Evaluating the Challenges and Opportunities of High-Frequency Trading
Table of Contents
Introduction. 3
High-Frequency Trading. 3
Key Issues in HFT/Challenges. 5
Social Bias. 5
Financial Costs to Small Organizations. 6
High Liquidity and Messaging. 6
Concerns to Regulators and Public. 6
Manipulation and Vulnerability of the Capital Markets. 7
State of Activity. 8
The Role of Big Data Technology. 8
The Relationship Between Latency and Trading Performance. 9
Importance of Validity, Liquidity, and Directional Trading Measures. 10
Future Directions and Opportunities. 10
Improved Technologies. 10
Addressing HFT Challenges. 11
Regulations and Transparency. 11
Addressing Entry Barriers. 12
Transforming Developing Countries. 12
Conclusion. 13
References. 14
Evaluating the Challenges and Opportunities of High-Frequency Trading
Introduction
There is a significant growth in financial technology by 201% globally (Monaco, 2019). Some of the key growing areas include online loans, automated investing, and data analytics. Notably, automation in the investments and big data transform the capital markets. Technology has also introduced fast trading and co-location activities. These elements allow the entrance of sophisticated investors in the capital market. Through high efficiency in computing, characterized by speed and performance, algorithmic traders (AT) have increased tremendously in the international stock exchange. Primarily, the behavioral changes of market entrants attract concern by policymakers and researchers due to vulnerability and manipulation. Total trading activity distinguishes into AT and non-algorithmic trading activity. Among the current concept in overall trading activity is high-frequency trading (HFT). This paper examines the HFT concept, key issues, state of action, and opportunities. Don't use plagiarised sources.Get your custom essay just from $11/page
High Frequency Trading
Regulatory agencies define HFT as automating the trading process and high-speed activities of orders. According to the United States Securities and Exchange Commission (SEC), HFT entails competent traders engaging in approaches that generate a vast number of daily trades. The European Markets in Financial Instruments Directive (MiFID II) defines the concept as an algorithmic trading approach characterized by advanced technology and infrastructure. Technology is manifested in co-location facilities, direct electronic access, and proximity hosting, which allow for the execution of orders without human activity. Agarwal (2012) define HFT traders as participants in the market, who react promptly to incoming news. Also, these traders react to low inventories and execute a high number of trades. Since its introduction in the 2000s, HFT presently represents at least 50% of US trading (Breckenfelder, 2019). HFT traders engage in two core trading types of speculative and market-making. In market-making, it is advantageous to investors based on the liquidity, while speculative trading increases the liquidity cost provision.
HFT is related to price discovery, liquidity, and application of technology. HFT is a new concept in the 21st century. Unlike the former financial markets approaches that were concerned with intermediaries, HFT relies on technology and algorithms. The brokers in previous financial markets facilitate the investor in meeting the objectives. In present fully automated stock exchanges, the main merits include improving the trading capacity and fostering for the use of technology. In automated financial markets, human makers have been replaced by technology. Similar to the traditional commercial marketing approach, HFTs also trade frequently and have a short holding time (Brogaard, Hendershott, & Riordan, 2014). The use of algorithms increases the number of stocks, and the amounts are more than that the market can absorb. Unlike the traditional trading approach, HFT allows for splitting larger orders into small ones that can be executed over hours, minutes, or days. The competition element among HFT is based on the commission pool related to trading using the algorithm. The main advantages of HFT are narrowing spreads, enhancing market efficiency, and increasing liquidity. Also, algorithms minimize trading risks through scattering the orders over a period. This is demonstrated in figure 1 below.
Figure 1: Market Impact vs. Timing Risk (Gomber, Arndt, Lutat, & Uhle, 2014).
Key Issues in HFT/Challenges
Although several merits, including efficiency and narrowing spread, characterize HFT, there are several critiques and challenges associated with the algorithmic trading approach.
Social Bias
HFT has created a social bias between slow and fast traders. According to Zook and Grote (2017), the late 20th century was characterized by the emergence of algorithmic trading and shift from the physical territories. The transformation heralds the reducing influence of the trading pit. In algorithmic trading, the order is placed by machine terminals without interference by the human element. In a smooth and fair operating market, the ability of traders to sell according to their wish refers to liquidity. Although HFT has improved how people trade, access to information is biased. Individuals that have invested in HFT technologies have access to more information which influences their trading efficiency. HFT has also introduced multilateral trading facilities (MTFs) and anonymous actors. The access to information for one to trade resultS in the social bias between those with the capital to invest in algorithms and slow traders who depend on financial news and sentimental analysis.
Financial Costs to Small Organizations
The social dimension of HFT is defined by how traders distribute risks while capturing profits. There is a significant connection between the assemblage of HFT and these risks. The majority of organizations not engaging in capital markets are affected by these uncertainties. HFT may destroy wealth to small firms for influencing the economic downtown (Aitken, Cumming, and Zhan, 2017). Notably, smaller organizations experience high volatility in stock prices, which increases their risks and financing costing.
High Liquidity and Messaging
HFT is characterized by a vast number of trades spread over short times. Notably, bid asking spread is, low and the liquidity cannot be accessed by slow traders (Linton & Mahmoodzadeh, 2018). The high number of messages, such as revisions and cancellation of orders, impose a negative effect on other traders, who depend on slow trading and have invested less in technology.
Concerns to Regulators and Public
The last decade has seen an increasing fear of regulators and the public about HFT. In 2013, the High-Frequency Trading Act was passed in Germany to mitigate the irrational and extreme fluctuations in prices influenced by HFT and disconnection from real economic development. The US also share concerns. As espoused by Zook and Grote (2017), there was high engagement in unexpected behavior by assemblages between 2006 and 2011. A significant example is the Flash Crash in 2010 May. During the incidence, the Dow Jones Industrial Average reduced approximately 7% within a few minutes and restored in less than 30 minutes. At this time, while some shares reduced to 1%, Apple traded at $ 100000 per share (Zook & Grote, 2017). The 2010 Flash Crash is shown in figure 1 below.
Figure 1: Flash Crash on May 6, 2010 (Zook & Grote, 2017).
Manipulation and Vulnerability of the Capital Markets
HFT is accessible to individuals that invest considerable resources in technology. The capital market is therefore influenced by these individuals that move colossal capital in the assemblage. However, the technology is vulnerable to external actors, and security risks may affect the trading activities. An example is the April 2013 hacking of the Associated Press Twitter account. Fake message was sent about President Obama being injured and explosions at the white house. HFT algorithms that scan for news reacted to this information resulting in a plunge of the stock market by 1% (Egele, Stringhini, Kruegel, & Vigna, 2015). Other challenges associated with vulnerability include scanning the market and acting fast by HFT traders. This results in slow traders being affected and incurring losses. Through strategies such as spoofing and quote stuffing, HFT traders manipulate and move the market. This creates inequality and unfairness in the stock market.
State of Activity
The Role of Big Data Technology
HFT is centered on big data technology in the financial market. In the last decade, the velocity, veracity, and vast data developed and consumed has risen exponentially and will persist due to artificial intelligence and machine learning. The goal of HFT is to make several trades within short periods to increase the profits. The short term predictions, according to Linton and Mahmoodzadeh (2018), are used to create a high number of moves within nanosecond periods. Although the wins are still plausible if entered by a human, the profits may not be high compared to algorithm trading. The use of complicated big data approaches is aimed at establishing better opportunities within short times. The algorithms are aimed at identifying buying and selling opportunities in the stock market and minimizing risks. The difference between the informed and uninformed individuals is explained from the stock prices model by Grossman and Stiglitz (1980). The model is provided in equation one below.
Equation 1: Stock Price Model (Grossman & Stiglitz, 1980)
The stock price model allows one to perform comparative analysis. The increasing sources of information influence the argument that information acquisition costs have reduced significantly while access to sufficient data has improved. However, the veracity of information, which in big data defines the permutation computations, has increased, which raises the cost of obtaining the data.
The Relationship Between Latency and Trading Performance
Latency affects trading performance. There are two channels through which traders reap from speed. These include proper risk management strategies and short-lived information. Fast traders are more aggressive to news and establishing the above stale quotes. Low latency, according to Baron, Brogaard, Hagstromer, and Kirilenko (2019), allows the liquidity providers to decrease their selection costs adversity through a revision on the stale quotes. HFT traders benefit from averting inventory risks. Baron et al. (2019) used regression equation 2 to show the relationship between latency and trading performance through the ordinary least squats (OLS).
……Equation 2
In this equation, is among the HFT performance measures, involving the revenues, returns, Sharpe ratio, and revenues traded or volume. These dependent variables include days, venues, and stocks. The trading volume and revenues are averaged across the days of trading, while revenues traded and returns are calculated from the firm’s observations per month. Based on Baron et al. (2019) findings, both revenues and latency affect the risk-adjusted performance measures, and the difference between the two is expressed from a market capitalization perspective.
Importance of Validity, Liquidity, and Directional Trading Measures
Volatility and liquidity measures are vital in HFT. According to Breckenfelder (2019), liquidity measures include bid-ask spreads, Kyle’s lambda, autocorrelation, and order execution shortfall. Volatility measures, on the other hand, include inter and intraday volatility. Intraday volatility refers to the summation of all squared returns according to high low intervals, while inter-day volatility involves different ranges. In HFT, directional trading measures are developed to capture the momentum. It is a speculative trading approach that considers different variants. An important variant, as described by Breckenfelder (2019), examines the trading turnover from trades on a day at an interval of 5 minutes in a given stock. The importance of measuring the volatility, liquidity, and direction of trading is to define the relationship between efficiency and trading strategies.
Future Directions and Opportunities
Improved Technologies
HFT is an evolution that advances based on big data technology. The transformations in stock markets are inexhaustive. Among the critical future directions is enhanced discoveries in artificial intelligence and machine learning, which aim at minimizing costs and maximizing profits. HFT is a dynamic concept. The introduction of new electronic execution strategies and machines are aimed at improving access to routing systems (Guilbaud & Pham, 2015). Also, there is a significant relationship between market participants and adopting new technologies. Similar to other big data technologies, a significant opportunity is to ensure sophisticated participants in the stock market have achieved better rewards from their capital investments.
Addressing HFT Challenges
Since the inception of HFT technology, there have been several concerns, particularly in social bias, vulnerability, and manipulation. According to Kirilenko, Kyle, Samadi, and Tuzin (2017), previous issues such as the Flash Crash in 2010 reflect on the potential challenges related to HFT. Addressing these setbacks is vital in ensuring stability in the capital market. In future markets, algorithmic advancements should aim at establishing safe approaches and detection of fake news. Also, big data in trading has continuously created social bias, which affects small organizations and slow traders through manipulation. Future opportunities should, therefore, aim at establishing normalization strategies such as improving access to information to both high and slow traders.
Regulations and Transparency
Among the common concerns of HFT technology is how it influences the market, which is a risk to economic downtown and unstable volatility. According to MiFID II, there is an urgency to regulate HFT to mitigate the manipulation risks. Countries such as Germany have already introduced regulation acts, which are aimed at reducing the risks associated with algorithmic technologies (Busch, 2016). Policymakers should have access to HFT reliability, robustness, and risk management strategies. Organizations implementing HFT systems should communicate their risk mechanisms and internal safeguards that prevent the recurrence of events such as Flash Crash in 2010 and the 2013 fake news in the US. The evolutions of innovative security markets should be monitored to ensure a balance in supply and demand and ensure price discovery to the benefit of market participants.
Addressing Entry Barriers
HFT primarily benefits large corporations with capital to invest in algorithm technology. New and small entrants encounter a number of barriers in engaging in the market due to low capital investment, latency demands, and co-location facilities. Also, the vast market data and acquisition of high volume are technological barriers that need to be considered in the future. According to Argwal (2012), although barriers to new entrants have reduced, there is a need for more research on how small firms can benefit from HFT. Some of the areas to focus on include bandwidth and field programming gate arrays.
Transforming Developing Countries
HFT has the potential of changing the stock market in developing nations, including Africa and Asia. Since the introduction of algorithmic trading, developed countries have registered high equity turnover, as shown in figure 2 below. The US and EU are among the areas that have benefited significantly from technology. This increase is also projected for developing nations, particularly those in the Asia-Pacific Region. According to Kauffman, Hu, and Ma (2015), developing countries are undergoing significant changes in technology. HFT will promote efficiency in the capital market. HFT will also encourage competition among these nations with the developed ones in the economic sphere, such as GDP. However, these nations have to invest in risk management, adaptation to advanced technologies, and quality management.
Figure 3: HFT Equity Turnover for EU and US 2005-2014 (Kauffman et al., 2015).
Conclusion
HFT also referred to as algorithmic trading, has transformed the capital market significantly. HFT is characterized by the use of technology to predict trading opportunities. Through HFT, traders enter various trades within a short time to maximize their profits while minimizing risks. The US SEC associate HFT with several characteristics, such as using high-speed computer programs to generate and execute orders and synthesizing data feeds and co-location to mitigate latencies and network. Several merits characterize HFT. These include increasing liquidity, narrowing spreads, and enhancing market efficiency. The adverse effects include high volatility, a disadvantage to small firms, and high capital investment. The creation of algorithms distinguishes fast and slow traders. Although high profits describe HFT, there are several concerns, such as manipulation of the market and vulnerability of the algorithms, as reflected in 2010 and 2013 events. Future strategies should, therefore, aim at addressing these concerns through advanced technology and regulations.
References
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