towards a better measure of business proximity: topic modeling for industry intelligence

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


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



technology



• Existing methods

• Common industry membership (Wang and Zajac 2007)

• Patent holdings (Stuart 1998, Mowery et al. 1998)

• Geographic distance (Mitsuhashi and Greve 2009)



technology



• 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


Our Big Data approach


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)









• Finer granularity (vs. discrete)

• Relaxed data requirement (vs. patent, location)


Main contributions


Main contributions

1. Propose a transformative data-analytic framework for understanding dynamic startup landscape


Main contributions


2. Construct an explicit network structure for understanding firm interactions


Main contributions


2. Construct an explicit network structure for understanding firm interactions

3. Implement a BI for competitive intelligence in U.S. high-tech industry


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


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


Data: networked business

8



8

• M&A: 1689 total

• cross-state: 62.6%

• cross-sector: 63.6%

• top 10 buyers: 14.3% (skewed)



8

• M&A: 1689 total




• Investments: 531 total



8

• M&A: 1689 total




• Investments: 531 total

• Job mobility: 19K total


Our approach on business proximity

10



• Objectives: data-driven, scalability, finer granularity, little data requirements

10




• Approach: topic modeling [Blei et al. 2003]

10





• unsupervised learning to discover latent “topics” from a large collection of documents

10






10

24K company descriptions






10

LDA24K company descriptions






10

LDA

Industry-wide topics







10

LDA

Industry-wide topics

Company’s topics



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


Business topic modelPer-word

business topic assignment

Observedbusiness

descriptions

Businesstopics

Per-firmbusiness

topics distrib.

Topic parameter

Proportions parameter

K: # topicsD: # companiesN: # words


LDA topic model with CrunchBase

13

Click here for the complete list of 50 topics

Video/music

Energy

Sports

Healthcare


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

])


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

])


Analysis on high-tech M&A network

18



• 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



• 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


ERGM for M&A network

19

degree selective mixing proximity



ERGM (Exponential Random Graph Model):

19




ERGM (Exponential Random Graph Model): • Probability of realizing a graph = a function of the

graph’s statistics [Robins et al. 2007]

19





graph’s statistics [Robins et al. 2007]• Inter-firm proximity: business, geo, social, invest

19





graph’s statistics [Robins et al. 2007]• Inter-firm proximity: business, geo, social, invest• Selective mixing: 50 states, 30 industry sectors

19





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



Estimation setup

20


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


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


ERGM results from a sample

21


Empirical results: M&A & proximity

22

+++



22

• Proximities are normalized for comparison

+++



22

• Proximities are normalized for comparison• 1.0 std increase in business proximity

+++



22


= 3.64 std increase in social proximity

+++



22


= 3.64 std increase in social proximity= 6.89 std increase in investment proximity

+++


Empirical results: complementarity

23

+++-



23

• Original term (+) / Squared term (-) -> reverse U-curve

+++-



23

• Original term (+) / Squared term (-) -> reverse U-curve• Interpretation: M&A transactions between two firms that

have complementarity but not substitutes

+++-



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

+++-


Transformative platform for M&A market

25


Transformative platform for M&A market• High-profile M&A involving startups (reference)

• Search for “fitted” startups in huge venture universe

25




• “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




• “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


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


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


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

●

●

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●

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top10top20top30top50top100

●

●

●

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●

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


Platform UI: Find competitors

29


Search firms by business components

30


Search firms by business components

31


Summary

33


Summary1. Proposed new machine learning-based business

proximity measures for industry intelligence

• Empirical validation with high-tech industry data

33





2. Built and estimated a statistical network model to understand M&A in high-tech industry

33





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


Implications

34


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


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


Future directions

35


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


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


Thank you!

36


Backup slides

37


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


Full topic model from CrunchBase

39

Back to main


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


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


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


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


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


Our M&A model (conditional form)

45

Back to Main


M&A model notations

46

Back to Main


Density and node degree

Degree > 2 coefficient is positive

Power law is observed

Edge coefficient is a constant for the model

47


Back to main


Selective mixing: state locations

• Selective mixing holds for state locations

• CA, MA, NJ, NY, TX, WA

48Back to main


Selective mixing: industry sectors

● Selective mixing holds for industry sectors, but it is coarse grained

● Proposed business proximity provides even finer grained measures49


Back to main


M&A in high-tech industryBack to main

towards a better measure of business proximity: topic modeling for industry intelligence

Data & Analytics