Economic Indicators

A subscriber requested, as in “Combine Market Trend and Economic Trend Signals?”, testing of a strategy that combines: (1) U.S. Economic Policy Uncertainty (EPU) Index, as described and tested separately in “Economic Policy Uncertainty and the Stock Market”; and, (2) U.S. stock market trend. We consider two such combinations. The first combines:

• 10-month simple moving average (SMA10) for the broad U.S. stock market as proxied by the S&P 500 Index. The trend is bullish (bearish) when the index is above (below) its SMA10 at the end of last month.
• Sign of the change in EPU Index last month. A positive (negative) sign is bearish (bullish).

The second combines:

• SMA10 for the S&P 500 Index as above.
• 12-month simple moving average (SMA12) for the EPU Index. The trend is bullish (bearish) when the EPU Index is below (above) its SMA12 at the end of last month.

We consider alternative timing strategies that hold SPDR S&P 500 (SPY) when: the S&P 500 Index SMA10 is bullish; the EPU Index indicator is bullish; either indicator for a combination is bullish; or, both indicators for a combination are bullish. When not in SPY, we use the 3-month U.S. Treasury bill (T-bill) yield as the return on cash, with 0.1% switching frictions. We assume all indicators for a given month can be accurately estimated for signal execution at the market close the same month. We compute average net monthly return, standard deviation of monthly returns, net monthly Sharpe ratio (with monthly T-bill yield as the risk-free rate), net compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key strategy performance metrics. We calculate the number of switches for each scenario to indicate sensitivities to switching frictions and taxes. Using monthly values for the EPU Index, the S&P 500 Index, SPY and T-bill yield during January 1993 (inception of SPY) through September 2020, we find that:

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Does quantified uncertainty in government economic policy reliably predict stock market returns? To investigate, we consider the U.S. Economic Policy Uncertainty (EPU) Index, created by Scott Baker, Nicholas Bloom and Steven Davis and constructed from three components:

1. Coverage of policy-related economic uncertainty by prominent newspapers.
2. Number of temporary federal tax code provisions set to expire in future years.
3. Level of disagreement in one-year forecasts among participants in the Federal Reserve Bank of Philadelphia’s Survey of Professional Forecasters for both (a) the consumer price index (CPI) and (b) purchasing of goods and services by federal, state and local governments.

They normalize each component by its own standard deviation prior to 2012 and then compute a weighted average of components, assigning a weight of one half to news coverage and one sixth each to tax code uncertainty, CPI forecast disagreement and government purchasing forecast disagreement. They update the index monthly at the beginning of the following month, potentially revising recent months. Using monthly levels of the EPU Index and the S&P 500 Index during January 1985 through September 2020, we find that: Keep Reading

How do different asset classes interact with the Chinese yuan-U.S. dollar exchange rate? To investigate, we consider relationships between WisdomTree Chinese Yuan Strategy (CYB) and the exchange-traded fund (ETF) asset class proxies used in the Simple Asset Class ETF Momentum Strategy (SACEMS) and the Simple Asset Class ETF Value Strategy (SACEVS) at a monthly measurement frequency. Using monthly dividend-adjusted closing prices for CYB and the asset class proxies during May 2008 (when CYB is first available) through August 2020 (147 months), we find that: Keep Reading

How do different asset classes interact with euro-U.S. dollar exchange rate? To investigate, we consider relationships between Invesco CurrencyShares Euro Currency (FXE) and the exchange-traded fund (ETF) asset class proxies used in the Simple Asset Class ETF Momentum Strategy (SACEMS) or the Simple Asset Class ETF Value Strategy (SACEVS) at a monthly measurement frequency. Using monthly dividend-adjusted closing prices for FXE and the asset class proxies since February 2006 as available through July 2020, we find that: Keep Reading

How do different asset classes interact with U.S. dollar valuation? To investigate, we consider relationships between Powershares DB US Dollar Index Bullish Fund (UUP) and the exchange-traded fund (ETF) asset class proxies used in the Simple Asset Class ETF Momentum Strategy (SACEMS) or the Simple Asset Class ETF Value Strategy (SACEVS) at a monthly measurement frequency. Using monthly dividend-adjusted closing prices for UUP and the asset class proxies since March 2007 as available through July 2020, we find that: Keep Reading

Do interest rate effects explain/predict the poor performance of value stocks over the past decade, and especially during 2017 through early 2020? In their May 2020 paper entitled “Value and Interest Rates: Are Rates to Blame for Value’s Torments?”, Thomas Maloney and Tobias Moskowitz investigate interactions between equity value factors and the interest rate environment. They first examine theoretical relationships and then explore relationships between several ways to measure the U.S. equity value premium and interest rates empirically, including interest rate level, change in short-term rates, change in long-term rates and slope of the yield curve. They look at subperiods and some international evidence. Finally, they assess ability of interest rate variables to predict the value premium and thereby inform factor timing strategies. Using U.S. interest rate and firm/stock data inputs for several ways of estimating the value premium as available since January 1954, and similar data for Japan, Germany and the UK since 1988, all through December 2019, they find that: Keep Reading

In response to “Exploiting Chicago Fed NFCI Predictive Power”, which tests practical use of the Federal Reserve Bank of Chicago’s National Financial Conditions Index (NFCI) for U.S. stock market timing, a subscriber suggested combining this strategy with stock market trend as in “Combine Market Trend and Economic Trend Signals?”. To investigate, we use the 40-week simple moving average (SMA40) for the S&P 500 Index to measure stock market trend. We then test two strategies that are each week in SPDR S&P 500 (SPY) or cash (U.S. Treasury bills, T-bills), as follows:

1. Combined (< Mean): hold SPY (cash) when either: (a) prior-week S&P 500 Index is above (below) its SMA40; or, (b) prior-week change in NFCI is below (above) its mean since since the beginning of 1973.
2. Combined (< Mean+SD): hold SPY (cash) when either: (a) prior-week S&P 500 Index is above (below) its SMA40; or, (b) prior-week change in NFCI is below (above) its mean plus one standard deviation of weekly changes in NFCI since the beginning of 1973.

The return week is Wednesday open to Wednesday open (Thursday open when the market is not open on Wednesday) per the NFCI release schedule. SMA40 calculations are Tuesday close to Tuesday close to ensure timely availability of signals before any Wednesday open trades. We assume SPY-cash switching frictions are a constant 0.1% over the sample period. Using weekly NFCI data since January 1973, weekly S&P 500 Index levels since April 1992, weekly dividend-adjusted opens of SPY and weekly T-bill yield since February 1993 (limited by SPY), all as specified through April 2020, we find that:

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In reaction to “Federal Reserve Holdings and the U.S. Stock Market”, a subscriber suggested analysis of market reactions to announcements (starts/ends) of major Federal Reserve System interventions, such as the series of quantitative easing (QE) initiatives. Reactions to such announcement should precede changes in actual holdings. To investigate, we look at cumulative returns of SPDR S&P 500 (SPY) and iShares Barclays 20+ Year Treasury Bond (TLT) during the 30 trading days after each of the following announcements:

• 11/25/08: QE-1 initiated
• 3/16/09: QE-1 expanded
• 3/31/10: QE-1 terminated
• 11/3/10: QE-2 initiated
• 6/29/12: QE-2 terminated
• 9/13/12: QE-3 initiated
• 12/12/12: QE-3 expanded
• 10/29/14: QE-3 terminated
• 3/23/20: “QE-4” initiated

Using daily dividend-adjusted prices for SPY and TLT spanning these dates, we find that: Keep Reading

“Chicago Fed ANFCI as U.S. Stock Market Predictor” suggests that weekly change in the Federal Reserve Bank of Chicago’s Adjusted National Financial Conditions Index (ANFCI) may be a useful indicator of future U.S. stock market returns. We test its practical value via two strategies that are each week in SPDR S&P 500 (SPY) when prior change in ANFCI is favorable and in cash (U.S. Treasury bills, T-bills) when prior change in ANFCI is unfavorable, as follows:

1. Change in ANFCI < Mean [aggressive]: hold SPY (cash) when prior-week change in ANFCI is below (above) its mean since the beginning of 1973, providing an initial 20-year calculation interval.
2. Change in ANFCI < Mean+SD [conservative]: hold SPY (cash) when prior-week change in ANFCI is below (above) its mean plus one standard deviation of weekly changes in ANFCI since the beginning of 1973.

The return week is Wednesday open to Wednesday open (Thursday open when the market is not open on Wednesday) per the ANFCI release schedule. We assume SPY-cash switching frictions are a constant 0.1% over the sample period. We use buying and holding SPY as the benchmark. Using weekly levels of ANFCI since January 1973 and weekly dividend-adjusted opens of SPY and T-bills since February 1993 (limited by SPY), all through April 2020, we find that: Keep Reading

Referring to “Chicago Fed NFCI as U.S. Stock Market Predictor”, a subscriber asked whether the Federal Reserve Bank of Chicago’s Adjusted National Financial Conditions Index (ANFCI) may work better as a U.S. stock market predictor. ANFCI “isolates a component of financial conditions uncorrelated with economic conditions to provide an update on financial conditions relative to current economic conditions.” Positive (negative) values of the ANFCI are associated with financial conditions that are tighter (looser) than than those suggested by prevailing macroeconomic conditions, with degree measured in standard deviations from the mean. The Chicago Fed releases ANFCI each week as of Friday on the following Wednesday at 8:30 a.m. ET (or Thursday if Wednesday is a holiday), renormalized such that the full series always has a mean of zero and a standard deviation of one (thereby each week changing past values, perhaps even changing their signs). To investigate its usefulness as a U.S. stock market predictor, we relate ANFCI and changes in ANFCI to future S&P 500 Index returns. Using weekly levels of ANFCI and weekly closes of the S&P 500 Index during January 1971 (limited by ANFCI) through April 2020, we find that: Keep Reading