Why Factor-Based Investing Works

factor-based investing 3

Why Factor-Based Investing Works

Contrary to what you may have been told by experts, gurus, and sages in the investment industry, individual investors are perfectly capable of constructing portfolios that produce non-trivial amounts of Alpha (excess returns above the market return), in a persistent and systematic way. This is the conclusion reached by several recent studies on the topic of Factor-Based Investing. (See the list of studies, articles, and other research sources at the end of this article.)

But not all factor-based strategies are effective, and not all individual investors have the skills to successfully execute the strategies that are. Of the roughly 128 million “retail” investors in the U.S., only about 18% invest “directly” by constructing and managing portfolios of individual securities. The other 82% of investors use pooled vehicles like mutual funds and ETFs to invest their savings.

Of the 23 million direct investors, most use an “ad hoc” approach, selecting stocks based on a wide range of metrics, but without the benefit of a unifying theme or methodology. That leaves just a tiny fraction of investors who use a systematic, factor-based screening strategy to select the stocks that are the most likely to outperform their peers.

The point of all this is that systematic, factor-based screening strategies are hardly a “mainstream” approach to DIY investing. Put differently, it’s not a crowded space in which to operate. As a result, the alpha that’s generated by these strategies is durable, as the literature shows.

A recent study of 600 factors found that nearly half produced zero to negative alpha. Only a handful of factors have survived out-of-sample testing and were proven to add alpha in a systematic and persistent manner. Among the most reliable sources of outperformance are valuation, company size, price momentum, earnings growth, and sponsorship. But most of the factors studied were found to be either insignificant or inconsistent sources of alpha.


The origins of factor-based investing

In 1964, William Sharpe introduced the Capital Asset Pricing Model (CAPM). In it, he explained the pricing of securities using a single factor (beta) which described how much a stock moved compared to the market. Stocks with a higher beta were viewed as more risky, and investors would therefore demand higher potential returns to offset the added risk. Sharpe’s approach has since been revised and enhanced to incorporate other factors that also help to explain the sources of stock returns.

By utilizing widely available stock screening tools, and choosing the right combination of factors for inclusion in the screening algorithm, investors can outperform the market… but only if certain conditions are met.

  • First, the investor must be willing to spend considerable time up front to build a robust screening strategy.
  • Second, the investor must have the patience and discipline to implement the strategy diligently and consistently.
  • And third, the investor must be prepared to bear the pain of occasional short-term underperformance in order to reap superior long-term performance.

But how can it be that a non-professional investor with limited time and resources can outperform not only the market, but also outperform the majority of professional mutual fund managers against whom he or she is competing for a finite amount of alpha?

The answer can be found in the way business is done on Wall Street. The two features I mentioned above —the potential for long-term outperformance, but periods of short-term underperformance—are critical. Professional fund managers are under tremendous pressure to beat their benchmarks. Because big-money investors are impatient, even a single down quarter can cause a significant outflow of capital.

This so-called “hot money” is a fact of life that fund managers must live with. And it causes them to make decisions that are beneficial to short-term performance but can be harmful to longer-term performance. An individual investor has no such constraints, and can turn this into an advantage.

The obvious question becomes, why don’t these factor-based advantages get arbitraged away by the market? The answer lies in two realities – the limits of arbitrage, and the compensation structure of the investment management industry.


The limits of arbitrage

For some factors there is a vast amount of research into the reasons behind their effectiveness. Low volatility and small cap are examples. But other factors like earnings estimate revisions and insider buying activity have not yet attracted enough attention to dilute their effectiveness.

Beyond the limits of arbitrage, there is an even more powerful force at work: factor diversification. By combining certain non-correlated factors into a screening algorithm, the investor can create a strategy that has unique characteristics that will not attract the attention of arbitrageurs.

An early example of this strategy was the combining of two strong factors into a single screen. Value and small-cap. For many years this strategy produced significant and persistent alpha. But eventually it attracted enough attention that the magnitude of alpha started to diminish. It still works to this day, but the alpha margins are much smaller than they once were.


The compensation structure of the investment management industry

Professional investors make a good living. And a significant part of their compensation comes in the form of a year-end bonus. In order to get the bonus, they have to deliver results. It’s very common in the industry for a manager to fall behind the market during the first half of the year, which then encourages him or her to scramble to make up the shortage by deviating from their normal strategy and taking on increasing amounts of risk. This rarely turns out well. It’s a problem that an individual investor doesn’t have to deal with, as long as they have personal discipline.


The ZenInvestor Top 7 Strategy


According to the efficient market hypothesis (EMH), past price behavior cannot reliably predict future price behavior. However, recent studies question the efficient market hypothesis and support the notion that stock market excess returns can be predicted and captured by thoughtfully designed factor screening algorithms.

Although conventional wisdom says the U.S. stock market is somewhat efficient, financial research indicates that security prices do not reflect all publicly available information at all times. I developed my own factor-based screening algorithm in 2005. The algorithm searches across the major stock exchanges and evaluates potential candidates on both technical and fundamental characteristics, such as price-to-sales ratio, market capitalization, positive price momentum, increasing trading volume, and accelerating earnings growth. Using a backtesting program, I built an algorithm that produced significant alpha over the five year period from 2000 to 2004. But the problem with backtesting is that in most cases, the results are not repeated after the sample period. The real-world, “out-of-sample” results can be very different from the backtested “in-sample” results.

In my case, the real-world results achieved by my clients have been as good, or better than, the backtested results.

Screening factors

Valuation ratios

Researchers have found several ways to identify undervalued stocks and predict excess returns. These include such factors as low price-to-earnings, price-to-book, and price-to-sales ratios. These researchers argue that stocks that have low values for these factors are not currently popular with investors and therefore create a potential for greater price appreciation.

Earnings momentum

Earnings prospects are considered to be an important driver of stock returns. Researchers note that screening on low price-to-earnings values would be successful in predicting returns only if earnings-per-share expectations are improving. They also note that changes in quarterly earnings estimates are one of the most reliable factors for predicting the potential for excess returns.

Earnings momentum is measured as the weighted average of quarterly growth rates in earning per share (EPS) over the past year. The recent quarters are given the most weight.

Company Size

Several studies have found a positive relationship between small firm size and stock returns. Market capitalization is the firm’s stock price multiplied by the number of common shares outstanding.

These studies found a significant relationship between firm size, book-to-market equity and excess returns. The optimal portfolios are those with the smallest firms and highest book-to-market equity. The portfolios with the largest firms and smallest book-to-market equity underperformed the market.

Price and volume characteristics

Relative price performance is a weighted average measure of past price changes of each stock in comparison to all stocks. Advocates of this measure contend that a stock will generally lose relative strength before a significant drop in price occurs. Relative price strength is a significant factor in selecting successful stock portfolios.

A sound strategy will consider changes in both price and volume. Volume serves as a proxy for commitment on the part of the buyers or sellers of a security. There is a measurable relationship between trading volume and price persistence.

Price changes are more likely to reverse following low trading volume. High volume indicates a greater likelihood that the trading is coming from informed and motivated investors.

Chartists and technicians have developed a variety of moving average rules. The length of the moving average period is typically between 50 and 200 days. The length chosen by the investor should coincide with the time horizon being considered.

The moving average indicator assumes that there are durable patterns in market prices, and that these patterns can be used in forecasting. If the current price moves above the moving average by a predetermined margin, a buy signal is generated. In this situation the chartist believes that the mood among investors has changed from negative to positive. Conversely, if the current price falls below the moving average, a sell signal results.

Until recently, moving averages were not considered to have much predictive value. However, advances in technology have enhanced the sophistication of these models and there is new evidence that shows how moving averages, when used in conjunction with other indicators, can add value to the stock selection process.

Sentiment and sponsorship

There is no getting around the fact that the stock market is heavily influenced by investor sentiment. In the old literature it was called “animal spirits.” In the new literature it’s called sentiment. But the idea is the same in both cases.

When investors are enthusiastic about the prospects for a company, they will bid up the price until it reaches an unsustainable level of valuation. At that point, buying interest begins to wane and the equilibrium shifts from the buyers to the sellers. It’s the natural law of supply and demand, and it will never go away.

Another version of sentiment is insider buying. During normal times, company officers and directors are net sellers of their company stock. That’s how they convert their stock options into cash bonuses. But sometimes the balance shifts, and there is net buying by insiders. This can mean that they know something good is coming down the road.

Lastly, there is sponsorship. There are certain mutual fund managers who are known for their expertise in a certain industry or niche of the market. When these professionals begin to build a position in a company that they know very well, it can mean that something good is coming. There are other indicators of sentiment, but these are some primary examples.

Screening methodology

The ideal screening model is one that efficiently constructs a portfolio of stocks that are not only undervalued but have relatively high growth potential. This model is created by combining the various factors described above. A judicious blend of these and other factors can create a synergy that arises from the interaction of these technical, fundamental, and sentiment factors.

A three-step approach

  1. Fundamental factors are used to screen for companies with low price-to-sales and price-to-book ratios, positive returns on equity, low debt burdens, and high earnings per share growth. These screens identify stocks that are financially sound and reasonably priced, relative to the industry group where they reside.
  2. Technical factors are used to select stocks that are above their moving averages and have high accumulation-distribution ratios (trading volume activity measured in dollars). Relative price strength vs. the market benchmark and vs. industry peers is considered.
  3. Sentiment factors are used to gauge investor motivation and commitment. Analyst upgrades, earnings estimate increases, and increasing trading volume are taken into account. Insider buying activity is also considered.

The out-of-sample results of this strategy (actual returns achieved in real time) cover eleven full years. This period includes both bull and bear markets, plus a long and painful economic recession, thereby encompassing a good mix of investing environments.

The strategy is designed to pick a portfolio of 5 to 7 stocks, and rebalance every four weeks. (Aggressive traders who have the time to spend can rebalance every week.) The number of stocks is limited to no more than 7, to make the portfolio financially accessible to the individual investor. The execution and rebalancing procedure is outlined below.

At the beginning of each 4-week period, the screening algorithm is executed and selects for purchase the top stocks from the universe that best fit the screening criteria. Dollars invested are distributed equally between the selected stocks. The total return for the selected portfolio is compared to the market return at the end of each period. The portfolio is then liquidated and a new search is made on the same criteria. Dollars are once again invested equally among selected stocks in the new portfolio.


Results by year


factor-based investing returns

Growth of an initial $1,000 investment in January 2000


factor-based investing returns 2




What accounts for the success of this screening algorithm? Four things. First, the results are consistent with delayed price reactions to company and analyst announcements. Many times, the market is slow to react because the big players who dominate trading have to convene a meeting of their firm’s investment selection committee before they get approval to act. This leaves opportunities for individual investors, who are much more nimble, to take advantage of those opportunities.

Second, the indicators are selected based on a review of the empirical literature. Only those indicators that proved successful in prior studies and out-of-sample experience are kept in the mix.

Third, both fundamental and technical filters are linked to take advantage of synergistic effects. And finally, strong industry sectors are given priority to account for current and changing macroeconomic conditions.

I don’t claim that this strategy is the best one out there. There are many ways to approach factor-based investing. I just happened to find one that makes sense to me, and to my clients who prefer direct investing. It has worked very well since I launched it back in 2005, with no diminution of alpha over the years.

If you are interested in trying factor-based investing, send me a message and I’ll send you the details.



  1. Gallup: Only 52% of American adults are invested in the stock market. http://www.gallup.com/poll/190883/half-americans-own-stocks-matching-record-low.aspx
  2. Fama and French, ‘A five-factor asset pricing model’, (2015). Journal of Financial Economics 116.1.
  3. Sharpe, ‘Capital asset prices: A theory of market equilibrium under conditions of risk’, (1964). Journal of Finance 19.3.
  4. Graham, Benjamin, Dodd and Cottle, ‘Security Analysis’, (1934). New York: McGraw-Hill.
  5. Fama and French, ‘The cross-section of expected stock returns’, (1992). Journal of Finance 47.2.
  6. Carhart, ‘On persistence in mutual fund performance’, (1997). Journal of Finance 52.1.
  7. Barberis, Shleifer and Vishny, ‘A model of investor sentiment’. (1998). Journal of Financial Economics 49.3.
  8. Hong, Lim and Stein, ‘Bad news travels slowly: Size, analyst coverage and the profitability of momentum strategies’, (2000). Journal of Finance 55.1.
  9. Givoly and Lakonishok, ‘The information content of financial analysts’ forecasts of earnings: Some evidence on semi-strong inefficiency’, (1979). Journal of Accounting and Economics 1.3.
  10. Womack, ‘Do brokerage analysts’ recommendations have investment value?’ (1996). Journal of Finance 51.1.
  11. Da and Warachka, ‘Cashflow risk, systematic earnings revisions and the cross section of stock returns’, (2009). Journal of Financial Economics. 94.3
  12. Computerized stock screening rules for portfolio selection, Steven C. Gold, Paul Lebowitz, Department of Finance, Accounting and MIS, Rochester Institute of Technology, College of Business, Max Lowenthal Bldg., 1 Lomb Memorial Drive, Rochester, NY 14623, USA





About the Author

Former head of equity trading, Northern Trust Bank, Chicago. Teacher, trainer, mentor, market historian, and perpetual student of all things related to the stock market and excellence in investing.

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