A subscriber proposed four alternative ways of timing the U.S. stock market based on simple moving averages (SMA) of the market price-earnings ratio (P/E) , as follows:

1. 5-Year Binary – hold stocks (cash) when P/E is below (above) its 5-year SMA.
2. 10-Year Binary – hold stocks (cash) when P/E is below (above) its 10-year SMA.
3. 15-Year Binary – hold stocks (cash) when P/E is below (above) its 15-year SMA.
4. 5-Year Scaled – hold 100% stocks (cash) when P/E is five or more units below (above) its 5-year SMA. Between these levels, scale allocations linearly.

To obtain a sample long enough for testing these rules, we use the monthly U.S. data of Robert Shiller. While offering a very long history, this source has the disadvantage of blurring monthly data as averages of daily values. How well do these alternative timing strategies work for this dataset? Using monthly data for the S&P Composite Index, annual dividends, annual P/E and 10-year government bond yield since January 1871 and monthly 3-month U.S. Treasury bill (T-bill) yield as return on cash since January 1934, all through August 2018, we find that: Keep Reading

Do exchange-traded funds (ETF) that track major currencies support a relative momentum strategy? To investigate, we consider the following four ETFs:

Invesco DB US Dollar Bullish (UUP)
Invesco CurrencyShares Euro Currency (FXE)
Invesco CurrencyShares Japanese Yen (FXY)
WisdomTree Chinese Yuan Strategy (CYB)

We each month rank these ETFs based on past return over lookback intervals ranging from one to 12 months. We consider portfolios of past winners reformed monthly based on Top 1 and on equally weighted (EW) Top 2 and Top 3 ETFs. The benchmark portfolio is the equally weighted combination of all four ETFs. We present findings in formats similar to those used for the Simple Asset Class ETF Momentum Strategy and the Simple Asset Class ETF Value Strategy. Using monthly adjusted closing prices for the currency ETFs during March 2007 (when three become available) through August 2018, we find that: Keep Reading

The Mojena Market Timing strategy (Mojena), developed and maintained by professor Richard Mojena, is a method for timing the broad U.S. stock market based on a combination of many monetary, fundamental, technical and sentiment indicators to predict changes in intermediate-term and long-term market trends. He adjusts the model annually to incorporate new data. Professor Mojena offers a hypothetical backtest of the timing model since 1970 and a live investing test since 1990 based on the S&P 500 Index (with dividends). To test the robustness of the strategy’s performance, we consider a sample period commencing with inception of SPDR S&P 500 (SPY) as a liquid, low-cost proxy for the S&P 500 Index. As benchmarks, we consider both buying and holding SPY (Buy-and-Hold) and trading SPY with crash protection based on the 10-month simple moving average of the S&P 500 Index (SMA10). Using the trade dates from the Mojena Market Timing live test, daily dividend-adjusted closes for SPY and daily yields for 13-week Treasury bills (T-bills) from the end of January 1993 through August 2018 (over 25 years), we find that: Keep Reading

Some stock market experts cite the year (1, 2, 3 or 4) of the U.S. presidential term cycle as a useful indicator of U.S. stock market returns. Game theory suggests that presidents deliver bad news immediately after being elected and do everything in their power to create good news just before ensuing biennial elections. Are some presidential term cycle years reliably good or bad? If so, are these abnormal returns concentrated in certain quarters? Finally, what does the stock market do in the period immediately before and after a national election? Using daily and monthly S&P 500 Index levels from January 1950 through August 2018 (nearly 69 years and about 17 presidential terms) and focusing on “political quarters” (Feb-Apr, May-Jul, Aug-Oct and Nov-Jan), we find that: Keep Reading

Below is a weekly summary of our research findings for 9/10/18 through 9/14/18. These summaries give you a quick snapshot of our content the past week so that you can quickly decide what’s relevant to your investing needs.

Subscribers: To receive these weekly digests via email, click here to sign up for our mailing list. Keep Reading

The monthly Cass Freight Index is a “measure of North American freight volumes and expenditures. …Data within the Index includes all domestic freight modes and is derived from $25 billion in freight transactions processed by Cass annually on behalf of its client base of hundreds of large shippers. These companies represent a broad sampling of industries including consumer packaged goods, food, automotive, chemical, OEM, retail and heavy equipment. …Volumes represent the month in which transactions are processed by Cass, not necessarily the month when the corresponding shipments took place. The January 1990 base point is 1.00. …Each month’s volumes are adjusted to provide an average 21-day work month. Adjustments also are made to compensate for business additions/deletions to the volume figures.” Cass typically publishes the index level for a month by the middle of the following month. Does this index usefully anticipate economic trend and thereby U.S. stock market returns? To investigate, we relate index changes to SPDR S&P 500 (SPY) returns. Using monthly Cass Freight Index levels and monthly/daily dividend-adjusted SPY returns during January 1999 (limited by the freight index) through mid-August 2018, we find that: Keep Reading

The Inflation Forecast now incorporates actual total and core Consumer Price Index (CPI) data for August 2018. The actual total (core) inflation rate for August is slightly lower than (lower than) forecasted.

Is there a best way for investors to measure firm profitability for global stock selection? In their August 2018 paper entitled “Constructing a Powerful Profitability Factor: International Evidence”, Matthias Hanauer and Daniel Huber investigate which measure of firm profitability best predicts associated stock returns. They consider six measures: return on equity; gross profitability; operating profitability calculated in two ways; cash-based operating profitability (excluding accruals); and, cash-based gross profitability (also excluding accruals). They construct a long-short profitability factor for each measure and test its power to predict stock returns both standalone and in combination with other kinds of factors (market, size, book-to-market, momentum, investment and accruals) and the other profitability factors. Using monthly returns and annual accounting data for non-financial common stocks in 49 countries (excluding the U.S.) during July 1989 through June 2016, they find that: Keep Reading

How, and how well, do institutional equity traders manage global stock trading frictions? In the April 2018 draft of their paper entitled “Trading Costs”, Andrea Frazzini, Ronen Israel and Tobias Moskowitz examine the real-world trading frictions of a large trader. They define trading frictions as the difference in results between a theoretical portfolio with zero frictions and a practical tracking portfolio with frictions. They account for all components of trading frictions: broker commissions, bid-ask spreads and price impacts of trading. They record market price at trade initiation, volume traded and execution price for each share traded, as well as type of trade (buy long, buy-to-cover, sell long or sell short). They describe how frictions vary by trade type, stock characteristics, trade size, time and exchange. Based on preliminary findings, they devise and test out-of-sample a price impact model based on market conditions, stock characteristics and trade size calibrated to actual U.S. and international trades. Using $1.7 trillion of orders and trade execution data from a large institutional money manager spanning 21 developed equity markets during August 1998 through June 2016, they find that: Keep Reading

Rainer Zitelmann prefaces his 2018 book, The Wealth Elite: A Groundbreaking Study of the Psychology of the Super Rich, as follows: “For this book, I succeeded in convincing 45 wealthy people to talk to me. …Without exception, the interviewees were entrepreneurs or investors… The interviews were conducted in person between September 2015 and March 2016, and each lasted between one and two hours. …every interviewee (with one exception) took a personality test consisting of 50 questions. …This work explores the personalities and patterns of behaviour exhibited by wealthy individuals. …their answers to my questions clearly demonstrate that the personality traits and patterns of behaviour described in this book have played a significant role in their extraordinary economic success. However, this is a study based on methods of qualitative social research and, as such, the interview subjects do not constitute a representative sample. Above all, their answers were not tested against a control group consisting of non-wealthy individuals.” Based on the body of wealth creation research and the set of in-depth interviews/personality tests, he concludes that: Keep Reading