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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.

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Do Any Sector ETFs Reliably Lead or Lag the Market?

Do any of the major U.S. stock market sectors systematically lead or lag the overall market, perhaps because of some underlying business/economic cycle? To investigate, we examine the behaviors of the nine sectors defined by the Select Sector Standard & Poor’s Depository Receipts (SPDR) exchange traded funds (ETF), all of which started trading in December 1998:

Materials Select Sector SPDR (XLB)
Energy Select Sector SPDR (XLE)
Financial Select Sector SPDR (XLF)
Industrial Select Sector SPDR (XLI)
Technology Select Sector SPDR (XLK)
Consumer Staples Select Sector SPDR (XLP)
Utilities Select Sector SPDR (XLU)
Health Care Select Sector SPDR (XLV)
Consumer Discretionary Select SPDR (XLY)

Using monthly dividend-adjusted closing prices for these ETFs, along with contemporaneous data for SPDR S&P 500 (SPY) as a benchmark, during December 1998 through December 2017 (229 months), we find that: Keep Reading

Combining Market, Unemployment and Interest Rate Trends

In reaction to “Combine Market Trend and Economic Trend Signals?”, a subscriber suggested adding an interest rate trend signal to those for the U.S. stock market and U.S. unemployment rate for the purpose of timing the S&P 500 Index (SP500). To investigate, we look at combining:

We consider scenarios when the SP500 trend is positive, the UR trend is positive, the T-bill trend is positive, at least one trend is positive (>=1), at least two trends are positive (>=2) or all three trends are positive (All). For total return calculations, we adjust the SP500 monthly with estimated dividends from the Shiller dataset. When not in the index, we assume return on cash from the broker is the specified T-bill yield. We focus on gross compound annual growth rate (CAGR), maximum drawdown (MaxDD) and annual Sharpe ratio as key performance metrics. We use the average monthly T-bill yield during a year as the risk-free rate for that year in Sharpe ratio calculations. While we do not apply any stocks-cash switching frictions, we do calculate the number of switches for each scenario. Using the specified monthly data through October 2017, we find that: Keep Reading

Combine Market Trend and Economic Trend Signals?

A subscriber requested review of an analysis concluding that combining economic trend and market trend signals enhances market timing performance. Specifically, per the example in the referenced analysis, we look at combining:

  • The 10-month simple moving average (SMA10) for the broad U.S. stock market. The trend is positive (negative) when the market is above (below) its SMA10.
  • The 12-month simple moving average (SMA12) for the U.S. unemployment rate (UR). The trend is positive (negative) when UR is below (above) its SMA12.

We consider scenarios when the stock market trend is positive, the UR trend is positive, either trend is positive or both trends are positive. We consider two samples: (1) dividend-adjusted SPDR S&P 500 (SPY) since inception at the end of January 1993 (24 years); and, (2) the S&P 500 Index (SP500) since January 1950, adjusted monthly by estimated dividends from the Shiller dataset, as a longer-term robustness test (67 years). Per the referenced analysis, we use the seasonally adjusted civilian UR, which comes ultimately from the Bureau of Labor Statistics (BLS). BLS generally releases UR monthly within a few days after the end of the measured month. When not in the stock market, we assume return on cash from the broker is the yield on 3-month U.S. Treasury bills (T-bill). We focus on gross compound annual growth rate (CAGR), maximum drawdown (MaxDD) and annual Sharpe ratio as key performance metrics. We use the average monthly T-bill yield during a year as the risk-free rate for that year in Sharpe ratio calculations. While we do not apply any stocks-cash switching frictions, we do calculate the number of switches for each scenario. Using the specified monthly data through October 2017, we find that: Keep Reading

The Decision Moose Asset Allocation Framework

A reader requested review of the Decision Moose asset allocation framework. Decision Moose is “an automated stock, bond, and gold momentum model developed in 1989. Index Moose uses technical analysis and exchange traded index funds (ETFs) to track global investment flows in the Americas, Europe and Asia, and to generate a market timing signal.” The trading system allocates 100% of funds to the index projected to perform best. 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 dividend-adjusted SPDR S&P 500 (SPY) over the same intervals. Using Decision Moose signals/performance data and contemporaneous SPY prices during 8/30/96 through 11/3/17 (21+ years), we find that: Keep Reading

OFR FSI as Stock Market Return Predictor

Is the Office of Financial Research Financial Stress Index (OFR FSI), described by Phillip Monin in his October 2017 paper entitled “The OFR Financial Stress Index”, useful as a U.S. stock market return predictor? OFR FSI is a daily snapshot of global financial market stress, distilling more than 30 indicators via a dynamic weighting scheme. The index drops and adds indicators over time as some become obsolete and new ones become available. Unlike some other financial stress indicators, past OFR FSI series values do not change due to any periodic renormalization and are therefore readily suitable for backtesting. To investigate OFR FSI power to predict U.S. stock market returns, we relate the level of and change in OFR FSI to SPDR S&P 500 (SPY) returns. Using daily and monthly values of OFR FSI and SPY total returns during January 2000 (OFR FSI inception) through October 2017, we find that:

Keep Reading

Consumer Credit and Stock Returns

Does expansion (contraction) of consumer credit indicate growing (shrinking) corporate sales, earnings and ultimately stock prices? The Federal Reserve collects and publishes U.S. consumer credit data on a monthly basis with a delay of about five weeks. Using monthly seasonally adjusted total U.S. consumer credit for January 1943 through June 2017 and monthly Dow Jones Industrial Average (DJIA) closes for January 1943 through August 2017 (almost 75 years), we find that: Keep Reading

Federal Regulations and Stock Market Returns

Do changes in the U.S. federal regulatory burden predict U.S. stock market returns? To check, we consider two measures of the regulatory burden:

  1. Annual number of pages in the Federal Register (FR) during 1936-2016 – “…in which all newly proposed rules are published along with final rules, executive orders, and other agency notices—provides a sense of the flow of new regulations issued during a given period and suggests how the regulatory burden will grow as Americans try to comply with the new mandates.”
  2. Annual number of pages in the Code of Federal Regulations (CFR) during 1975-2016 – “…the codification of all rules and regulations promulgated by federal agencies. Its size…provides a sense of the scope of existing regulations with which American businesses, workers, and consumers must comply.

Specifically, we relate annual changes in these measures to annual returns for the S&P 500 Index. Using the specified regulatory data and annual S&P 500 Index total returns during 1929 through 2016, we find that: Keep Reading

Do Hedge Funds Effectively Exploit Real-time Economic Data?

Do hedge funds demonstrate the exploitability of real-time economic data? In their June 2017 paper entitled “Can Hedge Funds Time the Market?”, Michael Brandt, Federico Nucera and Giorgio Valente evaluate whether all or some equity hedge funds vary equity market exposure in response to real-time economic data, and (if so) whether doing so improves their performance. Their proxy for real-time economic data available to a sophisticated investor is the 20-day moving average of an economic growth index derived from principal component analysis of purely as-released industrial output, employment and economic sentiment. They relate this data to hedge fund performance by:

  1. Applying a linear regression to measure the sensitivity (economic data beta) of each hedge fund to monthly changes in economic data.
  2. Sorting funds into tenths (deciles) based on economic data beta and calculating average next-month equally weighted risk-adjusted performance (7-factor alpha) by decile. The seven monthly factors used for risk adjustment are: equity market excess return; equity size factor; change in 10-year U.S. Treasury note (T-note) yield; change in yield spread between BAA bonds and T-notes; and trend following factor for bonds, currencies and commodities.

Using sample of 2,224 dead and alive equity hedge funds having at least 36 months of net-of-fee returns and average assets under management of at least $10 million, and contemporaneous daily values of the economic growth index, during January 1994 through December 2014, they find that:

Keep Reading

Consumer Inflation Expectations Predictive?

A subscriber noted and asked: “Michigan (at one point) claimed that the inflation expectations part of their survey of consumers was predictive. That was from a paper long ago. I wonder if it is still true.” To investigate, we relate “Expected Changes in Prices During the Next Year” (expected annual inflation) from the monthly final University of Michigan Survey of Consumers and actual U.S. inflation data based on the monthly non-seasonally adjusted consumer price index (U.S. All items, 1982-84=100). The University of Michigan releases final survey data near the end of the measured month. We consider two relationships:

  • Expected annual inflation versus one-year hence actual annual inflation.
  • Monthly change in expected  annual inflation versus monthly change in actual annual inflation.

As a separate (investor-oriented) test, we relate monthly change in expected annual inflation to next-month total returns for SPDR S&P 500 (SPY) and iShares Barclays 20+ Year Treasury Bond (TLT). Using monthly survey/inflation data since March 1978 (limited by survey data) and monthly SPY and TLT total returns since July 2002 (limited by TLT), all through May 2017, we find 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. Per the KCFSI release schedule, we use the market close on the first trading day of the month after the 7th for calculations. Using monthly data for KCFSI and the S&P 500 Index during February 1990 through early May 2017 (327 months), we find that: Keep Reading

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