Consumer Price Index (CPI)

Supporting:

• Stock Prices and Inflation by Ali Anari and James Kolari (2014): This study explores the relationship between stock prices and inflation, reconciling the seemingly contradictory short-term and long-term effects observed in previous research. The authors analyze data from six industrial countries and demonstrate that, while inflation negatively impacts stock returns in the short run, there is evidence of a positive long-term Fisher effect where stock prices adjust and eventually exceed the rate of inflation. The long-run Fisher elasticities of stock prices with respect to goods prices in the study range from 1.04 to 1.65, indicating that over time, stocks can act as a hedge against inflation. The research also finds that the initial negative response of stock prices to inflation shocks gradually turns positive, suggesting that investors who hold stocks over a longer period are likely to benefit from this adjustment.

Criticizing:

• The Impact of Inflation, GDP, Unemployment, and Money Supply on Stock Prices by Lena Shiblee (2009): This paper examines the effects of four key macroeconomic variables—inflation, GDP, unemployment, and money supply—on stock prices in the industrial sector, focusing on data from the New York Stock Exchange between 1994 and 2007. The study finds that while money supply has the strongest positive influence on stock prices, inflation (measured by CPI) also impacts stock prices, but this effect is inconsistent, exhibiting both positive and negative relationships across different companies. The paper highlights the complexity of inflation’s role in stock price movements, noting that while inflation often negatively affects stock returns in the short term, it may also provide insight into potential long-term hedging strategies, depending on a company’s ability to adjust prices. The analysis suggests that inflation’s effect on stock prices is significant but varies in direction and strength across different firms.

Gross Domestic Product (GDP)

Supporting:

• The Impact of Inflation, GDP, Unemployment, and Money Supply on Stock Prices by Lena Shiblee (2009): This paper explores the influence of key macroeconomic variables—particularly GDP, along with inflation, unemployment, and money supply—on stock prices within the industrial sector, using data from the New York Stock Exchange from 1994 to 2007. The study finds that GDP plays a crucial role in determining stock prices, with a strong positive correlation between GDP growth and stock market performance. As GDP increases, it signals economic expansion, leading to higher corporate profits and, consequently, rising stock prices. The paper emphasizes that GDP is one of the most significant predictors of stock prices, indicating that economic growth directly enhances investor confidence and drives market valuations upward. This relationship underscores the importance of GDP as a key indicator for forecasting stock market trends.

Criticizing:

• GDP Announcements and Stock Prices by Yoshito Funashima, Nobuo Iizuka, Yoshihiro Ohtsuka (2019, Journal of Economics and Business): This study investigates how the Japanese stock market responds to GDP announcements, providing valuable insights into the balance between the timeliness and accuracy of these announcements. The research reveals that the initial GDP announcement has a limited impact on stock prices, suggesting that provisional estimates often lack actionable information for investors. However, the first revision of the GDP data elicits a strong market reaction, indicating that investors place greater trust in revised figures. Conversely, the second revision tends to have a muted effect on stock prices. The study also highlights that revisions to different expenditure components of GDP can lead to overreactions or underreactions in the market, serving as destabilizing factors for stock prices. These findings underscore the complexity of market responses to GDP data and the importance of considering the nature and timing of such announcements.

Unemployment

Supporting:

• The Impact of Inflation, GDP, Unemployment, and Money Supply on Stock Prices by Lena Shiblee (2009): This study examines the impact of various macroeconomic factors on stock prices, with a particular focus on unemployment, alongside inflation, GDP, and money supply. The analysis, based on data from the New York Stock Exchange between 1994 and 2007, reveals that unemployment has a significant inverse relationship with stock prices. As unemployment rates rise, stock prices tend to decline, reflecting investor concerns about reduced consumer spending and overall economic slowdown. The paper emphasizes that high unemployment levels typically signal weaker economic conditions, leading to lower corporate earnings and decreased investor confidence. This relationship highlights the importance of unemployment as a key indicator in predicting stock market performance, as fluctuations in employment rates can significantly influence investor sentiment and market dynamics.

Criticizing:

• The Reaction of Stock Market Returns to Unemployment by Jesús Gonzalo and Abderrahim Taamouti (2015, Studies in Nonlinear Dynamics & Econometrics): This study investigates the short-term effects of both anticipated and unanticipated unemployment rates on stock prices, with a focus on the nonlinearity of the stock market’s reaction. Utilizing nonparametric Granger causality and quantile regression-based tests, the authors find that the anticipated unemployment rate significantly impacts stock prices, while the unanticipated rate does not have a strong influence. The study reveals that the impact of anticipated unemployment on stock returns varies across different quantiles of the return distribution. For example, in the quantile range of 0.35 to 0.80, an increase in the anticipated unemployment rate is associated with a rise in stock market prices, suggesting that the market interprets higher anticipated unemployment as a precursor to favorable monetary policy actions, such as interest rate cuts by the Federal Reserve. This nuanced analysis provides a deeper understanding of how unemployment data influences market behavior, particularly in the context of expected versus unexpected economic conditions.

University of Michigan Consumer Sentiment Index

Supporting:

• Investor Sentiment and the Cross-Section of Stock Returns by Malcolm Baker and Jeffrey Wurgler (2006, The Journal of Finance): This study examines the impact of investor sentiment on the cross-section of stock returns, focusing on how waves of sentiment disproportionately affect stocks that are difficult to value and harder to arbitrage. The authors find that when investor sentiment is low at the beginning of a period, subsequent returns are higher for stocks that are small, young, highly volatile, unprofitable, non-dividend-paying, extreme growth, and distressed. Conversely, when sentiment is high, these same categories of stocks tend to underperform. The study suggests that investor sentiment drives over- and under-valuation in these types of stocks, leading to predictable patterns in future returns based on the level of sentiment at the outset.
• Investor Sentiment and the Cross-Section of Stock Returns: New Theory and Evidence by Wenjie Ding, Khelifa Mazouz, and Qingwei Wang (2018, Review of Quantitative Finance and Accounting): This study extends the noise trader risk model to multiple risky assets, demonstrating how investor sentiment affects the cross-section of stock returns. The authors found that stocks more prone to investor sentiment tend to experience higher returns in the short run when sentiment is high but lower returns in the long run as sentiment reverts to the mean. The study decomposes investor sentiment into long- and short-run components and shows that short-run sentiment positively correlates with contemporaneous returns, while long-run sentiment negatively predicts future returns. The results highlight that sentiment-prone stocks, which are more difficult to arbitrage, exhibit greater sensitivity to sentiment changes. This study provides robust empirical evidence consistent with the theoretical predictions, confirming that investor sentiment significantly impacts stock returns across different market conditions.

Criticizing:

• None currently available.

Yield Curve (10y – 2y)

Supporting:

• Using the Yield Curve to Time the Stock Market by Bruce G. Resnick & Gary L. Shoesmith (2019, Financial Analysts Journal): This study extends the use of the probit model, traditionally employed for forecasting economic recessions, to predict bear markets in the stock market. The authors find that the yield curve spread, specifically the difference between the composite 10-year+ U.S. T-bond yield and the three-month T-bill yield, holds valuable predictive information regarding the likelihood of a forthcoming bear market. By simulating market-timing strategies based on this model, the study shows that investors could achieve superior returns compared to a simple buy-and-hold strategy. For example, using a 50% probability threshold, the simulated strategy yielded a compound annual return of 16.46%, outperforming the 14.17% return from a stock-only buy-and-hold approach. The findings suggest that market timers could potentially enhance returns by switching between stocks and T-bills based on the probability of a bear market as predicted by the yield curve spread.
• The Yield Curve and the Stock Market: Mind the Long Run by Gonçalo Faria & Fabio Verona (2019, Journal of Financial Markets): This study explores the predictive power of the yield curve’s term spread for forecasting the equity premium. The authors utilize wavelet filters to extract cycles from the term spread and analyze their effectiveness in predicting equity returns through linear models. The findings reveal that the trend of the term spread, when properly extracted, is a strong and reliable out-of-sample predictor of the equity premium, outperforming several other variables traditionally considered good predictors. The study supports recent asset pricing literature that emphasizes the importance of low-frequency components of macroeconomic variables in influencing equity market dynamics. The results suggest that both policymakers and financial market participants can benefit from focusing on the trend of the term spread as a critical variable for anticipating developments in equity markets.

Criticizing:

• None currently available