Value investing: a new SCORE model.
| Autor | Navas, Raul Daniel |
Introduction
The efficient market hypothesis (EMH) states that markets incorporate information into stock prices (Fama, 1998; Malkiel, 1987, 2003, 2005). The more information that is incorporated into stock prices and the faster that information is reflected in price fluctuations, the more efficient the market is. While most studies support some level of market efficiency, researchers have discovered some market anomalies that reveal patterns of trading strategies that earned higher ex-post returns than would be expected in efficient markets (Geyfman et al., 2016; Navas & Bentes, 2021).
This research investigates whether a basic accounting-based fundamental analysis method can affect the distribution of returns earned by an investor when applied to a board portfolio of higher growth stocks with high fundamentals (book value). We focus only on high market capitalization stocks, so we do not examine small caps. Extensive research shows the returns of a higher growth stock investment strategy in terms of earnings per share (EPS) and sales growth (e.g., Stallings, 2017; Yeh & Hsu, 2014). We look for companies with a high book-to-market ratio (BM) in addition to high EPS growth in the past and future. Research papers on high BM strategies include those of Cordeiro and Machado (2013), Fama and French (1992), Geyfman et al. (2016), and Piotroski (2000). The strategy's success depends on the high performance of a few firms. In this study, we do not engage in any short selling.
Recent studies on the impact of earnings forecasts have attempted to differentiate between value and growth firms (e.g., high and low book-to-market equity (BE/ ME) stocks). Growth stocks, as assessed by the BE/ME ratio, have an asymmetrically strong negative response to negative earnings surprises, according to Skinner and Sloan (2002). Jegadeesh et al. (2004) also believe that financial analysts promote growth stocks to attract institutional investors, who often invest more heavily in growth companies. Individual investors' interest in value stocks peaked, according to La Porta et al. (1997), due to forecast revisions as a result of these firms' better-than- expected earnings reports (Jong & Apilado, 2009).
The purpose of this article is to illustrate that by applying a basic screen based on previous financial performance, investors can build a stronger value portfolio. We show that the average return of a high BM and higher growth company can be significantly increased. Between 2000 and 2020, an investment strategy that buys and holds for one and two years generates an annual return of roughly 37%. The returns from this strategy have shown to be consistent over time and when compared to different investment strategies.
We propose a new SCORE model, inspired by Piotroski's (2000) well-known F-SCORE. But here we examine past, present, and future earnings forecasts in this binary model, which is also made up of nine signals. At the same time, we believe that this model is not only simple to design (allowing investors to use stock screeners, for example), but also contributes to the value investing theory, by targeting companies with strong balance sheets (high BM). Many academics have investigated other binary models, such as L-SCORE (Lev & Thiagarajan, 1993), PEIS (Wahlen & Wieland, 2011), and the Mohanram G-SCORE (Mohanram, 2005).
The main difference between our new SCORE and other scores is that existing scores are based solely on past events (past and present returns and metrics), but we propose (as noted in the previous paragraph) to include future earnings estimates in our binary model. This data is updated every quarter when a firm reports its quarterly results, and investors can also rely on it.
The next section reviews the literature on value investing and fundamental analysis (FA). Section 3 discusses the research strategy, while section 4 discusses the empirical findings. Section 5presents several robustness tests. Section 6 concludes the paper.
2 Literature review and motivation
Value investing, as described by Benjamin Graham and David Dodd, is based on three important features of financial markets. First, the prices of financial securities fluctuate significantly and arbitrarily. Every day, the market sets the price of securities at any given time, and it seems to buy or sell any financial asset. It is prone to a wide range of unpredictable "mood" swings that influence the price at which it is prepared to do business. Second, despite market price fluctuations, many financial assets have underlying or fundamental economic values that are reasonably constant and can be evaluated with reasonable accuracy by an attentive and disciplined investor. In other words, the intrinsic value of the asset is one thing; the current price at which it is traded is quite another (Hanauer et al., 2022). Though value and price may be the same on any given day, they often differ. And third, a technique of purchasing assets only when their market prices are much lower than their projected intrinsic value would yield higher long-term returns. Benjamin Graham refers to this gap between value and price as "the margin of safety"; ideally, the gap should be around half of fundamental value and no less than one-third of fundamental value (Greenwald et al., 2001).
Mutual fund managers have created, tested, and refined their investment theories over time. One of these ideologies is value investing, which combines FA with well-known concepts such as price-to-book ratio (P/B), margin of safety, competitive advantage, dividend yield, and price-to-earnings ratio (P/E). Several research papers, including those by Piotroski (2000), Beukes (2011), and Sareewiwatthana (2011), have examined the success of this investment strategy, which screens firms based on certain financial measures, as a proxy for value investing. Typically, these techniques outperform the market average while posing less risk (Holloway et al., 2013; Linnenluecke et al., 2017).
Some studies argue that the quality of earnings (as a result of solid fundamentals) may reflect higher returns rather than the reverse, i.e., returns are not directly related to earnings, but earnings are related to good fundamentals. According to Penman (1992) and Abarbanell and Bushee (1998), the core objective of FA should be to project accounting earnings rather than explaining security returns (Bradbury et al., 2021). The authors investigated the relationships between basic signals and future earnings changes, allowing them to directly assess the validity of the economic intuition that underpins the original signal formulation. Lev and Thiagarajan (1993) use a different, less direct method that is based on an assessment of the relationships between basic signals and contemporaneous returns.
2.1 The efficient market hypothesis (EMH)
The efficient market hypothesis (EMH) is a widely accepted theory in financial economics that suggests that stock prices reflect all available information in the market, and it is therefore impossible to consistently beat the market by trading based on publicly available information. However, there are several factors that can cause markets to deviate from this ideal, leading to under/over-reactions to information and an inability to fully incorporate all available information (Fama, 1998, 1970; Timmermann & Granger, 2004).
One major factor is the role of human behavior and emotions in financial decision-making. Behavioral finance studies have shown that investors often exhibit biases and heuristics that lead to irrational decision-making, such as overconfidence, loss aversion, and herding behavior (see, for example, Barberis & Thaler, 2003; Bondt & Thaler, 1985). These biases can lead to market inefficiencies and contribute to under/over-reactions to new information.
Another factor that can contribute to market inefficiencies is the presence of institutional investors and market frictions. Institutional investors, such as mutual funds and pension funds, often have large amounts of assets under management and can have a significant impact on stock prices (see, for example, Greenwood & Thesmar,
2011). In addition, market frictions such as transaction costs and liquidity constraints can prevent all available information from being fully incorporated into stock prices (see, for example, Amihud & Mendelson, 1986).
Overall, while the efficient market hypothesis is a useful theoretical framework for understanding financial markets, there are several factors that can cause deviations from market efficiency in practice. Researchers in finance and economics continue to study these factors and their implications for market behavior and investment strategies.
2.2 High book-to-market and growth of earnings per share strategy
Fundamental strength metrics have been shown to be predictive of future returns in both the accounting and financial literature (e.g., Bradbury et al., 2021; Kumsta & Vivian, 2020; Ng & Shen, 2020; Patari et al., 2022; Piotroski, 2000). Dechow et al. (2010) indicate that systematic inaccuracies in market expectations about long-term earnings growth can partially explain the success of contrarian investment techniques and the book-to-market effect. Companies with high book-to-market ratios provide a unique opportunity to test the capacity of simple fundamental analysis heuristics to distinguish between them (Caglayan et al., 2018; Patari et al., 2022; Piotroski, 2000).
Ball et al. (2020), Papadamou et al. (2017), and Piotroski (2000) argued that high BM value enterprises employ accounting indicators of financial soundness to distinguish really distressed firms from out-of-favor but financially strong firms. This is consistent with research that suggests that, although the return on growth or glamour companies is mostly driven by momentum (Asness, 1997), the evaluation of value stocks should be based on firm fundamentals as reflected in financial statements. Investing...
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