# The Impact of Monetary Policy on the U.S. Stock Market since the COVID-19 Pandemic

## Abstract

**:**

## 1. Introduction

## 2. Data and Methodology

_{i}is the ex-ante expected return on asset i, λ

_{0}is the return on the risk-free asset, β

_{ij}is the beta or factor loading of asset i to macroeconomic factor j, and λ

_{j}is the risk price associated with factor j. The ex-post realized return equals the sum of the expected return, a beta-weighted vector of unexpected changes in the macroeconomic factors, and an error term capturing the effects of idiosyncratic news:

_{i}is the ex-post realized return, f

_{j}represents news about macroeconomic factor j and ε

_{i}is a mean-zero error term.

_{i}− λ

_{0}is a 1 × T vector where R

_{i}represents the realized return on asset i and λ

_{0}is the return on the risk-free asset. X(λ,f) is a T × k matrix whose tith element equals f

_{it}+ λ

_{i}. β

_{i}is a 1 × k vector measuring asset i’s sensitivity to the macroeconomic factors. ε

_{i}is an i×T vector, where by assumption E(ε

_{1}, ε

_{2}, …, ε

_{n}) = 0

_{nT}, E(ε

_{1}, ε

_{2}, …, ε

_{n})’(ε

_{1}, ε

_{2}, …, ε

_{n}) = ∑⊗I

_{T}, and ∑

_{i,j}= cov(ε

_{i,t}, ε

_{j,t}).

## 3. Results

## 4. Conclusions

## Funding

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Notes

1 | See Milstein and Wessel (2021) for a discussion of monetary policy during the pandemic. |

2 | These data come from the statements accompanying Federal Open Market Committee meetings (https://www.federalreserve.gov/monetarypolicy/fomccalendars.htm accessed on 15 July 2023) and from the Federal Reserve Bank of St. Louis FRED database. |

3 | Campbell and Vuolteenaho (2004) decomposed assets’ betas into a component reflecting news about future cash flows and future discount rates. Bernanke and Kuttner (2005) employed the Campbell and Ammer (1993) methodology to divide asset returns into those driven by news of future discount rates, future risk premia, and future cash flows. Using a vector autoregression including monetary policy surprises, they reported that monetary policy shocks primarily impact future risk premia and future cash flows. |

4 | As a robustness check, monetary policy is also measured using the surprise monetary policy variables constructed by Bauer and Swanson (2022) (B&S). B&S modeled unexpected changes in monetary policy as the first principal component of the change in the first four Eurodollar futures contracts over the 30 min bracketing Federal Open Market Committee (FOMC) announcements. They then aggregated the intra-daily data into a monthly monetary policy shock series. The results using the B&S variable are similar to those reported below. |

5 | To obtain industry portfolios, Datastream begins with all stocks traded on the New York Stock Exchange (NYSE) and the National Association of Securities Dealers Automated Quotations (NASDAQ). It then uses the Refinitiv Business Classification (RBC) to assign firms to industries. RBC employs company filings, news articles, and other information to classify companies into industries. |

6 | |

7 | The monetary policy beta for the market portfolio is obtained from regressing the return on the S&P 500 on the BRW measure of monetary policy, unexpected inflation, the horizon premium, industrial production growth, and the change in expected inflation over the January 1994 to December 2019 period. |

8 | These data are available at: http://www.policyuncertainty.com/ (accessed on 14 July 2023). |

9 |

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**Figure 1.**The personal consumption expenditures (PCE) inflation rate and the monthly change in returns on the market portfolio associated with exposure to monetary policy. Note: The figure presents the personal consumption expenditures (PCE) inflation rate and the change in returns on the market portfolio associated with monetary policy. To calculate the change in returns associated with monetary policy, assets’ monetary policy betas are estimated. The betas are obtained from an iterated nonlinear seemingly unrelated regression of returns on 53 assets (minus the return on one-month Treasury bills) on the Bu et al. (2021) (BRW) measure of Fed policy surprises, the difference in returns between 20-year and one-month Treasury securities, the monthly growth rate in industrial production, unexpected inflation, and the change in expected inflation. The BRW measure is constructed so that an increase represents a contractionary monetary policy surprise. If investors believe that monetary policy will tighten, this will drive up the prices of assets that benefit from contractionary monetary policy (those with larger betas to the BRW variable) and drive down the process of assets that are harmed by contractionary monetary policy (those with smaller betas to the BRW variable). There should thus be a positive relationship between asset returns and assets’ BRW betas on months when investors foresee monetary policy tightening. For each month between April 2020 and April 2023, returns on the 53 assets are thus regressed on the assets’ monetary policy betas. To facilitate interpretation, the resulting regression coefficient is multiplied by the beta coefficient on the market portfolio obtained from regressing the return on the S&P 500 on the BRW measure of monetary policy, unexpected inflation, the horizon premium, industrial production growth, and the change in expected inflation over the January 1994 to December 2019 period. The change in returns associated with monetary policy in the figure thus represents the change in returns for the market portfolio. Since the market monetary policy beta is negative, positive values in Figure 1 indicate that investors expect easier policy and negative values indicate that they foresee tighter policy.

**Figure 2.**The consumer price index, the core consumer price index, and the number of new COVID-19 Cases in the U.S. Source: Federal Reserve Bank of St. Louis FRED database and Our World in Data (https://ourworldindata.org/covid-cases accessed on 5 September 2023).

**Figure 3.**The daily change in returns on the market portfolio associated with exposure to monetary policy between 1 April and 31 August 2022. Note: The figure presents the daily change in returns associated with monetary policy. To calculate the change in returns associated with monetary policy, assets’ monetary policy betas are estimated. The betas are obtained from an iterated nonlinear seemingly unrelated regression of returns on 53 assets (minus the return on one-month Treasury bills) on the Bu et al. (2021) (BRW) measure of Fed policy surprises, the difference in returns between 20-year and one-month Treasury securities, the monthly growth rate in industrial production, unexpected inflation, and the change in expected inflation. The BRW measure is constructed so that an increase represents a contractionary monetary policy surprise. If investors believe that monetary policy will tighten, this will drive up the prices of assets that benefit from contractionary monetary policy (those with larger betas to the BRW variable) and drive down the process of assets that are harmed by contractionary monetary policy (those with smaller betas to the BRW variable). There should thus be a positive relationship between asset returns and assets’ BRW betas on days when investors foresee tighter monetary policy. For each business day between 1 April 2022 and 31 August 2022, returns on the 53 assets are thus regressed on the assets’ monetary policy betas. To facilitate interpretation, the regression coefficient is multiplied by the monetary policy beta for the market portfolio obtained from regressing the return on the S&P 500 on the BRW measure of monetary policy, unexpected inflation, the horizon premium, industrial production growth, and the change in expected inflation over the January 1994 to December 2019 period. The change in returns associated with monetary policy in the figure thus represents the change in returns for the market portfolio. Since the market BRW beta coefficient is negative, positive values in Figure 3 indicate that investors expect easier policy and negative values indicate that they foresee tighter policy. The figure only reports days when there is a statistically significant relationship (at least the 10 percent level) between returns on the 53 assets and the assets’ monetary policy betas.

Variable | Unexpected Inflation | Horizon Premium | Change in Expected Inflation | Industrial Production Growth |
---|---|---|---|---|

Unexpected inflation | ||||

Horizon Premium | −0.215 | |||

Change in expected inflation | 0.145 | −0.156 | ||

Industrial production growth | −0.109 | −0.018 | 0.056 | |

Monetary policy (Bu et al. measure) | 0.034 | 0.027 | 0.023 | 0.008 |

**Table 2.**Iterated nonlinear seemingly unrelated regression estimates of the risk prices associated with macroeconomic factors.

(1) | (2) |
---|---|

Macroeconomic factor | Risk price |

Unexpected inflation | −0.0029 ** (0.0014) |

Horizon premium | −0.0105 * (0.0060) |

Change in expected inflation | −0.0020 ** (0.0009) |

Industrial production growth | −0.0127 *** (0.0043) |

Monetary policy (Bu, Rogers, and Wu Measure) | −0.0368 ** (0.0178) |

(1) | (2) | (3) |
---|---|---|

Asset | Monetary Policy Beta (Bu, Rogers, and Wu Measure) | Standard Error |

Aerospace/defense | −0.109 | 0.081 |

Aerospace | −0.141 | 0.089 |

Airlines | −0.056 | 0.129 |

Aluminum | −0.438 ** | 0.198 |

Apparel retail | −0.123 | 0.110 |

Auto and parts | −0.077 | 0.114 |

Auto parts | −0.034 | 0.099 |

Automobiles | −0.091 | 0.142 |

Basic materials | −0.297 *** | 0.087 |

Basic resources | −0.397 *** | 0.113 |

Beverages | −0.043 | 0.068 |

Broadcast and entertainment | −0.100 | 0.095 |

Brewers | −0.023 | 0.084 |

Building materials/fixtures | −0.068 | 0.093 |

Business supply services | −0.094 | 0.077 |

Chemicals | −0.274 *** | 0.080 |

Clothing and accessories | −0.187 * | 0.102 |

Commercial vehicles/trucks | −0.259 ** | 0.110 |

Computer hardware | −0.239 ** | 0.116 |

Computer services | −0.130 * | 0.079 |

Construction and materials | −0.239 ** | 0.093 |

Consumer electricity | −0.069 | 0.063 |

Consumer discretionary | −0.140 ** | 0.069 |

Consumer finance | −0.160 * | 0.097 |

Consumer goods | −0.078 | 0.069 |

Consumer staples | −0.038 | 0.054 |

Consumer services | −0.136 ** | 0.069 |

Container and packaging | −0.181 ** | 0.086 |

Defense | −0.057 | 0.087 |

Distillers and vintners | −0.016 | 0.083 |

Diversified industrials | −0.068 | 0.088 |

Drug retailers | −0.098 | 0.092 |

Durable household products | −0.201 * | 0.120 |

Electronic components and equipment | −0.228 ** | 0.094 |

Electricity | −0.070 | 0.063 |

Electronic and electrical equipment | −0.218 ** | 0.095 |

Food and drug retail | −0.077 | 0.066 |

Food producers | −0.073 | 0.056 |

Food retailers and wholesalers | −0.019 | 0.073 |

Financial services | −0.204 ** | 0.086 |

Financials | −0.130 | 0.082 |

Gold Bullion | −0.129 ** | 0.063 |

Gold mining (Americas) | −0.190 | 0.148 |

Gold mining (Australasia) | −0.318 * | 0.164 |

Gold mining (World) | −0.200 | 0.140 |

Health care | −0.088 | 0.058 |

Oil and gas | −0.200 ** | 0.080 |

Pharmaceuticals and biochemical products | −0.051 | 0.066 |

Real estate investment trusts | −0.019 | 0.083 |

Silver (S&P GSCI) | −0.411 *** | 0.118 |

Technology | −0.172 | 0.106 |

Telecom | −0.074 | 0.080 |

Utilities | −0.063 | 0.062 |

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## Share and Cite

**MDPI and ACS Style**

Thorbecke, W.
The Impact of Monetary Policy on the U.S. Stock Market since the COVID-19 Pandemic. *Int. J. Financial Stud.* **2023**, *11*, 134.
https://doi.org/10.3390/ijfs11040134

**AMA Style**

Thorbecke W.
The Impact of Monetary Policy on the U.S. Stock Market since the COVID-19 Pandemic. *International Journal of Financial Studies*. 2023; 11(4):134.
https://doi.org/10.3390/ijfs11040134

**Chicago/Turabian Style**

Thorbecke, Willem.
2023. "The Impact of Monetary Policy on the U.S. Stock Market since the COVID-19 Pandemic" *International Journal of Financial Studies* 11, no. 4: 134.
https://doi.org/10.3390/ijfs11040134