Towards Modeling M&A in High Tech Industry

December 4th 2013

Gene Moo LeeDepartment of Computer ScienceThe University of Texas at Austin

Research Preparation Exam

Startups in high tech industry

High tech startups are very active these days, thanks to many platforms including

Mobile Platforms Cloud Platforms Financial Platforms

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M&A is important in high tech

Mergers and acquisitions: buying, selling, dividing, combining companies

● Startups (sellers): M&A and IPO are the main exit strategies● Established companies (buyers): pursue innovation by acquisitions

M&A matching and challenges

Q: Can we model M&A matchings?Q: Which factors play important roles in M&A?

Challenges● How to measure proximities among companies

→ Topic modeling for business proximity● How to incorporate the interdependency of M&A deals

→ Random graph model (ERGM)● How to access venture data: mostly private

→ CrunchBase: wikipedia for venture industry● How to make the data accessible

→ Visualization with VentureMap interface

Buyer A Seller BWill they do M&A?

If so, why?

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Academic literature● M&A analysis

○ interview on 12 deals [Graebner, Eisenhardt, Admin. Science Quarterly, 2004]

○ geography [Erel et al., J. Finance, 2012] [Kalnins, Lafontaine, Amer. Econ. J., 2013]

○ social networks [Hochberg et al., J. Finance, 2007] [Cohen et al., J. Finance, 2010]● Matching problem

○ matching in graph [Mucha, Sankovski, Foundations of Computer Science, 2004]

○ kidney exchange [Roth et al, Quarterly Journal of Economics, 2004]

○ medical interns/residents [Roth, Journal of Political Economy, 1984]

● Link prediction in complex networks○ social networks [Liben-Nowell, Kleinberg, Conf. Info. Knowledge Mgmt., 2003]

○ biological networks [Yu et al., Science, 2008]

● Innovation & entrepreneurship○ two-sided market [Weyl, American Economic Review, 2010]

○ entrepreneurship [Glaser, Kerr, Ponzetto, Journal of Urban Economics, 2010]

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For complete reference list

Roadmap

1. Introductiona. Startups and M&A in high tech industryb. Problem definition

2. Modela. Proximity measuresb. M&A graphc. ERGM

3. Evaluation4. Platform

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Roadmap

1. Introductiona. Startups and M&A in high tech industryb. Problem definition

2. Modela. Proximity measuresb. M&A graphc. ERGM

3. Evaluation4. Platform

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Proximity measures

How do we quantify the closeness between firms?- hypothesis: companies with closer proximity measures are more likely to have M&A deals1. Business proximity [Haigu, Yoffie, J. Economic Perspectives, 2013]

- closeness on business area and intellectual property2. Social linkage [Hochberg et al., J. Finance, 2007] [Cohen et al., J. Finance, 2010]

- socially connected by board members, executives, developers3. Common ownership

- backed by same VCs or angels4. Geography [Erel et al., J. Finance, 2012] [Kalnins, Lafontaine, Amer. Economic J., 2013]

- distance matters in decision making

Firm A

Firm B

Firm C

sim(A,B)

sim(B,C)

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sim(A,C)

Business proximity & topic modeling● Topic modeling [Blei, Ng, Jordan, J. Machine Learning Research, 2002]

○ To discover abstract topics in a collection of documents○ Inputs: business descriptions and # of topics○ Outputs: (1) keywords in each topic, (2) distribution of topics

for each company description● Business proximity

○ Measure similarity in topic distribution

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More proximity measures

Social linkage● board members● executives● developers

Count common people in two firms

Common ownership● VCs● angels● institutions

Count shared investors of two firms

Geographic distance● lat, long● city● state

Use great circle distance of two coord.

* We can extend measures with multiple hop connections [LK, CIKM, 2003]

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More factors for M&A

● Selective mixing (homophily)○ Companies with same characteristics are likely to M&A○ Same state in the US: tax, regulations○ Same industry sector

● Power law○ Companies who acquired many startups are likely to make

more M&A transactions○ Or companies who already acquired many startups have

incentives to buy more

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Roadmap

1. Introductiona. Startups and M&A in high tech industryb. Problem definition

2. Modela. Proximity measuresb. M&A graphc. ERGM

3. Evaluation4. Platform

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M&A graphWe use graph models which incorporate the link interdependency

● M&A deals are interdependent● But conventional models (logit, probit) assume independency:

treat each M&A deal separately

photo photo

photo

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video blog

face recognition

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)

M&A graph

Want to explain an observed graph Y with statistics on E and V

Some notations before moving on...

13 / 34 For complete list of notations

Exponential Random Graph Model [Erdos, Renyi, Pub. Math., 1959], [Newman, SIAM Review, 2003], [Robins et al., Social Networks, 2007]

● Given a fixed set of n nodes, there are 2n(n-1)/2 possible graphs (Y)● Generative model to explain an observed graph

○ based on various properties on nodes and edges

In an ERGM, we want to estimate that maximizes P(Y=y), where

ERGM 101

where ● zk(y) is a certain property of the graph y

○ function of graph y and exogenous variables on nodes○ e.g. # of edges with nodes having the same category

● = parameter for kth statistic (want to estimate this)● = normalization constant (require exponential computation)● K = # of statistics we are interested in

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(ERGM vs logit comparison)

● Degree distribution○ t = # of M&A deals (network density)○ d2 = # of companies w/ 2+ deals (power law)

● Selective mixing (nodal attributes)○ hs

sta = # of deals within the same US state s (50 states)○ hc

cat = # of deals within the same industry c (30 categories)● Proximity (dyad attributes)

○ pb = sum of business proximities in all deals○ ps = sum of social proximities in all deals○ pf = sum of investment proximities in all deals○ pg = sum of geographic proximities in all deals

Our M&A model

degree selective mixing proximity

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(conditional form)

Roadmap

1. Introduction2. Model3. Evaluation

a. CrunchBaseb. Data analysisc. Statistical results

4. Platform

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Dataset:CrunchBase: “wikipedia” for venture industry● 8,772 M&A transactions● 180K high tech companies: startups, public● 10K financial orgs: venture capitals, investment banks● 200K people: execs, founders, developers, investors● Proximity measures: business, social, investment, geo

Click here for interactive map Click here for M&A search page

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Geographic locations

Active states: California, New York, Mass., Texas, Florida

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Industry sectors

Active sectors: software, web, ecommerce, advertising, mobile

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Business topics from topic modeling● Inputs: company profiles from CrunchBase● Unsupervised learning with minimum manual efforts

(selecting stop words)● Outputs: extracted 50 topics (topic=set of related keywords)

20 / 34 For complete list of 50 topics

Business proximity by topic model

● A 50-dimensional vector is assigned to each company● Business proximity

= cosine similarity

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M&A and proximity measures

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geographic distance business distance

Measure the distance of company pairs: M&A vs. random● geo distance (km) by great circle distance● business distance (0~1) by (1 - topic similarity)

M&A pairs have significantly lower distances than random pairs

Roadmap

1. Introduction2. Model3. Evaluation

a. CrunchBaseb. Data analysisc. Statistical results

4. Platform

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Evaluation

Dataset● US companies founded from 2008 to 2012: |V| = 25,692● M&A transactions within the US: |E| = 1,243● # of possible networks (Y) exceeds # of atoms in universe

Estimate our ERGM M&A model● Sample 25% companies from V: for computational feasibility● Run 100 times with different samples● Estimate model coefficients by following Markov chain Monte Carlo

(MCMC) maximum likelihood estimation (MLE)

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Proximity measures

● Business > social > investor >> geographic● Business proximity is statistically significant in our model

○ Even with the selective mixing of industries● Geographic distance is less significant

○ Due to selective mixing of states

degree selective mixing proximity

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Selective mixing: industry sectors

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

● Proposed business proximity provides even finer grained measures

degree selective mixing proximity

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Selective mixing: state locations

Selective mixing holds for state locations

CA, MA, NJ, NY, TX, WA

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1. Selective mixing holds for geography and industry2. Topic modeling results give very significant and fine-grained

proximity measures3. Social links play important roles4. Geographic distance play limited roles

a. state-level binary relation vs geographic distance

Implication: we can use the proposed proximity measures to understand/recommend/predict M&A deals

Evaluation summary

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Roadmap

1. Introduction2. Model3. Evaluation4. Platform

a. two-sided marketb. VentureMap interface

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● M&A market is a two-sided platform○ buyers: established companies○ sellers: startups

● We can increase the efficiency of this two-sided market by○ building interface, VentureMap, to make data accessible○ recommending matchings with our M&A model

● Potential beneficiaries○ Established firms: intelligence/M&A department○ Startups: identify opportunities, potential buyers○ Venture capitalists○ Market intelligence firms○ Researchers in finance field

Platform for M&A

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VentureMap: search M&A deals

● Search M&A deals by○ date, buyers, sellers, industry, etc.

Click here for VentureMap search page

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VentureMap: recommendation

Recommend M&A deals by proposed proximity measures

Click here for VentureMap search page32 / 34

We showed how Big Data analytics can serve the M&A market ● Proposed new business proximity measures● Built a generative model to explain M&A deals● Developed a new interface to support venture industry

Future directions● Improve proximity to distinguish complementarity & substitution● Scale up ERGM model using distributed systems● Build M&A prediction models

Concluding remarks

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Thank you!

Gene Moo Lee: gene@cs.utexas.edu

Center for Research in Electronic CommerceThe University of Texas at Austin

1. M&A analysisa. M. Graebner, K. Eisenhardt, The Seller’s Side of the Story: Acquisition as Courtship and

Governance as Syndicate in Entrepreneurial Firms, Administrative Science Quarterly, 20042. Link prediction

a. D. Liben-Nowell, J. Kleinberg., The Link Prediction Problem for Social Networks. Proc. 12th International Conference on Information and Knowledge Management (CIKM), 2003.

b. H. Yu, et al., High-Quality Binary Protein Interaction Map of the Yeast Interactome Network, Science, 2008

3. Matching problema. M. Mucha, P. Sankowski, Maximum Matchings via Gaussian Elimination, Proc. of Foundations of

Computer Science (FOCS), 2004b. A. Roth, T. Sonmez, M Unver, Kidney Exchange, Quarterly Journal of Economics, 2004c. A. E. Roth, The college admissions problem is not equivalent to the marriage problem, Journal of

Economic Theory, 1985d. A. E. Roth, The evolution of the Labor Market for Medical Interns and Residents: A Case Study in

Game Theory, Journal of Political Economy, 19844. Innovation and entrepreneurship

a. W. Kerr, Breakthrough inventions and migrating clusters of innovation, Journal of Urban Economics, 2010

5. Topic modelinga. D. Blei, A. Ng, M. Jordan, Latent Dirichlet allocation, Journal of Machine Learning Research, 2003

References

1. Random grapha. P. Erdos, A. Renyi, On random graphs, Publicationes Mathematicae, 1959b. M. Newman, The structure and function of complex networks, SIAM Reviews, 2003c. G. Robins, P. Pattison, Y. Kalish, D. Lusher, An introduction to exponential random graph models

for social networks, Social Networks, 20072. Business

a. A. Haigu, D. Yoffie, The New Patent Intermediaries: Platforms, Defensive Aggregators, and Super-Aggregators, Journal of Economic Perspectives, 2003

3. Geographya. I. Erel, R. Liao, M. Weisbach, Determinants of Cross-Border Mergers and Acquisitions, Journal of

Finance, 2012b. A. Kalnins, F. Lafontaine, Too Far Away? The Effect of Distance to Headquarters on Business

Establishment Performance, American Economic Journal: Microeconomics, 20134. Social links

a. L. Cohen, A. Frazzini, C. Malloy, Sell-Side School Ties, Journal of Finance, 2010b. Y. Hochberg, A. Ljungqvist, Y. Liu, Whom You Know Matters: Venture Capital Networks and

Investment Performance, Journal of Finance, 2007c. M. Conyon, M. Muldoon, The Small World of Corporate Boards, Journal of Business Finance &

Accounting, 20065. Two-sided markets

a. G. Weyl, A Price Theory of Multi-Sided Platforms, American Economic Review, 2010b. A. Haigu, Two-Sided Platforms: Product Variety and Pricing Structures, Journal of Economics &

Management Strategy, 2009

References

M&A and states

○ Many deals are within California or related to California○ Still cross state deal volume is substantial

M&A and industries

○ Many deals are within software/web industry○ Still cross industry deal volume is substantial

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Power law in M&A

Distribution on # of M&A follows the power law

Topics Back to main slide

NotationsBack to main slide

In logit/probit models, ● we assume that all the M&A deals are independent● calculate the probability of observing an individual M&A deal● maximize the product of each deals’ likelihood function

ERGM vs. Logit Model Back to main slide

In ERGM,● we assume that M&A deals are interdependent● calculate the probability of observing the whole M&A graph● maximize likelihood of the graph as a whole

Our M&A model (conditional form)Back to main slide

Density and node degree

● Degree > 2 coefficient is positive○ Power law is observed from the data

● Edge coefficient is a constant for the model

degree selective mixing proximity

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ERGM results