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Big Ideas

These blog entries offer some big ideas of lasting value relevant for investing and trading.

Testing Tactical Investment Rules

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

Investment Strategy Development Tournaments?

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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Compendium of Recent “Long Run” Research

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.

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).

Systemic Risk Impacts of Growth in Passive Investing

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

Trading Strategy Catalog

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

Financial Experts Ignoring Better Statistical Methods?

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

Effects of Factor Crowding

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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Cautions Regarding Findings Include…

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

Equity Factor Census

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

Relative Wealth Effects on Investors

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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