towards a better measure of business proximity: topic modeling for industry intelligence
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MISQ Workshop, Leuven, Belgium, August 2015
Towards A Better Measure of Business Proximity:
Topic Modeling for Industry Intelligence
1
August 13th 2015
Zhan (Michael) Shi Gene Moo Lee* Andrew B. Whinston
Arizona State University
University of Texasat Arlington
University of Texasat Austin
* presenter
MISQ Workshop, Leuven, Belgium, August 2015 2
Business proximity: motivation
MISQ Workshop, Leuven, Belgium, August 2015 2
Business proximity: motivation• To measure firms’ dyadic relatedness in spaces of product, market, and
technology
• Essential in competitive/industry intelligence
• Building block in strategy/industrial organization fields
MISQ Workshop, Leuven, Belgium, August 2015 2
Business proximity: motivation• To measure firms’ dyadic relatedness in spaces of product, market, and
technology
• Essential in competitive/industry intelligence
• Building block in strategy/industrial organization fields
• Existing methods
• Common industry membership (Wang and Zajac 2007)
• Patent holdings (Stuart 1998, Mowery et al. 1998)
• Geographic distance (Mitsuhashi and Greve 2009)
MISQ Workshop, Leuven, Belgium, August 2015 2
Business proximity: motivation• To measure firms’ dyadic relatedness in spaces of product, market, and
technology
• Essential in competitive/industry intelligence
• Building block in strategy/industrial organization fields
• Existing methods
• Common industry membership (Wang and Zajac 2007)
• Patent holdings (Stuart 1998, Mowery et al. 1998)
• Geographic distance (Mitsuhashi and Greve 2009)
• These approaches have strong data requirement
• Typically scarce for early stage high-tech startups
MISQ Workshop, Leuven, Belgium, August 2015 3
Our Big Data approach
MISQ Workshop, Leuven, Belgium, August 2015 3
Our Big Data approach• Our approach: a unified framework that integrates
MISQ Workshop, Leuven, Belgium, August 2015 3
Our Big Data approach• Our approach: a unified framework that integrates
• Machine learning (LDA topic model)
MISQ Workshop, Leuven, Belgium, August 2015 3
Our Big Data approach• Our approach: a unified framework that integrates
• Machine learning (LDA topic model)
• Statistical network model (ERGM)
MISQ Workshop, Leuven, Belgium, August 2015 3
Our Big Data approach• Our approach: a unified framework that integrates
• Machine learning (LDA topic model)
• Statistical network model (ERGM)
• Big Data technologies (Cloud, NoSQL, Condor)
MISQ Workshop, Leuven, Belgium, August 2015 3
Our Big Data approach• Our approach: a unified framework that integrates
• Machine learning (LDA topic model)
• Statistical network model (ERGM)
• Big Data technologies (Cloud, NoSQL, Condor)
• Outperforming existing approaches
MISQ Workshop, Leuven, Belgium, August 2015 3
Our Big Data approach• Our approach: a unified framework that integrates
• Machine learning (LDA topic model)
• Statistical network model (ERGM)
• Big Data technologies (Cloud, NoSQL, Condor)
• Outperforming existing approaches
• Automatic processing (vs. manual inspection)
MISQ Workshop, Leuven, Belgium, August 2015 3
Our Big Data approach• Our approach: a unified framework that integrates
• Machine learning (LDA topic model)
• Statistical network model (ERGM)
• Big Data technologies (Cloud, NoSQL, Condor)
• Outperforming existing approaches
• Automatic processing (vs. manual inspection)
• Dynamic industry definition (vs. static)
MISQ Workshop, Leuven, Belgium, August 2015 3
Our Big Data approach• Our approach: a unified framework that integrates
• Machine learning (LDA topic model)
• Statistical network model (ERGM)
• Big Data technologies (Cloud, NoSQL, Condor)
• Outperforming existing approaches
• Automatic processing (vs. manual inspection)
• Dynamic industry definition (vs. static)
• Finer granularity (vs. discrete)
MISQ Workshop, Leuven, Belgium, August 2015 3
Our Big Data approach• Our approach: a unified framework that integrates
• Machine learning (LDA topic model)
• Statistical network model (ERGM)
• Big Data technologies (Cloud, NoSQL, Condor)
• Outperforming existing approaches
• Automatic processing (vs. manual inspection)
• Dynamic industry definition (vs. static)
• Finer granularity (vs. discrete)
• Relaxed data requirement (vs. patent, location)
MISQ Workshop, Leuven, Belgium, August 2015 4
Main contributions
MISQ Workshop, Leuven, Belgium, August 2015 4
Main contributions
1. Propose a transformative data-analytic framework for understanding dynamic startup landscape
MISQ Workshop, Leuven, Belgium, August 2015 4
Main contributions
1. Propose a transformative data-analytic framework for understanding dynamic startup landscape
2. Construct an explicit network structure for understanding firm interactions
MISQ Workshop, Leuven, Belgium, August 2015 4
Main contributions
1. Propose a transformative data-analytic framework for understanding dynamic startup landscape
2. Construct an explicit network structure for understanding firm interactions
3. Implement a BI for competitive intelligence in U.S. high-tech industry
MISQ Workshop, Leuven, Belgium, August 2015 5
Roadmap1. CrunchBase Data
2. Data-Analytics based Business Proximity
3. Empirical Validation
4. Empirical Application on M&A Analysis
5. Industry Intelligence System
6. Conclusion and implication
MISQ Workshop, Leuven, Belgium, August 2015 6
Roadmap1. CrunchBase Data
2. Data-Analytics based Business Proximity
3. Empirical Validation
4. Empirical Application on M&A Analysis
5. Industry Intelligence System
6. Conclusion and implication
MISQ Workshop, Leuven, Belgium, August 2015
CrunchBase data
7
• CrunchBase: open database (“Wikipedia”) of high-tech industry
• Data collection time: April 2013 ~ April 2015
• 24,382 U.S. high-tech companies (1.4% public, 5.7 years old)
• HQ location, CB-defined industry sector, key personnels, M&A, investments, business summary
• States: CA, NY, MA, TX (stats page)
• Industries: software, web, e-commerce, ad, mobile
MISQ Workshop, Leuven, Belgium, August 2015
Data: networked business
8
MISQ Workshop, Leuven, Belgium, August 2015
Data: networked business
8
• M&A: 1689 total
• cross-state: 62.6%
• cross-sector: 63.6%
• top 10 buyers: 14.3% (skewed)
MISQ Workshop, Leuven, Belgium, August 2015
Data: networked business
8
• M&A: 1689 total
• cross-state: 62.6%
• cross-sector: 63.6%
• top 10 buyers: 14.3% (skewed)
• Investments: 531 total
MISQ Workshop, Leuven, Belgium, August 2015
Data: networked business
8
• M&A: 1689 total
• cross-state: 62.6%
• cross-sector: 63.6%
• top 10 buyers: 14.3% (skewed)
• Investments: 531 total
• Job mobility: 19K total
MISQ Workshop, Leuven, Belgium, August 2015 9
Roadmap1. CrunchBase Data
2. Data-Analytics based Business Proximity
3. Empirical Validation
4. Empirical Application on M&A Analysis
5. Industry Intelligence System
6. Conclusion and implication
MISQ Workshop, Leuven, Belgium, August 2015
Our approach on business proximity
10
MISQ Workshop, Leuven, Belgium, August 2015
Our approach on business proximity
• Objectives: data-driven, scalability, finer granularity, little data requirements
10
MISQ Workshop, Leuven, Belgium, August 2015
Our approach on business proximity
• Objectives: data-driven, scalability, finer granularity, little data requirements
• Approach: topic modeling [Blei et al. 2003]
10
MISQ Workshop, Leuven, Belgium, August 2015
Our approach on business proximity
• Objectives: data-driven, scalability, finer granularity, little data requirements
• Approach: topic modeling [Blei et al. 2003]
• unsupervised learning to discover latent “topics” from a large collection of documents
10
MISQ Workshop, Leuven, Belgium, August 2015
Our approach on business proximity
• Objectives: data-driven, scalability, finer granularity, little data requirements
• Approach: topic modeling [Blei et al. 2003]
• unsupervised learning to discover latent “topics” from a large collection of documents
10
24K company descriptions
MISQ Workshop, Leuven, Belgium, August 2015
Our approach on business proximity
• Objectives: data-driven, scalability, finer granularity, little data requirements
• Approach: topic modeling [Blei et al. 2003]
• unsupervised learning to discover latent “topics” from a large collection of documents
10
LDA24K company descriptions
MISQ Workshop, Leuven, Belgium, August 2015
Our approach on business proximity
• Objectives: data-driven, scalability, finer granularity, little data requirements
• Approach: topic modeling [Blei et al. 2003]
• unsupervised learning to discover latent “topics” from a large collection of documents
10
LDA
Industry-wide topics
24K company descriptions
MISQ Workshop, Leuven, Belgium, August 2015
Our approach on business proximity
• Objectives: data-driven, scalability, finer granularity, little data requirements
• Approach: topic modeling [Blei et al. 2003]
• unsupervised learning to discover latent “topics” from a large collection of documents
10
LDA
Industry-wide topics
Company’s topics
24K company descriptions
MISQ Workshop, Leuven, Belgium, August 2015
Business proximity from topic model
• Business proximity pb(i,j) between firms i and j
• Cosine similarity of topic vectors Ti and Tj
• Range: 0 (no commonality) ~ 1 (same business components)
11
MISQ Workshop, Leuven, Belgium, August 2015
Business topic modelPer-word
business topic assignment
Observedbusiness
descriptions
Businesstopics
Per-firmbusiness
topics distrib.
Topic parameter
Proportions parameter
K: # topicsD: # companiesN: # words
MISQ Workshop, Leuven, Belgium, August 2015
LDA topic model with CrunchBase
13
Click here for the complete list of 50 topics
Video/music
Energy
Sports
Healthcare
MISQ Workshop, Leuven, Belgium, August 2015 14
Roadmap1. CrunchBase Data
2. Data-Analytics based Business Proximity
3. Empirical Validation
4. Empirical Application on M&A Analysis
5. Industry Intelligence System
6. Conclusion and implication
MISQ Workshop, Leuven, Belgium, August 2015
Validation: compare with existent method
15
• Baseline: category match (industry co-membership)
• 0 (different industry): 0.06
• 1 (same industry): 0.12
• Pearson corr. coef. between bizprox and category match = 0.11 (t-stat 61.94, p-val < 2.2e-16)
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0.00
0.25
0.50
0.75
1.00
0 1category_match
busi
ness
pro
xim
ity ([
0, 1
])
MISQ Workshop, Leuven, Belgium, August 2015
Validation: Leading effect on business networks
16
• mean(business proximity)
• 0.293 (394 M&A pairs)
• 0.224 (129 invests pairs)
• 0.218 (9792 job mobility pairs)
• 0.068 (random pairs)
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0.00
0.25
0.50
0.75
1.00
M&A invest jobmob randomgroup
busi
ness
pro
xim
ity ([
0, 1
])
MISQ Workshop, Leuven, Belgium, August 2015 17
Roadmap1. CrunchBase Data
2. Data-Analytics based Business Proximity
3. Empirical Validation
4. Empirical Application on M&A Analysis
5. Industry Intelligence System
6. Conclusion and implication
MISQ Workshop, Leuven, Belgium, August 2015
Analysis on high-tech M&A network
18
MISQ Workshop, Leuven, Belgium, August 2015
Analysis on high-tech M&A network
• Objective: examine the relationship between likelihood of M&A matching and nodal/dyadic characteristics
• Nodal attributes: state, industry, previous M&A
• Dyadic proximities: business, geographic, investment, social
18
MISQ Workshop, Leuven, Belgium, August 2015
Analysis on high-tech M&A network
• Objective: examine the relationship between likelihood of M&A matching and nodal/dyadic characteristics
• Nodal attributes: state, industry, previous M&A
• Dyadic proximities: business, geographic, investment, social
• Challenges: recognize networked business environment
• Model all M&A deals as a network/graph
• Use statistical network model: ERGM or p* model
18
MISQ Workshop, Leuven, Belgium, August 2015
ERGM for M&A network
19
degree selective mixing proximity
MISQ Workshop, Leuven, Belgium, August 2015
ERGM for M&A network
ERGM (Exponential Random Graph Model):
19
degree selective mixing proximity
MISQ Workshop, Leuven, Belgium, August 2015
ERGM for M&A network
ERGM (Exponential Random Graph Model): • Probability of realizing a graph = a function of the
graph’s statistics [Robins et al. 2007]
19
degree selective mixing proximity
MISQ Workshop, Leuven, Belgium, August 2015
ERGM for M&A network
ERGM (Exponential Random Graph Model): • Probability of realizing a graph = a function of the
graph’s statistics [Robins et al. 2007]• Inter-firm proximity: business, geo, social, invest
19
degree selective mixing proximity
MISQ Workshop, Leuven, Belgium, August 2015
ERGM for M&A network
ERGM (Exponential Random Graph Model): • Probability of realizing a graph = a function of the
graph’s statistics [Robins et al. 2007]• Inter-firm proximity: business, geo, social, invest• Selective mixing: 50 states, 30 industry sectors
19
degree selective mixing proximity
MISQ Workshop, Leuven, Belgium, August 2015
ERGM for M&A network
ERGM (Exponential Random Graph Model): • Probability of realizing a graph = a function of the
graph’s statistics [Robins et al. 2007]• Inter-firm proximity: business, geo, social, invest• Selective mixing: 50 states, 30 industry sectors• Degree distribution: node degree, M&A experiences
19
degree selective mixing proximity
MISQ Workshop, Leuven, Belgium, August 2015
Estimation setup
20
MISQ Workshop, Leuven, Belgium, August 2015
Estimation setup• Dataset
• US companies founded from 2008 to 2012: |V| = 24,382
• All dyadic/nodal attributes collected in April 2013
• M&A transactions (April 2013~April 2015): |E| = 394
20
MISQ Workshop, Leuven, Belgium, August 2015
Estimation setup• Dataset
• US companies founded from 2008 to 2012: |V| = 24,382
• All dyadic/nodal attributes collected in April 2013
• M&A transactions (April 2013~April 2015): |E| = 394
• Estimate our ERGM M&A model
• Randomly sample 25% companies for computational feasibility
• Run 100 condor jobs with different samples
• Estimate model coefficients by Markov chain Monte Carlo (MCMC) maximum likelihood estimation (MLE)
20
MISQ Workshop, Leuven, Belgium, August 2015
ERGM results from a sample
21
MISQ Workshop, Leuven, Belgium, August 2015
Empirical results: M&A & proximity
22
+++
MISQ Workshop, Leuven, Belgium, August 2015
Empirical results: M&A & proximity
22
• Proximities are normalized for comparison
+++
MISQ Workshop, Leuven, Belgium, August 2015
Empirical results: M&A & proximity
22
• Proximities are normalized for comparison• 1.0 std increase in business proximity
+++
MISQ Workshop, Leuven, Belgium, August 2015
Empirical results: M&A & proximity
22
• Proximities are normalized for comparison• 1.0 std increase in business proximity
= 3.64 std increase in social proximity
+++
MISQ Workshop, Leuven, Belgium, August 2015
Empirical results: M&A & proximity
22
• Proximities are normalized for comparison• 1.0 std increase in business proximity
= 3.64 std increase in social proximity= 6.89 std increase in investment proximity
+++
MISQ Workshop, Leuven, Belgium, August 2015
Empirical results: complementarity
23
+++-
MISQ Workshop, Leuven, Belgium, August 2015
Empirical results: complementarity
23
• Original term (+) / Squared term (-) -> reverse U-curve
+++-
MISQ Workshop, Leuven, Belgium, August 2015
Empirical results: complementarity
23
• Original term (+) / Squared term (-) -> reverse U-curve• Interpretation: M&A transactions between two firms that
have complementarity but not substitutes
+++-
MISQ Workshop, Leuven, Belgium, August 2015
Empirical results: complementarity
23
• Original term (+) / Squared term (-) -> reverse U-curve• Interpretation: M&A transactions between two firms that
have complementarity but not substitutes• Can find this nonmonotonic relation because our
proximity has (1) comprehensiveness and (2) continuity
+++-
MISQ Workshop, Leuven, Belgium, August 2015 24
Roadmap1. CrunchBase Data
2. Data-Analytics based Business Proximity
3. Empirical Validation
4. Empirical Application on M&A Analysis
5. Industry Intelligence System
6. Conclusion and implication
MISQ Workshop, Leuven, Belgium, August 2015
Transformative platform for M&A market
25
MISQ Workshop, Leuven, Belgium, August 2015
Transformative platform for M&A market• High-profile M&A involving startups (reference)
• Search for “fitted” startups in huge venture universe
25
MISQ Workshop, Leuven, Belgium, August 2015
Transformative platform for M&A market• High-profile M&A involving startups (reference)
• Search for “fitted” startups in huge venture universe
• “Data-driven” platform for M&A matching and startup search
1. M&A executives to find M&A targets
2. entrepreneurs to position products
3. VCs to monitor niche markets
4. Analysts to examine the industry trends
25
MISQ Workshop, Leuven, Belgium, August 2015
Transformative platform for M&A market• High-profile M&A involving startups (reference)
• Search for “fitted” startups in huge venture universe
• “Data-driven” platform for M&A matching and startup search
1. M&A executives to find M&A targets
2. entrepreneurs to position products
3. VCs to monitor niche markets
4. Analysts to examine the industry trends
• Implemented a cloud-based IS based on proposed business proximity
25
MISQ Workshop, Leuven, Belgium, August 2015
Cloud-based platform design
26
Front end
Back end
Data collector (Python)
Raw data (MongoDB)
Industry data (Crunchbase, etc.)
Topic model builder(Scala)
Processed data (MongoDB)
Webpages(HTML/CSS, Javascript)
Company meta info
(JSON)
Business proximity (JSON)
UsersSearch
Company infoCloud DB
(Google Datastore, Google Cloud Storage)
API Engine(Google App
Engine)
Big Data and Cloud technologies: Cronjob, NoSQL, Python, Scala, Condor, Google Cloud (Storage, App Engine, Datastore) and more
MISQ Workshop, Leuven, Belgium, August 2015
Algorithm: Find N nearest competitors
27
• Goal: find nearest competitors based on business proximity
• Idea: • instead of exhaustive
comparison: O(n2)• leverage sparseness
of topic distributions• minimize # of
comparisons
MISQ Workshop, Leuven, Belgium, August 2015
Algorithm: accuracy and speed
28
● ●●
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90.0
92.5
95.0
97.5
100.0
th=0.00 th=0.10 th=0.15 th=0.20 th=0.30algorithms
Accu
racy
(%) variable
●
●
●
●
●
top10top20top30top50top100
●
●
●
●
●
●
0
100
200
300
brute−force fast(th=0.0) fast(th=0.1) fast(th=0.15) fast(th=0.20) fast(th=0.30)algorithms
Num
ber o
f com
paris
ons
(milli
on)
• Our algorithm can detect 50 nearest competitors • with 92.5% accuracy using only 3% of calculations• with 100% accuracy using 36% of calculations
MISQ Workshop, Leuven, Belgium, August 2015
Platform UI: Find competitors
29
MISQ Workshop, Leuven, Belgium, August 2015
Search firms by business components
30
MISQ Workshop, Leuven, Belgium, August 2015
Search firms by business components
31
MISQ Workshop, Leuven, Belgium, August 2015 32
Roadmap1. CrunchBase Data
2. Data-Analytics based Business Proximity
3. Empirical Validation
4. Empirical Application on M&A Analysis
5. Industry Intelligence System
6. Conclusion and implication
MISQ Workshop, Leuven, Belgium, August 2015
Summary
33
MISQ Workshop, Leuven, Belgium, August 2015
Summary1. Proposed new machine learning-based business
proximity measures for industry intelligence
• Empirical validation with high-tech industry data
33
MISQ Workshop, Leuven, Belgium, August 2015
Summary1. Proposed new machine learning-based business
proximity measures for industry intelligence
• Empirical validation with high-tech industry data
2. Built and estimated a statistical network model to understand M&A in high-tech industry
33
MISQ Workshop, Leuven, Belgium, August 2015
Summary1. Proposed new machine learning-based business
proximity measures for industry intelligence
• Empirical validation with high-tech industry data
2. Built and estimated a statistical network model to understand M&A in high-tech industry
3. Developed a prototype platform for industry intelligence
33
MISQ Workshop, Leuven, Belgium, August 2015
Implications
34
MISQ Workshop, Leuven, Belgium, August 2015
Implications1. For managers:
• Demonstrate the value of organizing unstructured data
• Understand the trade-off between old and new business proximities
• Provide a practical BI implementation based on business proximity
34
MISQ Workshop, Leuven, Belgium, August 2015
Implications1. For managers:
• Demonstrate the value of organizing unstructured data
• Understand the trade-off between old and new business proximities
• Provide a practical BI implementation based on business proximity
2. For researchers:
• Provide alternative/complementary approach for industry structure
• Show evidence on non-monotone relationship between business proximity and M&A matching
• Statistical network model to capture “networked” business
34
MISQ Workshop, Leuven, Belgium, August 2015
Future directions
35
MISQ Workshop, Leuven, Belgium, August 2015
Future directions1. Extending M&A analytics
1. Dynamic network model; Predictive analytics
2. Incorporate other data sources (e.g., firm size, patent, profitability, liquidity)
35
MISQ Workshop, Leuven, Belgium, August 2015
Future directions1. Extending M&A analytics
1. Dynamic network model; Predictive analytics
2. Incorporate other data sources (e.g., firm size, patent, profitability, liquidity)
2. Extending business proximity and topic model
1. Endogenize number of topics (Hierarchical Dirichlet process)
2. Topic model with word dependencies (Poisson Markov Random field)
35
MISQ Workshop, Leuven, Belgium, August 2015
Thank you!
36
MISQ Workshop, Leuven, Belgium, August 2015
Backup slides
37
MISQ Workshop, Leuven, Belgium, August 2015
Data: states and industries
38
AKSDWVWYNDMSMTNMARHIIDMEVTALIARIOKNELAKSDESCKYNHINWITNDCMONVCTUTMNMIORMDOHAZNCVACONJGAPAIL
WAFLTXMANYCA
0 2000 4000 6000 8000count
state
legalsemiconductor
securityeducation
searchcleantech
network_hostinghardware
public_relationsbiotech
enterpriseconsulting
games_videomobile
advertisingecommerce
otherweb
software
0 1000 2000 3000 4000count
industry
Back to main
MISQ Workshop, Leuven, Belgium, August 2015
Full topic model from CrunchBase
39
Back to main
MISQ Workshop, Leuven, Belgium, August 2015
Other existing proximity measures
40
• Common ownership • VCs • angels • institutions
• Count shared investors of two firms
• Geographic distance • lat, long • city • state
• Use great circle distance of two coord.
• Social linkage • board members • executives • developers
• Count common people in two firms
Back to Main
MISQ Workshop, Leuven, Belgium, August 2015
M&A and proximity measures
41
• Measure the distances of company pairs • M&A matched pairs (red) • Random pairs (green)
• M&A pairs have closer business/geo proximity values
0.00
0.25
0.50
0.75
1.00
0.00 0.25 0.50 0.75 1.00Empirical CDF
busi
ness
pro
xim
ity ([
0, 1
])
groupM&Arandom
0
1000
2000
3000
4000
5000
0.00 0.25 0.50 0.75 1.00Empirical CDF
geog
raph
ic d
ista
nce
(km
)
groupM&Arandom
Back to Main
MISQ Workshop, Leuven, Belgium, August 2015
ERGM for statistical network model• Exponential Random Graph Model (ERGM)
• Erdos and Renyi (1959), Newman (2003)
• Explain an observed graph based on node/edge properties
• Want to estimate that maximizes Pr(Y=y)
42
where • z_k(y) = a certain property of the graph y • theta_k = parameter for k-th statistic (want to estimate this) • Psi = normalization constant (require exponential computation) • K = # of statistics we are interested in
Back to Main
MISQ Workshop, Leuven, Belgium, August 2015
Why ERGM?
• M&A deals are interdependent
• Conventional statistical models (logit, probit) assume independency: treat each M&A deal separately
• Approach: use statistical network model “Exponential Random Graph Model (ERGM)”
43
photo photo
photo
video blog
face recognition
Back to Main
MISQ Workshop, Leuven, Belgium, August 2015
ERGM 101• Let Y = <V, E> be an M&A graph, where
• V is the set of companies (nodes)
• E is the set of M&A transactions (undirected edges)
44
Want to explain an observed graph Y with statistics on E and V. Some notations before moving on...
Back to Main
MISQ Workshop, Leuven, Belgium, August 2015
Our M&A model (conditional form)
45
Back to Main
MISQ Workshop, Leuven, Belgium, August 2015
M&A model notations
46
Back to Main
MISQ Workshop, Leuven, Belgium, August 2015
Density and node degree
Degree > 2 coefficient is positive
Power law is observed
Edge coefficient is a constant for the model
47
degree selective mixing proximity
Back to main
MISQ Workshop, Leuven, Belgium, August 2015
Selective mixing: state locations
• Selective mixing holds for state locations
• CA, MA, NJ, NY, TX, WA
48Back to main
MISQ Workshop, Leuven, Belgium, August 2015
Selective mixing: industry sectors
● Selective mixing holds for industry sectors, but it is coarse grained
● Proposed business proximity provides even finer grained measures49
degree selective mixing proximity
Back to main
MISQ Workshop, Leuven, Belgium, August 2015 50
M&A in high-tech industryBack to main
top related