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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.

Gold Return vs. Change in M2

A subscriber requested testing of the relationship between U.S. M2 Money Stock and gold, offered in one form via “Why Gold May Be Looking Cheap”: “[O]ne measure I’ve found useful is the ratio of the price of gold to the U.S. money supply, measured by M2, which includes cash as well as things like money market funds, savings deposits and the like. The logic is that over the long term the price of gold should move with the change in the supply of money… That equilibrium level is also relevant for future price action. When the ratio is low, defined as 25% below equilibrium, the medium 12-month return has been over 12%. Conversely, when the ratio is high, defined as 25% above equilibrium, the 12-month median return has been -6%. …This measure can be refined further. [G]old tends to trade at a higher ratio to M2 when inflation is elevated.” Because it defines specific valuation thresholds, this approach is susceptible to data snooping bias in threshold selection. We consider an alternative setup that relates monthly change in M2 to monthly gold return. We also consider the effect of inflation on this relationship. Using monthly seasonally adjusted M2 and end-of-month London gold price fix during January 1976 (to ensure a free U.S. gold market) through June 2018 (510 months), we find that: Keep Reading

Unemployment Claims Reports and Near-term Stock Market Returns

Each week the media report U.S. initial and continued unemployment claims (seasonally adjusted) as a potential indicator of future U.S. stock market returns. Do these indicators move the market? To investigate, we focus on weekly changes in unemployment claims during a period of “modern” information dissemination to release-day and next-week stock market returns. By modern period, we mean the history of S&P Depository Receipts (SPY), a proxy for the U.S. stock market. Using relevant news releases and archival data as available from the Department of Labor (DOL) and dividend-adjusted weekly and daily opening and closing levels for SPY during late January 1993 through mid-July 2018 (1,330 weeks), we find that:

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ADP Employment Report and Stock Returns

Since May 2006, the ADP National Employment Report has released a monthly estimate of U.S. nonfarm private sector employment growth using actual payroll data. The report is designed “to predict private-sector employment prior to the release of the CES [Bureau of Labor Statistics’ monthly Current Employment Statistics survey] report.” Do the ADP estimates affect or predict U.S. stock market returns on the release day or over the next month? To investigate, we consider both as-released (from press releases) and as-revised ADP data (from the extended downloadable historical dataset). Using monthly ADP report release dates and as-released employment growth estimates commencing April 2006, historically modeled ADP employment growth estimates commencing April 2002 and contemporaneous daily opening/closing and monthly dividend-adjusted closing prices of SPDR S&P 500 (SPY) through early July 2018, we find that: Keep Reading

Expert Estimates of 2018 Country Equity Risk Premiums and Risk-free Rates

What are current estimates of equity risk premiums (ERP) and risk-free rates around the world? In their April 2018 paper entitled “Market Risk Premium and Risk-free Rate Used for 59 Countries in 2018: A Survey”, Pablo Fernandez, Vitaly Pershin and Isabel Acin summarize results of a March 2018 email survey of international finance/economic professors, analysts and company managers “about the Risk Free Rate and the Market Risk Premium (MRP) used to calculate the required return to equity in different countries.” Results are in local currencies. Based on 5,173 specific and credible responses spanning 59 countries with more than five such responses, they find that: Keep Reading

Chemical Activity Barometer as Stock Market Trend Indicator

A subscriber proposed: “It would be interesting to do an analysis of the Chemical Activity Barometer [CAB] to see if it has predictive value for the stock market. Either [look] at stock prices when [CAB makes] a two percent pivot down [from a preceding 6-month high] as a sell signal and one percent pivot up as a buy signal…[or when CAB falls] below its x month moving average.” The American Chemistry Council claims that CAB “determines turning points and likely future trends of the wider U.S. economy” and leads other commonly used economic indicators. To investigate its usefulness for U.S. stock market timing, we consider the two proposed strategies, plus two benchmarks, as follows:

  1. CAB SMAx Timing – hold stocks (the risk-free asset) when monthly CAB is above (below) its simple moving average (SMA). We consider SMA measurement intervals ranging from two months (SMA2) to 12 months (SMA12).
  2. CAB Pivot Timing – hold stocks (the risk-free asset) when monthly CAB most recently crosses 1% above (2% below) its maximum value over the preceding six months. We look at a few alternative pivot thresholds.
  3. Buy and Hold (B&H) – buy and hold the S&P Composite Index.
  4. Index SMA10 – hold stocks (the risk-free asset) when the S&P Composite Index is above (below) its 10-month SMA (SMA10), assuming signal execution the last month of the SMA measurement interval.

Since CAB data extends back to 1912, we use Robert Shiller’s S&P Composite Index to represent the U.S. stock market. For the risk-free rate, we use the 3-month U.S. Treasury bill (T-bill) yield since 1934. Prior to 1934, we use Shiller’s long interest rate minus 1.59% (the average 10-year term premium since 1934). We assume a constant 0.25% friction for switching between stocks and T-bills as signaled. We focus on number of switches, compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key performance metrics. Using monthly data for CAB, the S&P Composite Stock Index, estimated dividends for the stocks in this index (for calculation of total returns) and estimated long interest rate during January 1912 through December 2017 (about 106 years), and the monthly T-bill yield since January 1934, we find that: Keep Reading

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

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:

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Expert Estimates of 2017 Country Equity Risk Premiums and Risk-free Rates

What are current estimates of equity risk premiums (ERP) and risk-free rates around the world? In their April 2017 paper entitled “Discount Rate (Risk-Free Rate and Market Risk Premium) Used for 41 Countries in 2017: A Survey”, Pablo Fernandez, Vitaly Pershin and Isabel Acin summarize results of a March 2017 email survey of international finance/economic professors, analysts and company managers “about the Market Risk Premium (MRP) or Equity Premium used to calculate the required return to equity in different countries.” Based on 4,368 specific and credible responses spanning 41 countries with at least 25 such responses, they find that: Keep Reading

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