An Analysis of Algorithmic Trading in Financial Markets

This report provides a comprehensive overview of algorithmic trading, beginning with its fundamental concepts and historical context, including the introduction of computerized trading systems in the 1970s. It delves into the advantages, such as faster execution and cost reduction, and disadvantages, like potential liquidity issues and the risk of flash crashes. The report details various algorithmic trading strategies, including arbitrage opportunities, trend following, mathematical model-based approaches, index fund rebalancing, and volume-weighted average price (VWAP) and time-weighted average price (TWAP) strategies. It also discusses the implementation of these strategies, including the importance of computer programming, network connectivity, and access to market data feeds. The conclusion emphasizes the benefits of algorithmic trading, such as faster execution times and reduced costs, while acknowledging the potential for market instability.

Running head: Academic & Business Research
Academic & Business Research
Name of the Student
Name of the University
Author Note

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Table of Contents
Introduction................................................................................................................................2
Discussion..................................................................................................................................2
Concept of Algorithmic Trading............................................................................................2
Advantages and Disadvantages of Algorithmic Trading.......................................................3
Benefits of Algorithmic Trading............................................................................................4
Strategies of Algorithmic Trading.........................................................................................5
Conclusion..................................................................................................................................8
References..................................................................................................................................9

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Introduction
Algorithmic trading is considered as the process through which it can execute orders
automatically and the pre-programmed instructions to the account which does include the
variables like price, volume and timing. In the algorithm trading, there is use of complex
formulas, which is combined with the mathematical models and human oversight, in making
decisions to buy or sell financial securities on an exchange. An algorithm does consist of a set
of directions, which help in solving the problems (Weller 2018). The computer algorithms are
being sent in small portions, so that it helps in fulfilling the order to the market over that span
of time. The algorithm traders do use of the high frequency technology, that does help in
making more amount compared to the other system. It can be sued through v a wide variety
of situations, which includes the order execution, arbitrage and trend trading strategies.
Discussion
Concept of Algorithmic Trading
The first computerized trading systems was introduced in American financial markets,
through which the trading increased because of the use of algorithms, in the period of 1970.
New York Stock Exchange introduced the Designated Order Turnaround (DOT) system in
the year 1976, that is been used for routing orders from the traders to the specialist for
exchanging floor.
In the recent years, there has been use of the practice of do-it yourself algorithm
trading, which has become an extensive. The use of hedge funds like Quantopian, there is the
use of crowd source algorithms, which is since the amateur programmers those who will
strive from win commissions for the writing from the most profitable code (Cartea, Donnelly

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and Jaimungal 2017). With this practice, it has become more possible through the spread of
internet and in expansion of the faster computers at a very cheap price. One of the emergent
technology that has been used is the machine learning which is by the Wall Street, has
enabled the computer programmers through which they have develop programs and can
develop themselves by using an iterative process that is called deep learning (Boehmer et al.
2018). The traders do use the developing algorithms; in which they can rely upon the deep
learning which will make themselves more profitable in future.
Advantages and Disadvantages of Algorithmic Trading
The Algorithmic trading is usually used by the big brokerage houses and institutional
investors, that will help in cutting down the cost which is associated with the trading. As per
the researcher, the algorithmic trading is being beneficial for the large orders which can be
compromised as the overall trading volume (Jia and Lau 2018). Thus, the market makers do
use the algorithmic trades in creating the liquidity. With the help of algorithm trading, it does
allow to make faster and in a much easier way through which it is easy to execute the orders,
and it makes the exchanges more attractive. It also means that the traders and investors can
use the quick book profits which has small changes in the price (Tseng, Mahmoodzadeh and
Gencay 2018). There is scalping trading strategy that is commonly been employed algorithms
as it is involved through rapid buying and selling of the securities in the small price
increments.
Some of the disadvantages of algorithmic trading are discussed here. The speed upon
execution of order, has an advantage upon the ordinary circumstance, which has become the
problem with the several orders which has been executed simultaneously without the human
intervention (Upson and Van 2017). Another drawbacks of the algorithm trades which is been
liquidity, that has been created through the rapid buy and sell orders, in which it can

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disappear in a moment, which does have eliminated some changes for the traders in the profit
off price changes. It has been seen that there has been a loss of liquidity. Through the
research, it has been uncovered that one of the major factor that does cause loss in the
currency markets because of the algorithmic trading after the Swiss franc discounted its Euro
peg in 2015.
Benefits of Algorithmic Trading
There are several benefits of Algorithmic Trading, which has been discussed below:
 The trading can be done in and executed way, which is at the best possible price.
 The trade order placement can be done instantly and very accurately, so that there is very
high chance of execution in the desired levels.
 The trades are needed to be timed correctly, so that it can be avoided instantly, so to avoid
price changes.
 The transaction cost can be reduced through this usage.
 There are simultaneous automated checks, which is depend upon the multiple market
conditions.
 The risk of manual errors can be reduced at the time of placing any trades.
 With the use of historical and real time data, the algorithm trading can be back tested, for
the use of viable trading strategy.
 It also reduces the possibility of mistakes which are usually done by human traders, as
there are emotional and psychological factors.
The Algorithmic trading do have many forms of the trading and investment activities,
which does include the forms of the trading and investment, in which the mid to long term
investors does invest on the pension funds, mutual funds, insurance companies with the use
of algorithm trading to the purchase of stock in large quantities, which they do not have any

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influence upon the stock prices with the large volume investments (MacKenzie 2018). There
are short term traders, which are the market makers that includes the brokerage, houses,
speculators and arbitrageurs, which have benefit from the automated trade execution, and in
addition there is also trading of goods which do have a sufficient; liquidity of the sellers in
the market (Cartea, Jaimungal and Kinzebulatov 2016). Another type of traders is the
systematic traders, in which there is trend followers, hedge funds or pair traders, in which
there is a market neutral strategy which do matches the long position with the short position,
with a pair of highly correlated instruments which are stock, exchange traded funds which is
much more efficient in programming the trading rules, that led the program trade
mechanically. Thus, the algorithmic trading does have a much systematic approach in which
there is more methods which is based upon the trader intuition or instinct.
Strategies of Algorithmic Trading
The strategy of Algorithmic Trading, does require an opportunity that can be
profitable a does help in the improved earnings or in the cost reduction. There are some
common strategies that are followed in the algorithmic trading:
 Arbitrage opportunities: At the time of buying any dual listed stock, at a much lower
price in one market and at the same time selling it in the another market, which do offers
the price differential that is risk free profit or arbitrage. Thus, the same operation can he
happened with the stock vs future instruments as there is existence of price differential
from the time to time (Mestel, Murg and Theissen 2018). So, an algorithm, is been
implemented for the identification of price differentials and in placing orders that will
help in efficiently allowing the profitable opportunities.
 Trend following Strategies: This is one of the most common algorithmic trading
strategies through which the trends can be followed like the channel breakouts, moving
averages, price level movements. These strategies are one of the easiest and simplest that

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can be implemented through the algorithmic trading as the strategies which are involved
does not need any predictions or forecast (Cartea, Jaimungal and Kinzebulatov 2018).
The trades are usually initiated which are based on the occurrence of the desirable trends
that are easy and straight forward which will be implemented through the help of
algorithms and without much complexity of the predictive analysis.
 Mathematical Model-based Strategies: There has been proven mathematical models
which does has the delta neutral strategies, which allows the trading upon the
combination of the options and underlying security. In this portfolio strategy, it does help
in consisting the multiple positions which can be offsetting positive and negative deltas
(Sezer and Ozbayoglu 2018). The ratio that has been compared is of the change in price
of assets and do have some marketable security, upon the changes that are corresponding
in the price of derivative. Thus, all the overall delta of assets comes to total zero.
 Index Fund Rebalancing: In the index funds, it does have defined periods of rebalancing
in which it brings holdings to the par that is in respective with the benchmark indices. It
helps in creating profitable opportunities for the algorithm traders, that help in
capitalizing the trades that is expected in the basic points from 20 to 80, which is
dependent upon the number of stocks in index fund rebalancing (Abergel, Huré and Pham
2020) . Thus, such trades are bene initiated through the algorithmic trading system,
which is for the timely execution and in best prices.
 Volume-weighted Average Price (VWAP): Through the use of Volume-weighted
Average Price strategy, it does breaks the large order and do releases vigorously that is
been determined with the smaller chunks of the order which is in the market using the
stock specific historical volume profile. It also aims to perform the order that is been
close to the Volume-weighted Average Price.

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 Trading Range (Mean Reversion): Through the use of Mean Reversion strategy, it is
usually based on the concept that is related to the higher and lower prices of the asset
upon the temporary phenomenon, which is to be revert to their mean value periodically. It
helps in the identification, also in defining the price range and in implementing the
algorithm that is based on the trades that is been allowed, which is to be placed
automatically at the time price of an asset breaks in and out of the defined range.
 Time Weighted Average Price (TWAP): The strategy of time weighted average price is
to break the large order and it also releases the dynamically that is been determined
through the smaller chunks upon the order to the market that does uses evenly into the
divided slots between the starting time and the ending time. It aims to execute the order
that has been closed in the average price between the start and end times, which there by
minimize the market impact.
The implementation of algorithms is being done with the commuter program which is
the final component of algorithmic trading, that is been accompanied by the back testing,
which is used in the historical periods in the past stock market performance which can be
seen profitable. There is a challenge which does help in transforming the strategy that has
been identified, which is into the integrated computerization process that can be accessed to
the trading account upon placing the orders (Frino et al. 2017). The computer programming
knowledge is the program that is been required for the trading strategy, that has the hired
programmers or the pre made trading software. It is also important to know the network
connectivity, and in assessing the trading platforms in placing the orders. It does have access
to the markets data feeds, which can be usually monitored through the algorithm for
opportunities in placing the orders. The infrastructure and ability to back test the system is
being built, before it even got the live market.

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Conclusion
From the above discussion, it can be concluded that the algorithmic trading is the
process and rules that has been based on the algorithms for the employment of strategies for
the executing of trades. It does provide several advantages like the faster execution time, and
reduced cost. Algorithmic trading does have a negative tendency that cause the flash crashes
and does occur an immediate loss to the liquidity (Weller 2016). There are several additional
challenges which does includes the system failures risk, network connectivity errors, time
lags between the trade orders and in execution, it is because of the imperfect algorithms.
Thus, the more complex algorithms do have more stringent back testing before it is even put
into the action.

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References
Abergel, F., Huré, C. and Pham, H., 2020. Algorithmic trading in a microstructural limit
order book model. Quantitative Finance, pp.1-21.
Boehmer, E., Fong, K.Y. and Wu, J., 2018. Algorithmic trading and market quality:
International evidence. In AFA 2013 San Diego Meetings Paper.
Cartea, A., Donnelly, R. and Jaimungal, S., 2017. Algorithmic trading with model
uncertainty. SIAM Journal on Financial Mathematics, 8(1), pp.635-671.
Cartea, Á., Jaimungal, S. and Kinzebulatov, D., 2016. Algorithmic trading with
learning. International Journal of Theoretical and Applied Finance, 19(04), p.1650028.
Cartea, A., Jaimungal, S. and Ricci, J., 2018. Algorithmic trading, stochastic control, and
mutually exciting processes. SIAM Review, 60(3), pp.673-703.
Farjam, M. and Kirchkamp, O., 2018. Bubbles in hybrid markets: How expectations
about algorithmic trading affect human trading. Journal of Economic Behavior &
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Frino, A., Prodromou, T., Wang, G.H., Westerholm, P.J. and Zheng, H., 2017. An
empirical analysis of algorithmic trading around earnings announcements. Pacific-Basin
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Jia, X. and Lau, R.Y.K., 2018, August. The Control Strategies for High Frequency
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Systems Engineering (ICCSSE) (pp. 49-52). IEEE.

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MacKenzie, D., 2018. ‘Making’,‘taking’and the material political economy of algorithmic
trading. Economy and Society, 47(4), pp.501-523.
Mestel, R., Murg, M. and Theissen, E., 2018. Algorithmic trading and liquidity: Long
term evidence from Austria. Finance Research Letters, 26, pp.198-203.
Sezer, O.B. and Ozbayoglu, A.M., 2018. Algorithmic financial trading with deep
convolutional neural networks: Time series to image conversion approach. Applied Soft
Computing, 70, pp.525-538.
Tseng, M., Mahmoodzadeh, S. and Gencay, R., 2018. Impact of Algorithmic Trading on
Market Quality: A Reconciliation. Available at SSRN 3038542.
Upson, J. and Van Ness, R.A., 2017. Multiple markets, algorithmic trading, and market
liquidity. Journal of Financial Markets, 32, pp.49-68.
Weller, B., 2016. Efficient prices at any cost: does algorithmic trading deter information
acquisition?. SSRN Scholarly Paper ID, 2662254, p.43.
Weller, B.M., 2018. Does algorithmic trading reduce information acquisition?. The
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