Can Investors Rely on the Effective Federal Funds Rate for Stock Market Decisions?

Investors often believe that changes in interest rates, specifically cuts, can lead to lower financing costs, increased lending, and ultimately a boost to the economy, and it leads to the stock price going up. This report examines the relationship between Federal Reserve (Fed) interest rate changes and the performance of the S&P 500 Index, highlighting the periods of rate cuts and hikes. The following are the historical periods when the Fed implemented interest rate cuts and hikes, using the FOMC Meeting date for the start and end points (from 1990 to 2024)

Rate Cut Periods:
August 1, 2019 to March 16, 2020
September 18, 2007 to December 16, 2008
January 31, 2001 to June 25, 2003
July 6, 1995 to November 17, 1998
July 13, 1990 to September 13, 1991

Rate Hike Periods:
March 17, 2022 to July 26, 2023
December 17, 2015 to December 20, 2018
June 30, 2004 to June 29, 2006
June 30, 1999 to May 16, 2000
February 4, 1994 to February 1, 1995

Data Source: Board of Governors of the Federal Reserve System, Yahoo Finance

Observations

While investors anticipate that rate cuts will stimulate economic activity and enhance stock prices, historical data suggests that these periods do not consistently correlate with notable increases in the S&P 500. Similarly, expectations surrounding rate hikes often lead to concerns about potential declines in stock prices. However, the performance of the S&P 500 during these periods has shown resilience, with no clear trend indicating a downturn solely due to rate increases.

Granger-Causality Test Results

To further investigate the relationship between the effective federal funds rate and the S&P 500, a Granger-causality test was conducted. The results are as follows:

import pandas as pd
import numpy as np
import statsmodels.api as sm
from statsmodels.tsa.stattools import adfuller
from statsmodels.tsa.api import VAR

def check_stationarity(series):
    result = adfuller(series)
    return result[1]  

p_values = {col: check_stationarity(data2[col]) for col in data2.columns}
print(p_values)

for col in data2.columns:
    if p_values[col] > 0.05:
        data2[col] = data2[col].diff().dropna()

model = VAR(data2)
results = model.fit(maxlags=15, ic='aic')

causality_results = results.test_causality('S&P500', 'effective_federal_funds_rate')
print(causality_results.summary())

Analysis of Granger-Causality Test

Since the p-value is much higher than the 0.05 significance level, we fail to reject the null hypothesis. This indicates that the effective federal funds rate does not have a predictive influence on the S&P 500. In other words, changes in the effective federal funds rate do not forecast movements in the S&P 500 index.

Conclusion

In conclusion, the data indicates that Fed policy turning points do not significantly influence the movement of the S&P 500 Index. A review of the S&P 500 Index during these periods reveals that the expectation that rate cuts or hikes are reliable predictors of future stock performance may be overstated.

Investors should look beyond interest rate movements when assessing market conditions, as a broader range of economic factors can provide crucial insights. Key indicators such as GDP growth, inflation trends, and consumer confidence reflect the overall health of the economy. Additionally, global events, sector performance, and central bank policies beyond just rate changes can significantly impact markets. All of these elements contribute to understanding potential scenarios, including “soft landing” and “hard landing” for the U.S. economy. Therefore, investors should base their investment decisions on a comprehensive evaluation of these factors to ensure compliance and informed strategy.

LEE YatMing

I love my cat who is called Sesame

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