PI is calculated using news articles from Thomson Reuters, Bloomberg, and other sources.
Uses Semantic Tree analysis and a benchmark sentence database for classifying sentiment.
Based on the ratio of positive vs. negative macroeconomic news.
PI ranges from -1 (extreme fear) to +1 (optimism), with 0 indicating neutral market conditions.
Opinion
The Panic Indicator (PI) is an innovative approach to quantifying market sentiment. By leveraging vast textual data, it aims to objectively measure investor psychology, setting it apart from traditional sentiment indicators.
The use of Semantic Tree analysis and a benchmark database allows for more context-aware sentiment analysis. However, potential biases in news selection and sentence classification could be limitations.
Despite these challenges, PI offers valuable market sentiment insights for investment decision-making. If integrated with machine learning and other advanced technologies, it could become a highly refined tool for investors.
Core Sell Point
The Panic Indicator (PI) quantifies investor sentiment through news article text analysis, making it a valuable tool for assessing market psychology. However, potential biases in sentiment classification remain a key limitation.
The paper "A Comprehensive Look at the Empirical Performance of Moving Average Trading Strategies" introduces the Panic Indicator (PI), which quantifies investor sentiment using the following methodology:
1. Collecting Text Data
Business analysis news articles are sourced daily from publishers such as Thomson Reuters, Bloomberg, The Wall Street Journal, and The New York Times Company.
The collected text database includes economic analysis articles, article headlines, and educational materials (e.g., CFA curriculum content).
2. Classifying Text Data (Positive vs. Negative)
Sentences likely to impact the stock market positively or negatively are selected from the text database.
The Semantic Tree method is used for subjective classification (Beth, 1955; Bondecka-Krzykowska, 2005; Liu, 2010).
A benchmark sentence database containing over 65,000 sentences from past market-affecting news is built.
News article headlines and summaries are compared with the benchmark database to classify sentiment as positive, negative, or neutral.
Traditional statistical methods, such as bad-good word counting, were found ineffective.
3. Calculating the Panic Indicator (PI)
PI is calculated based on the ratio of positive to negative macroeconomic news articles using the following formula: PI=Positive Macro News Count−Negative Macro News CountPositive Macro News Count+Negative Macro News CountPI = \frac{\text{Positive Macro News Count} - \text{Negative Macro News Count}}{\text{Positive Macro News Count} + \text{Negative Macro News Count}}PI=Positive Macro News Count+Negative Macro News CountPositive Macro News Count−Negative Macro News Count
Alternatively, the formula can be expressed in terms of pessimistic ratio (kp): PI=1−kp1+kp,kp=Negative Macro News CountPositive Macro News CountPI = \frac{1-kp}{1+kp}, \quad kp = \frac{\text{Negative Macro News Count}}{\text{Positive Macro News Count}}PI=1+kp1−kp,kp=Positive Macro News CountNegative Macro News Count
4. Panic Indicator (PI) Range
PI values range from -1 to +1:
PI close to -1 → Extreme fear (all macroeconomic news is negative).
PI close to 0 → Neutral market sentiment.
PI close to +1 → Optimistic market sentiment.
Summary
The Panic Indicator (PI) quantifies investor sentiment through text analysis of business news articles. It evaluates positive and negative macroeconomic news and calculates a sentiment ratio using a predefined formula. The PI value, ranging from -1 (extreme fear) to +1 (optimism), provides insights into market psychology.
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