Do you need this or any other assignment done for you from scratch?
We assure you a quality paper that is 100% free from plagiarism and AI.
You can choose either format of your choice ( Apa, Mla, Havard, Chicago, or any other)
NB: We do not resell your papers. Upon ordering, we do an original paper exclusively for you.
NB: All your data is kept safe from the public.
Experiments in High-Frequency Trading
Introduction
With the evolving technology the nature of trading is also changing drastically. High-frequency trading (HFT) is one example that enables firms, private businesses, and other institutions to execute a trade within fractions of seconds using certain algorithms and different computer programs. This method of trading has become very popular within the last century as it is a fast and efficient way to trade and make profits. High-frequency trading is based on various types of arbitrage strategies. This includes index arbitrage, volatility arbitrage, merger arbitrage or passive market making among many others (Bajpai, 2022). The computer programs are set up with complex algorithms that are coordinated to analyse different markets, access data bases, and execute a trade as fast as possible with minimum delay and minimum transaction costs. In the market of high frequency trading the traders who execute the fastest are the ones who are most profitable. Transaction profits are the results of the difference between the bid-ask-spread. Firms can trade from both sides, either by buying or selling. A common way to do it is by using limit orders.
As much as high-frequency trading seems to be an attractive way of trading to many businesses, this method of trading is still a controversial topic among policy makers. Thus, it is of a growing interest in looking into the market design and the coordination of trade among the participants of high frequency trading. One might question if the current market design of HFT really maximises the welfare of participating businesses and if it incorporates the best-responses of each participant, such as for the big-traders but also for the smaller investors?
Current literature shows how the market design in financial exchanges that is based on continuous limit order book (LOB) to match buyers and sellers creates a loss in social welfare due to the phenomenon of prisoners dilemma. More specifically, loss in social welfare is created due to the exploitation of trading opportunities (Budish et al., 2013). Some new market structures have been proposed that shall minimize the loss of social welfare and advocate market stability.
The structure and rules of a market is crucial for its outcome. Hence, the aim of this paper is to analyse which auction and market design is most suitable for the market of high-frequency trading by identifying its current flaws and comparing different types of auction design on the structure of HFT. Specifically, this paper focuses first on the structure of high-frequency markets with concentrating on its setting, traders, information availability, regulatory efforts, and fairness issues. Secondly, this paper discusses the frequent batch auction proposal as a market design and opposes it to continuous double auction. Moreover, a simulation approach that is performed under a double-auction microstructure will be incorporated into the discussion of how market design affects market liquidity and the magnitude of market crashes in high frequency trading. Lastly, some concluding remarks are being given on the market design of high frequency-trading.
Discussion of Literature
There is a sufficient amount of scientific theoretical, and practical research regarding high-frequency trading. Experiments in High-frequency Trading: Comparing Two Market Institutions, Online Appendices explores the design of the high-frequency trading market. The current Batch Auction model shows more relaxed trading behavior and lower transaction costs (Aldrich & Vargas, 2019). Strategies and Secrets of High Frequency Trading (HFT) Firms reveal the main strategies for operating HFT. Secrecy is inherent in HFT firms, often hiding their strategies and keys to success, which need to be changed (Bajpai, 2022). Market Microstructure Design and Flash Crashes: A Simulation Approach examines the possible strategies that the market uses to stabilize after disruptions. Switching to on-call auction mechanisms is the most effective intervention (Brewer, Cvitanic & Plott, 2013). Practical research is related to the need to switch to a new type of market organization in order to achieve the most incredible efficiency.
The High-frequency Trading Arms Race: Frequent Batch Auctions as a Market Design Response describes the HFT phenomenon as part of an imperfect market design. Financial exchanges are expected to use frequent batch auctions (Budish, Cramton & Shim, 2013). Finally, High Frequency Market Microstructure explores the impact of HFT on traders strategies and markets. To study new strategies, they should be applied, including practical-experimental ones (OHara, 2015). When analyzing the literature, it becomes clear that there are gaps associated with insufficient data on the new exchange trading strategy.
Structure of High Frequency Markets
In todays world there are markets that function in different ways. With the evolving technology everything is getting designed to be more efficient and effective; this is also the case with financial market. The traditional way of financial exchange is based on principles of clear legality, transparency, and risk management; however, it does not allow a trader to get a quick profit with minimal risks. More and more traders are choosing HFT because this method reduces risks. HFT is based on high-frequency algorithms that minimize the impact on the market and focus on profit. Thanks to these advantages, high-frequency trading is becoming popular. This section will discuss the nature and structure of high-frequency trading.
High-frequency trading includes a wide range of transactions while remaining a relatively closed area. HFT is built exclusively on high-tech solutions and vast amounts of computing. After the algorithm is launched, no adjustments are made to work, which is an essential distinguishing feature of relatively low-frequency system trading. Computer programs or algorithms automate the decision-making process, which does not require human intervention for each order or transaction. Opening and closing a position occurs in a concise time range. There is a high daily turnover of the portfolio of securities and a high proportion of placed orders concerning the number of transactions. The main difference between a traditional trader and an HFT trader is that the latter can trade faster and more often, and the holding time of the portfolio is meager. One operation of the standard HFT algorithm takes a millisecond, which traditional traders cannot match.
The HFT structure is based on technology, trader behavior, exchange features, and other markets. The development of HFT technology has predetermined the need for a central market for high-speed operations (OHara, 2015). The technical progress made it possible but created additional conditions for some traders. The current market structure is highly competitive, fragmented, and fast (OHara, 2015). It is also dominated by high-frequency traders, who make up half or more of the total trading volume (OHara, 2015). Understanding what high-frequency traders are doing is crucial to understanding why todays markets are so different from past times.
At this stage, high-frequency trading remains a closed area for many traders. Firms engaged in high-frequency trading use various strategies to achieve success. According to Bajpai (2022), strategies include various forms of arbitrage, stocks, and passive market formation. Basically, these strategies are aimed at maximizing profits and minimizing risks. One of the most effective forms of HFT firm is an independent private organization (Bajpai, 2022). Conscientious traders use many strategies to make money for their firms.
Firms are engaged in market making, creating a profit from the difference between the spread of supply and demand. These transactions are performed by high-speed computers using algorithms. In addition, HFT firms make a profit by providing liquidity (Bajpai, 2022). Another way is to search for price discrepancies between securities on different exchanges (Bajpai, 2022). A firm may seek to cause a spike in stock prices by using a series of trades with a goal. These companies must work on risk management because they are expected to solve operational and technological problems.
Frequent Batch Auction as a Market Design Proposal
After seeing the structure, setting, and information available in high-frequency trading markets, discussing different auction and market designs proposed in current literature is worthwhile. Budish et al. (2013) offer private batch double auctions held every second as a market reaction. The model shows that the arms race is wasteful in itself and leads to widening spreads and narrowing markets for fundamental investors (Budish, Cramton & Shim, 2013). In the future, batch double auctions can have a positive impact on the well-being of the population.
The main argument for the benefit of refocusing the system on batch double auctions is that continuous markets are not such. Market correlations functioning properly on human timescales are disrupted on high-frequency timescales (Budish, Cramton & Shim, 2013). Secondly, this gap creates technical arbitrage opportunities, which, in turn, encourages HFT firms to spend large amounts of money on small speed advantages (Budish, Cramton & Shim, 2013). In a distributed market, it is no longer possible to receive rent for information that all market participants see almost simultaneously. The important practical question is to determine the optimal dosing interval. Further research is needed, especially given the attention practitioners pay to market stability.
Frequent Batch Auction vs. Continuous Double Auction Design
According to the form of the organization, there are frequent batch auctions and continuous double auctions. These two terms can be confusing, as continuous double auctions are usually referred to as double auctions. A simple auction involves competition of sellers with insufficient solvent demand or competition of buyers with excessive demand. The model of frequent batch auctions is proposed as a final solution to the problem of capital inefficiency. An additional advantage is a particular provision of a more liquid market by eliminating external management. Previously, it was assumed that the market, organized as a continuous double auction, is highly efficient. However, the efficiency of CDA distribution would be significantly reduced if each transaction did not force agents to send new orders.
The main difference between FBA and CDA is related to frequency. During a continuous double auction, the traders can bid as often as they want. A double auction can only allow a certain limited number of bids for a certain period, seeking to solve the problem of institutional liquidity. Certain types of institutions are at risk if they cannot fulfill their orders at a certain point in time or as close to it as possible. Mutual funds usually conduct only one evaluation per day. If there should be many transactions resulting from a buyout or purchase, then the least risk is that the transaction is completed at this time.
The main proposal is to move from continuous trading to frequent batch auctions. To the human eye, trading will be essentially continuous, but the works will effectively be collected every second for a brief blind auction. A double auction is based on simultaneous competition between the seller and the buyer. A continuous auction is based on the recording of oral bids in the bid book or their fixation on an electronic scoreboard on the exchanges trading floor. This kind of auction can also take place directly on the exchange platform. Purchase and sale are carried out at the highest price at the time of purchase and the lowest price at the time of sale.
Currently, experiments are being conducted to study the best way to organize a trading market. Aldrich and López Vargas are comparing the performance of FBA and a CDA that organizes trades on most exchanges worldwide (2019). During the experiment, it becomes clear that more and more subjects are choosing the role of market makers in the FBA strategy. Compared to CDA, FBA demonstrates a higher level of liquidity, less predatory behavior, and less investment in communication technologies (Aldrich & López Vargas, 2019). In addition, FBA is associated with lower transaction costs and higher information efficiency (Aldrich & López Vargas, 2019). Deviations of transaction prices from the value of the underlying asset are also lower in the FBA system (Aldrich & López Vargas, 2019). The sensitivity of traders behavior to temporary shocks is assessed; sensitivity is statistically absent in the FBA (Aldrich & López Vargas, 2019). Thus, at the moment, the market is reoriented to the use of FBA due to high-performance indicators.
Microstructure Design and Market Instability
A flash crash is one of the dangers that the market may face. The ability to quickly cope with this phenomenon demonstrates the markets stability. The flash crash phenomenon is an event in the electronic securities markets when the withdrawal of orders for shares intensifies the fall in prices and then quickly recovers. The result may be a quick sell-off of securities, which may occur within a few minutes and lead to a sharp drop. By the end of the trading day, when prices rebound, it may seem that there was no sudden collapse. A flash crash refers to a rapid drop in the market price or stock value due to the withdrawal of orders, but then it quickly recovers, usually during the same trading day. Companies engaged in high-frequency trading bear much responsibility for sudden disruptions in recent times. U.S. regulators have taken swift steps to prevent disruptions, such as installing circuit breakers and banning direct access to telephone exchanges.
Researchers are concerned about the flash crash problem and are modeling situations to regulate this phenomenon. Brewer, Cvitanich, and Platt study the consequences of regulatory interventions in limit order markets to stabilize the market after the flash crash (2013). It is assumed that switching to a call auction is the most effective in restoring liquidity and price levels (Brewer, Cvitanic, & Plott, 2013). Among the goals of the central regulatory bodies is to prevent sudden large price drops, or at least to make them short-term (Brewer, Cvitanic, & Plott, 2013). The proposed interventions are intended to have a soothing effect in times of market instability.
If a rapid stock market change is a temporary reaction to a random event, regulatory intervention can help restore stability. If the change is long-term, a sudden failure can help accelerate the transition to a new equilibrium, and regulatory interventions can slow down this process (Brewer, Cvitanic, & Plott, 2013). Sudden failures can be caused by events and actions that destroy liquidity and create subsequent volatility. Short-term orders that are close in price to the equilibrium of supply and demand do not significantly impact market stability (Brewer, Cvitanic, & Plott, 2013). Big orders, on the contrary, can seriously affect the market if short-term orders do not balance them. If traders do not react to such an order, the markets stability will be preserved.
Intervention in markets affected by large orders should be aimed at restoring liquidity. An effective intervention policy variant includes a requirement for private traders to constantly provide liquidity (Brewer, Cvitanic, & Plott, 2013). Switching to on-call auctions before prices stabilize may be helpful (Brewer, Cvitanic, & Plott, 2013). The demand for rest time, on the contrary, does not have a significant impact on the market (Brewer, Cvitanic, & Plott, 2013). High-frequency trading firms, which have been accused of provoking the effect of a sudden failure, often use their broker-dealers code for direct access to exchanges. Such measures cannot wholly eliminate flash accidents, but they have been able to reduce the damage they can cause.
Conclusion
High-frequency trading (HFT) is becoming increasingly popular with private businesses and traders. HFT allows traders to make transactions within fractions of seconds using various algorithms. The increased popularity is associated with the speed of transactions, ease of profit, and lower risks. Scientific research related to HFT is aimed at studying the effectiveness of various market microstructures. Frequent Batch Auction is currently becoming the most effective, as it demonstrates high liquidity with minimal transaction costs.
References
Aldrich, E. M., & López Vargas, K. (2019). Experiments in high-frequency trading: Comparing two market institutions, online appendices. SSRN Electronic Journal.
Bajpai, P. (2022). Strategies and secrets of high frequency trading (HFT) firms. Investopedia.
Brewer, P., Cvitanic, J., & Plott, C. R. (2013). Market microstructure design and flash crashes: A simulation approach. Journal of Applied Economics, 16(2), 223-250.
Budish, E. B., Cramton, P., & Shim, J. J. (2013). The high-frequency trading arms race: Frequent batch auctions as a market design response. SSRN Electronic Journal.
OHara, M. (2015). High frequency market microstructure. Journal of Financial Economics, 116(2), 257-270.
Do you need this or any other assignment done for you from scratch?
We assure you a quality paper that is 100% free from plagiarism and AI.
You can choose either format of your choice ( Apa, Mla, Havard, Chicago, or any other)
NB: We do not resell your papers. Upon ordering, we do an original paper exclusively for you.
NB: All your data is kept safe from the public.