Economic Indicators

The U.S. economy is a very complex system, with indicators therefore ambiguous and difficult to interpret. To what degree do macroeconomics and the stock market go hand-in-hand, if at all? Do investors/traders: (1) react to economic readings; (2) anticipate them; or, (3) just muddle along, mostly fooled by randomness? These blog entries address relationships between economic indicators and the stock market.

Page 1 of 1412345678910...Last »

The Decision Moose Asset Allocation Framework

A reader suggested a review of the Decision Moose asset allocation framework of William Dirlam. “Decision Moose is an automated framework for making intermediate-term investment decisions.” Decision Moose focuses on asset class momentum, as augmented by monetary policy, exchange rate and interest rate indicators. Its signals tell followers when to switch from one index fund to another among nine encompassing a broad range of asset classes, including equity indexes for several regions of the globe. The trading system is a long-only approach that allocates 100% of funds to the index “having the highest probability of price appreciation.” The site includes a history of switch recommendations since the end of August 1996, with gross performance. To evaluate Decision Moose, we assume that switches and associated trading returns are as described (out of sample, not backtested) and compare the returns to those for the dividend-adjusted S&P 500 Depository Receipts (SPY) over the same intervals. Using Decision Moose signals and performance data during 8/30/96 through 6/5/15 (nearly 19 years), we find that: Keep Reading

Inflation Forecast Update

The Inflation Forecast now incorporates actual total and core Consumer Price Index (CPI) data for May 2015. The actual total (core) inflation rate for May is a little higher than (about the same as) forecasted.

A Few Notes on Invest with the Fed

In the introduction to their 2015 book entitled Invest with the Fed: Maximizing Portfolio Performance by Following Federal Reserve Policy, authors Robert Johnson, Gerald Jensen and Luis Garcia-Feijoo state: “Our purpose in writing this book is to provide a general overview of the Fed’s role in the financial markets, but, more important, to offer investors a road map that can be used in designing an investment portfolio that takes account of Fed policy. In detailing our road map for investors, we offer a rationale for each investment strategy along with empirical evidence supporting the efficacy of the strategy. Most important, the recommended strategies come with clear explanations and easy-to-follow descriptions of the processes needed to execute the strategies.” The essential Fed policy discriminator they use is whether monetary conditions are expansive (decreasing discount rate and decreasing federal funds rate), restrictive (increasing discount rate and increasing federal funds rate) or indeterminate (one rate increasing and the other decreasing). Based on their research, they conclude that: Keep Reading

KCFSI as a Stock Market Return Predictor

A subscriber suggested the Kansas City Financial Stress Index (KCFSI) as a potential U.S. stock market return predictor. This index “is a monthly measure of stress in the U.S. financial system based on 11 financial market variables. A positive value indicates that financial stress is above the long-run average, while a negative value signifies that financial stress is below the long-run average. Another useful way to assess the current level of financial stress is to compare the index to its value during past, widely recognized episodes of financial stress.” The paper “Financial Stress: What Is It, How Can It Be Measured, and Why Does It Matter?” describes the 11 financial inputs for KCFSI and its methodology, which involves principal component analysis and normalization. Is it useful for U.S. stock market investors? To investigate, we relate S&P 500 Index returns to values of KCFSI. Since KCFSI releases occur about eight days after ends of measured months, we use stock market data for the close on the eighth of each month (or the next trading day if the eighth is not a trading day). Using monthly data for KCFSI and the S&P 500 Index during February 1990 through March 2015 (302 months), we find that: Keep Reading

Dollar-Euro Exchange Rate, U.S. Stocks and Gold

Do changes in the dollar-euro exchange rate reliably interact with the U.S. stock market and gold? For example, do declines in the dollar relative to the euro indicate increases in the dollar value of hard assets? Are the interactions coincident or exploitably predictive? To investigate, we relate changes in the dollar-euro exchange rate to returns for U.S. stock indexes and spot gold. Using end-of-month and end-of-week values of the dollar-euro exchange rate, levels of the S&P 500 Index and Russell 2000 Index and spot prices for gold during January 1999 (limited by the exchange rate series) through February 2015, we find that: Keep Reading

Year-end Global Growth and Future Asset Class Returns

Does fourth quarter global economic data set the stage for asset class returns the next year? In their February 2015 paper entitled “The End-of-the-year Effect: Global Economic Growth and Expected Returns Around the World”, Stig Møller and Jesper Rangvid examine relationships between level of global economic growth and future asset class returns, focusing on growth at the end of the year. Their principle measure of global economic growth is the equally weighted average of quarterly OECD industrial production growth in 12 developed countries. They perform in-sample tests 30 countries and out-of-sample tests for these same 12 countries (for which more data are available). Out-of-sample tests: (1) generate initial parameters from 1970 through 1989 data for testing during 1990 through 2013 period; and, (2) insert a three-month delay between economic growth data and subsequent return calculations to account for publication lag. Using global industrial production growth as specified, annual total returns for 30 country, two regional and world stock indexes, currency spot and one-year forward exchange rates relative to the U.S. dollar, spot prices on 19 commodities, total annual returns for a global government bond index and a U.S. corporate bond index, and country inflation rates as available during 1970 through 2013, they find that: Keep Reading

Credit Risk Premium Magnitude and Dynamics

Is the reward for holding risky bonds material and distinct from the reward for holding stocks and the reward for holding longer term bonds? In their February 2015 paper entitled “Credit Risk Premium: Its Existence and Implications for Asset Allocation”, Attakrit Asvanunt and Scott Richardson measure and explore the predictability and diversification power of the credit (or default) risk premium associated with corporate bonds. They focus on the premium associated with creditworthiness of bonds by first removing the influence of duration/interest rates. They also test whether the credit risk premium diversifies the equity risk premium and the bond term premium. Using data for U.S. corporate bonds, the U.S. stock market, U.S. Treasury securities and economic indicators during 1927 through 2014 and for credit default swaps (CDS) during 2004 through 2014, they find that: Keep Reading

Gas Prices and Future Stock Market Returns

Some experts argue that high (low) gasoline prices mean that consumers must allocate more (less) spending power to fuel, and therefore less (more) to other industries and stocks. Do data support this argument? To check, we relate U.S. stock market returns to changes in U.S. gasoline price changes. Using weekly average retail prices for regular gasoline in the U.S. and contemporaneous levels of the S&P 500 Index from late August 1990 through early February 2015 (1,279 weeks, with a six-week gap in gas prices at the turn of 1990 and a one-week gap in the S&P 500 Index in 2001), we find that: Keep Reading

Mojena Market Timing Model

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 11 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 year by year. 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 availability of SPDR S&P 500 (SPY) as a conveniently investable 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) over the period 1/29/93 through January 2015 (22 years), we find that: Keep Reading

Do Any Style ETFs Reliably Lead or Lag the Market?

Do any of the various U.S. stock market size and value/growth styles systematically lead or lag the overall market, perhaps because of some underlying business/economic cycle? To investigate, we consider the the following six exchange-traded funds (ETF) that cut across capitalization (large, medium and small) and value versus growth:

iShares Russell 1000 Value Index (IWD) – large capitalization value stocks.
iShares Russell 1000 Growth Index (IWF) – large capitalization growth stocks.
iShares Russell Midcap Value Index (IWS) – mid-capitalization value stocks.
iShares Russell Midcap Growth Index (IWP) – mid-capitalization growth stocks.
iShares Russell 2000 Value Index (IWN) – small capitalization value stocks.
iShares Russell 2000 Growth Index (IWO) – small capitalization growth stocks.

Using monthly dividend-adjusted closing prices for the style ETFs and S&P Depository Receipts (SPY) over the period August 2001 through December 2014 (161 months, limited by data for IWS/IWP), we find that: Keep Reading

Page 1 of 1412345678910...Last »
Login
Current Momentum Winners

ETF Momentum Signal
for July 2015 (Final)

Winner ETF

Second Place ETF

Third Place ETF

Gross Compound Annual Growth Rates
(Since August 2006)
Top 1 ETF Top 2 ETFs
13.8% 14.1%
Top 3 ETFs SPY
14.0% 7.5%
Strategy Overview
Current Value Allocations

ETF Value Signal
for 3rd Quarter 2015 (Final)

Cash

IEF

LQD

SPY

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
13.4% 9.4% 8.4%
Strategy Overview
Recent Research
Popular Posts
Popular Subscriber-Only Posts