Slower Economic Growth Impacting Stock Market
The wheels of the stock market’s discounting machine are spinning rapidly these days as the crowd continues to price in the risk of slower economic growth.
The S&P 500 (NYSEARCA:SPY) is off a bit more than 8% for the year so far and is lower by nearly 7% over the past 12 months in total-return terms through yesterday (Jan 25). Formal estimates of GDP are on board with the market’s bias for downsizing expectations. The main debating point at this stage centers on one question: How much deceleration is lurking?
The Capital Spectator’s average estimate via several econometric forecasts sees Q4 GDP advancing at a sluggish 1.4% rate (seasonally adjusted annual rate), down from 2.0% in Q3 (seasonally adjusted annual rate). The new forecast is also down a touch from last month’s Q4 estimate.
The Capital Spectator’s current prediction represents the optimistic view in the current climate. Indeed, several forecasts from other sources anticipate an even slower pace for the official Q4 GDP report that’s scheduled for release on Friday (Jan. 29) via the US Bureau of Economic Analysis. The Atlanta Fed’s widely followed GDPNow model expects growth of only 0.7% for Q4. But wait, it gets worse: Wells Fargo’s latest prediction advises that growth will be virtually flat at 0.2% in last year’s final three months.
Economists overall see a firmer pace of Q4 growth: 1.4%, based on the average estimate via The Wall Street Journal’s survey data for this month–a rise that matches The Capital Spectator’s revised projection.
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The common theme that unites all the estimates is the assumption that Friday’s release for Q4 GDP growth will print at a substantially lesser rate vs. the previous quarter. The sharp slide in US stocks since the new year dawned tells us that Mr. Market’s in full agreement with the decision to manage macro expectations down.
Here’s a visual summary of estimates from various sources:
Here are the various forecasts that are used to calculate CapitalSpectator.com’s average estimate:
As updated estimates are published, based on incoming economic data, the chart below tracks the changes in the evolution of The Capital Spectator’s projections.
Finally, here’s a brief profile for each of The Capital Spectator’s GDP forecast methodologies:
R-4: This estimate is based on a multiple regression in R of historical GDP data vs. quarterly changes for four key economic indicators: real personal consumption expenditures (or real retail sales for the current month until the PCE report is published), real personal income less government transfers, industrial production, and private non-farm payrolls. The model estimates the statistical relationships from the early 1970s to the present. The estimates are revised as new data is published.
R-10: This model also uses a multiple regression framework based on numbers dating to the early 1970s and updates the estimates as new data arrives. The methodology is identical to the 4-factor model above, except that R-10 uses additional factors—10 in all—to forecast GDP. In addition to the data quartet in the 4-factor model, the 10-factor forecast also incorporates the following six series: ISM Manufacturing PMI Composite Index, housing starts, initial jobless claims, the stock market (Wilshire 5000), crude oil prices (spot price for West Texas Intermediate), and the Treasury yield curve spread (10-year Note less 3-month T-bill).
ARIMA GDP: The econometric engine for this forecast is known as an autoregressive integrated moving average. This ARIMA model uses GDP’s history, dating from the early 1970s to the present, for anticipating the target quarter’s change. As the historical GDP data is revised, so too is the forecast, which is calculated in R via the “forecast” package, which optimizes the parameters based on the data set’s historical record.
ARIMA R-4: This model combines ARIMA estimates with regression analysis to project GDP data. The ARIMA R-4 model analyzes four historical data sets: real personal consumption expenditures, real personal income less government transfers, industrial production, and private non-farm payrolls. This model uses the historical relationships between those indicators and GDP for projections by filling in the missing data points in the current quarter with ARIMA estimates. As the indicators are updated, actual data replaces the ARIMA estimates and the forecast is recalculated.
VAR 4: This vector autoregression model uses four data series in search of interdependent relationships for estimating GDP. The historical data sets in the R-4 and ARIMA R-4 models noted above are also used in VAR-4, albeit with a different econometric engine. As new data is published, so too is the VAR-4 forecast. The data sets range from the early 1970s to the present, using the “vars” package in R to crunch the numbers.
ARIMA R-NIPA: The model uses an autoregressive integrated moving average to estimate future values of GDP based on the datasets of four primary categories of the national income and product accounts (NIPA): personal consumption expenditures, gross private domestic investment, net exports of goods and services, and government consumption expenditures and gross investment. The model uses historical data from the early 1970s to the present for anticipating the target quarter’s change. As the historical numbers are revised, so too is the estimate, which is calculated in R via the “forecast” package, which optimizes the parameters based on the data set’s historical record.