Big Ideas

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

Return-based Analysis of Demographics as Stock Market Predictor

Analyses such as those described in “Demographic Headwind for U.S. Stock Market?” and “Classic Research: Demography and the Stock Market” assess the impact of demographic changes on the stock market by focusing on market valuation as measured by price-earnings ratio (P/E). What story would a more direct analysis of demographics and stock market returns tell? To investigate, we: (1) collect historical U.S. age demographics; (2) construct an annual series of the ratio of middle-age cohort (ages 40–49) population to the old-age cohort (ages 60–69) population (designated M/O, similar to the metric described in “Demographic Headwind for U.S. Stock Market?”) to capture the joint behavior of presumed equity investors and equity disinvestors; and, (3) relate M/O to annual U.S. stock market returns. Using estimated annual (July1) age demographics for 1900-2009, 2010 census age demographics, annual S&P 500 Index returns (June 30 to June 30) for 1950 through 2011, annual Dow Jones Industrial Average (DJIA) returns (June 30 – June 30) for 1929 through 2011 and annual Consumer Price Index (CPI) data (June) for 1913 through 2011, we find that: Keep Reading

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

Notes on Variability of Stock Market Returns

How should the variability of stock market returns shape the outlooks of short-term traders and long-term investors? How strong is the tailwind of the general drift upward in stock prices? How powerful is the turbulence of variability? Does the tailwind ever overpower the turbulence? Using weekly closes for the S&P 500 Index during for January 1950 through May 2011 (3,204 weeks or about 61 years), we find 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

Current Momentum Winners

ETF Momentum Signal
for November 2015 (Final)

Winner ETF

Second Place ETF

Third Place ETF

Gross Compound Annual Growth Rates
(Since August 2006)
Top 1 ETF Top 2 ETFs
12.2% 12.5%
Top 3 ETFs SPY
12.8% 7.4%
Strategy Overview
Current Value Allocations

ETF Value Signal
for November 2015 (Final)





The asset with the highest allocation is the holding of the Best Value strategy.
Gross Compound Annual Growth Rates
(Since September 2002)
Best Value Weighted 60-40
12.8% 9.9% 8.0%
Strategy Overview
Recent Research
Popular Posts
Popular Subscriber-Only Posts