In the field of quantitative investing, factors, also known as signals, play a pivotal role. These factors, traditionally rooted in the fundamental attributes of the companies in question, are the subject of extensive study by quantitative managers. However, a recent trend has seen investors turning to unconventional and unstructured data sources in an attempt to gain a strategic edge.
The concept of value, first introduced by Graham and Dodd in 1934, is a fundamental aspect of financial analysis and investment strategy. It refers to the intrinsic worth of a company or asset, which can be measured using various methods and has been the subject of extensive academic research.
Research by Basu (1977) found that stocks with low Price-to-Earnings (P/E) ratios, such as Apple Inc. in the early 2000s, tend to yield higher returns. This suggests that value stocks, or stocks that are undervalued compared to their intrinsic value, tend to deliver superior returns.
Fama and French (1993) formally defined value investing by proposing the book-to-market ratio as a measure of value and growth. This ratio compares a company’s book value (the value of a company according to its financial statements) to its market value (the value of a company according to the stock market), as seen in the case of Berkshire Hathaway’s investment strategy.
The reason why value stocks tend to deliver superior returns is a topic of debate. Fama and French (1992, 1993, 1996) suggested that the value premium exists to compensate investors for the greater likelihood that these companies will experience financial distress. Conversely, Lakonishok, Shleifer, and Vishny (1994) suggested that the effect is a result of behavioral biases on the part of the typical investor rather than compensation for higher risk.
Value factors can be based on various fundamental performance metrics of a company, such as dividends, earnings, cash flow, Earnings Before Interest and Taxes (EBIT), Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA), and sales. Investors often adjust these value factors for industry (and/or country) and historical differences, as seen in the valuation of multinational corporations like Amazon and Alibaba.
Most valuation ratios can be computed using either historical (also called trailing) or forward metrics. Historical metrics use past data, while forward metrics use projected future data, as seen in the valuation of tech startups.
Price momentum is a key factor in asset classes across various countries and forms the foundation of quantitative investing. It suggests that stocks that have performed well over the past 12 months, known as “winners”, tend to outperform those that have performed poorly, known as “losers”. This trend of outperformance is observed to persist for the next 2 to 12 months.
Price momentum is often attributed to behavioral biases, such as overreaction to information. However, it is also associated with extreme tail risk. For instance, during the three-month period from March to May 2009, a simple price momentum strategy resulted in a loss of 56%.
One way to mitigate this downside risk is by neutralizing the effect of sector exposure from momentum factor returns. This modified version is referred to as the “sector-neutralized price momentum factor”.
Investors often employ a growth strategy, focusing on a company’s potential for expansion. This potential is evaluated using growth factors, which can be determined through the company’s historical growth rates or projected future growth rates.
Growth factors are metrics used to assess a company’s growth potential. They can be divided into two categories:
A growth rate higher than the market or sector average is often seen as a potential indicator of strong future stock price performance. However, growth in certain metrics, such as assets, may lead to weaker future stock price performance.
Investors frequently utilize accounting ratios and share price data, known as fundamental style factors, to evaluate companies. With the vast array of accounting data available, more intricate factors have been developed. A prime example is the accruals factor, introduced by Richard Sloan in his 1996 paper on earnings quality. Sloan suggested that stock prices do not fully incorporate the information in the accrual and cash flow components of current earnings. However, the accruals anomaly factor’s performance is often cyclical.
There are numerous other potential factors based on a company’s fundamental data. These include profitability, balance sheet and solvency risk, earnings quality, stability, sustainability of dividend payout, capital utilization, and management efficiency measures. Another factor, known as analyst sentiment, refers to the phenomenon of sell-side analysts revising their forecasts of corporate earnings estimates, a process known as earnings revision .
With the advent of more data, analysts have begun to include cash flow revisions, sales revisions, ROE revisions, sell-side analyst stock recommendations, and target price changes as variables in the “analyst sentiment” category. A new and promising area of research involves news sentiment. Instead of solely relying on the output of sell-side analysts, investors can use natural language processing (NLP) algorithms to analyze the large volume of news stories and quantify the news sentiment on stocks.
Practice Questions
Question 1: In the context of quantitative investing, certain elements, often referred to as signals, are studied extensively. These elements are traditionally based on the fundamental characteristics of the underlying companies. However, a recent trend has seen many investors shifting their focus towards unconventional and unstructured data sources. What is the traditional basis for these elements or signals in quantitative investing?
- The market trends of the companies
- The fundamental characteristics of the companies
- The financial performance of the companies
Answer: Choice B is correct.
The traditional basis for these elements or signals in quantitative investing is the fundamental characteristics of the companies. Quantitative investing is a strategy that uses mathematical algorithms and computations to identify trading opportunities. Traditionally, these algorithms are based on the fundamental characteristics of the companies, such as earnings, cash flow, book value, and dividends. These characteristics provide a snapshot of a company’s financial health and potential for growth, which are crucial factors in investment decisions. The fundamental characteristics are often used to create models that predict future price movements, allowing investors to make informed decisions about when to buy or sell securities. This approach is based on the assumption that the market may misprice securities in the short term, but in the long term, the price will reflect the fundamental value of the company.
Choice A is incorrect. While market trends can be a part of the analysis in quantitative investing, they are not the traditional basis for the elements or signals used in this strategy. Market trends are more commonly associated with technical analysis, which focuses on price movements and patterns rather than the underlying fundamentals of the companies.
Choice C is incorrect. The financial performance of the companies is a part of the fundamental characteristics that are used in quantitative investing. However, it is not the only basis for the elements or signals. Other fundamental characteristics, such as earnings, cash flow, book value, and dividends, are also considered. Therefore, saying that the financial performance is the traditional basis for these elements or signals is too narrow and does not fully capture the range of factors considered in quantitative investing.
Question 2: The concept of value investing has been a topic of interest in the financial world for many years. It was first introduced by Graham and Dodd in 1934 and has since been explored and defined by various researchers. One of the key aspects of value investing is the use of certain ratios and metrics to determine the value of a stock. In this context, Fama and French (1993) proposed a specific ratio as a measure of value and growth. The ratio proposed for this purpose is
- Price-to-Earnings (P/E) ratio
- Book-to-market ratio
- Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA) ratio
Answer: Choice B is correct.
Fama and French, in their 1993 research, proposed the Book-to-Market ratio as a measure of value and growth. The Book-to-Market ratio is a financial valuation metric used to compare a company’s book value to its market value. The book value is the value of a company according to its balance sheet, and the market value is the value of a company according to the stock market. A high Book-to-Market ratio indicates that the company’s stock is undervalued, and it may be a good investment opportunity, which is a key principle of value investing. Fama and French’s research showed that companies with high Book-to-Market ratios tend to outperform those with low ratios, suggesting that the Book-to-Market ratio is a useful tool for identifying value stocks.
Choice A is incorrect. The Price-to-Earnings (P/E) ratio is a valuation ratio used to compare a company’s current share price to its per-share earnings. While it is a commonly used ratio in value investing, it was not the specific ratio proposed by Fama and French in their 1993 research for measuring value and growth.
Choice C is incorrect. The Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA) ratio is a measure of a company’s operating performance. It is not a valuation ratio and was not the ratio proposed by Fama and French for measuring value and growth. EBITDA is a profitability metric and does not provide information about a company’s market value relative to its book value, which is the key concept in value investing.
Portfolio Management Pathway Volume 1: Learning Module 2: Active Equity Investing: Strategies; LOS 2(e): Analyze factor-based active strategies, including their rationale and associated processes