Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for September 2022 (Final)
Cash TLT LQD SPY

Momentum Investing Strategy (Strategy Overview)

Allocations for September 2022 (Final)
1st ETF 2nd ETF 3rd ETF

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.

Asset Class ETF Interactions with the Yuan

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

Interest Rates and the Equity Value Premium

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

Combining Market Trend and Chicago Fed NFCI Signals

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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Returns After QE Announcements

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

Exploiting Chicago Fed ANFCI Predictive Power

“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

Chicago Fed ANFCI as U.S. Stock Market Predictor

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

Exploiting Chicago Fed NFCI Predictive Power

“Chicago Fed NFCI as U.S. Stock Market Predictor” suggests that weekly change in the Federal Reserve Bank of Chicago’s National Financial Conditions Index (NFCI) 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 NFCI is favorable and in cash (U.S. Treasury bills, T-bills) when prior change in NFCI is unfavorable, as follows:

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

Chicago Fed NFCI as U.S. Stock Market Predictor

A subscriber suggested that the Federal Reserve Bank of Chicago’s National Financial Conditions Index (NFCI) may be a useful U.S. stock market predictor. NFCI “provides a comprehensive weekly update on U.S. financial conditions in money markets, debt and equity markets, and the traditional and ‘shadow’ banking systems.” It consists of 105 inputs, including the S&P 500 Implied Volatility Index (VIX) and Senior Loan Officer Survey results. Positive (negative) values indicate tight (loose) financial conditions, with degree measured in standard deviations from the mean. The Chicago Fed releases NFCI 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 NFCI and changes in NFCI to future S&P 500 Index returns. Using weekly levels of NFCI and weekly closes of the S&P 500 Index during January 1971 (limited by NFCI) through April 2020, we find that: Keep Reading

Testing Zweig’s Combined Super Model

A subscriber requested testing Martin Zweig’s Combined Super Model, which each month specifies an equity allocation based on a system that assigns up to eight points from his Monetary Model and 0 or 2 points from his Four Percent Model. We consider two versions of the Combined Super Model:

  1. Zweig-Cash – Allocate to Fidelity Fund (FFIDX) as equities, with the balance in cash earning the 3-month U.S. Treasury bill (T-bill) yield.
  2. Zweig-FGOVX – Allocate to FFIDX as equities, with the balance in Fidelity Government Income Fund (FGOVX)

The benchmark is buying and holding FFIDX. We focus on compound annual growth rate (CAGR), maximum drawdown (MaxDD) and annual Sharpe ratio, with average monthly T-bill yield during a year as the risk-free rate for that year. We ignore impediments to mutual fund trading and any issues regarding timeliness of allocation changes for end-of-month rebalancing. Using monthly Combined Super Model allocations and monthly fund returns/T-bill yield during December 1986 through March 2020, we find that: Keep Reading

Evolving Equity Index Earnings-returns Relationship

Why does the coincident relationship between U.S. aggregate corporate earnings growth and stock market return change from negative in older research to positive in recent research? In their January 2020 paper entitled “Assessing the Structural Change in the Aggregate Earnings-Returns Relation”, Asher Curtis, Chang‐Jin Kim and Hyung Il Oh examine when the change in the aggregate earnings growth-market returns relationship occurs. They then examine factors explaining the change based on asset pricing theory (expected cash flow and expected discount rate). They calculate aggregate earnings growth as the value-weighted average of year-over-year change in firm quarterly earnings scaled by beginning-of-quarter stock price. They consider only U.S. firms with accounting years ending in March, June, September or December, and they exclude firms with stock prices less than $1 and firms in the top and bottom 0.5% of quarterly earnings growth. They calculate corresponding quarterly stock market returns from one month prior to two months after fiscal quarter ends to capture earnings announcement effects. Using quarterly earnings and returns data as specified for a broad sample of U.S. public firms from the first quarter of 1970 through the fourth quarter of 2016, they find that:

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