Stock market pattern recognition using symbol entropy analysis

Abstract

Market uncertainty and asset pricing are closely related. The emergence of global and local shocks provokes price patterns in stock markets that threaten overall market stability. Using a combination of symbol entropy analysis, regression models, and forecast variance decomposition estimates, we examine the relationships between the frequency of states associated with rising and falling price patterns of the S&P 500 index, its implied volatility, and the fluctuations in the global stock market uncertainty. We find a positive (negative) relationship between the increase (decrease) in global uncertainty and the frequency of extreme bear (bull) episodes. Similarly, we observe the same relationship between increases (decreases) in the implied volatility and the frequency of extreme bear (bull) episodes. Furthermore, joint increases (decreases) in implied volatility and global uncertainty are positively (negatively) associated with the frequency of bear (bull) episodes in the S&P 500 index. Finally, interconnectedness studies confirm the relevance of the mutual influence of the phenomenon and its dynamic nature, which increases the probability of observing shock waves of financial instability in global and local stock markets. Our work highlights the need to consider price patterns and their links to uncertainty as factors of stock market instability. For policymakers, governments, and practitioners, our findings are a call to include events with high global/local impact in the radar of threats to financial stability.