Corporate governance and price strategies

Corporate governance is the system of rules and procedures by which a company is directed and controlled at various levels. Essentially it involves balancing (not enhancing) the interests of the many stakeholders in the company (public or private), the ability to react to external and internal challenges imposed to the organization, and to ensure the fulfilment of legal obligations imposed to the firm by law and in its line of business. In particular, corporate governance oversees the framing of effective transfer pricing policies³⁸³. Such policies and practices include <<maintaining robust transfer pricing documentation, alignment between transfer pricing rules and business realities, proper execution of inter-company agreements, maintaining clean records for inter-company transactions, and periodic review of transfer pricing arrangements>>³⁸⁴. The fact is that transfer pricing is a key taxation issue faced by multinational enterprises, so whether a transfer price policy is or is not tax efficient is a quite relevant question, as it is if the transfer pricing model is in line with the risk performed. From this discussion, it is clear that a correct pricing can led to savings in money and to optimise resources for a company through its corporate governance structure. It also throws light to the big versatility of the concept of ‘price’.

Next, we consider a crucial step of in a firm growth and development.

As for the role of corporate governance³⁸⁵ in initial public offerings, Hartzell, et. al. (2008) found that firms with stronger governance structures have higher IPO valuations and better long-term operating performance than their peers³⁸⁶. Another interesting result presented by Boulton, et. al. (2010), shows that IPO underpricing is higher in countries with corporate governance that strengthens the

383 A transfer price is the price at which divisions of a company transact with each other, such as trading supplies or labour between departments. Transfer prices are used when individual entities of a larger multi-entity firm are treated and measured as separately business run entities. Source:

‘Transfer Price’. Investopedia. Available at: http://www.investopedia.com/terms/t/transferprice.asp.

384 Jain, Ajit Kumar. ‘Transfer Pricing from a Corporate Governance Perspective’. Linkedin. Available at: https://www.linkedin.com/pulse/transfer-pricing-corporate-governance-ajit-kumar-jain.

385 Corporate governance is not about enhancing shareholder value. It is about enhancing economic growth, entrepreneurship, innovation and value creation. We do not actually care about shareholder returns or shareholder value per se, except insofar as they contribute to achieving these objectives.

386 Hartzell, Jay C., Kallberg, Jarl G. and Liu, Crocker H. “The Role of Corporate Governance in Initial Public Offerings: Evidence from Real Estate Investment Trusts”. Cornell University School of Hotel Administration, August, 2008.

126 position of investors relative to insiders. The authors conjecture that <<when countries give outsiders more influence, IPO issuers underprice in order to generate excess demand for the offer, which in turn leads to ownership dispersion and reduces outsiders' incentives to monitor the behaviour of corporate insiders>>. In other words, underpricing is both a tool and a cost that insiders pay to maintain control when legal systems doesn’t support them enough.³⁸⁷

387 Boulton, Thomas Jason, Smart, Scott and Zutter, Chad J. “IPO Underpricing and International Corporate Governance”. Journal of International Business Studies, Vol. 41, February/March, 2010.

127

Discussion of results and conclusions

Significant progress has been made in designing algorithms for complex financial product pricing, attempting to capture within the underlying models the structural and organizational aspects of financial reality. My small contribution to this research is to delve further into key decision points and clarify areas of ambiguity, in an attempt to set a ground for further computational theory development.

I defined the concepts of computational price, pricing algorithm and computational price theory. Both a contextualization and a characterisation of pricing algorithms is provided, allowing a more comprehensive assessment of computational financial techniques, an understanding of the role of computational finance experts, knowledge of software methodologies availability, and identification of high-frequency pricing problems and their possible solutions. The case studies contribute to the discussion by exemplifying the ideas in the text. I tried to privilege elegance over expansiveness and gain insight into the topic always through disciplined thought.

The Master thesis succeeds in:

1. Contextualizing the topic of pricing algorithms according to its relation to other algorithmic topic which it has a relation with: allocation algorithms, parametrized algorithms and planning algorithms were identified as identified as the more closely related.

2. Establishing an structural relation between computer science and economics

‒ Economic scarcity and computational complexity fulfill similar roles in either discipline.

‒ The validity of the abstract economic concept of Efficient Market Hypothesis is conceptually equivalent to the condition P = NP being true.

‒ The possibility to implement the Turing test in both contexts, in which the interpretation of the results although conceptually different, serve similar purposes.

‒ Reducing the abstraction of economic profit to an endowment of computational resources.

All of these suggesting a deeper connection between both scientific fields deeper that initially suspected.

3. Defining the concepts of price (computer theory perspective), pricing algorithm, and computational price theory. The proposed pricing algorithm definition is flexible enough as to allow, if necessary, the generation of vectors of prices capturing a subset of market scenarios, thus providing a useful tool for market condition assessment. Priceable assets are described, as they constitute the domain for pricing algorithms. The mechanics of the derivation of pricing algorithms are presented, showing how a pricing kernel unfolds into a pricing engine.

4. Defining commercial algorithmic pricing.

5. Uncover the internals of financial pricing algorithms, presenting the reader a schema of the corresponding component organization.

Relevant and significant conclusions were reached:

1. The way reality is conceptualized (discrete vs. continuous) juxtaposed to how modelling is conceptualized (also discrete vs. continuous) accounts for an abstract template for financial and computational finance modelling.

2. From comparing the different financial system architectures both coincidences (the quest for security/privacy, a friendly GUI, high performance) and discrepancies (structural design is highly dependent on the specific application type) were identified

3. Prices are like windows, natural interfaces between agents, and thus a set a basis for interaction and coordination, relating specific events to specific price fluctuations, thus permitting to some extent at least, an assessment of the market condition from an informational perspective.

4. Not all the prices in a financial market or an economy can be algorithmically estimated, and not every financial instrument has an algorithmic representation (therefore implying the intractability of some pricing problems).

5. Commercial algorithmic pricing effectively affects the demand of products, thus enabling some degree of market power to the enterprises.

128 6. Pricing algorithms introduce in fact, a new way of categorizing financial agents’ profiles, besides expectations, preferences, risk-aversion, etc., because which algorithms they use is a factor in determining their behaviour and their decision-making.

7. Pricing algorithms are instrumental in understanding the economy of information.

8. Generally speaking, pricing algorithms are not scalable, but they can they can be evaluated according to a set of criteria.

9. Basket derivative (instruments which depend on two or more underling assets), because of their composition, represent a pricing problem difficult to solve. Increased reliability for option pricing comes not only through the development of new pricing models, but also from improvements in computational processing techniques (parallelization of algorithms, more efficient memory management).

10. Rather than formulating general templates for pricing algorithms, it is better to produce a set of stereotypical description, which are acquired by a process of certain pattern abstraction from examples.

Finally, on the termination of the thesis, I became aware that a line of thought emerges in terms of information:

Basic information patterns ― Dynamic information economy ― Information-based modelling of transactions (time series) ―

CH1 CH2 CH3

Information models for computational finance Implementation of informational objects

CH4 CH5 / CH6

The incrementally focused topic description give me certainty the Master thesis is logically organized.

■

129

References

Research articles

Aït-Sahalia, Y. and Lo, A. W. (2000) “Nonparametric risk management and implied risk aversion”.

Journal of Econometrics 94, pp. 9-51.

Agre, Philip E. (1995) “Computational research on interaction and agency”. Artificial Intelligence 72, pp. 1-52.

Belomestny, Denis; Ma, Shujie and Härdle, Wolfgang Karl. (2015) “Pricing Kernel Modeling”. SFB 649 Discussion Paper 2015-001.

Biagini, Sara and Cont, Rama. (2007) “Model-free Representation of Pricing Rules as Conditional Expectations”. In: Akahori, Jiro; Ogawa, Shigeyoshi & Watanabe, Shinzo. Stochastic Processes and Applications to Mathematical Finance: Proceedings of the 6th Ritsumeikan International Symposium, Ritsumeikan University, Japan, 6-10 March 2006, pp. 53-66. World Scientific.

Biagini, Sara and Cont, Rama. (2006) “Model-free representation of pricing rules as conditional expectations”.

Canina, Linda and Figlewski, Stephen. (1993) “The Informational Content of Implied Volatility”. The Review of Financial Studies, Volume 6, Issue 3, pp. 659–681.

Ciardelli, Ivano. (2016) “Questions as information types”. Synthese An International Journal for Epistemology, Methodology and Philosophy of Science.

Gomer, Peter; Schmidt, Claudia and Weinhardt, Christof. (1998) “Auctions in Electronic Commerce - Efficiency versus Computational Tractability –“. Paper presented at the International conference of Electronic Commerce (ICEC) ’98. Seoul, Korea.

Hartmanis, J. (1972) “On non-determinancy in simple computing devices”. Acta Informatica, Vol. 1, Issue 4, pp. 336-344. Springer.

Herek, Gregory M. (2010) “Developing a Theoretical Framework and Rationale for a Research Proposal”. In Pequegnat, Willo; Stover Ellen & Boyce, Cheryl Anne (Editors), How to Write a Successful Research Grant Application– A Guide for Social and Behavioral Scientists, pp. 137-145.

Springer.

Horswill, Ian. (1996) “Analysis of Adaptation and Environment”. MIT Artificial Intelligence Laboratory.

In Agre, Philip E. and Rosenschein, Stanley J. (Editors). Computational Theories of Interaction and Agency. The MIT Press.

Jianxin, Xiong and Dingxing, Wang. (1996) “Analyzing Nondeterminacy of Message Passing Programs”. Proceedings of the Second International Symposium on Parallel Architectures, Algorithms, and Networks. © IEEE.

Julio, Brandon and Deng, Quian. (2005) “The Informational Content of Implied Volatility Around Stock Splits”. Available at SSRN: https://ssrn.com/abstract=831144.

Krige, D.G. and Austin, J. D. (1980) “Theshold rates of return on new mining projects as indicated by market investment results”. Journal of the South African Institute of Mining and Metallurgy.

Manfredo, Mark R. and Sanders, Dwight R. (2002) “The Information Content of Implied Volatility from Options on Agricultural Futures Contracts”. Paper presented at the NCR-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management. St. Louis, Missouri.

Melvin, Michael and Sultan, Jahangir. (1990) “South African Political Unrest, Oil Prices, and the Time-Varying Risk Premium in the Gold Futures Market”. The Journal of Futures Market, Vol. 10.

Moris, Joseph N. (2004) “Augmenting Types with Unbounded Demonic and Angelic Nondeterminacy”. In: Kozen, Dexter & Shankland, Carron (Editors). Mathematics of Program Construction: 7th International Conference, MPC 2004, Proceedings, Scotland, UK, Springer-Verlag.

130 Pras A. and Schoenwaelder J. (2003) ‘On the Difference between Information Models and Data Models’. Available at: https://tools.ietf.org/html/rfc3444#section-3.

Rosenberg, Joshua V. and Engle, Robert F. (2002) “Empirical pricing kernels”. Journal of Financial Economics 64, pp. 341–372.

Shagrir, Oron. (2010) “Marr on Computational-Level Theories”. PM/PHOS10157/2010/77/4.

Verykios, Vassilios S.; Houstis, Elias; Tsoukalas, Lefteri H. and Pantazopoulos, Kostas. (2011)

”Automating the Analysis of Option Pricing Algorithms through Intelligent Knowledge”. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, Vol. 31, No. 6.

Weyl, E. Glen. (2014) “Price Theory”. Journal of Economic Literature. April, 2014.

Yavneh, Irad. (2006) “Why Multigrid Methods Are So Efficient”. Computing in Science and Engineering Journal, Vol 6, Issue 6, pp. 12-22. November-December 2006.

[NUM-01] L’Floch, Fabien. (2010) “TR-BDF2 for Stable American Option Pricing”. Available at SSRN: https://ssrn.com/abstract=1648878 or http://dx.doi.org/10.2139/ssrn.1648878.

[NUM-02] Jeong, Darae; Li, Yibao; Choi, Yongho and Kim, Junseok. (2013) “An Adaptive Multigrid Technique for Option Pricing Under the Black–Scholes Model”. Journal of the Korean Society for Industrial and Applied Mathematics (J-KSIAM), Vol. 17, No. 4, pp. 295-306.

[NUM-03] Boyle, Phelim P.. (1988) “A Lattice Framework for Option Pricing with Two State Variables”. Journal of Financial and Quantitative Analysis, Vol. 23, No. 1.

[ANT-01] Ju, Nengjiu. (2002) “Pricing Asian and Basket Options via Taylor Expansion”. Journal of Computational Finance, Vol. 5, No. 3.

[ANT-02] Korn, Ralf and Zeytun, Serkan. (2013) “Efficient basket Monte Carlo option pricing via a simple analytical approximation”. Journal of Computational and Applied Mathematics, Vol. 243, pp.

48-59.

[OPT-01] Dai, Tian-Shyr; Liu, Min-Li and Lyuu, Yuh-Dauh. (2008) “Linear-time option pricing algorithms by combinatorics”. Computers and Mathematics with Applications, Vol. 55, Issue 9, pp.

2142-2157.

[PAR-01] Doan, Viet_Dung; Gaikwad, Abhijeet; Bossy, Mireille; Baude, François and Stokes-Rees, Ian. (2010) “Parallel pricing algorithms for multi-dimensional Bermudan/American options using Monte Carlo methods”. Mathematics and Computers in Simulation, Vol. 81, Issue 3, pp. 568-577.

[MEM-01] Savage, John E. and Zubair, Mohammad. (2010) “Linear-time option pricing algorithms by combinatorics”. ACM Transactions on Mathematical Software (TOMS), Vol. 37, Issue 1, Article No. 7.

Thesis

Hamdi, Ali. (2013) “Some aspects of optimal switching and pricing Bermudan options”, p. 1-2.

Doctoral Thesis. Stockholm, Sweden.

Books

Agre, Philip E. Computation and Human Experience. Chapter four: “Abstraction and implementation”, p. 68 © Cambridge University Press 1997.

Gopnik, A., & Meltzoff, A. Words, Thoughts, and Theories. Cambridge: MIT Press, 1997.

131 Musiela, Marek and Rutkowski, Marek. Martingale Methods in Financial Modelling. Second edition, Springer, 2005.

Tavella, Domingo. Quantitative Methods in derivatives pricing: An Introduction to Computational Finance. Wiley, 2002

Web resources

Board, Simon and Meyer-ter-Vehn, Moritz. “Writing Economic Theory Papers”. UCLA. October, 2014. At: http://www.econ.ucla.edu/sboard/teaching/WritingEconomicTheory.pdf.

Briggs, William, L. ‘A Multigrid Tutorial’. Center for Applied Scientific Computing Lawrence Livermore National Laboratory. Available at: https://www.math.ust.hk/~mawang/teaching/math532/mgtut.pdf Clark, Jeff. ‘There's no reason to own a gold ETF’. Business Insider. April 9, 2016. Available at:

http://www.businessinsider.com/theres-no-reason-to-own-a-gold-etf-2016-4?r=US&IR=T&IR=T

‘Ergomics’ https://www.le.ac.uk/oerresources/psychology/ergonomics/page_07.htm.

EU Regional School 1st Short Course. 2013. Available at:

https://www.aices.rwth- aachen.de/en/media-and-seminars/archives/scientific-seminars/eu-regional-school/eu-regional-school-2013-part-1

Falgout, Robert D. ‘An Algebraic Multigrid Tutorial’. IMA Tutorial – Fast Solution Techniques, Lawrence Livermore National Laboratory, November 28 - 29, 2010. Available at:

http://user.it.uu.se/~maya/Projects/3phase/AMG_parallel_Falgout.pdf

“Front-End Loaded Tender Offers: The Application of Federal and State Law to an Innovative Corporate Acquisition Technique” (Editorial). University of Pennsylvania Law Review, Vol 131, pp.

389-422, 1982. Available at: http://scholarship.law.upenn.edu/penn_law_review/vol131/iss2/3/ . Hayes, David and Miller, Allisha. ‘What Is A Fair Price? - And Who Gets to Decide?’. At http://www.hospitalitynet.org/news/4049159.html, November 17, 2010.

Lalley, Steven and Mykland Per. “Brownian motion”. Lecture notes‒Statistics 313: Stochastic Processes II course. The University of Chicago. Spring, 2013.

(http://galton.uchicago.edu/~lalley/Courses/313/)

Lowther, George. ‘Filtrations and Adapted Processes’. Almost Sure blog. November 8, 2009. At:

https://almostsure.wordpress.com/2009/11/08/filtrations-and-adapted-processes/

Martingale and filtration’. Stack Exchange. At:

https://math.stackexchange.com/questions/13605/martingale-and-filtration

Moffatt, Mike. ‘What Is a Pricing Kernel in Econometrics?’ Thought Co. Available at:

https://www.thoughtco.com/pricing-kernels-in-econometrics-1147073

Nejadmalayeri, Ali. “Basics of Asset Pricing”. Lecture notes. Oklahoma State University. January, 2007.

‘Organizing Your Social Sciences Research Paper: Theoretical Framework’. USC Libraries.

Research Guides. © University of Southern California, 2017. Available at:

http://libguides.usc.edu/writingguide/theoreticalframework.

Petrov, Krassimir. ‘A Primer on Martingales’. Petrov Financial. 1998. Available at:

http://www.petrovfinancial.com/?page_id=880

Pollard, David “Brownian motion (BM)”. Lecture notes‒ Statistics 251b/551b - Stochastic processes.

Yale University. Spring, 2004. (http://www.stat.yale.edu/~pollard/Courses/251.spring04/)

Shakir. ‘Marr's Levels of Analysis’. The Spectator Machine Learning Blog. April 29, 2013. Available at: http://blog.shakirm.com/2013/04/marrs-levels-of-analysis/

132 South African rand (ZAR) reference rates. European Central Bank. Available at:

http://www.ecb.europa.eu/stats/policy_and_exchange_rates/euro_reference_exchange_rates/html/

eurofxref-graph-zar.en.html

Spieksma, Flora and van Zanten, Harry (adaptator). “An Introduction to Stochastic Processes in Continuous Time…”. Lecture notes‒ Stochastic processes - Fundamentals course. University of Leiden. Spring, 2016. (http://www.math.leidenuniv.nl/~spieksma/SPspring08.html)

Rouse, Margaret. ‘Fair and reasonable price’. At:

http://searchitchannel.techtarget.com/definition/fair-and-reasonable-price. July, 2007.

‘Theoretical Models and Frameworks’. Library of Rush University Medical Centre and McCormick Educational Technology Centre (MECT). Available at: http://rushu.libguides.com/c.php?g=694134.

Weiskopf, Daniel A. ‘The Theory-Theory of Concepts’. Internet Encyclopedia of Philosophy.

Available at: http://www.iep.utm.edu/th-th-co/.

Weyl, E. Glen. ‘What Is “Price Theory”? Marginal Revolution. July 29, 2015. Available at:

http://marginalrevolution.com/marginalrevolution/2015/07/what-is-price-theory.html

In document Pricing algorithms and computational price theory : the building blocks of computational finance and IT business applications (sivua 140-147)