Factor investing

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Factor investing is an investment approach that involves targeting quantifiable firm characteristics or "factors" that can explain differences in stock returns. Security characteristics that may be included in a factor-based approach include size, low-volatility, value, momentum, asset growth, profitability, leverage, term and carry. [1] [2] [3]

Contents

A factor-based investment strategy involves tilting investment portfolios towards or away from specific factors in an attempt to generate long-term investment returns in excess of benchmarks. Proponents claim this approach is quantitative and based on observable data, such as stock prices and financial information, rather than on opinion or speculation. [4] [5] Factor premiums are also documented in corporate bonds [6] and across all major asset classes including currencies, government bonds, equity indices, and commodities. [7]

Critics of factor investing argue the concept has flaws, such as relying heavily on data mining that does not necessarily translate to real-world scenarios. [8]

History

The earliest theory of factor investing originated with a research paper by Stephen A. Ross in 1976 on arbitrage pricing theory, which argued that security returns are best explained by multiple factors. [9] Prior to this, the Capital Asset Pricing Model (CAPM), theorized by academics in the 1960s, held sway. CAPM held that there was one factor that was the driver of stock returns and that a stock's expected return is a function of its equity market risk or volatility, quantified as beta. The first tests of the Capital Asset Pricing Model (CAPM) showed that the risk-return relation was too flat. [10]

Sanjoy Basu was the first academic to document a value premium in 1977. [11] The roots of value investing date to decades earlier, and were formalized by Benjamin Graham and David Dodd as outlined in their 1934 book Security Analysis . Their student Warren Buffett outlined their findings and application in his 1984 article "The Superinvestors of Graham-and-Doddsville". In 1981 a paper by Rolf Banz established a size premium in stocks: smaller company stocks outperform larger companies over long time periods, and had done so for at least the previous 40 years. [12]

In 1992 and 1993, Eugene F. Fama and Kenneth French published their seminal three-factor papers that introduce size and value as additional factors next to the market factor. [13] In the early 1990s, Sheridan Titman and Narasimhan Jegadeesh showed that there was a premium for investing in high momentum stocks. [14] [15] In 2015 Fama and French added profitability and investment as two additional factors in their five-factor asset pricing model. [16] Profitability is also referred to as the quality factor. [17] Other significant factors that have been identified are leverage, liquidity and volatility. [18] [19] [20]

Value factor

The most well-known factor is value investing, which can be defined primarily as the difference between intrinsic or fundamental value and the market value. The opportunity to capitalize on the value factor arises from the fact that when stocks suffer weakness in their fundamentals, leading the market to overreact and undervalue them significantly relative to their current earnings. A systematic quantitative value factor investing strategy strategically purchases these undervalued stocks and maintains the position until the market adjusts its pessimistic outlook. [21] Value can be assessed using various metrics, including the P/E ratio, P/B ratio, P/S ratio, and dividend yield.

Low-volatility factor

Low-volatility investing is a strategy that involves acquiring stocks or securities with low volatility while avoiding those with high volatility, exploiting the low-volatility anomaly. The low-volatility anomaly was identified in the early 1970s but gained popularity after the 2008 global financial crisis. Different studies demonstrate its effectiveness over extended periods. [18] Despite widespread practical use, academic enthusiasm varies, and notably, the factor is not incorporated into the Fama-French five-factor model. Low-volatility tends to reduce losses in bear markets, while often lagging during bull markets, necessitating a full business cycle for comprehensive evaluation.

Momentum factor

Momentum investing involves buying stocks or securities with high returns over the past three to twelve months and selling those with poor returns over the same period. Despite its establishment as a phenomenon, there is no consensus explanation, posing challenges to the efficient market hypothesis and random walk hypothesis. Due to the higher turnover and no clear risk-based explanation the factor is not incorporated into the Fama-French five-factor model. Seasonal effects, like the January effect, may contribute to the success of momentum investing.

Criticism

In a 2019 paper, Rob Arnott, Campbell Harvey and colleagues identify several problems with factor investing. [8] They assert that due to data mining, very few of hundreds of identified factors have statistical significance in real-world scenarios. They also argue factors may not offer promised diversification under all market conditions, as factors may change in their level of correlation over time.

In a 2016 paper, Arnott and colleagues noted that many factors become popular among investors, leading to high valuations among such stocks and subsequent expected poor returns. [22]

Daniel Peris, an asset manager at Federated Hermes, argues that factor investing risks treating stocks like mathematical abstractions rather than ownership in companies, and furthermore states factor investing is not as objective or neutral as commonly believed: “...since the 1970s, factor investing has come to dominate the market, value being one of the two great investing styles (along with 'growth'). But those characteristics say more about market participants’ opinions about a company’s condition and future than the reality already embedded in a dividend stream." [23]

See also

Related Research Articles

<span class="mw-page-title-main">Financial economics</span> Academic discipline concerned with the exchange of money

Financial economics is the branch of economics characterized by a "concentration on monetary activities", in which "money of one type or another is likely to appear on both sides of a trade". Its concern is thus the interrelation of financial variables, such as share prices, interest rates and exchange rates, as opposed to those concerning the real economy. It has two main areas of focus: asset pricing and corporate finance; the first being the perspective of providers of capital, i.e. investors, and the second of users of capital. It thus provides the theoretical underpinning for much of finance.

<span class="mw-page-title-main">Capital asset pricing model</span> Model used in finance

In finance, the capital asset pricing model (CAPM) is a model used to determine a theoretically appropriate required rate of return of an asset, to make decisions about adding assets to a well-diversified portfolio.

<span class="mw-page-title-main">Efficient-market hypothesis</span> Economic theory that asset prices fully reflect all available information

The efficient-market hypothesis (EMH) is a hypothesis in financial economics that states that asset prices reflect all available information. A direct implication is that it is impossible to "beat the market" consistently on a risk-adjusted basis since market prices should only react to new information.

In finance, arbitrage pricing theory (APT) is a multi-factor model for asset pricing which relates various macro-economic (systematic) risk variables to the pricing of financial assets. Proposed by economist Stephen Ross in 1976, it is widely believed to be an improved alternative to its predecessor, the capital asset pricing model (CAPM). APT is founded upon the law of one price, which suggests that within an equilibrium market, rational investors will implement arbitrage such that the equilibrium price is eventually realised. As such, APT argues that when opportunities for arbitrage are exhausted in a given period, then the expected return of an asset is a linear function of various factors or theoretical market indices, where sensitivities of each factor is represented by a factor-specific beta coefficient or factor loading. Consequently, it provides traders with an indication of ‘true’ asset value and enables exploitation of market discrepancies via arbitrage. The linear factor model structure of the APT is used as the basis for evaluating asset allocation, the performance of managed funds as well as the calculation of cost of capital. Furthermore, the newer APT model is more dynamic being utilised in more theoretical application than the preceding CAPM model. A 1986 article written by Gregory Connor and Robert Korajczyk, utilised the APT framework and applied it to portfolio performance measurement suggesting that the Jensen coefficient is an acceptable measurement of portfolio performance.

In finance, the beta is a statistic that measures the expected increase or decrease of an individual stock price in proportion to movements of the stock market as a whole. Beta can be used to indicate the contribution of an individual asset to the market risk of a portfolio when it is added in small quantity. It refers to an asset's non-diversifiable risk, systematic risk, or market risk. Beta is not a measure of idiosyncratic risk.

The equity premium puzzle refers to the inability of an important class of economic models to explain the average equity risk premium (ERP) provided by a diversified portfolio of equities over that of government bonds, which has been observed for more than 100 years. There is a significant disparity between returns produced by stocks compared to returns produced by government treasury bills. The equity premium puzzle addresses the difficulty in understanding and explaining this disparity. This disparity is calculated using the equity risk premium:

In finance, Jensen's alpha is used to determine the abnormal return of a security or portfolio of securities over the theoretical expected return. It is a version of the standard alpha based on a theoretical performance instead of a market index.

A market anomaly in a financial market is predictability that seems to be inconsistent with theories of asset prices. Standard theories include the capital asset pricing model and the Fama-French Three Factor Model, but a lack of agreement among academics about the proper theory leads many to refer to anomalies without a reference to a benchmark theory. Indeed, many academics simply refer to anomalies as "return predictors", avoiding the problem of defining a benchmark theory.

Kenneth Ronald "Ken" French is the Roth Family Distinguished Professor of Finance at the Tuck School of Business, Dartmouth College. He has previously been a faculty member at MIT, the Yale School of Management, and the University of Chicago Booth School of Business.

Momentum investing is a system of buying stocks or other securities that have had high returns over the past three-to-twelve months, and selling those that have had poor returns over the same period.

Style investing is an investment approach in which securities are grouped into categories, and portfolio allocation is based on selection among "styles" rather than among individual securities.

Robert (Bob) Arthur Haugen was a financial economist and a pioneer in the field of quantitative investing and low-volatility investing. He was President of Haugen Custom Financial Systems and also consulted and spoke globally.

Fundamentally based indexes or fundamental indexes, also called fundamentally weighted indexes, are indexes in which stocks are weighted according to factors related to their fundamentals such as earnings, dividends and assets, commonly used when performing corporate valuations. This fundamental weight may be calculated statically, or it may be adjusted by the security's fundamental to market capitalization ratio to further neutralize the price factor between different securities. Indexes that use a composite of several fundamental factors attempt to average out sector biases that may arise from relying on a single fundamental factor. A key belief behind the fundamental index methodology is that underlying corporate accounting/valuation figures are more accurate estimators of a company's intrinsic value, rather than the listed market value of the company, i.e. that one should buy and sell companies in line with their accounting figures rather than according to their current market prices. In this sense fundamental indexing is linked to so-called fundamental analysis.

There are several concepts of efficiency for a financial market. The most widely discussed is informational or price efficiency, which is a measure of how quickly and completely the price of a single asset reflects available information about the asset's value. Other concepts include functional/operational efficiency, which is inversely related to the costs that investors bear for making transactions, and allocative efficiency, which is a measure of how far a market channels funds from ultimate lenders to ultimate borrowers in such a way that the funds are used in the most productive manner.

In finance, momentum is the empirically observed tendency for rising asset prices or securities return to rise further, and falling prices to keep falling. For instance, it was shown that stocks with strong past performance continue to outperform stocks with poor past performance in the next period with an average excess return of about 1% per month. Momentum signals have been used by financial analysts in their buy and sell recommendations.

In asset pricing and portfolio management the Fama–French three-factor model is a statistical model designed in 1992 by Eugene Fama and Kenneth French to describe stock returns. Fama and French were colleagues at the University of Chicago Booth School of Business, where Fama still works. In 2013, Fama shared the Nobel Memorial Prize in Economic Sciences for his empirical analysis of asset prices. The three factors are:

  1. Market excess return,
  2. Outperformance of small versus big companies, and
  3. Outperformance of high book/market versus low book/market companies
<span class="mw-page-title-main">Low-volatility anomaly</span>

In investing and finance, the low-volatility anomaly is the observation that low-volatility securities have higher returns than high-volatility securities in most markets studied. This is an example of a stock market anomaly since it contradicts the central prediction of many financial theories that higher returns can only be achieved by taking more risk.

In portfolio management, the Carhart four-factor model is an extra factor addition in the Fama–French three-factor model, proposed by Mark Carhart. The Fama-French model, developed in the 1990, argued most stock market returns are explained by three factors: risk, price and company size. Carhart added a momentum factor for asset pricing of stocks. The Four Factor Model is also known in the industry as the Monthly Momentum Factor (MOM). Momentum is the speed or velocity of price changes in a stock, security, or tradable instrument.

Low-volatility investing is an investment style that buys stocks or securities with low volatility and avoids those with high volatility. This investment style exploits the low-volatility anomaly. According to financial theory risk and return should be positively related, however in practice this is not true. Low-volatility investors aim to achieve market-like returns, but with lower risk. This investment style is also referred to as minimum volatility, minimum variance, managed volatility, smart beta, defensive and conservative investing.

Style drift occurs when a mutual fund's actual and declared investment style differs. A mutual fund’s declared investment style can be found in the fund prospectus which investors commonly rely upon to aid their investment decisions. For most investors, they assumed that mutual fund managers will invest according to the advertised guidelines, this is however, not the case for a fund with style drift. Style drift is commonplace in today’s mutual fund industry, making no distinction between developed and developing markets according to studies in the United States by Brown and Goetzmann (1997) and in China as reported in Sina Finance.

References

  1. ↑ Fisher, Gregg; Shah, Ronnie; Titman, Sheridan (2015-03-23). "Combining Value and Momentum". Journal of Investment Management. 14. SSRN   2472936.
  2. ↑ Harvey, Campbell R.; Liu, Yan; Zhu, Heqing (2016-01-01). "… and the Cross-Section of Expected Returns". Review of Financial Studies. 29 (1): 5–68. doi: 10.1093/rfs/hhv059 . ISSN   0893-9454.
  3. ↑ Swedroe, Larry (2016-10-07). Your Complete Guide to Factor-based Investment. BAM ALLIANCE Press. ISBN   978-0692783658.
  4. ↑ Fama, Eugene F.; French, Kenneth R. (1992). "The Cross-Section of Expected Stock Returns". The Journal of Finance. 47 (2): 427–465. CiteSeerX   10.1.1.556.954 . doi:10.2307/2329112. JSTOR   2329112.
  5. ↑ Maymin, Philip; Fisher, Gregg (2011-04-11). "Past Performance is Indicative of Future Beliefs". Risk and Decision Analysis. 13 (3): 145–150. doi:10.3233/RDA-2011-0038. S2CID   15665310. SSRN   1746864.
  6. ↑ Houweling, Patrick; van Zundert, Jeroen (2017). "Factor Investing in the Corporate Bond Market". Financial Analysts Journal. 73 (2): 100–115. doi:10.2469/faj.v73.n2.1. ISSN   0015-198X.
  7. ↑ Baltussen, Guido; Swinkels, Laurens; van Vliet, Pim (2021). "Global Factor Premiums". Journal of Financial Economics. doi:10.2139/ssrn.3325720. S2CID   159122441. SSRN   3325720.
  8. 1 2 Arnott, Robert D. and Harvey, Campbell R. and Kalesnik, Vitali and Linnainmaa, Juhani T., Alice’s Adventures in Factorland: Three Blunders That Plague Factor Investing (April 10, 2019). Available at SSRN: https://ssrn.com/abstract=3331680 or http://dx.doi.org/10.2139/ssrn.3331680
  9. ↑ Ross, Stephen A. (1976). "The Arbitrage Theory of Capital Asset Pricing" (PDF). Journal of Economic Theory. 13 (3): 341–360. doi:10.1016/0022-0531(76)90046-6.
  10. ↑ Black, F.; Jensen, M. C.; Scholes, M. (1972). "The capital asset pricing model: Some empirical tests". Studies in the Theory of Capital Markets. 81 (3): 79–121.
  11. ↑ Basu, S. (1977). "Investment Performance of Common Stocks in Relation to Their Price-Earnings Ratios: A Test of the Efficient Market Hypothesis". The Journal of Finance. 32 (3): 663–682. doi:10.2307/2326304. ISSN   0022-1082. JSTOR   2326304.
  12. ↑ BANZ, Rolf W. (1981). "The relationship between return and market value of common stocks". Journal of Financial Economics. 9: 3–18. CiteSeerX   10.1.1.554.8285 . doi:10.1016/0304-405X(81)90018-0.
  13. ↑ Fama, E. F.; French, K. R. (1993). "Common risk factors in the returns on stocks and bonds". Journal of Financial Economics . 33: 3–56. CiteSeerX   10.1.1.139.5892 . doi:10.1016/0304-405X(93)90023-5.
  14. ↑ Jegadeesh, Narasimhan (July 1990). "Evidence of Predictable Behavior of Security Returns" (PDF). The Journal of Finance. 45 (3): 881–898. doi:10.1111/j.1540-6261.1990.tb05110.x.
  15. ↑ Jegadeesh, Narasimhan; Titman, Sheridan (1993). "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency". The Journal of Finance. 48 (1): 65–91. CiteSeerX   10.1.1.597.6528 . doi:10.1111/j.1540-6261.1993.tb04702.x. JSTOR   2328882. S2CID   13713547.
  16. ↑ Fama, Eugene F.; French, Kenneth R. (2015-04-01). "A five-factor asset pricing model". Journal of Financial Economics. 116 (1): 1–22. doi:10.1016/j.jfineco.2014.10.010. ISSN   0304-405X.
  17. ↑ Novy-Marx, Robert (2013-04-01). "The other side of value: The gross profitability premium". Journal of Financial Economics. 108 (1): 1–28. doi:10.1016/j.jfineco.2013.01.003. ISSN   0304-405X.
  18. 1 2 Blitz, David; van Vliet, Pim (2007). "The Volatility Effect: Lower Risk Without Lower Return". Journal of Portfolio Management. doi:10.3905/jpm.2007.698039. S2CID   154015248. SSRN   980865.
  19. ↑ Bollen, Nicolas; Fisher, Gregg (2012-07-03). "Send in the Clones? Hedge Fund Replication Using Futures Contracts". The Journal of Alternative Investments. 16 (2): 80–95. doi:10.3905/jai.2013.16.2.080. S2CID   219222562. SSRN   2102593.
  20. ↑ Pástor, Ľ; Stambaugh, R.F. (2003). "Liquidity risk and expected stock returns". Journal of Political Economy. 111 (3): 642–685. doi:10.1086/374184.
  21. ↑ "Factors from Scratch | O'Shaughnessy Asset Management". osam.com. Retrieved 2018-09-06.
  22. ↑ "How Can "Smart Beta" Go Horribly Wrong?". researchaffiliates.com. Retrieved 2019-07-24.
  23. ↑ Peris, Daniel (2024). The Ownership Dividend: The Coming Paradigm Shift in the U.S. Stock Market. London: Routledge. ISBN 978-1032270524
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