the benchmark is the large capitalisation index, S&P100, with equal weighting methodology used.

Number of Stocks in the Portfolio

Determining the right number of stocks to hold in a portfolio is a balancing act between maximising diversification benefits and minimising transaction costs. As more stocks are added to a portfolio, company-specific risks would decline. These risks can be diversified by holding stocks in different sectors or industries. This is often referred to as the benefit of diversification. On the other hand, as the number of companies invested in the portfolio rises, it becomes difficult to differentiate from the market index, and transaction costs would increase. Holding a larger number of stocks can also increase transaction costs. This is why investors seek an optimal number of stocks offering maximum diversification benefits.

Research on the optimal number of stocks for diversification has suggested different numbers. Wagner, Lau (1971) and Evans, Archer (1968) suggested that portfolios with 10 to 15 stocks were sufficient.

As this research focuses on the 74 largest US companies that have at least $100bn market capitalisation, portfolios are constructed with ten companies. Note that a more concentrated portfolio might be appropriate for an investor who is willing to take more risk for potentially higher returns and wants to differentiate from the market index as much as possible, while a more diversified portfolio might be suitable for a risk-averse investor.

Portfolio Returns

The time series data for stock prices are obtained from Reuters Eikon Refinitiv, and the monthly and annual returns are calculated as follows:

The return for stock i period t is calculated as:

 Rti =  Pti – Pt-1,i / Pti

Rt is a one-period return for stock i for period t. Price data are dividend and split/reverse split-adjusted. Returns for more than one period are calculated by compounding the period returns.

Sharpe Ratio

The Sharpe ratio, introduced by William Sharpe, is a widely used risk-adjusted performance metric in finance (Sharpe, 1964). It provides a way to evaluate the excess return of an investment relative to its risk, considering the volatility of the investment's returns. The formula for the Sharpe ratio is as follows:

Sharpe Ratio = Rp−Rf / σp

Where:

Rp is the Quality portfolio’s monthly return

Rf is the risk-free rate of return which is calculated for monthly periods from the yield on government bonds

σp is the standard deviation of the portfolio's returns, representing its monthly volatility

The Sharpe ratio quantifies the excess return an investment generates for each unit of risk it takes on. A higher Sharpe ratio indicates that the investment provides more excess return per unit of risk, making it a more attractive investment choice. It's a valuable tool for comparing the risk-adjusted performance of different investments and portfolios.

Maximum Drawdown

Maximum Drawdown measures the largest decrease in portfolio value from a peak to a trough over a specified period (Riley, Yan, 2022). It's used to measure the worst-case scenario for an investment or portfolio, giving investors an idea of the total risk involved in a particular investment strategy. It's expressed as a percentage of the peak value. A lower maximum drawdown is preferable as it indicates less downside risk.

Quality investing seeks to reduce risk by focusing on financially stable and low-risk companies. A lower maximum drawdown would suggest the strategy successfully mitigates downside risk during market downturns.

Investors often consider both the magnitude and duration of the Drawdown. A strategy with a deep but short-lived drawdown might be more tolerable than one with a shallower drawdown that persists over a longer period (Hemert et. al, 2020).

Investors can compare the Maximum Drawdown of a quality investing strategy with other investment approaches to gauge its relative risk profile.

These measures provide comprehensive risk and performance assessment tools for investing decision-makers. It's important to consider all these measures collectively, as they each provide different insights into the risk-return characteristics of an investment. It provides insights into how much an investment could decline during adverse market conditions, helping investors evaluate the strategy's resilience and risk management capabilities.

24 Quality portfolios were constructed from the three dimensions of Quality: Profitability, Growth, and Safety. The first set of constituents is calculated for 31 July 2014 using the fundamental data of the previous year. Every year, at the last trading date of July, the Quality scores are calculated, and with the updated scores, all the portfolio constituents are reallocated.

3.4 Research Settings: Financial Ratios

Throughout the existing literature, certain metrics have consistently been associated with higher quality and have gained acceptance as quality indicators. The concept of quality can be broken down into four main categories, as highlighted by Asness et al. (2015):

Profitability: This area centres on a company's ability to generate profits. It can be assessed through various metrics such as gross profits, profit margins, earnings, accruals, and cash flows. Metrics tied to profitability have been extensively explored in the literature, with studies such as those by Graham, Dodd (1934), Haugen & Baker (1996), and Novy-Marx (2014) emphasising the connection between profitability and quality. This metric assesses a company's ability to generate profits relative to a specific accounting value, such as book equity, book assets, or sales. It typically involves using indicators like gross profits, earnings, or cash flows, which are then scaled by the chosen accounting value.

Payout: Payout refers to the portion of profits distributed to shareholders. Higher payout ratios are often seen as positive since they can help mitigate agency issues by reducing cash holdings through dividends and share repurchases, as suggested by Jensen (1986). Payout refers to the fraction of a company's profits distributed to shareholders through dividends or share buybacks. It can be seen as an indicator of how shareholder-friendly a company's management is, focusing on providing returns to shareholders.

Growth: Growth reflects a company's potential for expansion and is usually measured by changes in essential fundamental variables, such as profits or margins. Growth-related indicators have also been recognised as quality markers, with studies by Lakonishok, Shleifer, Vishny (1994) and Mohanram (2005) highlighting the relationship between growth and quality. This measures the growth rate of a profitability metric over a specific period, often the trailing five years. It reflects how much a company's profitability has increased, indicating its potential for continued growth.

Safety: Safety relates to a broad range of variables that can be market-based, like bid-ask spreads, volatility, betas or fundamental, like leverage and balance sheet liquidity. These factors contribute to the perception of a company's stability and reliability. Measures associated with safety have gained attention in the quality literature. Research by Ang et al. (2006), Campbell et al. (2008), and George & Hwang (2010) underscore the importance of safety-related metrics in defining quality. This characteristic emphasises that safer companies are associated with lower required investor returns. Various measures can indicate safety, including return-based factors such as market beta and volatility and fundamental-based factors like low leverage, low volatility of profitability, and low credit risk.

These four areas collectively contribute to assessing a company's quality, providing investors with a comprehensive framework to evaluate different dimensions of a company's financial health and potential for long-term success. These established connections between these metrics and quality indicate their significance as factors in understanding and assessing the quality characteristics of stocks.

This research demonstrates that various authors, including Hsu, Kalesnik, Kose (2019) and Novy-Marx (2014), have put forth diverse proxies and interpretations of quality. Moreover, these studies suggest that combining different proxies enhances the quality score's performance, a concept also supported by Asness, Frazzini, and Pedersen (2019). This underscores the significance of considering multiple quality indicators and their combination to achieve a more robust and effective quality assessment.

In this research, quality definition is structured along three dimensions: Profitability, Growth, and Safety.

Profitability

Profitability metrics are widely studied and used in Quality Investing as they are considered a major fundamental aspect that investors and researchers often focus on when evaluating the quality of a company. The profitability metric provides insights into a company's ability to generate earnings and cash flows, which are crucial indicators of its financial health and sustainability.

Fama and French (2015), Novy-Marx (2014), Hou, Xue, and Zhang (2014) all contribute to the understanding that more profitable firms tend to outperform less profitable firms over time. These findings are in line with the expectation that investors are willing to pay a premium for companies that demonstrate strong and consistent profitability, as it suggests their ability to generate healthy returns on investments.

In addition, research has shown that a growth portfolio constructed from high-profitability stocks tends to outperform a growth portfolio constructed from high-valuation stocks. This finding is consistent with the idea that companies with high profitability are better positioned to generate sustainable growth over time.

When considering growth strategies, it's essential to account for the quality of growth. High-profitability companies not only have the potential for growth, but their ability to generate strong earnings and cash flows also provides a solid foundation for that growth. As a result, high-profitability growth stocks may be more likely to fulfil their growth potential and deliver positive returns to investors.

The alpha observed in Fama-French five-factor model when adjusting for the value characteristic suggests that high-profitability stocks possess attributes that enable them to outperform their less profitable counterparts even when accounting for the influence of value factors. This further underscores the significance of profitability as a key determinant of quality and its impact on investment outcomes.

Major Profitability metrics used in this research are Return on Invested Capital (ROIC), Return on Equity (ROE) and Return on Assets (ROA).

Return on Invested Capital (ROIC)

Return on Invested Capital (ROIC) is a fundamental metric that shows the profitability and efficiency of a company's capital investments. It provides insights into how effectively a company generates returns from its invested capital, both equity and debt. ROIC is a key indicator investors and analysts use to assess the quality of a company's operations and its ability to generate value.

ROIC measures how efficiently a company generates profits from its capital investments. Companies with higher ROIC are considered more efficient in capital utilisation, often associated with higher-quality operations. Quality investors look for companies that generate consistent and sustainable returns on their invested capital. In addition, companies with higher ROIC are generally better at allocating their capital to investments that generate positive returns (Koller et al., 2020).

ROIC = Net Operating Profit After Taxes (NOPAT) / Total Invested Capital

The Return on Invested Capital metric shows how effectively a company uses its capital, accounting for the working capital needed to operate its business and the long-term investments it's made in fixed assets.

Return on Assets

Return on Assets (ROA) is a fundamental ratio that measures a company's ability to generate profit from its total assets. It provides insight into how efficiently a company utilises its assets to generate earnings. ROA is a fundamental indicator used by investors and analysts to assess a company's profitability and operational efficiency.

Quality investors look for companies that generate consistent and sustainable profits from their assets. A company with a history of high and stable ROA is seen as having strong operational quality. ROA is an essential metric for evaluating a company's fundamental performance and its ability to create returns for shareholders. It is particularly relevant in quality investing strategies, where the emphasis is on identifying companies with strong fundamentals, efficient operations, and sustainable profitability.

ROA = Net Income / Total Assets

Return on Asset Ratio's (ROA) focus on the return generated from all of a company's financing, regardless of whether it comes from debt or equity, is particularly relevant for assessing overall efficiency. This makes ROA a useful metric from a firm's financing perspective. Considering ROA as a profitability proxy, there is a significant connection between high ROA and the Quality of a company (Mohanram, 2005).

Return on Equity (ROE)

The Return on Equity (ROE) measure is one of the commonly used metrics by investors as a profitability metric when considering quality stocks. ROE represents the financial return that a company produces on the money invested by its shareholders. It indicates how effectively a company uses investors' equity to generate profits. A high ROE typically indicates a more profitable company, which can be an attractive characteristic for investors.

ROE = Net Income / Shareholders' Equity

Return on Equity (ROE) is an effective metric which is an indicator of future returns. Stocks with high ROE tend to yield higher average returns than those with low ROE (Hou et al., 2020).

The findings of Haugen and Baker (1996), Frankel and Lee (1998), and Hou et al. (2020) further support the idea that ROE is a useful measure for forecasting stock returns and evaluating a firm's financial health and potential profitability. Fama and French's (2015) integration of ROE into their five-factor model and the observed predictive power of ROE in their analysis emphasise its role in explaining average returns and shaping asset pricing factors.

Revenue Growth

Revenue growth is a key financial metric that measures the percentage increase in a company's total revenue or sales over a specific period of time, usually on an annual or quarterly basis. It is a fundamental indicator of a company's ability to expand its customer base, increase market share, and generate higher sales figures (Jerab, 2023).

Revenue Growth = ((Current Year's Revenue - Previous Year's Revenue) / Previous Year's Revenue) * 100

In quality investing, revenue growth is often considered an important characteristic of high-quality companies. Companies that consistently achieve positive and substantial revenue growth are often viewed favourably because it indicates that they successfully attract new customers, introduce new products or services, and effectively manage their market presence.

EBITDA Growth

EBITDA growth measures how a company's Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA) have changed over a specific period. EBITDA is often used as a proxy for a company's operating performance, as it measures its earnings before accounting for interest, taxes, and non-cash expenses like depreciation and amortisation.

EBITDA Growth = ((Current Year's EBITDA - Previous Year's EBITDA) / Previous Year's EBITDA) * 100

EBITDA growth is an important metric in quality investing as it gives insight into a company's ability to generate operating profits and manage its operations efficiently. Companies with positive and consistent EBITDA growth are typically viewed as higher quality because they demonstrate the ability to increase earnings from their core operations. Investors use EBITDA growth to assess a company's operational health, especially in industries where capital expenditures and depreciation play a significant role. Positive EBITDA growth can suggest that a company effectively manages its costs, expands its business, or improves its profitability (Maxim, 2023).

Operating Cash Flow / Sales

The Operating Cash Flow / Sales is a financial metric measuring the efficiency of a company's operating cash flow relative to its total sales or revenue. This ratio provides insights into a company's ability to convert its sales into actual cash flow, an important indicator of its operational efficiency and financial health. In addition, operating cash flow affects companies' profitability and returns for investors (Ghodrati, Abyak, 2014).

Operating Cash Flow / Sales = Operating Cash Flow / Total Sales

A higher Operating Cash Flow to Sales ratio is generally considered favourable, meaning that the company's sales are being converted into cash flow, which suggests that the company is efficient in managing its operating activities and turning its revenue into actual cash. A strong Operating Cash Flow to Sales ratio can signify that a company has good working capital management and effective cost controls. It also suggests that the company relies less on external financing to support its operations.

Debt to Equity Ratio: This ratio measures a company's financial leverage by comparing its total debt to shareholders' equity. A lower debt-to-equity ratio suggests lower financial risk and greater safety, as the company relies less on debt to finance its operations.

In quality investing, safety metrics help investors identify companies with strong financial foundations, well-managed risks, and the ability to deliver consistent performance even during challenging market environments (Wulandari et al., 2022). These metrics play a crucial role in constructing portfolios that focus on high-quality companies capable of delivering sustainable returns over the long term.

Cash Flow Adequacy: Analysing a company's cash flow generation relative to its operational and investing activities helps gauge its ability to fund ongoing operations, repay debts, and invest for growth.

Debt / Equity Ratio = Total Debt / Total Equity

A higher metric indicates that the company relies more on debt financing, which can increase financial risk and interest expenses. On the other hand, a lower ratio suggests that the company has a stronger equity position and may be less dependent on borrowed funds. A lower ratio can be considered a positive quality characteristic, suggesting that a company has a stronger financial position and is better positioned to manage its financial obligations.

Cash Flow / Debt

The Cash Flow / Debt is a financial ratio that assesses a company's ability to generate sufficient operating cash flow to cover its outstanding debt obligations. The Cash Flow / Debt can provide insights into a company's financial health and capacity to manage debt.

Cash Flow / Debt = Operating Cash Flow / Total Debt

A higher ratio indicates that a company is generating more cash flow relative to its debt levels, which suggests that it can better meet its debt obligations without relying heavily on external financing.

4.2 Research Empirical Findings

The first objective of this research is to present a more comprehensive and universally accepted definition that can help improve investment decision-making processes. This study evaluates the effectiveness of various Quality indicators for stock selection. It considers metrics Return on Invested Capital (ROIC), Return on Equity (ROE) and Return on Assets (ROA) under Profitability. The Growth dimension includes Revenue Growth, EBIT, EBITDA Growth, and Operating Cash Flow to Sales Ratio. Safety is assessed using Debt to Equity and Cash Flow to Debt Ratios. Each dimension provides a unique lens to gauge a company's performance, financial health, and risk profile.

The second purpose of this study is to identify which of the defined quality definitions provides better results. All the defined Quality portfolios performed better than the benchmark Index, S&P 100, during the investment period, even though they generated different returns and Sharpe Ratios. The Quality portfolio, which has the highest Sharpe Ratio, is the 23rd Portfolio formed using the Return on Equity (ROE), Operating Profit (EBIT) Growth and Cash Flow to Debt ratios.

The best performer Quality portfolio has the highest Sharpe Ratio of 1.14. The Portfolio provides better risk-adjusted returns compared to other Quality portfolios and the benchmark index. The average Sharpe Ratios of other portfolios are 0.93, and the benchmark`s Sharpe Ratio is 0.62.

4.3 Fama-French Five-Factor Model

The third aim of this research is to investigate whether a portfolio formed from top-quality stocks by scoring and selecting stocks based on fundamental quality indicators generates statistically significant alpha in the long term.

The Fama-French Five-Factor Model is an asset pricing model introduced by Fama and French (2015). It extends the original Fama-French Three-Factor Model (1992) by adding two additional factors to explain better the stock returns cross-section. The model aims to provide a more comprehensive framework for understanding the sources of stock returns beyond the traditional Capital Asset Pricing Model (CAPM).

The Fama-French Five-Factor Model includes the following five factors:

Market Risk (Market Return): This factor represents the excess return of the overall market compared to the risk-free rate. It captures the systematic risk associated with investing in the market.

Size (SMB, Small Minus Big): This factor captures the historical outperformance of small-cap stocks over large-cap stocks. It measures financial return differences between small market capitalisation stocks and large market capitalisation stocks. 

Value (HML, High Minus Low): The value factor captures the historical outperformance of value stocks over growth stocks. It measures the return difference between a portfolio of high-value stocks and growth stocks.

Profitability (RMW, Robust Minus Weak): This factor captures the historical outperformance of profitable companies over less profitable companies. It measures the return difference between a portfolio of companies with high operating profitability and a portfolio with low operating profitability.

Investment (CMA, Conservative Minus Aggressive): The investment factor reflects the historical outperformance of conservative (low investment) companies over aggressive (high investment) companies. It measures return differences between conservative investing companies and aggressive investing companies.

The Fama-French Five-Factor Model is designed to provide a more accurate explanation of stock returns by accounting for market return, size, value, profitability, investment factors, and market risk. Researchers and investors use this model to understand the sources of risk better and return in the equity market and assess the performance of investment strategies.

It's important to note that while the Five-Factor Model offers a more comprehensive explanation of stock returns than the Capital Asset Pricing Model (CAPM), it is still a model. Not all variations in stock returns can be fully explained by these factors, and additional factors or market dynamics may play a role in specific cases.

In this analysis, the performance of portfolios constructed based on Quality strategies is compared against a benchmark, which is a large capitalisation overall market index, S&P100. The portfolio constituents are the company shares with the highest Quality scores and equal weights in the portfolio. The portfolio constituents are reshuffled yearly at the end of July with updated quality scores. The goal here is to investigate whether quality investing strategies yield excess returns. This analysis aims to uncover whether quality-based investing provides additional value beyond these core factors.

The Fama-French Five-Factor Model (2015) is an improved version of their original three-factor model by incorporating two additional factors, profitability and investment, which are believed to further contribute to the explanation of stock returns. This expanded model aims to provide a more comprehensive explanation for stock returns by considering a wider range of firm characteristics. The formula for the Fama and French Five-Factor Model is as follows:

R = α + β1(Rm-Rf) + β2SMB + β3HML + β4RMW + β5CMA + ϵi

Where:

R represents the excess return of the Quality Portfolio

Rf denotes the risk-free rate

Rm is the market return which is calculated by value-weighted returns of all US companies listed on the New York Stock Exchange (NYSE)

Rm – Rf is the excess return of the market is calculated by subtracting the risk-free rate from the market return.

SMB represents the size premium, the return difference between small and big companies

HML stands for the value premium, the return difference between high and low book-to-market ratio stocks.

RMW represents the profitability factor, capturing the return difference between stocks with robust operating profitability and those with weak profitability

CMA denotes the investment factor, capturing the return difference between conservatively investing companies and aggressively investing ones

α is the abnormal return for the Quality Portfolio

β coefficients measure the sensitivity of the stock's returns to the various factors

ϵi represents the residual error term

Fama and French's (2015) research demonstrated that the five-factor model had more explanatory power than the three-factor model. By adding the profitability and investment factors, the model could provide an explanation of a larger portion of the observed patterns in stock returns, especially for specific subsets of stocks that the three-factor model struggled to explain. This enhanced model provided a more accurate representation of the risk and return relationship in the financial markets by improving the model's overall predictive ability. The five-factor model's success in explaining stock returns was a significant development in asset pricing theory, contributing to a better understanding of the various factors influencing stock prices and helping investors make more informed decisions.

These portfolios are used as explanatory variables in the regression analysis to understand their impact on the Quality Investing strategy's performance. The goal is to determine if the quality investing strategy provides returns above what can be explained by these market variables and factor models.

The results from the Fama-French five-factor model are presented for the quality portfolios on the table below.

The first objective is to establish a comprehensive and widely accepted definition of quality to enhance investment decision-making. The study evaluates various quality indicators across three dimensions: Profitability (ROIC, ROA, ROE), Growth (Revenue Growth, EBIT, EBITDA Growth, Operating Cash Flow to Sales Ratio), and Safety (Debt to Equity, Cash Flow to Debt Ratios). These metrics provide different insights into a company's performance, financial health, and risk profile. Twenty-four different Quality portfolios were created with these metrics.

The second objective is to identify which of the defined quality definitions yields superior results. All the quality portfolios outperformed the benchmark S&P 100 Index over the investment period despite varying returns and Sharpe Ratios. Notably, the 23rd Portfolio, constructed using ROE, EBIT Growth, and Cash Flow to Debt ratios, exhibited the highest Sharpe Ratio of 1.14, surpassing both the average Sharpe Ratios of peer portfolios (0.93) and the benchmark's Sharpe Ratio (0.62).

The third objective investigates whether constructing a portfolio of top-quality stocks based on fundamental quality indicators can generate statistically significant alpha in the long term. The study reveals that the Quality Portfolio consistently outperformed the S&P 100 Index, delivering an annualized alpha of 5.88%. This alpha is statistically significant at a 5% level, demonstrating that a quality investment strategy can produce superior returns compared to the benchmark, as confirmed by a Fama-French five-factor model.

In summary, this research highlights the importance of defining quality in investment decision-making and assesses various quality indicators. It demonstrates that portfolios constructed based on these indicators consistently outperform the benchmark index. The standout Portfolio, using ROE, EBIT Growth, and Cash Flow to Debt metrics, provides substantial alpha, reinforcing the effectiveness of a quality-focused investment approach.

Defining Quality: The study seeks to identify key fundamental ratios that can effectively define and calculate Quality scores for companies. These ratios encompass measures of profitability, growth, and safety, aligning with metrics used in prior research.

Portfolio Performance: Among the defined Quality portfolios, the study investigates which one yields the highest risk-adjusted returns. All Quality portfolios displayed superior performance compared to the S&P 100 benchmark index over the investment period. Notably, Portfolio 23, constructed based on Return on Equity, Operating Profit Growth, and Cash Flow to Debt ratios, demonstrated the highest Sharpe Ratio, indicating better risk-adjusted returns.

Quality Investing Success: The core question of whether Quality Investing outperforms the S&P 100 benchmark index and if it proves to be a viable long-term investment strategy is addressed. Portfolio 23, the standout performer, generated a statistically significant annualised alpha of 5.88%. This reinforces the conclusion that a Quality Investing strategy, evaluated through the Fama-French five-factor model, has the potential to achieve consistent excess returns.

This research focuses on three dimensions—Profitability, Growth, and Safety—comprising the definition of Quality. It explores the effectiveness of various investing metrics and ratios within these dimensions. Key indicators such as Return on Invested Capital (ROIC), Return on Assets (ROA), Return on Equity (ROE), Revenue Growth, Operating Profit Growth (EBIT), EBITDA Growth, Operating Cash Flow to Sales Ratio, Debt to Equity, and Cash Flow to Debt Ratio are used to assess Quality. The study employs equally weighted portfolios to calculate combined Quality scores.

Significantly, this research focuses on large capitalisation stocks in the U.S. market and their comparison against the S&P 100 Index. It holds potential implications for investment decision-makers, offering insights into fundamental value and quality indicators that could enhance long-term investment performance.

The study has some limitations. Primarily, it concentrates solely on U.S. large capitalisation stocks and does not incorporate banks or financial institutions, considering only companies with available fundamental data. Moreover, transaction costs and liquidity constraints are not considered, yet the impact is minimal due to the focus on well-capitalised firms with readily available investment opportunities. The analysis also omits sectoral differences, which could contribute to a more comprehensive understanding of Quality Investment. Moreover, while this research is limited to the U.S. market, its methodology can also be applied to other developed country markets. Despite these limitations, the research serves as a valuable exploration of Quality Investing within the context of large-capitalisation U.S. stocks.

6. Conclusion

This research explores the concept of quality investing in U.S. large capitalisation stocks to determine its potential to generate abnormal returns. Using company fundamental metrics of profitability, growth, and safety dimensions to calculate a quality score, this study constructs high-quality portfolios and investigates their performances.

In this study, the framework of the research onion model by Saunders et al. (2018) is employed. It adopts a positivist philosophical approach, favouring objective measurement and data. Inductive reasoning is utilised, where observations lead to broader generalisations and theories. The research methodology leans toward a quantitative approach, emphasising numerical data and statistical analyses. Strategically, the study takes on an experimental design, aiming to discern cause-effect relationships. The research extends over a prolonged period, characterised by its longitudinal time horizon, and primarily leverages secondary data for analysis using pre-existing datasets.

The study targets three main questions on the effectiveness and significance of Quality Investing:

Fundamental Ratios for Quality Definition: The first question seeks to identify the primary fundamental ratios that can serve as robust indicators for defining the quality of companies and calculating Quality scores. These ratios encompass profitability, growth, and financial safety, crucial components in assessing a company's quality.

Best-Performing Quality Portfolio: The second question targets the performance aspect, with a focus on determining which of the defined quality portfolios delivers the highest risk-adjusted returns. This evaluation is critical in discerning which quality definition or set of indicators is most effective in generating favourable investment outcomes.

Effectiveness of Quality Investing: The third question addresses the broader effectiveness of Quality Investing as an investment strategy. It assesses whether constructing a portfolio comprised of top-quality stocks, selected based on fundamental quality indicators, can generate statistically significant alpha over the long term. This investigation ultimately gauges the practical viability and success of Quality Investing.

The research focuses on Quality Investing by considering both academic and practical perspectives. It strives to contribute to developing a more comprehensive and universally accepted definition of Quality that can enhance investment decision-making processes. The methodology involves collecting historical financial data, formulating an investment strategy, implementing it in a real-world portfolio, backtesting it on historical data, and evaluating its performance through statistical analysis.

This study extends the methodology of previous research, particularly the work of Asness et al. (2018), by creating portfolios in the U.S. large-capitalisation stock market spanning the years 2013 to 2023. These portfolios are constructed based on quality indicators, and their performance is rigorously compared against the large capitalisation overall market index, S&P 100. The analysis leverages quantitative approaches to fundamental financial data and utilises time series data of company fundamentals and stock prices spanning the past decade.

Quality is defined and structured along three dimensions: Profitability, Growth, and Safety. In the context of stock selection based on Quality indicators, this study addresses the question of which investing metrics and ratios are most effective. Return on Invested Capital (ROIC), Return on Assets (ROA), and Return on Equity (ROE) metrics are used from the Profitability dimension. For Growth, Revenue Growth, Operating Profit Growth (EBIT), EBITDA Growth and Operating Cash Flow to Sales Ratio were used. Debt to Equity and Cash Flow to Debt Ratio is used for the Safety dimension of Quality. Twenty-four portfolios are built based on the Quality definition. The first set of constituents is calculated for 31 July 2014 using the fundamental data of the previous year. Every year, at the last trading date of July, the Quality scores are calculated with the most recent data, and with the updated scores, all the portfolio constituents are reallocated.

Even though they had different performances, all Quality-defined portfolios generated better risk-adjusted returns and had higher Sharpe Ratios compared to the large-capitalisation Index, S&P 100. They provided absolute outperformance vs. the benchmark index as well. Among the company fundamental ratios examined, the Quality portfolio defined with a combination of Return on Equity (ROE), Operating Income Growth and Cash Flow to Debt ratios has provided the most successful results and better risk-adjusted returns than other portfolios and the broader market.

The findings of this research conclude that a high-quality portfolio defined with three dimensions of Quality -profitability, growth, and safety- can consistently secure a statistically significant annual alpha of 5.88% at 5% significance level. These results provide evidence that by focusing on Return on Equity (ROE), Operating Profit (EBIT) Growth and Cash Flow to Debt ratio, investors can achieve abnormal returns by favouring high-quality stocks when investing in the U.S. large capitalisation stocks. The conclusions of this study may be significant for investment decision-makers interested in investments in U.S. large capitalisation stocks, offering insights into utilising quantitative analysis of companies' fundamental ratios to improve their long-term performances.

This research offers a detailed look into U.S. large-cap stocks, leaving out banks and financial institutions. While the study doesn't consider transaction costs or liquidity, these elements do not significantly impact results. Sectoral differences are overlooked, suggesting room for a more thorough exploration. Even though the analysis is U.S.-focused, its methods can be applied to other developed markets. By incorporating non-quantifiable quality factors into the analysis, returns can be improved. Overall, the study provides a crucial perspective on Quality Investing in the U.S. large capitalisation companies and underscores opportunities for further research.

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