Computational finance: a motivational entrée to a rugged landscape

1 Introduction

1.2 Computational finance: a motivational entrée to a rugged landscape

The first ideas that usually comes to mind to when the words “computational finance” are heard among the non-technical audience resemble something like “beating the market” through trading;

the abstract concept of financial markets as a source of “infinite wealth”; or the existence of some gurus that possess a “higher understanding” and therefore are able to make extraordinary profits where everyone else cannot²⁴, or that are pulling the strings of the economy behind the scenes with the aid of technology toward dark objectives. But generally all of these ideas can be resumed in the perception of a fundamental ambivalence in the financial system (and of computational finance as natural appendix to it): a minority of happy-go-lucky people getting immensely rich and a blue-collar hard-working majority engaged in a never ending struggle to recover from the impact of financial crises, let alone recurrent news regarding firms’ CEO’s corruption and misbehaviour. While the true nature of the financial environment in general, and of computational finance in particular, is somewhat beyond the grasp of the mere uneducated mortals, some of the former ideas, although naïve, might capture nevertheless some relevant features of the financial system.

Recently, the idea of a mythical money-making algorithm set in the imaginary of the people is being exploited in many ways. One of the most visible is the sheer amount of offers to make money out of

“trading” binary options²⁵: mind-blowing algorithms which the platform runners in a sudden fit of algorithm to take the necessary advantage. A post in a related forum proposes: <<Without going into too much detail: The stock market in general, individual sectors, and individual corporations can be predicted mathematically, through pattern recognition and the ability to mathematically describe the patterns. You must be able to mathematically describe the progression of wave-like patterns over time, and then you can predict the timing and significance/degree of high points and low points during rising and falling periods. If you can do this, there should really be no limit to the amount of success you can have. >> Sources:

Limitless IMDb card at http://www.imdb.com/title/tt1219289/. Accessed online 15.04.2015.

Limitless: Stock Market Can Be Predicted Using Equations. ‘The Journey’ blog. Posted on December 31, 2012 at http://www.abovetopsecret.com/forum/thread913573/pg1. Accessed online 15.04.2015.

25 Technically, a binary option a type of option (financial contract) in which the payoff is structured to be either a fixed amount of compensation if the option expires “in the money” (meaning that the option is worth money so the holder can turn around and sell or exercise it) or nothing at all if the option expires “out of the money” (meaning that is worthless). Investors may find binary options attractive because of their apparent simplicity: it involves only a guess. For example, the guess as whether the price of XYZ Company will be above 1 euro tomorrow before the market closes is a

7 godliness want to share with the public, so more and more people can become financially unconcerned or even loaded. The stories are sometimes adorned with a conspiracy setting, in which an adversary figure (the government or the current masters of the financial markets) want to preclude the public to have access to the algorithms. While the majority of the so-called platforms present themselves as high-risk of scam situations²⁶, others look more serious, even professional²⁷. But the important thing to notice is that everything eventually comes up to the existence of “laws” or

“patterns” prices abide to, so price prediction can be tackled by skillful computer programming. Even though pricing algorithms are concerned with the calculation of prices rather than their prediction, the whole issue shows that the concepts of “price” and “algorithm” are started to be connected in the people’s minds, just not in the right way.

Besides the scam situations mentioned, a part of the public thinks that computers might be deciding which price is right for the consumers to pay, especially in online transactions: here, pricing mechanisms are viewed as “black-boxes”, allowing price manipulation through technology. These subjective appreciations might be not be very far from reality. The automation of price determination and price manipulation are real. I present two case studies.

Case study 1.1 ‒ Price manipulation in the Amazon Marketplace.

A conspiracy allegedly involving sellers in the Amazon Marketplace who wanted to raise the price of posters, prints, and art on the Amazon e-commerce website. David Topkins, former Director of the Trend Division of Art.com, an e-commerce seller of wall décor, and his co-conspirators agreed to

<<fix, increase, maintain, and stabilize prices>> of certain posters sold in the United States by agreeing <<to adopt specific pricing algorithms for the agreed-upon posters with the goal of coordinating changes in their respective prices>>. These actions involved using an algorithm to set prices in conformity with the anticompetitive agreement between the sellers. The algorithm utilized was said to be a commercially available pricing software, which uses competitor pricing information in accordance with rules set by the seller. Mr. Topkins customized the code so that the software could determine price changes based on which posters were popular, and share that information with other Marketplace sellers so that they would coordinate their respective prices.

binary option, which automatically exercises if that is the case, and the holder gets a preset amount of cash. While occasionally traded on platforms regulated by the SEC and other regulatory agencies, binary options are most likely traded over the Internet outside of regulations, and because of that investors are at greater risk of fraud. Source:

Binary Option. Investopedia. At http://www.investopedia.com/terms/b/binary-option.asp. Accessed online 27.03.2016.

26 Binary option websites are gambling sites, pure and simple. These sites promote themselves as offering controlled risk (the amount of money that the player can lose is bounded), a low cost-big gains experience for many players, and ease of use—it is possible to trade from home whenever markets are open and set up an account with a credit card. A professional online pocker player estimated that an investor must be right 55% of the time in order for her bet to have a neutral expected value (Source: Pape, Gordon. ‘Don't Gamble on Binary Options’.at http://www.forbes.com/sites/investor/2010/07/27/dont-gamble-on-binary-options/. July 27, 2010.

Accessed online 28.032016). What is interesting is the “technical argument” offered as support, namely the algorithmic structure. Here are some examples:

30 Day Change Program “Winning software” http://thirtydaychange.com/?campaign=1504 Profit from Home System Pattern Recog. Software http://www.pfhsystem.com/visitor.php

Amanda Richards Prediction Software http://www.mymoneymakingapp.com/

Quick Cash System “The Pattern” Algorithm http://quickcashsystem.com/index.php

The constant reference to computer science jargon only helps people to believe. In some cases, a more detailed explanation of the business model is provided, which show also the rules the players have to comply with in case of either winning or losing, suggesting it is a 50-50 deal. For an example check: https://iqoption.com/promo/binary-options_en/.

27 Check for example the interview made about the managers of Ultimate4Trading: Milbar, John.

‘The First Ever Proven Money-Making Trading Algorithm is successfully Demonstrated!’. At http://www.start-up365.net/Pages/Top10/Ultimate4Trading/2.php. Accessed 28.03.2016.

8 According to the Justice Department, by sharing information about sales among a number of other vendors in order to fix prices, Topkins violated the Sherman Act, because the U.S. federal antitrust law prevents the merchants colluding to sell items at “non-competitive” rates. On April 6, 2015, the U.S. Antitrust Division announced that Topkins, had pled guilty to a one-count felony charge for conspiring to fix the prices through the Amazon Marketplace from September 2013 to January 2014, agreed to pay a $20,000 criminal fine and to cooperate with the department’s ongoing investigation, in what represents a new front for the criminal enforcement of price fixing. The Amazon Marketplace competes with eBay Inc. by allowing third-party sellers to offer goods on Amazon’s site for a fee.

Interestingly, Amazon as an enterprise was not charged in the price-fixing scheme, because the Marketplace is separate from Amazon’s regular business, where the company directly sells goods to consumers. ²⁸ This case raise the following reflections:

1. As pricing mechanisms shift from traditional markets to computational techniques, new forms of collusion are also expected to arise. The old image of collusion as “smoke-filled hotel rooms”

where executives expressly collude is being replaced by a stereotype consisting of pricing algorithms continually monitoring and adjusting to price movements and market data. Under this tacit collusion view, a closed oligopolistic market might unsuspectingly turn into an open one, based on price anomalies.²⁹ Interestingly, when computer algorithms take over the role of market players, the spectrum of possible infringements widens uncontrollably. Computers may limit competition without market competitors being aware. Consider for example a hypothetical market in which market participants start making extensive usage of very similar computer algorithms to predict each other’s reactions and dominant strategies. Such a market would resemble a monopoly, increasing the price at the expense of the consumers’ value, with the additional advantage of avoiding the normal behavioural biases normal markets exhibit, and also being less susceptive to possible deterrent effects generated through antitrust enforcement.

2. Even though Amazon was not charged for the price-fixing scheme due to the fact that it was considered separated from its main line of business, it is nevertheless uncertain if Amazon could have done more in detecting the price-fixing manipulation, or if the company should have done so. After all, pricing and other market-oriented algorithms operate on online platforms which provide them with the input information they need. The fact that price-fixing scheme took place on the Marketplace, with disregard of business lines considerations, is a failure for Amazon’s corporate governance.

3. This case also raises the issue of how to punish a misuse of computational pricing techniques, i.e. the problem of the application of competition law to a computerised trading environment. A violation of the Sherman Act, carries a maximum sentence of 10 years and a fine of $1 million for offenders. The maximum fine may be increased to twice the gain derived from the crime or twice

28 Halleck, Thomas. ‘Feds Uncover Amazon Marketplace Price-Fixing Scheme’, July 4, 2015. Available at: http://www.ibtimes.com/feds-uncover-amazon-marketplace-price-fixing-scheme-1871504.

Department of Justice, Office of Public Affairs. ‘Former E-Commerce Executive Charged with Price Fixing in the Antitrust Division's First Online Marketplace Prosecution’, April 6, 2015. Available at:

https://www.justice.gov/opa/pr/former-e-commerce-executive-charged-price-fixing-antitrust-divisions-first-online-marketplace.

O'Neill, Patrick Howell. ‘Amazon price-fixing scheme lands programmer with $20,000 fine’, April 6, 2015. At: http://www.dailydot.com/crime/amazon-marketplace-price-fixing-art/.

Cadwalader Wickersham & Taft LLP. ‘Department of Justice Antitrust Division charges former e-commerce executive with price-fixing in first ever online marketplace prosecution’, April 7, 2015.

Available at:

9 the loss suffered by the victims, if either of those amounts is greater than the statutory maximum fine. So in this case the punishment is non-severe, and the damage caused to the final customers is not calculated or even mentioned as a provision in the legal procedure. Moreover, the sense of detachment between algorithms, the programmers who designed them and the individuals who misuse them, also reveals a potential failure to prevent misconducts, as algorithms are not susceptible to traditional deterrents, such as jail, monetary fines, or shaming.³⁰. Computing presents a particular case for understanding the role of technology in moral responsibility.³¹ This case shows how coordinated, accommodating, or interdependent responses among computers raise challenging technical, law enforcement and ethical questions.

Collusion as a result of manipulative activities based on computational techniques deserves a more detailed discussion. Regarding the use of computer techniques by the market participants, Ezrachi and Stucke (2015) identify four non-exclusive categories of collusion:³²

Model of

31 ‘Computing and Moral Responsibility’ Stanford Encyclopedia of Philosophy. 2012. Available at:

https://plato.stanford.edu/entries/computing-responsibility/

32 Ezrachi Ariel and Stucke, Maurice E. “Artificial Intelligence & Collusion: When Computers Inhibit Competition, The University of Oxford Centre for Competition Law and Policy, Working Paper CCLP (L) 40, April 8, 2015.

10 So, a company that utilizes an algorithm-based pricing software needs to take particular care that its rules are decided independently of those of other competitors, especially when doing business in an online marketplace that utilizes an auction model. The authors even suggest that inside the markets a Darwinian interaction between the algorithms might occur: <<one should acknowledge that evolution dictates that the stronger, more powerful algorithms will likely prevail and dominate the technology market>>. This is of course, pure speculation, and possibly will not even ever happen, for the simple reason that in an natural setting, all individuals in the ecosystem face identical environment conditions, and the different probabilities of survival arise as a consequence of their own individual characteristics, while in an actual market, not only the participants may exhibit different characteristics (different business models, different levels of risk-aversion, different financial ratios, different network arrangements with other agents, etc.) but also the market might not present itself in the same way to all of them. Anyway, a referential framework for collusion or any other illegal or unethical practices, comes handy for policy-makers as well as computational finance practitioners.

Case study 1.2 ‒The importance of financial management: the downfall of Nokia and the economic situation in Finland.

As a second case regarding the potential effect of a technology issue on an economy, I will now discuss the crash of the Finnish company Nokia, and how this unfortunate event has put the country’s economy to edge. Financial and technology management have become intertwined, and that constitutes the core of the field of computational finance.

As of the writing this work, the financial situation of Finland is a difficult one. At the beginning of the 21^st century Finland, and its former prime enterprise, Nokia, were doing well, with the economy growing over 4,5% annually. However, the technological decline of Nokia and the world’s financial crisis of 2008 hit severely the Finnish economy³³. In 2012 and 2013 the Finnish gross domestic product (GDP) was negative, and around zero in 2014, which means the economy will be stagnant for a few years according to the economists. Even though the country has complied with the strict austerity policies ordered from Berlin, the deficit of the government public balance is expected to reach 3,4% of the GDP this year, which is above the limits of financial sustainability. Moreover, the public debt has doubled during the last decade, and represents 60% of the GDP, which is the upper bound established by the European Union. Direct investments experienced a sharp drop during the last economic cycle, the competitiveness has fallen down in relation to the neighbouring countries (Sweden and Germany), and also the contraction of the domestic demand is relevant as it precludes a fast recovery by implementing emergency measures.

There are different factors that can be identified as the cause of this economical sub-performance, and as often happens in these cases, there is a sort of mixture of structural weaknesses, bad luck and worse decisions:³⁴

33 The financial crisis of 2007–2008, also known as the Global Financial Crisis, is considered to have been the worst financial crisis since the Great Depression of the 1930s. It threatened the total collapse of large financial institutions, which was prevented by the bailout of banks by national governments, but stock markets still dropped worldwide. In many countries unemployment escalated. The crisis played a significant role in the failure of key businesses, declines in consumer wealth, and a downturn in economic activity leading to the 2008–2012 global recession and contributing to the European sovereign-debt crisis.

Source: http://en.wikipedia.org/wiki/Financial_crisis_of_2007-08 34 The sources of the following paragraphs are:

Soto, Adrián. “La estrella finlandesa pierde su brillo” (“Finland’s shinning star looses its brightness”). El País, March 8, 2015. Accessed online 27.03.2015.

(http://economia.elpais.com/economia/2015/03/05/actualidad/1425575662_045977.html Martín, Javier. “El gran traidor” (“The great traitor”). El País, September 13, 2013. Accessed online 27.03.2015

(http://tecnologia.elpais.com/tecnologia/2013/09/03/actualidad/1378225641_315612.html)

 Inability to carry out the so-called “structural reforms” (the reform concerning the municipalities, social security system, the age of retirement, among others).

 Basically, all sectors of the Finnish economy are experiencing setbacks. During 2014, the output of the industry of technology, which is crucial for the economic activity, fell short 4,6% compared to the previous year, and it is in the red for the fifth year in a row. The paper industry, the other pillar of the economy has also deteriorated quite a lot. Production of paper and cardboard was reduced from 14,5 millions of tons in 2007 to 10,6 million in 2013. Finally the metallurgical and naval industries registered a decline in its business.

 The most publicized factor is the one regarding the Finnish “star”: Nokia. Here, the legendary tale of the wooden horse of ancient Troy is belittled. The modern horse name is Stephen Elop. Sent by Microsoft to prepare the acquisition of the mobile division of Nokia by the American information technology giant, he managed to destroy Nokia from the inside in just three years, betraying the company and the country that hosted him³⁵. Notably, Nokia alone was responsible for a stunning 24% of Finnish real GDP growth from 1997 to 2007.

 Uncompetitive export base and labour market rigidities.

 The political turmoil resulting from the Russian-Ukrainian crisis. This situation has weakened Russian economy, along with the so-called “sanctions” imposed by the United Stated. Since Russia’s business represent around 10% of Finnish foreign trade and 50% of the tourism that comes to Finland also comes from Russia, the political turmoil only makes matter worse at the worst time possible.

 The country's demographics –not very different from the rest of Europe– include a shrinking working-age population and a growing aging population, meaning a decreasing active population and a higher dependant ratio on the working population.³⁶

 Finland uses the euro, which means it can't adjust fiscal or monetary policy to its specific needs.

In an interview given to CNBC on October 2014³⁷, the back then Finland’s Prime Minister, Alexander Stubbs, suggested that Apple could be to blame for the demise of its two biggest industries (iPhone killed Nokia and iPad killed the paper industry), which in turn led to an economic downturn and a ratings downgrade for the country³⁸. The reasons the PM had to give this biased and clearly inaccurate information are unknown to me, but the downgrading of Finland's sovereign debt rating was real. On October 2014 Standard & Poor’s (S&P) downgraded Finland from AAA to AA+ ³⁹. More recently, on 3 June 2016, the credit rating agency Moody’s Investors Service downgraded Finland’s long-term issuer rating from Aaa to Aa1. The outlook was changed from ‘negative’ to ‘stable’.

In document Pricing algorithms and computational price theory : the building blocks of computational finance and IT business applications (sivua 21-0)