Corporate Governance and Algorithmic Trading
The prevailing definition of corporate governance had its roots in the Cadbury Report 1992 where it is described as, “the system by which companies are directed and controlled” (Cadbury Report, 1992). Shareholders, therefore, appoint directors and other officers whom they task with the mandate to govern and control the organisation (Harrigan, 2012). According to the OECD Principles of Corporate Governance, “Corporate governance involves a set of relationships between a company’s management, its board, its shareholders and other stakeholders”. Corporate governance creates a framework under which company objectives are established, defined, met and monitored (OECD, 2015). The objective of corporate governance is illuminated in the UK Corporate Governance Code, hereinafter cited as ‘the Code 2016’; as the advancement of “effective, entrepreneurial and prudent management” so as to achieve lifelong success for the organisation (Roach, 2014). The aforementioned statements outlined characteristics of corporate governance which contribute to its definition. However, like its predecessors, the Cadbury Code of 1992 and the Combined Code of 1998 who had no legal forces as illustrated in Re Astec (BSR) PLC ChD 1999, the current UK Code is voluntary.
The following essay aims to critically examine algorithmic traders in the UK’s financial industry in order to evaluate the legal versus social compliance of this group of financial firms. This evaluation will be conducted in the context of corporate governance by highlighting the limitations of the Code 2016 in addressing intentional misrepresentation and the protection of stakeholders.
As mentioned above, the core focus of this essay is algorithmic trading. Algorithmic trading can be described as the use of computers or computer programmes to buy and sell on equities markets without human intervention; the programmes rely on algorithmic codes to identify aspects such as timing, price and quantity of the equities in question (Pasquale, 2015). The algorithms in use are developed from a statistical model; the programme will monitor when a particular group of shares are overvalued or undervalued and trade based on this information (Chu, 2018). There are various forms of algorithmic trading, the most common being high-frequency trading which involves the automated use of sophisticated programmes and algorithms on super-fast computers in order to take advantage of price differences in the market (Thompson, 2015).
The Markets in Financial Instruments Directive II (MiFID II) under Article 4(1) 39 defines algorithmic trading as any trade in financial instruments in which computer algorithms automatically determine the specific parameters of the order such as timing, price and management after submission with little to no human intervention. However, this does not include any system used for the sole purpose of routing orders across different platforms. This is the definition adopted by the Financial Conduct Authority which is tasked with the mandate of protecting stakeholders such as consumers, regulating the conduct of institutions and enhancing integrity in the UK market (Yonge, 2013).
The Pros and Cons of Algorithmic Trading
In the UK, there has been an increased use of algorithms for trading activity due to the rise in electronic trading platforms. Additionally, the increased availability of data facilitated by technological advancements has made it easier to develop and use algorithms which are used in decision making with respect to execution and investment concerns (FCA, 2018). The increased application of algorithmic trading in the UK has led to calls by regulators for firms to ensure proper management of potential risks which may arise from good or bad practice. Regulators are insisting on the development of proper systems and controls to ensure any rising issues from the increased speed and complexity of this particular market are well managed to avoid extreme widespread implications (Langton, 2018).
According to Thompson (2015), automated trading services like algorithmic trading serve some societal purpose. For example, asset managers use algorithmic trading as a platform to quickly and efficiently rebalance their portfolios. The application of algorithms into the financial trading market has led to the elimination of intermediaries this, in turn, has led to a reduction in trading costs which has subsequently led to an increase in the performance of pensions and annuities which essentially benefit the society as a whole (Lowrey, 2012). However, regardless of the evident societal benefits to stakeholders, algorithmic trading is also facilitating certain harms to the financial market which translate to an overall social detriment.
In the US, 60 to 70 percent of trades in the stock market are comprised of high-frequency trading by hedge funds, investment funds and other firms (Freeman, 2012). The increase in investments has led to a rise in concerns over the social detriments of algorithmic trading to stakeholders. The main concern is that very little is known about how automated high-speed trading works and whether it can be used to manipulate markets or lead to a financial crisis. Prewitt (2012) outlined the concerns associated with high-frequency algorithmic trading by recognising that it increases systemic risk by causing or catalysing crises, it makes it easy for manipulators to go undetected and some of its supposed benefits such as liquidity are questionable.
An example of a crisis is the US Flash Crash of 2010 which many scholars and economists have attributed to the use of high-frequency algorithmic trading. In May of 2010, the New York Stock Exchange experienced one of its largest stock plunges in a span of 20 minutes as the Dow Jones index lost close to 9 percent of its value leading to hundreds of billions of dollars in shares being lost for large companies like Proctor & Gamble and General Electric. The official report by the Securities and Exchange Commission and the Commodity Futures Trading Commission revealed that high-frequency trading had led to the rapid plunge in stock prices (Treanor, 2015). In late 2016, the British pound experienced a drop in value by up to 6 percent against the dollar; it fell to $1.18 in a matter of minutes marking a 31 year low before recovering to $1.24. This plunge was reflective of a flash crash which experts attributed to algorithmic trading (Condliffe, 2016).
Limitations of UK Corporate Governance Code(2016)
The uncertainties arising from algorithmic trading create a societal detriment to stakeholders in that they could easily facilitate a financial crisis which would lead to a drop in the economy. These uncertainties also lead to a reduction of the market’s integrity as manipulators can easily go undetected. It has also been argued that algorithmic traders create a false sense of liquidity in the market which encourages more investors (Pewitt, 2012). This may be a benefit in encouraging investments, however, in the event of a crisis, high-frequency traders take up the little liquidity in the market leading to a loss of investor confidence and consequently the economy as a whole suffers. Integrity and transparency are elements which coincide with the concept of corporate governance, it is for this reason that regulators are encouraging firms to tighten their approval processes, their testing and deployment, documentation, inventories and risk management so as to ensure stakeholders, both internal and external are protected from the potential risks arising from algorithmic trading.
As previously mentioned, the UK Code of Governance 2016 provides the framework under which companies can effectively incorporate corporate governance into their activities. The Code highlights five core principles of good corporate governance; each core principle is supported by various principles and code provisions (Ridley, 2013). The essential principles are; leadership, effectiveness, accountability, remuneration and relations with shareholders (FRC, 2016). According to the FRC, the Code’s provisions merely provide one way of achieving the core principles; they are not conclusive (Davies, 2010). In addition to these principles, as aforementioned, the lack of legal force presents another feature of corporate governance practice in the UK as the Code has been voluntary from the Cadbury Report to date. As such, based on company size, structure and nature of business, different companies adopt different approaches to governance under the guidance of the Code (Chapman, 2011).
However, the incorporation of the principles of governance into statute has given the Code a sense of legal force; companies are required to comply with the provisions of the Code or provide an explanation where non-compliance is inevitable (French, et al., 2015). Essentially, compliance is voluntary however; failure to comply or explain can garner certain consequences such as exposure for failure to comply or in extreme cases being delisted from the stock market (Tricker, 2012). Compliance with the provisions of the Code 2016 is, therefore, a mandatory requirement of the Listing Rules with non-compliance requiring an explanation. This approach to enforcement reflects the ‘comply or explain’ model.
Conclusion
The main limitation arising from the current Code 2016 is that it is still essentially a voluntary, principle-based code. As such, it does not place any legal liability on institutions; it merely proposes principles on which institutions can build their corporate governance practices. On these grounds, the Code 2016 is limited in tackling the potential risks arising from algorithmic trading. From the discussion above it is evident that there is a lot of ambiguity and uncertainty attached to this category of financial firms. They create a false sense of liquidity and there is little understanding among investors as to the functioning of high-frequency trading creating room for potential manipulation. These aspects can be construed as intentional misrepresentation on the part of algorithmic traders.
The ‘comply and explain’ model provides a mechanism under which regulators can monitor and control algorithmic trading. Algorithmic traders are required to exercise transparency by complying with the principles of the Code 2016 among which is the principle of accountability which would require an element of transparency among traders. If they opt not to comply they are required to provide a valid explanation as to why they opted not to comply. They should be transparent regarding their sources of information which inform the algorithms and how the algorithms are applied to facilitate trade. This transparency reduces the chances of manipulation and also increases investor confidence and market integrity.
However, where the Code 2016 falls short, UK regulators have adopted and developed other sources of regulation to ensure stakeholders are protected. The Financial Conduct Authority, as aforementioned, relies on the requirements of the MiFID II to inform algorithmic traders on focus areas of compliance. According to a report published by the authority in early 2018, firms are expected to establish proper methods of identifying algorithmic trading and managing significant changes. This is facilitated by maintaining a detailed inventory of algorithmic trading activities within the business. This requirement facilitates transparency and accountability, a significant element of corporate governance and stakeholder protection. Additionally, risk controls should be developed and frequently assessed to identify, monitor, and decrease potential risks arising from algorithmic trading which may facilitate greater societal detriment in the long run. Another core focus area is governance as firms are expected to have in place a proper governance framework to oversee the implementation and monitoring of the risk management and compliance aspects. Finally, firms are expected to assess the impact of their algorithmic trading activities on market integrity.
Conclusion
The discourse above as exhaustively examined the financial category of algorithmic trading in the UK and their societal purpose. Essentially, algorithmic traders facilitate fast and efficient trading which is beneficial to the society as it reduces the cost of trade and also makes it easy for asset managers to balance their portfolio. This is particularly beneficial for managers in pension funds and as such translate into social benefit. However, the uncertainties associated with algorithmic trading due to the lack of clear understanding of how they function and the increasing speeds of the market create a social detriment. Algorithmic trading can easily create or catalyse a crisis such as a flash crash, additionally, as there is little information as to how they function; they create an environment that is easily susceptible to manipulation. Further, in the event of a crisis, high-frequency traders take up the liquidity in the market further deepening the crises. All these scenarios are detrimental to investors and the society at large as they risk the stability of the economy. From the discussion above it is evident that good corporate governance practices, particularly those that ensure accountability and transparency, are essential to reducing the risks associated with algorithmic trading. The UK Code 2016, is one of the ways in which regulators encourage firms to exercise accountability; the ‘comply or explain’ mechanism ensures that firms are held accountable for their practices towards ensuring stakeholder protection. However, the Code is limited to the extent that it is voluntary. As such, regulators such as the FCA have adopted other mechanisms to ensure algorithmic firms practice good corporate governance to ensure stakeholders are protected.
References
Cadbury Report, 1992. Report of the Committee on the Financial Aspects of Corporate Governance, s.l.: Burgess Science Press.
Chapman, R. J., 2011. Simple Tools and Techniques for Enterprise Risk Management. s.l.: John Wiley & Sons.
Chu, B., 2018. Stock markets: What is algorithmic trading? And is it really responsible for the current financial mayhem?. [Online]
Available at: https://www.independent.co.uk/news/business/analysis-and-features/stock-market-algorithmic-trading-what-is-it-responsible-financial-correction-us-uk-latest-news-a8202161.html
[Accessed 15 May 2018].
Condliffe, J., 2016. Algorithms Probably Caused a Flash Crash of the British Pound. [Online]
Available at: https://www.technologyreview.com/s/602586/algorithms-probably-caused-a-flash-crash-of-the-british-pound/
[Accessed 16 May 2018].
Davies, P. L., 2010. Introduction to Company Law. 2nd ed. Oxford: OUP.
FCA, 2018. Algorithmic Trading Compliance in Wholesale Markets, London: Financial Conduct Authority.
FRC, 2016. The UK Corporate Governance Code, London: Financial Reporting Council.
Freeman, K., 2012. Secret Weapon: How Economic Terrorism Brought Down the U.S. Stock Market and Why it Can Happen Again. Washington: Regnery Publishing.
French, D., Mayson, S. & Ryan, C., 2015. Mayson. French & Ryan on Company Law. 32nd ed. Oxford: OUP.
Harrigan, B., 2012. Company Law. 3rd ed. Oxford: Oxford University Press.
Langton, J., 2018. U.K regulators review algorithmic trading. [Online]
Available at: https://www.investmentexecutive.com/news/from-the-regulators/u-k-regulators-review-algorithmic-trading/
[Accessed 15 May 2018].
Lowrey, J., 2012. Social benefits of high-frequency trading. [Online]
Available at: https://www.ft.com/content/7b1a0d2c-e207-11e1-8e9d-00144feab49a
[Accessed 16 May 2018].
OECD, 2015. G20/OECD Principles of Corporate Governance, s.l.: OECD.
Pasquale, F., 2015. The Black Box Society: The Secret Algorithms That Control Money and Information. London: Harvard University Press.
Pewitt, M., 2012. High-Frequency Trading: Should Regulators Do More?. Michigan Telecommunications and Technology Law Review, 19(1), pp. 131-214.
Re Astec (BSR) PLC (1999) ChD.
Ridley, A., 2013. Key Facts Company Law. 4th ed. London: Routledge.
Roach, L., 2014. Company Law Concentrate: Law Revision and Study Guide. 3rd ed. Oxford: OUP.
Thompson, G., 2015. Globalization Revisited: Critical Issues in Global Politics. Oxon: Routledge.
Treanor, J., 2015. The 2010 ‘flash crash’: how it unfolded. [Online]
Available at: https://www.theguardian.com/business/2015/apr/22/2010-flash-crash-new-york-stock-exchange-unfolded
[Accessed 16 May 2018].
Tricker, R. I., 2012. Corporate Governance Principles, Policies and Practices. 2nd ed. Oxford: OUP.
Yonge, W., 2013. New UK financial regulation architecture takes effect. [Online]
Available at: https://www.lexology.com/library/detail.aspx?g=5623e279-383b-461b-ae37-246611e2151b
[Accessed 15 May 2018].
Order an Essay Now & Get These Features For Free:
Turnitin Report
Formatting
Title Page
Citation
Outline
