The document discusses modeling mergers and acquisitions (M&A) in the high tech industry. It proposes using topic modeling to measure business proximity between companies and an exponential random graph model (ERGM) to model the interdependent relationships between M&A deals. Evaluation of the models using M&A transaction data from CrunchBase found that business proximity is a significant factor in M&A deals, even after accounting for industry and geographic selective mixing. A proposed interface called VentureMap could utilize the models to recommend potential M&A matches.
Eric van Heck - Congres 'Data gedreven Beleidsontwikkeling'ScienceWorks
De presentatie van Eric van Heck, tijdens de parallelle sessie 'Methoden en technieken voor data-analyse' van het congres 'Data gedreven Beleidsontwikkeling' in Den Haag op 28 november 2017.
Eric van Heck - Congres 'Data gedreven Beleidsontwikkeling'ScienceWorks
De presentatie van Eric van Heck, tijdens de parallelle sessie 'Methoden en technieken voor data-analyse' van het congres 'Data gedreven Beleidsontwikkeling' in Den Haag op 28 november 2017.
Developing A Big Data Analytics Framework for Industry IntelligenceGene Moo Lee
Researchers often model industry as a network where each node corresponds to an organization and an edge represents an inter-organizational relationship (e.g., competition, acquisition, alliance). Structural holes are an important construct in identifying network opportunity structures. While there have been significant theoretical and empirical works around this concept, there has been limited fine-grained empirical research on the operationalization of the structural hole concept based on organizational self-identified strategic posturing. In this project, we propose an innovative method to quantify self-identified strategic posturing structural holes using a machine learning approach called doc2vec, which transforms textual documents into numeric vector representations. Specifically, we apply the doc2vec model to the collection of 10-K annual reports from U.S. public firms in the 1995-2016 period. To show the effectiveness of our measure, we conducted empirical analyses on firm birth (i.e., IPO) and firm mortality (i.e., delisting) using Compustat data. First, our firm birth analysis, using the generalized linear model, shows that new organizations have an increasing birth rate in structural holes between a pair of existing firms. Second, using the Cox proportional hazard model, we show that organizations entering into a structural hole have a significant decrease in mortality rates. This is the first large-scale empirical study to use self-identified strategic posturing structural holes in the analysis of industry dynamics, and as such provides an advance to both the industry dynamics and network literature.
In this webinar we will talk about some of outcomes of recent projects along with the implications of those outcomes and how you can adopt some of the ideas into your own projects.
Tutorial on Countering Bias in Personalized Rankings: From Data Engineering t...Mirko Marras
ICDE 2021 Tutorial on Countering Bias in Personalized Rankings:
From Data Engineering to Algorithm Development
Website: https://biasinrecsys.github.io/icde2021
Digital strategy design & proptech resources Jonas Canton
"Down the rabbit hole of Proptech" was presented at the event ProptechRiga. It provides real estate companies with the necessary resources and framing to design their digital strategy. The second part of the presentation focuses various references of content to train employees about Proptech.
The new and improved Construction Lead Generation - The Definitive Guide will share the results of our most recent national survey on construction lead generation. Importantly, the guide details the current state of the top two construction lead services: Dodge Data & Analytics, and ConstructConnect. Next, the guide identifies several other construction lead services that tend to specialize in regions, type of construction project or service offerings. Finally, other types of lead sources are identified and categorized by Traditional, Internet, and Social Media.
Paper Explained: Deep learning framework for measuring the digital strategy o...Devansh16
Companies today are racing to leverage the latest digital technologies, such as artificial intelligence, blockchain, and cloud computing. However, many companies report that their strategies did not achieve the anticipated business results. This study is the first to apply state of the art NLP models on unstructured data to understand the different clusters of digital strategy patterns that companies are Adopting. We achieve this by analyzing earnings calls from Fortune Global 500 companies between 2015 and 2019. We use Transformer based architecture for text classification which show a better understanding of the conversation context. We then investigate digital strategy patterns by applying clustering analysis. Our findings suggest that Fortune 500 companies use four distinct strategies which are product led, customer experience led, service led, and efficiency led. This work provides an empirical baseline for companies and researchers to enhance our understanding of the field.
Check out my other articles on Medium. : https://rb.gy/zn1aiu
My YouTube. It's a work in progress haha: https://rb.gy/88iwdd
Reach out to me on LinkedIn. Let's connect: https://rb.gy/m5ok2y
My Twitter: https://twitter.com/Machine01776819
My Substack: https://devanshacc.substack.com/
If you would like to work with me email me: devanshverma425@gmail.com
Live conversations at twitch here: https://rb.gy/zlhk9y
To get updates on my content- Instagram: https://rb.gy/gmvuy9
Get a free stock on Robinhood: https://join.robinhood.com/fnud75
Presentation delivered during 9th Seminar on Media and the Digital Economy (21-22 March 2019).
http://fsr.eui.eu/event/annual-scientific-seminar-on-media-and-the-digital-economy-9th-edition/
Zeotap: Data Modeling in Druid for Non temporal and Nested DataImply
Druid has been the production workhorse for the past 2+ years at Zeotap powering the core Audience planning across our Connect and Targeting products. Though Druid is best suited for data having time as a dimension as it partitions data based on time first, we have used Druid to serve ML powered enhanced insights and Estimation of potential dataset sizes, to assist us with our core business case of Audience planning. These are datasets without timestamp a.k.a non-temporal with high scale and having nested dimensions. These have been achieved using nuanced data modelling to store the data sets and achieve millisecond latency retrieval on top of the same. The core of the presentation would be on the data modelling journey to achieve these use cases detailing the query access patterns. We also delve upon the architecture - ingestion into druid sink and processing including ML. In the end we go over the production setup and configurations and provide the performance tunings applied. The presentation would have the following heads:
The presentation would have the following heads
* Business case in Ad-Tech and Mar-Tech vertical
* Audience Planner Usecase 1 - Insights
-Lambda Architecture and data flow
-Deep dive on data model
-Takeaways
*Audience Planner Usecase 2 - Estimator
-Architecture and data flow
-Stratified sampling explained
-Data model to solve nested data - deep dive
-Takeaways
*Audience Planner Usecase 3 - Skew correction
-Skew correction model
-Query Access
-Data model in Druid to accommodate output from ML models
-Takeaways
*Production setup, config and Tunings
*Production Operation experience takeaways
Tech M&A Monthly: What Happens If You Don’t Sell?Corum Group
Not every company sells right away—deals fall apart, valuations don’t meet expectations, or the market just isn’t ready. What do you do now?
80% of self-managed M&A efforts initially end in failure. However, if you’ve run the process right, this may be a golden opportunity to actually increase the value you finally get for your company.
Procurement strategy in major infrastructure: The AS-IS and STEPS - D. Makovš...OECD Governance
Presented at the OECD expert meeting "Construction Risk Management in Infrastructure Procurement: The Loss of Appetite for Fixed-Price Contracts", held on 17 May 2023 at the OECD, Paris and online.
Developing A Big Data Analytics Framework for Industry IntelligenceGene Moo Lee
Researchers often model industry as a network where each node corresponds to an organization and an edge represents an inter-organizational relationship (e.g., competition, acquisition, alliance). Structural holes are an important construct in identifying network opportunity structures. While there have been significant theoretical and empirical works around this concept, there has been limited fine-grained empirical research on the operationalization of the structural hole concept based on organizational self-identified strategic posturing. In this project, we propose an innovative method to quantify self-identified strategic posturing structural holes using a machine learning approach called doc2vec, which transforms textual documents into numeric vector representations. Specifically, we apply the doc2vec model to the collection of 10-K annual reports from U.S. public firms in the 1995-2016 period. To show the effectiveness of our measure, we conducted empirical analyses on firm birth (i.e., IPO) and firm mortality (i.e., delisting) using Compustat data. First, our firm birth analysis, using the generalized linear model, shows that new organizations have an increasing birth rate in structural holes between a pair of existing firms. Second, using the Cox proportional hazard model, we show that organizations entering into a structural hole have a significant decrease in mortality rates. This is the first large-scale empirical study to use self-identified strategic posturing structural holes in the analysis of industry dynamics, and as such provides an advance to both the industry dynamics and network literature.
In this webinar we will talk about some of outcomes of recent projects along with the implications of those outcomes and how you can adopt some of the ideas into your own projects.
Tutorial on Countering Bias in Personalized Rankings: From Data Engineering t...Mirko Marras
ICDE 2021 Tutorial on Countering Bias in Personalized Rankings:
From Data Engineering to Algorithm Development
Website: https://biasinrecsys.github.io/icde2021
Digital strategy design & proptech resources Jonas Canton
"Down the rabbit hole of Proptech" was presented at the event ProptechRiga. It provides real estate companies with the necessary resources and framing to design their digital strategy. The second part of the presentation focuses various references of content to train employees about Proptech.
The new and improved Construction Lead Generation - The Definitive Guide will share the results of our most recent national survey on construction lead generation. Importantly, the guide details the current state of the top two construction lead services: Dodge Data & Analytics, and ConstructConnect. Next, the guide identifies several other construction lead services that tend to specialize in regions, type of construction project or service offerings. Finally, other types of lead sources are identified and categorized by Traditional, Internet, and Social Media.
Paper Explained: Deep learning framework for measuring the digital strategy o...Devansh16
Companies today are racing to leverage the latest digital technologies, such as artificial intelligence, blockchain, and cloud computing. However, many companies report that their strategies did not achieve the anticipated business results. This study is the first to apply state of the art NLP models on unstructured data to understand the different clusters of digital strategy patterns that companies are Adopting. We achieve this by analyzing earnings calls from Fortune Global 500 companies between 2015 and 2019. We use Transformer based architecture for text classification which show a better understanding of the conversation context. We then investigate digital strategy patterns by applying clustering analysis. Our findings suggest that Fortune 500 companies use four distinct strategies which are product led, customer experience led, service led, and efficiency led. This work provides an empirical baseline for companies and researchers to enhance our understanding of the field.
Check out my other articles on Medium. : https://rb.gy/zn1aiu
My YouTube. It's a work in progress haha: https://rb.gy/88iwdd
Reach out to me on LinkedIn. Let's connect: https://rb.gy/m5ok2y
My Twitter: https://twitter.com/Machine01776819
My Substack: https://devanshacc.substack.com/
If you would like to work with me email me: devanshverma425@gmail.com
Live conversations at twitch here: https://rb.gy/zlhk9y
To get updates on my content- Instagram: https://rb.gy/gmvuy9
Get a free stock on Robinhood: https://join.robinhood.com/fnud75
Presentation delivered during 9th Seminar on Media and the Digital Economy (21-22 March 2019).
http://fsr.eui.eu/event/annual-scientific-seminar-on-media-and-the-digital-economy-9th-edition/
Zeotap: Data Modeling in Druid for Non temporal and Nested DataImply
Druid has been the production workhorse for the past 2+ years at Zeotap powering the core Audience planning across our Connect and Targeting products. Though Druid is best suited for data having time as a dimension as it partitions data based on time first, we have used Druid to serve ML powered enhanced insights and Estimation of potential dataset sizes, to assist us with our core business case of Audience planning. These are datasets without timestamp a.k.a non-temporal with high scale and having nested dimensions. These have been achieved using nuanced data modelling to store the data sets and achieve millisecond latency retrieval on top of the same. The core of the presentation would be on the data modelling journey to achieve these use cases detailing the query access patterns. We also delve upon the architecture - ingestion into druid sink and processing including ML. In the end we go over the production setup and configurations and provide the performance tunings applied. The presentation would have the following heads:
The presentation would have the following heads
* Business case in Ad-Tech and Mar-Tech vertical
* Audience Planner Usecase 1 - Insights
-Lambda Architecture and data flow
-Deep dive on data model
-Takeaways
*Audience Planner Usecase 2 - Estimator
-Architecture and data flow
-Stratified sampling explained
-Data model to solve nested data - deep dive
-Takeaways
*Audience Planner Usecase 3 - Skew correction
-Skew correction model
-Query Access
-Data model in Druid to accommodate output from ML models
-Takeaways
*Production setup, config and Tunings
*Production Operation experience takeaways
Tech M&A Monthly: What Happens If You Don’t Sell?Corum Group
Not every company sells right away—deals fall apart, valuations don’t meet expectations, or the market just isn’t ready. What do you do now?
80% of self-managed M&A efforts initially end in failure. However, if you’ve run the process right, this may be a golden opportunity to actually increase the value you finally get for your company.
Procurement strategy in major infrastructure: The AS-IS and STEPS - D. Makovš...OECD Governance
Presented at the OECD expert meeting "Construction Risk Management in Infrastructure Procurement: The Loss of Appetite for Fixed-Price Contracts", held on 17 May 2023 at the OECD, Paris and online.
DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...Wasswaderrick3
In this book, we use conservation of energy techniques on a fluid element to derive the Modified Bernoulli equation of flow with viscous or friction effects. We derive the general equation of flow/ velocity and then from this we derive the Pouiselle flow equation, the transition flow equation and the turbulent flow equation. In the situations where there are no viscous effects , the equation reduces to the Bernoulli equation. From experimental results, we are able to include other terms in the Bernoulli equation. We also look at cases where pressure gradients exist. We use the Modified Bernoulli equation to derive equations of flow rate for pipes of different cross sectional areas connected together. We also extend our techniques of energy conservation to a sphere falling in a viscous medium under the effect of gravity. We demonstrate Stokes equation of terminal velocity and turbulent flow equation. We look at a way of calculating the time taken for a body to fall in a viscous medium. We also look at the general equation of terminal velocity.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
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Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
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Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
1. Towards Modeling M&A in High Tech Industry
December 4th 2013
Gene Moo Lee
Department of Computer Science
The University of Texas at Austin
Research Preparation Exam
2. Startups in high tech industry
High tech startups are very active these days, thanks to
many platforms including
Mobile Platforms Cloud Platforms Financial Platforms
2 / 34
3. 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
4. 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 B
Will they do M&A?
If so, why?
4 / 34
5. 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]
5 / 34
For complete reference list
6. Roadmap
1. Introduction
a. Startups and M&A in high tech industry
b. Problem definition
2. Model
a. Proximity measures
b. M&A graph
c. ERGM
3. Evaluation
4. Platform
6 / 34
7. Roadmap
1. Introduction
a. Startups and M&A in high tech industry
b. Problem definition
2. Model
a. Proximity measures
b. M&A graph
c. ERGM
3. Evaluation
4. Platform
6 / 34
8. Proximity measures
How do we quantify the closeness between firms?
- hypothesis: companies with closer proximity measures are more likely
to have M&A deals
1. Business proximity [Haigu, Yoffie, J. Economic Perspectives, 2013]
- closeness on business area and intellectual property
2. Social linkage [Hochberg et al., J. Finance, 2007] [Cohen et al., J. Finance, 2010]
- socially connected by board members, executives, developers
3. Common ownership
- backed by same VCs or angels
4. 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)
7 / 34
sim(A,C)
9. 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
8 / 34
10. 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]
9 / 34
11. 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
10 / 34
12. Roadmap
1. Introduction
a. Startups and M&A in high tech industry
b. Problem definition
2. Model
a. Proximity measures
b. M&A graph
c. ERGM
3. Evaluation
4. Platform
11 / 34
13. M&A graph
We 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
12 / 34
video blog
face recognition
14. 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
15. 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
14 / 34
(ERGM vs logit comparison)
16. ● 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
15 / 34
(conditional form)
21. 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
22. Business proximity by topic model
● A 50-dimensional vector is assigned to each company
● Business proximity
= cosine similarity
21 / 34
23. M&A and proximity measures
22 / 34
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
25. 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)
24 / 34
26. 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
25 / 34
27. 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
26 / 34
28. Selective mixing: state locations
Selective
mixing holds for
state locations
CA, MA, NJ,
NY, TX, WA
27 / 34
29. 1. Selective mixing holds for geography and industry
2. Topic modeling results give very significant and fine-grained
proximity measures
3. Social links play important roles
4. 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
28 / 34
31. ● 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
30 / 34
32. VentureMap: search M&A deals
● Search M&A deals by
○ date, buyers, sellers, industry, etc.
Click here for VentureMap search page
31 / 34
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
33 / 34
35. Thank you!
Gene Moo Lee: gene@cs.utexas.edu
Center for Research in Electronic Commerce
The University of Texas at Austin
36. 1. M&A analysis
a. M. Graebner, K. Eisenhardt, The Seller’s Side of the Story: Acquisition as Courtship and
Governance as Syndicate in Entrepreneurial Firms, Administrative Science Quarterly, 2004
2. 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 problem
a. M. Mucha, P. Sankowski, Maximum Matchings via Gaussian Elimination, Proc. of Foundations of
Computer Science (FOCS), 2004
b. A. Roth, T. Sonmez, M Unver, Kidney Exchange, Quarterly Journal of Economics, 2004
c. A. E. Roth, The college admissions problem is not equivalent to the marriage problem, Journal of
Economic Theory, 1985
d. A. E. Roth, The evolution of the Labor Market for Medical Interns and Residents: A Case Study in
Game Theory, Journal of Political Economy, 1984
4. Innovation and entrepreneurship
a. W. Kerr, Breakthrough inventions and migrating clusters of innovation, Journal of Urban
Economics, 2010
5. Topic modeling
a. D. Blei, A. Ng, M. Jordan, Latent Dirichlet allocation, Journal of Machine Learning Research, 2003
References
37. 1. Random graph
a. P. Erdos, A. Renyi, On random graphs, Publicationes Mathematicae, 1959
b. M. Newman, The structure and function of complex networks, SIAM Reviews, 2003
c. G. Robins, P. Pattison, Y. Kalish, D. Lusher, An introduction to exponential random graph models
for social networks, Social Networks, 2007
2. Business
a. A. Haigu, D. Yoffie, The New Patent Intermediaries: Platforms, Defensive Aggregators, and Super-
Aggregators, Journal of Economic Perspectives, 2003
3. Geography
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Finance, 2012
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Establishment Performance, American Economic Journal: Microeconomics, 2013
4. Social links
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b. Y. Hochberg, A. Ljungqvist, Y. Liu, Whom You Know Matters: Venture Capital Networks and
Investment Performance, Journal of Finance, 2007
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5. Two-sided markets
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Management Strategy, 2009
References
38. M&A and states
○ Many deals are within California or related to California
○ Still cross state deal volume is substantial
39. M&A and industries
○ Many deals are within software/web industry
○ Still cross industry deal volume is substantial
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40. Power law in M&A
Distribution on # of M&A follows the power law
43. 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
45. 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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