Big Ideas

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Book Preview – Chapter 6

Here is this Friday’s installment of Avoiding Investment Strategy Flame-outs, a short book we are previewing for subscribers. Chapter previews will continue for the next three Fridays.

Chapter 6: “Modeling at the Portfolio Level”

“Evaluating strategies based only on trade-level performance, as often presented by trading advisory (“education”) services, may mislead. Some strategies concentrate opportunities, at times identifying more trades than can reasonably be addressed with a limited amount of capital and at other times identifying no trades.

“Moreover, evaluating strategies based only on a list of closed trades, with the performance of contemporaneous open trades unknown, may mislead because open trades may be losers that at times absorb all the capital and preclude further trading.

“Modeling profitability at the portfolio level in such cases may be complicated and tedious, but is essential for understanding effects of a trading strategy on wealth. Portfolio-level modeling means carefully accounting for the allocations of all capital in a portfolio at all decision points in time series.”

Book Preview – Chapter 5

Here is this Friday’s installment of Avoiding Investment Strategy Flame-outs, a short book we are previewing for subscribers. Chapter previews will continue for the next four Fridays.

Chapter 5: “Checking for Market Adaptation”

“The market is a complex system with many interacting parts, and external influences. As in other social settings, there are two aspects to market evolution: (1) adaptation to changes in external influences; and, (2) adaptation to adjust internal imbalances.

“External influences include economic forces, political shifts, monetary policies, regulatory initiatives and information technology enhancements. For example:

    • Economic globalization broadens the universe of assets available in the market, but tends to increase co-movement of assets.
    • Political shifts may favor one industry over another or affect portfolio-level after-tax profitability of investing.
    • Loose monetary policy may favor the financial industry.
    • Regulatory actions on broker fees, quote granularity, short selling and margin levels impact investment frictions (profitability of trading) and cash requirements (portfolio-level returns).
    • Mass availability of historical data and investing knowledge, computing power, analysis software and real-time trading accelerate market identification of and response to all market opportunities.

“Investor adaptation to such influences is generally strategic.

“Some investors continuously strive to identify and exploit internal market imbalances (pricing anomalies) through fundamental and technical analysis, both asset-specific and marketwide. They express perceived imbalances in different ways, such as:

    • Undervalued versus overvalued
    • Overbought versus oversold
    • Too fearful versus too complacent
    • Risk-on versus risk-off
    • Informed versus noise

“When many investors compete in exploiting an imbalance, they supply negative feedback that suppresses it. When more investors compete, suppression is faster. More generally and abstractly, acts of exploiting characteristics of an inferred distribution of investing returns change the distribution. (There is an extensive body of countering research that attributes perceived internal market “imbalances” to rational equilibriums based on actual, but sometimes subtle, risks. The counter-counter is a proposition that people are not even grossly rational, let alone subtly rational.)

“The following sections discuss ways to detect and deal with market adaptation.”

Book Preview – Chapter 4

Here is this Friday’s installment of Avoiding Investment Strategy Flame-outs, a short book we are previewing for subscribers. Chapter previews will continue for the next five Fridays.

Chapter 4: “Accounting for Implementation Frictions”

“Investment frictions (costs) include such burdens as broker transaction fee, bid-ask spread, impact of trading (for large trades), borrowing cost for shorting, cost of leverage, costs of data, software and hardware for research, fund loads, cost of advisory services and cost of an investment manager.

“These costs vary considerably by category of investor (retail or institutional), over time, across countries and across types of assets. For example:

“Transaction fees vary by broker.

“Transaction fees are generally higher percentage-wise for small trades than large trades, and therefore for investors with small accounts than those with large accounts. Sophisticated traders may be able to suppress frictions via broker arrangements and order placement algorithms.

“Market liquidity tends to be lower in emerging markets than developed markets, generally indicating higher bid-ask spreads and impacts of trading in emerging markets.

“Both transaction fees and bid-ask spreads were generally much higher in past decades than now due to regulatory changes (ending of fixed commissions and decimalization) and technological advances (lower cost of execution and lower barrier to entry for discount brokers). This variation is problematic for long backtests.

“Frictions are generally higher percentage-wise for option trades than equity trades of similar sizes. Frictions for futures trades are comparatively low.

“Frictions for aesthetic assets such as art and wine are very large compared to those for financial assets.

“Cost of an investment manager subsumes the other costs (perhaps with economies of scale) but adds incremental fees for administration and management.

“Realistic modeling of frictions is often very difficult, especially for samples spanning long time periods. Many researchers set a goal of analyzing gross risk premiums or anomalies and therefore ignore frictions in measuring returns and alphas (returns adjusted for widely accepted risk factors). However, research findings based on net results may differ substantially from those based on gross results, to the extent of rendering realistic implementations unprofitable. The following sections cover some considerations and approaches for modeling trading frictions.”

Book Preview – Chapter 3

Here is this Friday’s installment of Avoiding Investment Strategy Flame-outs, a short book we are previewing for subscribers. Chapter previews will continue for the next six Fridays.

Chapter 3: “Avoiding or Mitigating Snooping Bias”

“Snooping bias, also called mining bias and more loosely benefit of hindsight, is a notorious artificial booster of backtest performance. It takes multiple forms:

    • Picking the best of many rules/indicators (strategies, models) for a given data sample
    • Optimizing rule parameters for a given data sample
    • Restricting a data sample to find favorable performance of a given rule
    • Running an investment contest among many individuals

“A sentiment shared among researchers in stochastic fields is: “If you torture the data long enough, it will confess to anything.” Because returns are noisy (substantially random), trying many combinations of rules, parameter settings and data samples will generate strategies that outperform benchmarks by extreme good luck. A prosecutor (an investor) satisfied with false confessions is likely to lose in court (the market).

“To illustrate, Figure 3-0 depicts the net cumulative values of $1.00 initial investments in each of 12 variations of the simple asset class momentum strategy introduced in Figure 1-1. This strategy shifts each month to the one of nine asset class proxies with the highest total return over a past return measurement (ranking) interval. Most of the proxies are exchange-traded funds (ETF). The 12 variations differ by the length of the ranking interval, from one to 12 months. All variations impose a switching friction of 0.25% whenever the strategy switches funds.

“Does the top-performing variation (dotted line) represent a premium earned by extracting truly valuable information from market prices, or just the payout from being the lucky winner of a lottery? The following sections address this question.”

Figure 3-0: Performance of 12 Asset Class Momentum Strategy Variations

Figure-3-0

Evaluating Strategy Backtests

How should investors assess the credibility of investment strategy backtests? In his October 2013 paper entitled “Telling the Good from the Bad and the Ugly: How to Evaluate Backtested Investment Strategies”, Patrick Beaudan recommends ways to judge investment strategies and backtests based on his years of experience in evaluating, managing and investing in algorithmic strategies. His perspective is that of investors choosing among strategies proposed by investment managers. Using examples to illustrate his points, he concludes that: Keep Reading

Book Preview – Chapter 2

Here is this Friday’s installment of Avoiding Investment Strategy Flame-outs, a short book we are previewing for subscribers. Chapter previews will continue for the next seven Fridays.

Chapter 2: “Making the Strategy Logical”

“Making an investment/trading strategy logical essentially means making it testable and implementable, with inputs, outputs and rules clearly defined, properly sequenced and inclusive of all material factors. Clearly defined inputs, outputs and rules enable verification and extension. Definitions that require subjective interpretation are not clear. Properly sequenced inputs, outputs and rules fit the real world, representing an analysis and implementation scenario available to an investor in real time. Some strategies are more forgiving of tight sequencing than others. Including all material factors means accounting for all significant contributions to (capital gains, dividends, interest) and debits from (costs of data, trading frictions, cost of shorting, cost of leverage) investment outcome. The materiality of factors varies with strategy specifics.

“How can investors make sure their strategies are logical?”

Book Preview – Introduction and Chapter 1

Starting today and continuing for the next eight Fridays, we are previewing for subscribers a short book entitled Avoiding Investment Strategy Flame-outs.

The initial installments are:

“Introduction”

“Why do investment/trading strategies that test well on historical data flame out when put to actual use? Are there steps investors can take to improve the odds that strategies they develop will perform as tested? This book draws upon reviews of hundreds of academic and practitioner studies that seek to predict asset prices and exploit the predictions. It focuses on widespread weaknesses and limitations in these studies to help investors: (1) avoid or mitigate the weaknesses in developing their own strategies; and, (2) perform due diligence on strategies offered by others.”

Chapter 1: “Some Statistical Practices that Make Sense”

“Financial systems, such as stock markets, involve a large number of interacting decisions based on many different time-varying levels of knowledge, processing capabilities, motivations and financial resources. Due to this complexity, theories of financial system behavior cannot determine future prices and returns. Said differently, the models termed “financial theories” are actually just working hypotheses generally formed retrospectively (empirically) to fit the past.

“Lack of solid theories leaves researchers to explore a jungle of empirical data via statistical inference, constructing samples and looking for past conditions (indicators) that relate strongly to future outcomes (returns) within those samples. Investors then make the leap (despite limitations in empirical research and changes in the market conditions) that future data is enough like past data to apply findings from such inferences to investment decisions.

“How should investors generate and interpret research findings in such an environment?”

To make room for Avoiding Investment Strategy Flame-outs on the CXOAdvisory.com main menu, we are retiring our “Investment Demons” (largely subsumed by the book). The demons will, however, remain available here.

Navigating the Data Snooping Icebergs

Iterative testing of strategies on a set of data introduces snooping bias, such that a winning (losing) strategy is to some degree lucky (unlucky). Sharing of strategies across a community of researchers carries the luck forward, with accretion of additional bias from testing by subsequent researchers. Is there a rigorous way to account for this accumulation of snooping bias? In the October 2013 version of their paper entitled “Backtesting”, Campbell Harvey and Yan Liu describe three types of adjustment for snooping bias and apply them to quantify the snooping bias “haircut” appropriate for any reported Sharpe ratio (in lieu of a 50% rule-of-thumb discount). Using mathematical derivations and examples, they conclude that: Keep Reading

Measuring Investment Strategy Snooping Bias

Investors typically employ backtests to estimate future performance of investment strategies. Two approaches to assess in-sample optimization bias in such backtests are:

  1. Reserve (hold out) some of the historical data for out-of-sample testing. However, surreptitious direct use or indirect use (as in strategy construction based on the work of others) of hold-out data may contaminate its independence. Moreover, small samples result in even smaller in-sample and hold-out subsamples.
  2. Randomize the data for Monte Carlo testing, but randomization assumptions may distort the data and destroy real patterns in them. And, the process is time-consuming.

Is there a better way to assess data snooping bias? In their September 2013 paper entitled “The Probability of Backtest Overfitting”, David Bailey, Jonathan Borwein, Marcos Lopez de Prado and Qiji Zhu derive an approach for assessing the probability of backtest overfitting that depends on the number of trials (strategy alternatives) employed to select it. They use Sharpe ratio to measure strategy attractiveness. They define an optimized strategy as overfitted if its out-of-sample Sharpe ratio is less than the median out-of-sample Sharpe ratio of all strategy alternatives considered. By this definition, overfitted backtests are harmful. Their process is very general, specifying multiple (in-sample) training and (out-of-sample) testing subsamples of equal size and reusing all training sets as testing sets and vice versa. Based on interpretation of mathematical derivations, they conclude that: Keep Reading

Insidiousness of Overfitting Investment Strategies via Iterative Backtests

Should investors worry that investment strategies available in the marketplace may derive from optimization via intensive backtesting? In the September 2013 update of their paper entitled “Backtest Overfitting and Out-of-Sample Performance”, David Bailey, Jonathan Borwein, Marcos Lopez de Prado and Qiji Zhu examine the implications of overfitting investment strategies via multiple backtest trials. Using Sharpe ratio as the measure of strategy attractiveness, they compute the minimum backtest sample length an investor should require based on the number of strategy configurations tried. They also investigate situations for which more backtesting may produce worse out-of-sample performance. Based on interpretations of mathematical derivations, they conclude that: Keep Reading

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