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Big Ideas

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

Demographic Headwind for U.S. Stock Market?

Will disinvestment of the baby boom generation retard U.S. equities? In their August 2011 letter entitled “Boomer Retirement: Headwinds for U.S. Equity Markets?”, flagged by a reader, Zheng Liu and Mark Spiegel revisit the relationship between U.S. age demographics and U.S. equity valuation as indicated by the lagged price-earnings ratio (P/E). They calculate P/E based on year-end level of the S&P 500 Index adjusted for inflation and inflation-adjusted S&P 500 earnings over the prior 12 months. They specify a critical demographic metric, M/O, based on the ratio of the middle-age cohort (ages 40–49) to the old-age cohort (ages 60–69), epitomizing equity investors and equity disinvestors, respectively. Using annual data for 1954 through 2010, they find that: Keep Reading

Effects and Prediction of Extreme Returns

Are financial market returns from extreme outlier days mostly good or bad for investors? Is the occurrence of such days usefully predictable? In his August 2011 paper entitled “Where the Black Swans Hide & The 10 Best Days Myth”, Mebane Faber examines the effects and predictability of daily market return outliers. Using daily returns for the broad U.S. stock market for September 1928 through December 2010 and shorter samples through 2010 for 15 other country stock markets (as in “The (Worldwide) Futility of Market Timing?”), he finds that: Keep Reading

Technical Trend-following: Fighting the Last War?

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

Overview of Financial Market Regime Change

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

Model What You Trade?

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

Inside the Realm of the Black Swan

“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

Post-1960 Financial Cycles

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

Enhancing/Streamlining Asset Rotation

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

Capital Management with Clustered Signals

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

A Few Notes on Super Boom

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

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