Market prices, analysts' predictions, and Covid19

Area of Science:

• Econometrics
• Financial Market Analysis
• Time Series Analysis

Background:

• Accurate analysis of financial market time-series data is crucial for economic stability.
• Traditional methods may not fully capture sudden shifts or structural breaks in market behavior.
• Analyst stock price prediction accuracy is a key indicator of market efficiency.

Purpose of the Study:

• To apply a novel statistical method for analyzing US market price time-series.
• To identify periods of significant structural breaks in market prices, particularly during the Covid-19 pandemic.
• To evaluate the long-term predictive accuracy of market analysts.

Main Methods:

• Utilized a new statistical technique for time-series analysis of US market prices.
• Examined historical market data to detect structural breaks.
• Assessed the statistical significance of analyst stock price predictions over an extended period.

Main Results:

• The Covid-19 pandemic correlated with the most substantial structural breaks in US market prices.
• Analysts' stock price predictions were not statistically significant at the 5% level since 1993.
• The new statistical method provides enhanced capabilities for market price analysis.

Conclusions:

• The study highlights the impact of global events like Covid-19 on market volatility.
• Evidence suggests a consistent inability of analysts to predict market stock prices accurately over decades.
• The employed statistical method offers a more robust tool for understanding financial market dynamics.