When the Dust Flies
Examining how volatility events and post-peak events, across a 68-year span, affect asset class performance.
Investors often use their views of future GDP as an input when forecasting stock and bond market returns. It seems reasonable to turn this relationship around and argue that the bond, stock and real estate markets, which quickly incorporate investors’ current expectations about the future, might contain useful signals regarding the economy’s future direction. In fact, the Conference Board’s Leading Economic Index contains both a stock market and a bond market signal as leading indicators.
Using market data since 1970, PGIM’s Institutional Advisory & Solutions (IAS) examines several market signals and their ability to explain and predict next year’s GDP and the change in GDP from the previous year. The slope of the Treasury yield curve, the change in the Aaa-Baa quality corporate spread, the change in stock market valuation (CAPE) and real estate cap rate spread, carry information about future GDP growth, both in level and change. Fitting the market signals to the full data period, the signals explained 48% of the variation in next year’s GDP and 44% of the variation in the change in GDP from previous year.
More insights from IAS
Forecasting Long-Term Equity Returns
Evaluating the out-of-sample historical performance of two common approaches, or methodologies, for estimating 10-year equity market returns.
Emerging Market Equity Benchmarks For Japanese Investors
What type of baseline EM exposure might be most suitable for Japanese investors?
Note: Rolling regression uses rolling 10-year of market signal data to fit model and predict next year’s GDP and GDP changes. Avg in-sample R2 is the average of R2 for all iterations of rolling regressions performed. Out-of-sample RMSE compares the rolling regression prediction versus actual GDP and GDP Change.
Source: Federal Reserve Bank of St. Louis, FRED; Professor Shiller’s website; NCREIF; Datastream; and PGIM IAS.