Smart stock chart annotations to understand jump risk
Explaining jumps in stock prices by using annotations. Annotations establish context between data and the underlying narrative.
Making sense of price movements is a hard thing to do. Looking at quantitative data only gives us an understanding of how prices jumped. It does not tell us why they jumped. Combining price jumps with reasons for why people bought / sold assets provides relevant context.
Jumps in financial price time series are identified using statistical methodology.
Machine-readable news headlines provide context to identified jumps. Using machine learning methods allows to match cause (news) and effect (price movement).
Using tags allows to group together similar causes to identify the frequency and severity of reasons for different event categories.
|Tag||#the number of associated jumps within the time period||1-day changethe on-day price impact of the new information||5-day changethe price development following the next five days||Description|
|Conduct||3||1.86%||-7.69%||News about fraud or any other wrongdoing|
|Operations||3||0.15%||1.32%||News related to actions taken|
|Strategy||2||-6.23%||-8.94%||Strategy-related news about the business directon|
|Outlook||3||-1.78%||-5.90%||News related to overall market expectations|
"the arrival of important new information about that stock that has more than a marginal effect on price." - Merton (1975, p.4)Since then the how generated ample interest in the field whilst the why got sidelined. So let's pick up where Robert Merton left things more than 40 years ago.