Our quarterly S&P 500 forecast for 2024 Q2 (average price returns) indicates a 7.2 percent increase over the first quarter of 2024. Our monthly forecast for April is slightly higher than March’s average. Geopolitical problems such as the Russia-Ukraine, the Red Sea crisis, and the Gaza conflict cannot be captured in a forecast model. Thus, any uncertainty concerning these issues makes the 95 % confidence interval around the point forecast rather wide.
The US Congress has passed a new Covid-19 relief package. The new aid bill totals $484 bn. With Thursday’s bill, the total relief package amounts to about $3 trillion. Apart from topping up the ‘Paycheck Protection Program’ with additional $310 bn, the new bill will allocate $75 bn to hospitals some of which will be used for Covid-19 testing.
The previous relief program was criticized for helping some large public companies, big restaurant chains. It remains to be seen if the new Covid-19 relief package will provide sufficient support for the SME companies.
We expect the new stimulus package to mitigate the second quarter drop in GDP. Our initial ex-relief package estimate for the quarterly drop in GDP is therefore, revised upwards. We estimate 11% GDP drop in 2020 Q2 after taking into account the latest stimulus bill.
Even after the lifting of the lockdown, the stock market is expected to stay volatile with sporadic bullish rallies. However, without a successful drug or vaccination, it is unlikely the US economy to spring back to the pre-coronavirus state swiftly. After the stock market crash of 1929, the Dow Jones Industrial Average index had also experienced short-lived bullish rallies. Nevertheless, the underlying trend remained downward until 1932 when the prices bottomed out. In order to have a sustainable recovery in the stock markets, there should be a convincing amelioration of consumer demand and corporate profits. If the ‘new normal’ imposes rules such as fewer tables in restaurants, empty seats in planes or cinemas, lower earnings may persist till 2021. We expect the supply and demand shocks to linger even in the third quarter as a result of the social-distancing rules.
Meanwhile the new initial unemployment claims in the week ending April 18, was 4.43 million. This brings the total number of jobless to 26.4 million since mid-March (source: U.S. Department of Labor). The prospect of some states planning to reopen and the 15.4% fall in the initial claims from the previous week led to a S&P 500 rally. We expect the April average of S&P 500 higher than March. The advance seasonally adjusted insured unemployment rate was 11.0 percent for the week ending April 11. This marks the highest level in the history of the seasonally adjusted series.
We believe that the downward trend in initial unemployment claim may continue as the US states relax the lock-down rules. The keyword search in Google trends for the word ‘unemployment’ has not yet demonstrated a convincing downturn. The chart below shows the index of clicks on the keyword ‘unemployment’ dramatically rising since March. The Covid-19 relief plan has not so far affected unemployment.
In the meantime, the oil price has also been grabbing headlines. Last Monday West Texas Intermediate crude oil price became negative – $36.96 a barrel from $18.31 on Friday (Source: FRED). This was largely caused by the technical factors in the oil futures market. In order to avoid the problem of storing actual oil, the expiring contracts forced the oil traders to drop the price to negative for one day. The price sprang back next day to $9. This unprecedented incident is alarming as it exposes the scale of the lack of demand in the oil market. Generally, oil price changes positively correlate with the stock market and the price of oil is a leading indicator for the stock market. Despite the historic deal between OPEC, its allies and Russia to curb production, the expected global downturn is too severe for the oil demand to recover any time soon.
This blog presents the S&P 500 forecasts based on fundamental econometric models and historical economic and financial data.
Our blog refers to a quote attributed to Mark Twain, with the conviction that a thorough study of the past data throws some light into the future, as long as one is aware of the caveats that the future is not an exact replica of past events. Thus, our mission is to detect the ‘rhyming’ signals by fitting the best model to the historical data, bearing in mind that the future never plays out exactly the same; there are forecast risks associated with ‘’known unknowns” and “unknown unknowns”.
Our data is based on official sources, and not third-party data providers. Our facts are regularly checked for inconsistencies.
In contrast to the various websites that present the forecasts of hundreds of financial instruments ranging from stock market indices to crypto-currencies (e.g. tradingeconomics), our forecasts are focused on a few instruments and are labor-intensive. Our forecasts are the product of meticulous analysis of the data, composed of carefully chosen economic indicators based on theory and expert knowledge. Furthermore, our forecasts entail vigorous analysis via sophisticated econometric methods that have been back-tested and cross-validated. We ensure that the coefficient signs of the explanatory variables are in line with our economic intuition.
Our empirical findings are not an output of a black-box optimization process. Having said this, we also test the explanatory power of all the chosen economic indicators using machine-learning algorithms. We use algorithms such as Deep-learning, Logit, Random-Forest classifiers, K-nearest Neighbor, XGBoost, Extra Trees Classifier, and Support Vector Machine, to ensure that the choice of the indicators sufficiently discriminates the positive growth from negative growth signals in the forecast variables.
The S&P 500 predictions involve multi-equation models where some of the endogenous explanatory variables such as corporate profits and advance retail sales are themselves forecast using our proprietary models. Our models employ a totally original set of ‘keyword-based indicators. To the best of our knowledge, these indicators have never been used before. These indicators seem to capture the fluctuations in consumer tendencies rather well. Our models adjust all these indicators for seasonal variations. Thus, any changes in consumer tendencies due to various seasonal factors are taken into account.
The model outputs shown in this blog are point forecasts computed on the date shown in the graphs. They should be viewed as positive or negative signals for the direction where the markets are headed. The confidence intervals with regard to the accuracy of the forecasts refer to a wider range of possible outcomes. Depending on various irregular events/news, the actual outcome can be closer to the lower or upper confidence bound.
