Big Ideas

How can investment strategy researchers best address the randomness inherent in market data and the ability of investors/markets to adapt to changing conditions? In his September 2019 paper entitled “Tactical Investment Algorithms”, Marcos Lopez de Prado reviews three methods for testing the performance of an investment rule:

1. Walk-forward (WF) tests a rule against an actual historical data series, implicitly assuming that market behaviors are neither largely random nor changing (that the rule being tested is “all-weather”).
2. Resampling (RS) addresses randomness in market behaviors by assuming that resampling of past observations can usefully generate possible future price paths. 
3. Monte Carlo (MC) addresses both randomness and adaptation in market behaviors by simulating possible future price paths based on models of price generation derived from theory and statistical analysis of actual data.

Based on his knowledge of financial markets and testing methods, he concludes that: Keep Reading

Is there a way that asset managers can share knowledge/data across proprietary boundaries with many researchers to advance development of investment strategies? In their September 2019 paper entitled “Crowdsourced Investment Research through Tournaments”, Marcos Lopez de Prado and Frank Fabozzi describe highly structured tournaments as a crowdsourcing paradigm for investment research. In each such tournament, the organizer poses one investment challenge as a forecasting problem and provides abstracted and obfuscated data. Contestants pay an entry fee, develop models and provide forecasts, retaining model ownership by running calculations on their own hardware/software. Based on this hypothetical tournament setup and their experience, they conclude that:

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The following list links to summaries of recent (since 2010) investment research using long data samples. These summaries may be helpful in developing strategic allocations and tactical wariness for long-horizon investments.

• “Commodity Futures Risk Premium Over the Long Run” – estimates the historical risk premium of dead and live commodity futures contract series as available during 1871 through 2018 (148 years).
• “Bond Returns Over the Very Long Run” – assesses returns to investment grade bonds (federal, state/local and corporate) in the U.S. during 1793 through 2013 (221 years).
• “National Election Cycle and Stocks Over the Long Run” – examines the behavior of the U.S. stock market across the U.S. presidential term cycle (years 1, 2, 3 or 4) from the end of December 1872 through August 2019 (almost 147 years).
• “Stock Market Earnings Yield and Inflation Over the Long Run” – investigates how U.S. stock market earnings yield interacts with the U.S. inflation rate during January 1871 through June 2019 (148 years).
• “‘Sell in May’ Over the Long Run” – tests whether conventional wisdom to “Sell in May” works for the U.S. stock market during April 1871 through April 2019 (148 years).
• “Asset Class Short-term Momentum Over the Long Run” – measures short-term return predictability within global samples for five asset classes (equity indexes, government bonds, treasury bills, commodity futures and currencies) in U.S. dollars as available during 1800 through 2018 (219 years).
• “Commodity Futures Strategies Over the Very Long Run” – evaluates momentum (nearest contract 12-month excess return), value (spot price change from one year ago to five years ago) and basis (12-month average ratio of nearest to next-nearest contract prices) premiums for commodity futures as available during 1877 through 2017 (141 years).
• “Global Factor Premiums Over the Very Long Run” – checks the reliability of six widely accepted factor premiums across four asset classes using data as available during 1800 through 2016 (217 years).
• “January Barometer Over the Long Run” – tests support for the belief that “as goes January, so goes the rest of the year” for the the U.S. stock market during 1871 through 2017 (147 years).
• “Trend Following with Intrinsic Momentum over the Very Long Run” – investigates whether time series (intrinsic or absolute) return momentum works for 67 assets as available during 1880 through 2016 (137 years).
• “(Some) Commodities over the Long Run” – examines whether commodity futures are attractive as available during 1877 through 2015 (139 years).
• “Illiquid Asset Returns over the Long Run” – tests whether illiquid assets (houses, farmland, art, stamps, wine, violins, gold, silver and diamonds) are competitive as investments with liquid financial assets during 1900 through 2014 (115 years).
• “Asset Class Price Momentum Over the Very Long Run” – examines reliability of price momentum within and across country equity markets, global equity sectors, government bonds, commodities, currencies and individual U.S. stocks as available during 1800 through 2014 (215 years).
• “Market Timing with Moving Averages Over the Very Long Run” – explores which moving average rules and measurement (lookback) intervals work best for the U.S. stock market during 1860 through 2009 (150 years).
• “Currency Carry Trade Over the Long Run” – tests whether the currency carry trade (financing short-term deposits in currencies with high interest rates with short-term loans in currencies with low interest rates) generates a reliably attractive return during 1900 through 2012 (113 years).
• “Earnings per Share Growth in the Long Run” – investigates what long-run real return investors should expect from the U.S. stock market and whether popular metrics reliably indicate when it is overvalued using data as available during 1871 through 2013 (143 years).
• “Trend Following over the Very Long Run” – tests whether prices exhibit persistently exploitable trends for commodities, currencies, stock indexes and bonds as available during 1800 through 2010 (211 years).
• “January Effect Over the Long Run” – assesses support for belief in the January effect (exceptionally strong performance by the U.S. stock market during the month of January) during 1871 through 2013 (143 years).
• “Stock Price Momentum Over the Very Long Run” – analyzes momentum in U.S. stock prices during 1800 through December 2012 (213 years).
• “10-Month SMA Timing Signals Over the Long Run” – tests usefulness of a 10-month simple moving average (SMA) trading rule on the U.S. stock market during 1871 through 2012 (142 years).
• “Moving Average Rules Over the Long Run” – examines performances of 10,800 moving average trading rule variants as applied to then-current components of the Dow Jones Industrial Average during October 1928 through December 2011 (83 years).
• “Reversion of Stock Markets to Value Over the Long Run” – investigates whether investors can count on stock markets reverting to some valuation benchmark using annual index levels during 1900 through 2008 (109 years).
• “Trading Frictions Over the Long Run” – compiles a history of U.S. stock trading frictions spanning 1900 through 2000 (101 years).
• “A Long Run Demographic Stock Market Outlook” – assesses whether the demographic mix of a set of U.S. investors affects aggregate demand for yield, and thereby total returns, from equity investments during 1900 through 2008 (109 years).

Another long run source is the annual update of the work summarized in Triumph of the Optimists (Chapter-by-Chapter Review).

Some general cautions regarding such studies are:

• Reconstruction of price series from, for example, old newspapers involves missing data and potentially inconsistent reporting approaches. In other words, quality of old data is suspect.
• The number of asset class price series available may be small in early parts of sample periods.
• Some studies may impound survivorship bias via omission of assets that were important in the past but are no longer tracked in source databases.
• For studies using Shiller data, monthly levels are averages of monthly values, blurring monthly statistics and modestly blurring annual statistics. Results based on end-of-month values may differ.
• Reported returns are gross, not net. Accounting for costs of maintaining a tracking fund for a portfolio/index of commodities would reduce returns. Also:
  • Studies involving shorting (such as factor premium analyses) typically do not address the cost/feasibility of shorting.
  • Costs of maintaining tracking funds may vary by asset class, by country and over time, confounding comparisons. For example, commodity futures indexes generally assume monthly rolling of many contract series.
  • Investment capacities of some assets may be especially limited early in sample periods.
  • Tax consequences of trading vary considerably across countries and over time.
• Historical timeliness of data collection/processing for periodic trading (for example, for portfolio rebalancing) may be especially problematic in early parts of sample periods.
• Economies and markets change over time, making it difficult to assess the relative importance of older versus newer data.
• Distant past availability of retrospectively constructed indexes may have altered contemporaneous market behaviors (induced market adaptations).

How does a shift in emphasis from active to passive investing affect the financial market risk landscape? In their September 2019 paper entitled “The Shift From Active to Passive Investing: Potential Risks to Financial Stability?”, Kenechukwu Anadu, Mathias Kruttli, Patrick McCabe, Emilio Osambela and Chaehee Shin analyze how a shift from active to passive investing affects:

1. Investment fund redemption liquidity risks.
2. Market volatility.
3. Asset management industry concentration.
4. Co-movement of asset returns and liquidity.

They also assess how effects are likely to evolve if the active-to-passive shift continues. Based on their framework/analysis, they conclude that: Keep Reading

Where can investors find a large catalog of trading strategies spanning asset classes? In the September 2019 update of their paper entitled “151 Trading Strategies”, Zura Kakushadze and Juan Andres Serur make freely available their book describing over 150 trading strategies, including over 550 formulas. These strategies encompass many asset classes and trading styles, including: stocks, options, fixed income, futures, exchange-traded funds, commodities, foreign exchange, convertibles, structured assets (collateralized debt and mortgage-backed), volatility, real estate, distressed, crypto, weather, energy, inflation, global macro, infrastructure and tax arbitrage. Some strategies incorporate machine learning. The paper does not include empirical studies, numerical simulations or backtests, but does contain annotated source code for out-of-sample testing, references and a glossary of acronyms and mathematical terms. Based on experience, they observe that: Keep Reading

Why are expert economic and financial (econometric) forecasters so inaccurate? In his April 2019 presentation package for a graduate course at Cornell entitled “The 7 Reasons Most Econometric Investments Fail”, Marcos Lopez de Prado enumerates shortcomings of standard econometric statistical methods, which concentrate on multivariate linear regressions. In contrast, advanced computational methods that exploit machine learning are ascendant in many other scientific fields, because they avoid (likely unrealistic) assumptions regarding actual data generation (such as linearity). Based on reviews of econometric texts and the body of econometric research, he concludes that: Keep Reading

Does crowding of factor investing strategies reliably predict returns for those strategies? In his March 2019 paper entitled “The Impact of Crowding in Alternative Risk Premia Investing”, Nick Baltas explores mechanics of alternative risk (factor) premium crowding and implications of crowding for future performance. He classifies factor premiums as: divergent (such as momentum), inherently destabilizing due to positive feedback loops and lack of fundamental anchors; or, convergent (such as value), having self-correcting negative feedback loops and fundamental anchors. To test crowding effects, he considers the following premiums: equity value (book-to-market), size (market capitalization), momentum (from regression of return from 12 months ago to one month ago versus volatility), quality (return on assets) and low beta (versus the MSCI World Index); commodities momentum (12-month return); and, currencies value (purchasing power parity) and momentum (12-month return). Each premium consists of returns from a hedge portfolio that is each week long (short) the equal-weighted assets with the highest (lowest) expected returns. For equities, he uses top and bottom tenths. For commodities and currencies, he uses top and bottom thirds. His crowding metric (CoMetric) is average pairwise correlation of factor-adjusted returns of assets within the long or short sides of premium portfolios over the last 52 weeks (except 260 weeks for value). He defines the 20% of weeks with the highest (lowest) CoMetrics as most (least) crowded. Using the specified factor and return data for liquid developed market stocks since September 2004, 24 constituents of the S&P GSCI Commodity Index since January 1999, and 26 developed and emerging markets currency pairs versus the U.S. dollar since January 2000, all through May 2018, he finds that:

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What are common cautions regarding exploitation of academic and practitioner papers on financial markets? To investigate, we collect, collate and summarize our cautions on findings from papers reviewed over the past year. These papers are survivors of screening for relevance to investors of a much larger number of papers, mostly from the Financial Economics Network (FEN) Subject Matter eJournals and Journal of Economic Literature (JEL) Code G1 sections of the Social Sciences Research Network (SSRN). Based on review of cautions in 109 summaries of papers relevant to investors posted during mid-March 2018 through mid-March 2019, we conclude that: Keep Reading

Should investors trust academic equity factor research? In their February 2019 paper entitled “A Census of the Factor Zoo”, Campbell Harvey and Yan Liu announce a comprehensive database of hundreds of equity factors from top academic journals and working papers through January 2019, including a link to citation and download information. They distinguish among six types of common factors and five types of firm characteristic-based factors. They also explore incentives for factor discovery and reasons why many factors are lucky findings that exaggerate expectations and disappoint in live trading. Finally, they announce a project that allows researchers to add published and working papers to the database. Based on their census of published factors and analysis of implications, they conclude that: Keep Reading

How does investor competitiveness (a goal of relative rather than absolute wealth) affect optimal allocations? In their February 2019 paper entitled “The Growth of Relative Wealth and the Kelly Criterion”, Andrew Lo, Allen Orr and Ruixun Zhang compare optimal portfolios for maximizing relative wealth versus absolute wealth at both short and long investment horizons. They define an individual’s relative wealth as fraction held of total wealth of all investors. Their model assumes that investors allocate to two assets, one risky and one riskless. They identify when an investor should allocate according to the Kelly criterion (series of allocations that maximize terminal wealth over the long run) and when the investor should deviate from it. Based on derivations and modeling, they conclude that:

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