Big Ideas

When do simple moving averages (SMA) serve as useful trading rules? Do they exploit some hidden pattern in asset price behavior? In their July 2011 paper entitled “The Trend is not Your Friend! Why Empirical Timing Success is Determined by the Underlying’s Price Characteristics and Market Efficiency is Irrelevant “, flagged by a subscriber, Peter Scholz and Ursula Walther investigate the relationship between the performance of technical trend-following rules and the characteristics (statistics) of the target asset return series. They use timing rules based on SMAs of different intervals (5, 10, 20, 38, 50, 100 and 200 trading days) as examples of trend-following rules. They consider the effects on SMA rule performance of variations in four asset price series statstics: the first-order trend (drift); return autocorrelation (return persistence); volatility of returns; and, volatility autocorrelation (volatility persistence/clustering). Analyses are long-only and ignore trading frictions, dividends, return on cash and buffering tactics such as stop-loss. They use a robust array of risk and performance measures to compare SMA rule performance to a buy-and-hold approach. Using both simulated price series and ten years of daily prices (2000-2009) for 35 country stock market indexes, they find that: Keep Reading

Financial markets sometimes switch states (regimes), with key investment decision statistics (such as average return and volatility of returns) shifting dramatically for extended intervals. A simple example of financial market regimes is the designation of bull and bear stock market states, estimated (for example) by a broad index being above or below its long-interval simple moving average. What is the big picture on the concepts, estimation and application of regime changes in investing? In their June 2011 paper entitled “Regime Changes and Financial Markets”, Andrew Ang and Allan Timmermann review the basics of modeling regime switches and applying such models to asset allocation decisions. Drawing on prior theoretical and empirical research, they conclude that: Keep Reading

Do strategies modeled using major indexes translate cleanly to the exchange-traded funds (ETF) that track them? ETF returns may deviate from underlying index levels because: (1) ETFs incorporate trading frictions from rebalancing and management fees; (2) ETF composition may differ slightly from that of the underlying index due to trading cost considerations; (3) ETFs accumulate dividends in a non-interest bearing account for periodic lump sum distribution; and, (4) ETFs trade until 4:15 p.m., while indexes close at 4:00 p.m. To investigate, we compare return distribution statistics over rolling 52-week histories for three index-ETF pairs:

SPDR S&P 500 (SPY) versus S&P 500 Index, since late January 1993.
SPDR Dow Jones Industrial Average (DIA) versus Dow Jones Industrial Average (DJIA), since late January 1998.
PowerShares QQQ (QQQ) versus NASDAQ 100 Index, since early March 1999.

Using weekly closes for both indexes and dividend-adjusted ETFs from ETF inception through June 2011, we find that: Keep Reading

“The Fourth Quadrant: No Realm for the Normal” summarizes Nassim Taleb’s description of the realm of the Black Swan, concluding that in this realm “normal” statistical metrics and associated risk management methods do not work and that redundancy, not optimization, is key to risk management. This Fourth Quadrant encompasses return distributions that have infrequent, large, unpredictable observations (shocks) that contribute materially to return distribution statistics. In the February 2011 version of his essay entitled “Antifragility, Robustness, and Fragility inside the ‘Black Swan Domain'”, he explores this realm further in the context of models as fragile (shocks involve largely negative returns) versus anti-fragile (shocks involve largely positive returns). For investors and traders, “model” means an investment strategy or a trading setup. Using a mostly theoretical approach, he argues that: Keep Reading

Are there recognizable country and global financial cycles over the past half century? If so, what are their characteristics? In their April 2011 paper entitled “Financial Cycles: What? How? When?”, Stijn Claessens, Ayhan Kose and Marco Terrones employ regression models to investigate cycles in credit, housing and equity markets for developed countries since 1960. They define a downturn as peak to trough and an upturn as trough to level of previous peak (not trough to new peak). They define series peaks and troughs with the constraints that complete cycle duration must be at least five quarters and each upturn and downturn must be at least two quarters. The main characteristics of cyclical phases are their duration, amplitude and slope, with crunches/busts (booms) defined as downturns (upturns) in the bottom (top) fourth of all observations. They examine pre-globalization (1960-1985) and globalization (1986-2007) subperiods, with phases globally synchronized (highly synchronized) when more than 40% (50%) of countries experience the same phase. Using quarterly data for aggregate loans to the private sector, house/land price indexes and value-weighted stock market indexes for 21 developed countries over the period 1960 through 2007, seasonally adjusted as necessary, they find that: Keep Reading

Can investors systematically benefit from the perspective that trading is the exchange of one asset for another, not the buying and selling of a single asset? In his paper entitled “Optimal Rotational Strategies Using Combined Technical and Fundamental Analysis”, third-place winner for the 2011 Wagner Award presented by the National Association of Active Investment Managers, Tony Cooper presents methods and tools designed to exploit the precept that valuations are relative. An organizing concept for these methods and tools is the Binary Decision Chart (BDC), which in one form addresses simultaneous analysis of two competing investments for the purpose of switching or weighting and in an extended form addresses combining technical analysis (based on observed price action) and fundamental analysis (indicator-based prediction). BDCs are cumulative return charts, but the horizontal axis may be a technical or fundamental indicator rather than time. More specifically, using various asset price series and indicators, he illustrates the following methods/tools: Keep Reading

Trading rules that generate clustered signals present capital allocation problems. Sometimes unpredictable scarcity of signals idles capital, and other times unpredictable clustering of signals presents too many opportunities to exploit. Portfolio-level performance therefore falls considerably short of trade-level promise. Are there ways to optimize capital allocation for such trading rules? In his paper entitled “Buying Power – The Overlooked Success Factor”, winner of the 2011 Wagner Award presented by the National Association of Active Investment Managers, Thomas Krawinkel examines the interplay of capital constraints and clustered trading signals. Using example trading rules for illustration, he concludes that: Keep Reading

In his 2011 book Super Boom: Why the Dow Will Hit 38,820 and How You Can Profit from It, author Jeffrey Hirsch (editor-in-chief of the Stock Trader’s Almanac) states, regarding the book’s title and a target date of 2025: “…I believe it will happen” and more cautiously “the coming super boom is not only plausible, but mathematically and historically probable. Moves of this magnitude have happened several times throughout history, and they have always been preceded by tumultuous times and economic weakness. In fact, big moves happen with such regularity and clear cause that we have successfully identified why they happen, how they happen, and when they happen. Most importantly, we show what to invest in before and as they happen.” Derived from approximately a century of historical observations, some notable points from the book are: Keep Reading

Can portfolios exhibit properties not evident from, or even contrary to, average properties of their component assets? In the April 2011 draft of their paper entitled “The Sources of Portfolio Returns: Underlying Stock Returns and the Excess Growth Rate”, Jason Greene and David Rakowski provide a framework for distinguishing two sources of portfolio return: (1) weighted average growth rates of component assets; and, (2) portfolio “excess growth rate” derived from diversification (component return volatilities and correlations). They apply this framework to investigate equity portfolio equal-weighting versus value-weighting, and to isolate the sources of the size effect and the value premium. They establish consistency in return measurements by matching rebalancing frequency and return measurement interval. Using monthly returns and firm characteristics for a broad sample of U.S. stocks over the period 1960 through 2009, they find that: Keep Reading

In their March 2011 paper entitled “The Shrinking Space for Anomalies”, George Jiang and Andrew Zhang investigate the robustness of ten well-known anomalies by iteratively “shrinking the stock space” in two ways to determine whether and how the anomalies really work. The ten anomaly variables are: size, book-to-market ratio, momentum, two liquidity measures, idiosyncratic volatility, accrual, capital expenditure, sales growth and net share issuance. The first way of “shrinking the stock space” involves: (1) ranking the universe of stocks by each of the ten anomaly variables into deciles; (2) iteratively trimming deciles from side of a variable distribution that a hedge portfolio would sell and the side that a hedge portfolio would buy; and, (3) retesting the strength of the anomaly associated with the variable after each iterative trimming. The second way of “shrinking the stock space” involves: (1) trimming from the sample stocks with the smallest market capitalizations and the most extreme book-to-market ratios until size, book-to-market and momentum no longer have significant four-factor alphas for value-weighting and equal equal-weighting (thereby “perfecting” the sample for the four-factor model); and, (2) retesting the strength of the anomalies associated with the other seven variables using the perfected sample. This approach obviates weaknesses in alpha measurement via the commonly applied but imperfect three-factor (market, size, book-to-market) and four-factor (plus momentum) risk models. Using firm characteristics and trading data for all non-financial NYSE, AMEX, and NASDAQ common stocks over the period July 1962 through December 2007, they find that: Keep Reading