Investment Ideas That Work
In a hidden laboratory, an alchemist toiled tirelessly, his eyes reflecting the flicker of mysterious flames. Through endless nights and countless experiments, his time was wasted pursuing the elusive secret to transmutation, turning base metals into gold.
Similarly, in the world of finance, investors toil for the formula to change today's base facts into golden opportunities. Few are successful. Among professional money managers, only 17% of professionally managed stock (equity) funds are “winners” - both surviving and outperforming their market benchmarks over the past 20 years. 1
Winners don't keep winning, either. Of the equity investments that were initially top performers, only ~1 in 5 funds (22%) stayed on top during the next period. 2
Just like alchemy, the balance between theory and reality remains a perpetual enigma that no one has successfully cracked.
Rather than hope for a golden ticket, we keep investment evidence that actually works – the kind that has been peer-reviewed and time-tested – at the root of most everything we do with investors. The medical profession relies on sound research from peer-reviewed medical journals to manage your health. At Open Window, we follow a similar course. We invest by rigorously adhering to research from leading academics and Nobel Laureates. The alternative pursuit of alchemy – trying one’s luck guessing at the future – is a much tougher (and likely more costly) pursuit.
This begs the question: Which specific investment ideas actually work, and can they be relied upon going forward?
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Put these ideas to work for you:
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An exhaustive account of every meaningful contribution would be a lengthy list indeed, but it helps to be familiar with the most important academic insights that, in aggregate, offer us a clearer pathway with which to navigate the world's daily twists and turns.
Modern Portfolio Theory (MPT)
Harry Markowitz, “Portfolio Selection,” The Journal of Finance, 1952
For our purposes, most of the tenets underlying today’s reliable investment strategies originate in the 1950s with Modern Portfolio Theory (MPT), so we’ll begin there.
MPT represents one of the greatest equalizing breakthroughs in financial economics, paving the way for a radically different approach to investing. Prior to MPT, it was generally assumed that the best way to invest was to look at each security (or hire someone to do so), pick a few of the “best,” and hope you were right. For every winning trade, there had to be an equal and opposite losing one in the market’s transactional zero-sum game. This meant that individual success was a dicey proposition indeed, especially net of costs.
MPT suggested that investors could abandon the cut-throat competition and play with rather than against the forces of the market by adopting a portfolio-wide approach to capturing returns. It introduced several, now widely accepted principles, including a strong relationship between market risk and expected returns, and the vital role that diversification plays in managing that risk. Most importantly, MPT described a way for any participant to earn market returns, simply by being patient and waiting for businesses to do their thing (be profitable).
Separation Theorem
James Tobin, “Liquidity Preference as Behavior Towards Risk,” The Review of Economic Studies, 1958
The Separation Theorem played an important role by proposing that investors could form portfolios of riskier stocks (equity), but temper that risk by offsetting it with an allocation to bonds (fixed income). Today, it’s widely assumed that a portfolio should consist of an appropriate mix of stocks and bonds reflecting the investor’s individual risk tolerances. In the 1950s, this notion was groundbreaking.
Capital Asset Pricing Model (CAPM)
William F. Sharpe, “Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk,” The Journal of Finance, 1964
How is a price set? CAPM offered us some early, data-driven insights on this front. The Separation Theorem indicates that investors might be better off ignoring individual stock performance and focusing instead on their portfolio’s relative overall exposure to stocks versus bonds. This also was known as the single factor of general market risk. Sharpe expanded on the theme by analyzing newly available data on historical rates of returns. While CAPM left considerable room for additional inquiry, it established a stronger, data-driven platform from which to build.
Efficient Market Hypothesis (EMH, or “Random Walk” Pricing)
Eugene F. Fama, “The Behavior of Stock-Market Prices,” The Journal of Business, 1965; and Paul Samuelson, “Proof That Properly Anticipated Prices Fluctuate Randomly,” Industrial Management Review, 1965
In the mid-1960s, Samuelson, Fama, and others contributed to what collectively became the Efficient Market Hypothesis. Complementing CAPM price-setting, EMH also crunched available data to determine that a security’s next price seemed effectively unpredictable – like trying to track a drunkard’s “random walk.” Instead, prices were generally established by the market’s collective wisdom in lieu of individual “smart” trades. The evidence continued to mount that investors seem better off consistently capturing wide swaths of the markets’ expected risks and rewards, instead of trying to chase individual stocks or particular market climates.
Behavioral Finance
Amos Tversky and Daniel Kahneman, Judgment under Uncertainty: Heuristics and Biases,” Science, 1974
Parallel to financial economics, behavioral finance empirically analyzes the “human factor” in factor-based investing. Tversky and Kahneman published one of the earliest inquiries in this ongoing field, describing a number of behavioral biases to which investors seem predisposed. Particularly as improved brain-imaging techniques have advanced, so too has our understanding of what is going on in the deepest recesses of our brains that may be causing us to make seemingly irrational investment decisions, to the detriment of our end returns.
The Role of Bonds/Fixed Income
Eugene F. Fama, “Term premiums and default premiums in money markets,” Journal of Financial Economics, 1986
Turning to the bond side of the bond/stock mix, Fama and others have shed additional light on why it is usually a good idea to diversify one’s portfolio into a measure of stocks and bonds. With bonds, the primary risks – and thus sources for expected returns – include a bond’s term (its maturity date) and credit rating (the likelihood it might default on its obligations). These and other differences contribute to a relatively low correlation between stock and bond markets’ differing risks, expected returns, and price movements. This, in turn, helps us understand why bonds are better suited to serve as a stabilizing rather than a returns-generating force within an investor’s total portfolio, offsetting stocks’ more volatile mood swings.
The Three-Factor Model
Eugene F. Fama and Kenneth R. French, “Common risk factors in the returns on stocks and bonds,” Journal of Financial Economics, 1993
In one of the most important advances beyond the single-factor model for stock pricing, Fama and French provided key, data-driven evidence indicating that three distinct market factors went further than a single factor in explaining why one stock portfolio would be expected to perform better or worse than another over time. This became known as the Three-Factor Model. The three factors include: the market factor (stocks vs. bonds), the size factor (small-company vs. large-company stocks), and the stock-price factor (value vs. growth companies, typically as measured by price-to-earnings ratios).
The Three-Factor Model has been monumental in enabling fund companies to create practical, low-cost solutions for building diversified portfolios in which holdings can be tilted toward or away from each of these factors, so investors can efficiently tailor their expected levels of risk and return.
International Diversification
Steven L. Heston, K. Geert Rouwenhorst, and Roberto E. Wessels, “The structure of international stock returns and the integration of capital markets,” Journal of Empirical Finance, 1995
One way to differentiate persistent results from fleeting patterns is whether the results are repeatable in other samples. In financial economics, this often means determining whether a factor shows up in multiple markets around the globe. This landmark study (among others) shored up existing evidence by testing a number of markets across a dozen European countries and the U.S., and finding that they shared multiple risk factors in common. The study also initiated exploration into potential benefits of diversifying not only across risk factors, but also across various global markets.
What Does the Future Hold?
In the latest capital market research, scholars cited here as well as the next generation of their peers have been exploring whether the Three-Factor Model should evolve into a model incorporating additional factors – such as stock-price momentum, company profitability and company reinvestment costs. Do these factors represent additional, distinct sources of expected return? If so, can we expect them to remain persistent over time? Can they be practically implemented after the costs involved?
These are the questions unfolding even as we publish this piece. They get to the heart of why we feel an evidence-based investment approach is crucial to our own and our clients’ well-being.
As always, we begin by harnessing the most robust evidence available today, using the practical solutions built from that evidence to help our clients invest confidently toward their long-term goals. Keeping a watchful eye on additional evidence as it emerges, we also remain vigilant to new possibilities, applying rigorous criteria to assess their credibility. In this context, we believe the best way to participate in ever-uncertain markets was, is and will remain those strategies and solutions that are grounded in the most durable academic evidence.
While we can never promise certain success, an evidence-based strategy gives investors their best shot at likely success. That’s one factor we don’t see changing over time.
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Footnotes
There is no guarantee investment strategies will be successful. Past performance is no guarantee of future results.
1 - The sample includes funds at the beginning of the 20-year period ending December 31, 2022. Each fund is evaluated relative to its primary prospectus benchmark. Survivors are funds that had returns for every month in the sample period. Winners are funds that survived and outperformed their benchmark over the period. Where the full series of primary prospectus benchmark returns is unavailable, non-Dimensional funds are instead evaluated relative to their Morningstar category index. Data Sample: The sample includes US-domiciled, USD-denominated open-end and exchange-traded funds (ETFs) in the following Morningstar categories. Non-Dimensional fund data provided by Morningstar. Dimensional fund data is provided by the fund accountant. Dimensional funds or subadvised funds whose access is or previously was limited to certain investors are excluded. Index funds, load-waived funds, and funds of funds are excluded from the industry sample. Morningstar Categories (Equity): Equity fund sample includes the following Morningstar historical categories: Diversified Emerging Markets, Europe Stock, Foreign Large Blend, Foreign Large Growth, Foreign Large Value, Foreign Small/Mid Blend, Foreign Small/Mid Growth, Foreign Small/Mid Value, Global Real Estate, Japan Stock, Large Blend, Large Growth, Large Value, Mid-Cap Blend, Mid-Cap Growth, Mid-Cap Value, Miscellaneous Region, Pacific/Asia ex-Japan Stock, Real Estate, Small Blend, Small Growth, Small Value, Global Large-Stock Blend, Global Large-Stock Growth, Global Large-Stock Value, and Global Small/Mid Stock. Morningstar Categories (Fixed Income): Fixed income fund sample includes the following Morningstar historical categories: Corporate Bond, High Yield Bond, Inflation-Protected Bond, Intermediate Core Bond, Intermediate Core-Plus Bond, Long-Term Bond, Intermediate Government, Long Government, Muni California Intermediate, Muni California Long, Muni Massachusetts, Muni Minnesota, Muni National Intermediate, Muni National Long, Muni National Short, Muni New Jersey, Muni New York Intermediate, Muni New York Long, Muni Ohio, Muni Pennsylvania, Muni Single State Intermediate, Muni Single State Long, Muni Single State Short, Muni Target Maturity, Short Government, Short-Term Bond, Ultrashort Bond, Global Bond, and Global Bond-USD Hedged. Index Data Sources: Index data provided by Bloomberg, MSCI, Russell, FTSE Fixed Income LLC, and S&P Dow Jones Indices LLC. Bloomberg data provided by Bloomberg. MSCI data © MSCI 2023, all rights reserved. Frank Russell Company is the source and owner of the trademarks, service marks, and copyrights related to the Russell Indexes. FTSE fixed income indices © 2023 FTSE Fixed Income LLC. All rights reserved. S&P data © 2023 S&P Dow Jones Indices LLC, a division of S&P Global. All rights reserved. Indices are not available for direct investment. Their performance does not reflect the expenses associated with management of an actual portfolio. US-domiciled mutual funds and US-domiciled ETFs are not generally available for distribution outside the US. There is no guarantee investment strategies will be successful. Past performance is no guarantee of future results.
2- This study evaluated fund performance over rolling periods from 2003 through 2022. Each year, funds are sorted within their category based on their previous five-year total return. Those ranked in the top quartile of returns are evaluated over the following five-year period. The chart shows the average percentage of top-ranked equity and fixed income funds that kept their top ranking in the subsequent period. Data Sample: The sample includes US-domiciled, USD-denominated open-end and exchange-traded funds (ETFs) in the following Morningstar categories. Non-Dimensional fund data provided by Morningstar. Dimensional fund data is provided by the fund accountant. Dimensional funds or subadvised funds whose access is or previously was limited to certain investors are excluded. Index funds, load-waived funds, and funds of funds are excluded from the industry sample. Morningstar Categories (Equity): Equity fund sample includes the following Morningstar historical categories: Diversified Emerging Markets, Europe Stock, Foreign Large Blend, Foreign Large Growth, Foreign Large Value, Foreign Small/Mid Blend, Foreign Small/Mid Growth, Foreign Small/Mid Value, Global Real Estate, Japan Stock, Large Blend, Large Growth, Large Value, Mid-Cap Blend, Mid-Cap Growth, Mid-Cap Value, Miscellaneous Region, Pacific/Asia ex-Japan Stock, Real Estate, Small Blend, Small Growth, Small Value, Global Large-Stock Blend, Global Large-Stock Growth, Global Large-Stock Value, and Global Small/Mid Stock. Morningstar Categories (Fixed Income): Fixed income fund sample includes the following Morningstar historical categories: Corporate Bond, High Yield Bond, Inflation-Protected Bond, Intermediate Core Bond, Intermediate Core-Plus Bond, Long-Term Bond, Intermediate Government, Long Government, Muni California Intermediate, Muni California Long, Muni Massachusetts, Muni Minnesota, Muni National Intermediate, Muni National Long, Muni National Short, Muni New Jersey, Muni New York Intermediate, Muni New York Long, Muni Ohio, Muni Pennsylvania, Muni Single State Intermediate, Muni Single State Long, Muni Single State Short, Muni Target Maturity, Short Government, Short-Term Bond, Ultrashort Bond, Global Bond, and Global Bond-USD Hedged. Index Data Sources: Index data provided by Bloomberg, MSCI, Russell, FTSE Fixed Income LLC, and S&P Dow Jones Indices LLC. Bloomberg data provided by Bloomberg. MSCI data © MSCI 2023, all rights reserved. Frank Russell Company is the source and owner of the trademarks, service marks, and copyrights related to the Russell Indexes. FTSE fixed income indices © 2023 FTSE Fixed Income LLC. All rights reserved. S&P data © 2023 S&P Dow Jones Indices LLC, a division of S&P Global. All rights reserved. Indices are not available for direct investment. Their performance does not reflect the expenses associated with management of an actual portfolio. US-domiciled mutual funds and US-domiciled ETFs are not generally available for distribution outside the US. There is no guarantee investment strategies will be successful. Past performance is no guarantee of future results.