Methods and Tools for Studying Discrete Events

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  • 1. Quantitative Methods & Toolsfor Studying Discrete Eventswww.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 ResearchManagement 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 timeEvents“ • Organizational events (competitive moves) • M&A / divestitures / joint-ventures / alliances • Earnings announcements • Stock splitsIndividual • …. Finance &Events • Industry events Strategy • Regulatory change • Technological shocks • Environmental catastrophes • …. • Sequences of eventsSequences • 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 moves1 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 structure1 • Competitive pressure (IV): Competitive pressure refers to the aggregate number of actions taken by a firms 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 firms 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 action2 Impact of market shock (IV/MV): Impact and fading effect of a recent market shock3 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 firms inclination to take new competitive action. H2: A trend of rivals to pursue a distinct competitive action type increases a firms inclination to take new competitive action (of this type).BaselineNew { Market shocks imply uncertainty; Uncertainty breeds opportunities to outmaneuver rivals; Mutual forbearance equilibriums will fail } H3: Market shocks increase a firms 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 firms 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 firms 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 FindingsVariables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Coef. SE Coef. SE Coef. SE Coef. SE Coef. SE Coef. SEFirm 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 pressureMarket shock x Imitation -0.2** (0.1) -0.2** (0.1)Observations 2467 2467 2467 2467 2467 2467Log-Likelihood -16831.52 -16826.30 -16825.89 -16823.33 -16819.65 -16819.61Chi-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-SeriesMaking 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 streamPress 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 PersonalAdvice 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). Competitors 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 thats 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