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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 monthly demeaning of inputs, monthly normalization of the overall indicator to have historical standard deviation one and principal component analysis. This process changes past values in the series, perhaps even changing their signs. Is KCFSI useful for U.S. stock market investors? To investigate, we relate monthly S&P 500 Index returns to monthly values of, and changes in, 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 (limited by KCFSI) through May 2019, we find that: Keep Reading

Weekly Summary of Research Findings: 6/10/19 – 6/14/19

Below is a weekly summary of our research findings for 6/10/19 through 6/14/19. These summaries give you a quick snapshot of our content the past week so that you can quickly decide what’s relevant to your investing needs.

Subscribers: To receive these weekly digests via email, click here to sign up for our mailing list. 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 inception in January 1971.
  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 inception in January 1971.

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 as of May 2019 since January 1971 and weekly dividend-adjusted opens of SPY and T-bills since February 1993 (limited by SPY), all through May 2019, 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 May 2019, we find that: Keep Reading

Inflation Forecast Update

The Inflation Forecast now incorporates actual total and core Consumer Price Index (CPI) data for May 2019. The actual total (core) inflation rate for May is a little lower than (a little lower than) forecasted.

U.S. Corporate Bond Index Return Model

Is there a straightforward way to model the returns on U.S. Corporate bond indexes? In his April 2019 paper entitled “Give Credit Where Credit is Due: What Explains Corporate Bond Returns?”, Roni Israelov models returns on these indexes based on four intuitive factors:

  1. Positive exposure to government bond yields, quantified via duration-matched government bonds.
  2. Negative exposure to rate volatility from bond call provisions (uncertainty in duration), quantified via delta-hedged options on 10-year Treasury note futures.
  3. Positive exposure to firm values due to default risk, quantified via index constituent-weighted equities.
  4. Negative exposure to firm stock volatility due to default risk, quantified via index constituent-weighted delta-hedged single-name equity options.

Exposures 1 and 2 are general (systematic), while exposures 3 and 4 contain both systematic and firms-specific (idiosyncratic) components. He tests this 4-factor model on six Bank of America Merrill Lynch U.S. corporate bond indexes: Investment Grade, High Yield, 1-3 Year Corporate, 3-5 Year Corporate, 5-10 Year Corporate, and 10+ Year Corporate. All duration-specified indexes are investment grade. He also tests two Credit Default Swap (CDS) indexes: investment grade and high yield. He further devises and tests a Risk-Efficient Credit strategy on the six bond indexes that isolates and exploits compensated risk premiums by buying bond index futures, buying equity index futures, selling delta-hedged equity index options and selling delta-hedged options on bond index futures, with allocations sized to match respective historical exposures of each index. Using monthly data for the eight bond/CDS indexes and the four specified factors and their components during January 1997 through December 2017, he finds that:

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Tax-efficient Retirement Withdrawals

Considering taxes, in what order should U.S. retirees consume different sources of retirement savings/income? In their August 2018 paper entitled “Constructing Tax Efficient Withdrawal Strategies for Retirees with Traditional 401(k)/IRAs, Roth 401(k)/IRAs, and Taxable Accounts”, James DiLellio and Daniel Ostrov describe and illustrate an algorithm that computes individualized tax-efficient consumption for U.S. retirees of:

  • Tax-deferred retirement accounts [Traditional IRA/401(k)].
  • Post-tax retirement accounts [Roth IRA/Roth 401(k)].
  • Other taxable retirement accounts.
  • Other sources of money subject to income tax, including: earned income, some pensions, annuities bought with pre-tax money, earnings from annuities bought with post-tax money and sometimes Social Security benefits.
  • Other sources of money that do not affect tax rates of retirement accounts, such as: tax-free gifts, Health Savings Accounts, some pensions, principal from annuities bought with post-tax money and sometimes Social Security benefits.

Their model adapts to individual retiree circumstances and accommodates typical changes in tax policies (changes in marginal rates and number of brackets). For tractability, they make simplifying assumptions. The principal simplification is that  return on stocks, stock dividend yield, inflation rate, tax brackets and rates, other income sources and consumption rates are known each year (not random variables). When the goal is to optimize a bequest, inputs also include year of retiree death, marginal tax rate of the heir and rate the heir consumes inherited retirement accounts. They do not attempt to determine the optimal mix of  stocks and bonds/cash within retirement accounts (their deterministic model would prefer all stocks). Using illustrations of algorithm outputs based on varying input assumptions, they find that: Keep Reading

Are U.S. Equity Momentum ETFs Working?

Are U.S. stock and sector momentum strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider five momentum-oriented U.S. equity ETFs with assets over $100 million, all currently available (in order of decreasing assets):

  • iShares Edge MSCI USA Momentum Factor (MTUM) – holds U.S. large-capitalization and mid-capitalization stocks with relatively high momentum.
  • First Trust Dorsey Wright Focus 5 (FV) – holds five equally weighted sector and industry ETFs selected via a proprietary relative strength methodology, reformed twice a month.
  • PowerShares DWA Momentum Portfolio (PDP) – invests at least 90% of assets in approximately 100 U.S. common stocks per a proprietary methodology designed to identify powerful relative strength characteristics, reformed quarterly.
  • First Trust Dorsey Wright Dynamic Focus 5 ETF (FVC) – similar to FV but with added risk management via an increasing allocation to cash equivalents when relative strengths of more than one-third of the universe diminish relative to a cash index, reformed twice a month.
  • SPDR Russell 1000 Momentum Focus (ONEO) – tracks the Russell 1000 Momentum Focused Factor Index, picking U.S. stocks that have recently outperformed.

Because some sample periods are very short, we focus on daily return statistics, but also consider cumulative returns and maximum drawdowns (MaxDD). We use two benchmark ETFs, iShares Russell 1000 (IWB) and iShares Russell 3000 (IWV), according to momentum fund descriptions. Using daily returns for the five momentum funds and the two benchmarks as available through mid-May 2019, we find that: Keep Reading

Weekly Summary of Research Findings: 6/3/19 – 6/7/19

Below is a weekly summary of our research findings for 6/3/19 through 6/7/19. These summaries give you a quick snapshot of our content the past week so that you can quickly decide what’s relevant to your investing needs.

Subscribers: To receive these weekly digests via email, click here to sign up for our mailing list. Keep Reading

Intrinsic (Time Series) Momentum Everywhere?

Do all kinds of assets and long-short equity factor premiums exhibit exploitable time series (intrinsic or absolute momentum)? In their September 2018 paper entitled “Trends Everywhere”, Abhilash Babu, Ari Levine, Yao Hua Ooi, Lasse Pedersen and Erik Stamelos test intrinsic momentum on 58 traditional (studied in prior research) assets, 82 alternative (futures, forwards, and swaps not previously studied) assets and 16 long-short equity factors. They include only reasonably liquid (investable) assets and strategies. For equity factors, they each month: (1) classify over 4,000 U.S. common stocks as big or small according to NYSE median market capitalization; (2) within each size group, reform for each factor a value-weighted hedge portfolio that is long (short) the 30% of stocks with the highest (lowest) expected returns; and, (3) for each factor, average big and small hedge portfolio returns. They focus on a 12-month lookback interval for calculating momentum, taking a long (short) position in an asset/factor with positive (negative) return over this interval. For comparability of assets, they scale each position to an estimated 40% annualized volatility based on exponentially-weighted squared past daily returns. They assess diversification potentials by looking at pairwise correlations between momentum series, and between portfolios of momentum series and benchmark indexes (S&P 500 Index, MSCI World Index, Barclays Aggregate Bond Index and S&P GSCI Index). Using daily excess returns for the selected assets, factors and benchmarks as available during January 1985 through December 2017, they find that:

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