1. The document discusses quantitative methods and tools for studying discrete events, such as event study analysis, event history analysis, and optimal matching analysis. 2. These methods can be used to analyze various types of discrete events, including organizational events, industry events, and sequences of events. 3. The document provides examples of how these methods have been applied in research on topics like competitive dynamics after market shocks and the implications of growth patterns for firms' resistance to economic shocks. 4. Advice is given on potential challenges of event-driven research and available "apps" or tools to help with tasks like data collection, coding events, and statistical analysis

1. Quantitative Methods & Tools
for Studying Discrete Events
www.eventstudytools.com

2. How To Research Discrete Events?
Finance Theories:
• Market Efficiency Something about a…
Phenomenon
Hypothesis • Specific event type
• Theories on: regulation, • Sequence of events
optimal capital structure,
dividends, stock splits
and repurchases Research
Management Theories:
Theoretical Methods & Tools
• Behavioral Theory
Perspective
• Population Ecology
• Event Study Analysis1
Theory
• Event History Analysis2
• Resource Based View
• Optimal Matching2
• (…)
• …
Notes: 1 Cross-sectional method, 2 Longitudinal method © www.eventstudytools.com (2012) // Slide 2

3. Phenomena
„Discrete
Incidents of interest that occur at specific moments in time
Events“
• Organizational events (competitive moves)
• M&A / divestitures / joint-ventures / alliances
• Earnings announcements
• Stock splits
Individual • …. Finance &
Events • Industry events Strategy
• Regulatory change
• Technological shocks
• Environmental catastrophes
• ….
• Sequences of events
Sequences • Patterns of firm behavior Strategy &
of Events • Occurrences of economic shocks Economics
• … © www.eventstudytools.com (2012) // Slide 3

4. Methods (1/3): Event Study Methodology (ESM)
Astra-Zeneca merger announcement on April 6, 1999
• Event studies numerically capture the
impact of discrete events on firm values
• ESM grounds on the efficient-market
hypothesis. It assumes that new information
is timely reflected in stock prices.
event
• ESM yields a short-term performance window
measures (ARs, CARs, CAARs)
Prediction Models:
Market Model, CAPM,
Dependent Variables Fama-French 3 Factor
Event Window:
Model, APT
leakage, post-event
Abnormal Returns (ARs): ‚digestion period‘
Grouping:
Cumulative Abnormal Returns (CARs): Setting the grouping
variable
Cumulative Average Abnormal Returns (CAARs):
© www.eventstudytools.com (2012) // Slide 4

5. Methods (2/3): Event History Analysis (EHA)1
Hurrikane Katrina • EHA allows analyzing the social
August 25, 2005
processes that lead towards the
Restructuring moves
Duration / Spell length occurrence of events (thus its
Firm 1 name ‚event history‘)
Firm 2
• Grounds on failure-likelihoods
Firm 3
Firm 4
derived from ‚spell lengths‘
Firm 5 • Yields coefficients that express
Firm 6
how different IVs/covariates
t0 0 >> Clock Variable >> t impact the failure-likelihoods
Dependent Variable
H Katrina Example: The inverted clock-variable has a negative impact on the Hazard Rate/the
instantaneous probability that firms engage in restructuring moves
1 Synonym names: Survival Analysis, Duration Analysis (Economics), Failure Time Analysis, Hazard Time
© www.eventstudytools.com (2012) // Slide 5
Analysis, Transition Analysis, Reliability Analysis (Engineering)

6. Methods (3/3): Optimal Matching Algorithm (OMA)
• Events
• Action types
• OMA is a technique for the analysis of Sequences (Si) of Events • Scaling attributes
sequence data ( process theory) • Magnitude
Sequence 1 • Exploration/
• Methodologically, OMA is a propensity Exploitation
Sequence 2
score method to optimize/minimize
Sequence 3
the distance between „matched-
(…)
pairs‟ of sequences
Benchmarks: Typical Patterns (T) or ‚Gestalt„ Properties (G)
• Technically, OMA is an OR algorithm
minimizing the costs associated with Market Entry (T)
individual transformation functions Rhythm (G)
Dependent Variables (contingent on use)
Comparison The distance between individual sequences (S vs. S) / sequences and
of Sequences typologies/Gestalt properties (S vs. T, resp. S vs. G)
Through clustering of closely related sequences, one can inductively derive
Clustering typologies. These can then be used as dependent or independent
variables in further analysis (i.e., as dummy variables).
© www.eventstudytools.com (2012) // Slide 6

7. RESEARCH EXAMPLE & TOOLS
© www.eventstudytools.com (2012) // Slide 7

8. Example (1/6): The Phenomenon
"When Industries Shake: How Market Shocks Affect Competitive Behavior"
in USD bn,
indexed to 2007 Insured catastrophe losses 1998-2007
120
Earthquake/tsunami Katrina
100
Man-made disasters
Weather-related Nat Cats
80
60
Lothar/Bart
40
9/11
20
0
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
2001-09-11 2005-08-25
Source: Sigma Research
© www.eventstudytools.com (2012) // Slide 8

9. Example (2/6): Choice of Research Method
"When Industries Shake: How Market Shocks Affect Competitive Behavior"
Theory on Stock Market Perception:
Event
Study
Following market shocks stock markets respond differently to XYZ.
Theory on Firm Behavior Event
(Focus on Firms‟ Inclinations to do sth and its Determinants): History
Following market shocks firms are more/less inclined to do XYZ. Analysis
Theory on Firm Behavior (Focus on Process/Event Sequence):
Optimal
Matching
Before and after market shocks patterns of XYZ differ.
© www.eventstudytools.com (2012) // Slide 9

10. Example (3/6): “Competitive Dynamics”
"From Crisis to Opportunity: How Market shocks Impact Interfirm Rivalry"
Time
Firm resources Change in firm
resources
1 3
Rival action Firm action(s) Rival action
2
Industry structure and events Change in industry
structure
1
• Competitive pressure (IV): Competitive pressure refers to the aggregate number of actions taken
by a firm's rivals and expresses the pressure rivals jointly exert on a firm to take new competitive
action (Zuchhini & Kretschmer, 2011; Hsieh & Chen, 2010)
• Trend toward the respective action type (IV): Given that also a firm's choice of competitive action
may be driven by prior rival actions (Lieberman & Asaba, 2006/Asaba & Lieberman, 2012), we
capture trends in the overall body of rival actions prior to the focal action
2 Impact of market shock (IV/MV): Impact and fading effect of a recent market shock
3 Inclination to take new competitive action (DV): Similar to Hsieh & Chen (2010) and Yu & Canella
(2007), we study how firms' inclinations to take action change upon variations in a set of explanatory
variables (Event history analysis; Cox model). © www.eventstudytools.com (2012) // Slide 10
Figure adopted from Smith, Ferrier, and Ndofor (2001: 348)

11. Example (4/6): Theory and Hypotheses
H1: Higher degrees of competitive pressure increase a firm's inclination to take
new competitive action.
H2: A trend of rivals to pursue a distinct competitive action type increases a
firm's inclination to take new competitive action (of this type).
Baseline
New
{ Market shocks imply uncertainty; Uncertainty breeds opportunities to outmaneuver
rivals; Mutual forbearance equilibriums will fail }
H3: Market shocks increase a firm's inclination to take new competitive action.
{ Imitation of rival actions that have lost their validity due to fundamental changes in the
environment does not make sense; Individual exposures to the shock require
individual responses }
H4: Market shocks negatively moderate the relationship between competitive
pressure and a firm's inclination to take new competitive action.
H5: Market shocks negatively moderate the relationship between the trend of
rivals to pursue a distinct competitive action type and a firm's inclination to
engage in this competitive action type.
© www.eventstudytools.com (2012) // Slide 11

12. Example (5/6): Empirical Strategy
1 Press release scraping Industry Context
Website 1.1 Sample • Structural industry characteristics
Website A
Company
Website selection • Industry events
Website AA
Company
Company
Company A
Competitive Behavior
• Chronology, composition, and density of
competitive action sequence
Press release
archive • Patterns of and deviations from typical
1.2 Mass download competitive behavior
1.3 Consolidation Categorization Competitive Action
and clean-up of of news items
• Date
news items 3
• Type of competitive action
• Abnormal returns
2 4
• Press release text (for additional coding)
Identification of Event study
announcement dates
© www.eventstudytools.com (2012) // Slide 12

13. Example (6/6): Research Findings
Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Coef. SE Coef. SE Coef. SE Coef. SE Coef. SE Coef. SE
Firm size (0.1† (0.1) (0.1† (0.1) (0.1† (0.1) (0.1† (0.1) (0.1† (0.1) (0.1† (0.1)
Business scope -0.2 (0.3) -0.2 (0.3) -0.2 (0.3) -0.2 (0.3) -0.2 (0.3) -0.2 (0.3)
Diversification level (0.3 (0.3) (0.4 (0.3) (0.4 (0.3) (0.3 (0.3) (0.3 (0.3) (0.3 (0.3)
Reinsurance strategy -0.1 (0.4) -0.1 (0.4) -0.1 (0.4) -0.1 (0.4) -0.1 (0.4) -0.1 (0.4)
Financial leverage (1.7 (1.9) (2.0 (1.9) (2.0 (1.9) (2.0 (1.9) (2.0 (1.9) (2.0 (1.9)
Investment strategy (0.5 (0.4) (0.6 (0.4) (0.6 (0.4) (0.6 (0.4) (0.6 (0.4) (0.6 (0.4)
Past performance (1.5 (1.7) (1.0 (1.7) (1.0 (1.7) (0.9 (1.7) (1.0 (1.7) (1.0 (1.7)
Slack 3.1*** (0.7) 3.0*** (0.7) 3.0*** (0.7) 2.9*** (0.7) 2.9*** (0.7) 2.9*** (0.7)
Competitive pressure (0.03* (0.0) (0.03* (0.0) (0.03* (0.0) (0.03* (0.0) (0.03* (0.0)
Imitation (0.01* (0.0) (0.01* (0.0) (0.01* (0.0) (0.01* (0.0)
Market shock (0.7* (0.3) (0.7* (0.3) (0.7* (0.3)
Market shock x -0.0 (0.0) -0.0 (0.0)
Competitive pressure
Market shock x Imitation -0.2** (0.1) -0.2** (0.1)
Observations 2'467 2'467 2'467 2'467 2'467 2'467
Log-Likelihood -16831.52 -16826.30 -16825.89 -16823.33 -16819.65 -16819.61
Chi-Square 26.79*** 32.58*** 36.68*** 36.65*** 66.18*** 69.66***
© www.eventstudytools.com (2012) // Slide 13

14. Operational Challenges of Event-Driven Research
1 2 3 4
Allocation of Coding of Statistical
Data Collection
Events in Time Events Analyses
• „Secondary • Manual step • Manual review • Excel
databases“ (e.g., • Regular • Computer-aided • Eventus
Thomson One expressions text analysis • EST
Banker) • STATA / SPSS
• „Web harvesting“
Turning Qualitative Text into Quantified
Metrics and Time-Series
Making sense out of unstructured texts from the Research trends: “News Analytics”
Internet  Web 3.0 (aka the „semantic web“) (Finance), text analysis (Strategy)
© www.eventstudytools.com (2012) // Slide 14

15. „Apps‟ for Studying Discrete Events
2012 Best
Conference PhD
Paper Prize (SMS) 2012 William H.
2010 Best Paper Newman Award
Journal of Strategy Finalist (AOM)
and Management
.com
News Retrieval „Abnormal Return
RegEx-Based
Services Calculator“
„Date Identifier“
Data Downlink for Computer-Aided „Text
Yahoo!Finance Analyzer“ with Scaling and
Categorization Functions
© www.eventstudytools.com (2012) // Slide 15

16. The “News Analytics” – Framework
1 Press release scraping
Website 1.1 Sample selection
Website A
Company
Website
Website AA
Company
Company
Company A
Corporate News Stream
• Chronology, composition, and density of
corporate news stream
Press release
archive • Patterns of and deviations from typical
information flow
1.2 Mass download
1.3 Consolidation Categorization Corporate News
and clean-up of of news items
news items • Date
3
• Type of competitive action
• Abnormal returns
2 4
• Press release text (for additional coding)
Identification of Event study
announcement dates
© www.eventstudytools.com (2012) // Slide 16

17. My Personal
Advice on Writing a
„Quant Thesis‟
Based on Events
© www.eventstudytools.com (2012) // Slide 17

18. “Inspiring References” for Finding an own Thesis Topic (1/2)
Eberhard, K. (2012). Implications of pace, mechanisms, and rhythm of growth for
firms' resistance to economic shocks. St. Gallen: University of St. Gallen,
Institute of Management.
Fama, E. F. (1976). Foundations of finance. New York: Basic Books.
Ketchen, D. J. & Palmer, T. (1999). Strategic responses to poor organizational
performance: A test of competing perspectives. Journal of Management, 25: 683-
706.
Laughlin, R. C. (1991). Environmental disturbances and organizational transitions
and transformations: Some alternative models. Organizational Studies: 209.
Lieberman, M. B. & Asaba, S. (2006). Why do firms imitate each other? Academy of
Management Review, 31 (2): 366-385.
Lowenstein, J. & Ocasio, W. (2005). Vocabularies of organizing: How language links
culture, cognition, and action in organizations. Unpublished manuscript.
MacMillan, I., McCaffrey, M. & Van Wijk, G. (1985). Competitor's responses to easily
imitated new products: Exploring commercial banking product introductions.
Strategic Management Journal, 6: 75-86.
Meyer, A. D., Brooks, G. R. & Goes, J. B. (1990). Environmental jolts and industry
revolutions: Organizational responses to discontinuous change. Strategic
Management Journal, 11 (Summer special issue): 93-110.
© www.eventstudytools.com (2012) // Slide 18

19. “Inspiring References” for Finding an own Thesis Topic (2/2)
Miller, D. & Friesen, P. (1980). Momentum and revolution in organizational
adaptation. Academy of Management Journal, 23 (4): 591-614.
Nixon, R. D., Hitt, M., Lee, H. & Jeong, E. (2004). Market reactions to announcement
of corporate downsizing actions and implementation strategies. Strategic
Management Journal, 25 (11).
Schimmer, M. (2012). Competitive dynamics in the global insurance industry: strategic
groups, competitive moves, and firm performance. Wiesbaden:
SpringerGabler.
Staw, B. M. (1981). The escalation commitment to a course of action. Academy of
Management Review, 6 (4): 577-587.
Tetlock, P. C. (2008). More than words: Quantifying language to measure firms'
fundamentals. Journal of Finance, 63 (3): 1437-1467.
Tetlock, P. C. (2011). All the news that's fit to reprint: Do investors react to stale
information? Review of Financial Studies, 24 (5): 1481-1512.
Wang, L. & Zajac, E. J. (2007). Alliance or acquisition? A dyadic perspective on
interfirm resource combinations. Strategic Management Journal, 28: 1291-1317.
Yu, T. & Cannella, A. A. (2007). Rivalry between multinational enterprises: An event
history approach. Academy of Management Journal, 50: 665-686.
Zhang, Y. & Wiersema, M. (2009). Stock market reaction to CEO certification: The
signalling role of CEO background. Strategic Management Journal, 30.
© www.eventstudytools.com (2012) // Slide 19

20. ++ Stock Market Response Analysis ++ News Analytics ++
(Data Scraping // Date Identifier // CATA-Tool // Abnormal Return Calculator)
http://www.eventstudytools.com
© www.eventstudytools.com (2012) // Slide 20