Introducing Automated Trading Systems: The Possible Outcomes in Saudi Arabia

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Table of Contents

Executive summary
Introduction
Review of literature
Study methodology
Results
Conclusion
References

Executive summary

This paper aims at evaluating the benefits of improving the quality and transparency of the trading system in the country. First, the paper provides a brief introduction and background to the topic ‘trading systems and automation. Secondly, it provides a comprehensive review of literature on the topic both from broad and automated perspectives. Thirdly, it presents the procedure used to develop an empirical study to answer the research questions identified for the study. A final section will make a comprehensive conclusion, to sum up, the findings and make them available to interested parties in the Saudi Arabian trading system as well as academics.

Introduction

In the modern context, most developing nations are increasingly finding better ways to improve their trading systems and enhance general economic growth. One of the most important methods that are increasingly being applied is the use of technology to update and automate trading systems, with an aim of taking the advantage of the existing and dynamic state of modern technology (Black, 2001). Indeed, research has shown that several nations have benefitted from the automation and update of their trading systems using technology. For instance, empirical studies on Israel (Amihud & Mendleson, 2007) and Singapore (Naidu & Rozeff, 2004) provided evidence that the use of technology to enhance trading systems in these nations proved effective, with most of them gaining the ability to attract order flows and improve their liquidity through enhanced quality of execution and level of transparency (Naidu & Rozeff, 2004). In particular, the computerization of the stock market is an important aspect of automation of trading systems, which has become a common phenomenon in most countries in the modern world.

The problem of the study

In this regard, the recent efforts to use the most modernized technology in Saudi Arabia is no doubt an important step towards enhancing the country’s trading system and the national economy in general. Nevertheless, there is little research evidence to show that the country’s trading systems will improve to the levels of other nations such as Singapore and Israel, taking into consideration a number of other economic, political and social factors likely to affect the systems.

Research questions

Within the context of trading systems, the automation of Saudi Arabia’s stock markets is expected to improve trading in a variety of ways, especially due to the expected enhancement of the effectiveness of the mechanism involved, the size of the stock traded as well as the level of transparency. To examine these variables, this research will be answering the following questions;

What is the nature of Saudi Arabia’s organization of trading systems and how will automation be integrated within the mechanism?
What impacts will the automation make on the system from the point of view of investors, traders and the government?
What is the overall ability of the trading system to take the advantage of automation technology?

Study hypothesis

The research hypothesizes that the automation of the trading system in Saudi Arabia is expected to enhance trading in terms of volume of trade, transparency and efficiency in the long-term, but the short-term effect might be disorganization of the system and reduction of efficiency, at least for a short time.

Review of literature

Trading systems

According to Biais (2008), a trading system is defined by the presence of a set or group of rules and regulations that aims to determine the points of entry as well as an exit for stock (equities, bonds and securities) within a given stock market. According to this definition, the entry and exit points refer to the signals that function by prompting the instant time for the parties involved to execute an exchange of the stock. Thus, system trading refers to a mathematical approach to stock markets, which is defined to provide an edge in the stock market. The rules of the system tend to predefine the decisions required to place trades. According to Biais (2008), technical analysis is best defined as a method for determining effective ideas for trading based on an analysis of the prices in the past (Black, 2001). Trading systems basically concern individuals seeking to invest in stocks, which means that they are black boxes in nature, where the udders do not know the technical analysis used inside the code of the system (Black, 2001). For instance, the code can be based on a number of arithmetical algorithms such as oscillators, moving averages, entry signals, profit-taking exits, market filters, worst-case stop loss and re-entry methods. Amihud and Mendelson (2007), there is a significant difference between a trading system and a platform. While a trading platform is merely a front-end piece of a system that allows individuals to make orders for trading, trading systems are not a market letter because they are based on specific rules and regulations programmed into a code.

An important aspect of trading systems is based on the nature of their rules, which are tested within the context of data obtained from past pricing methods and statuses. This allows individuals to find out how the rules of a set of rules would have behaved in the past. Quite obviously, the 20/20 notion applies here, where the risk of losing is quite high. Nevertheless, the ability to look back towards history is a defining aspect of trading systems (Black, 2001).

Trading systems maintain the consistency of trading, which ensures that emotions are avoided from the trading environment. They execute their rules regardless of the events taking place in the outside world. The purpose is to help in practical matters such as the elimination of the need for investors to keep fixed to the trading position waiting to place an order. In addition, they have a long volatility profile, given that their internal codes or makeup look to risk a fixed amount of investment and allow profits to run at the same time.

Researchers have shown that when the number of participants in a trading system increases, the precision of stock prices tends to increase (Schwartz, 2009). It is based on the temporal consolidation of the orders. According to Biais (2008), it allows for an easy way of determining a single price for a large number of or all the transactions involved. Moreover, it has been shown that it incites investors to their orders, which results in an overall improvement of the liquidity of the stock market.

According to Amihud and Mendelson (2007), the NYSE is one of the most important bourses that show evidence of higher volatility during the opening session. Nevertheless, the study has shown that the high degree of volatility in the call auction does not have a correlation with the trading method involved (Black, 2001).

Automation of trading systems

According to Domowitz and Steil (2009), most researchers have asserted that the process of automating trading has negative impacts on liquidity if human interactions determine or control transactions at a stock market (Naidu & Rozeff, 2004). According to Biais (2008), automation of a trading system can cause a decrease in liquidity because it does not permit direct negotiations between the investors or traders in the system when important transactions are in process (Naidu & Rozeff, 2004). In addition, it does not allow these traders to have control over the conditions involved in the trading system. On the other hand, some studies tend to argue otherwise. For instance, according to Pirrong (2006), automated trading systems make the liquidity deeper than open outcry trading. Moreover, Naidu and Rozeff (2004) indicated that an increase of volatility as well as liquidity followed by an improvement in the efficiency of trading, are some of the most prominent features of automation of trading systems. They have the example of the Singapore stock exchange, where automation took place in the 1990s. They found that automation has an impact on speeding up the process of disseminating stock prices, which increases volatility. Indeed, they have also shown that the increase in volatility takes place mostly at the specific time of information hitting the market (Black, 2001). These observations were also confirmed in a number of other trading systems that have recently been automated, including Morocco and Israel.

Automation and its impact on transparency

Research indicates that the degree of transparency is higher in centralized markets than in non-centralized markets such as fragmented trading systems. For instance, most research studies have shown that it is easy for centralized stock markets to allow traders to observe the flow of both prices and transactions. This allows these individuals to make informed decisions when making strategies to participate in the trading system. Fragmented stock markets have a lower level of transparency than centralized markets (Black, 2001). As such, suppliers of liquidity are in a better position to take the advantage of this phenomenon to increase their fortunes in an illegal manner (Black, 2001).

According to Pagano and Roell (2006), transparency is the possibility of observing the direction and size of the flow of orders in a stock market (Naidu & Rozeff, 2004). The impact of transparency is to improve the degree of liquidity by reducing the probability of dealers taking the advantage of misinformed or less informed individuals participating in the trading system. Thus, it helps in reducing volatility, spread as well as pricing errors (Naidu & Rozeff, 2004). However, other studies have shown that some investors are likely to prefer a less transparent system because their private information is likely to remain confidential throughout the trading process.

Study methodology

As indicated, the purpose of this paper is to study the impact of automation of the trading system adopted at the Saudi Arabian stock market. In particular, it looks into the variables of liquidity, volatility and pricing error as well as the behavior of stock returns. This study notes that the process of transferring the stocks from the previous system is likely to present a new structure of the market at the micro-level, especially in cases where government agents or authorities have the overall responsibility of controlling the process (Naidu & Rozeff, 2004).

Study design

The study is quantitative research that seeks to examine the variables from a statistical perspective and use this data to characterize and describe the phenomenon by answering the research questions (Black, 2001). Therefore, statistical data will be obtained from the highly traded stock at the Saudi Arabian stock market, which has been collected between 2008 and 2013.

Data collection

Data were obtained from the Saudi Arabian stock exchange under the authority of the relevant authorities controlling trade at the stock market. Two forms of data were obtained. The study considered data of 40 stocks as the study sample, which was taken from the Saudi bourse. This set of data was used as the control or reference set for the study. Secondly, data on 20 stocks listed in the bourse after automation was obtained and measured for all the variables.

Data analysis

Data analysis was done using algorithms developed at the university of Harvard for the purpose of calculating the effectiveness of automation of the trading system. In this research, each of the four variables was assigned the respective algorithm for calculation as follows:

Liquidity

In theory, the trading volume of any security is an increasing function of the security’s liquidity when all other variables are held constant (equal) (Black, 2001). Therefore, the theory attempts to argue that an increase in trading volume in a particular market after the transfer has been implements provides a clear reflection of the consequent increase in the degree of liquidity. The researcher tested two empirical results for this variable. First, VR_it (the relative volume of every stock) for every week t was determined with the following formulae; t€ [-50, 50]⁶ (Black, 2001)

The following algorithm was used for this variable;

VR_it= log V_it/log (V_mt)

Where V_it represents the cumulative volume of the stock on the week of test (t) and V_mt represents that cumulative volume on the week of test, t.

The relative volume of the 20 stocks of the test sample (stocks after automation) is tested as follows;

VR_mt=

(Naidu & Rozeff, 2004)

Behavior of stock price

This variable was tested on the assumption that improving the microstructure of a stock market under automation of trading system has the capacity of affecting positively the prices of the stocks. The following formulae was adopted to determine weekly returns after the adjustment of prices was considered;

Art = 1/N

(Naidu & Rozeff, 2004)

Stock volatility

According to Sato (2010), the volatility of stock rises in an automated trading system due to overshooting or undershooting because participants who use screens are blinded from the reasons for the motion of the prices, which is similar to traders in a trading floor.

The tests were carried out on both sets of data- data on stocks before automation and data on stock after automation (2008-2012).

Results

The Saudi Stock market has recently adopted automated trading systems, which has been inspired by automation in other stocks such as Singapore, NYSE, LSE, and regional stock markets in the UAE, Tunisia, Morocco and Egypt.

The statistical tests on the two sets of data for the two main variables provided some significant indication of improved trading systems due to automation (Black, 2001). First, it was found that an interruption of the order quotations has been an important observation prior and after the Saudi Stock market adopted automated trading systems (Naidu & Rozeff, 2004). As a result of reforms undertaken by the controllers of the market to enhance transparency through automation, it was found that there was a short abnormal return, which is about 11% for all the securities taken as the sample data. This means that this is not a negative reaction to the automation of the system. It further implies that the automation of the system did not impose a constraint on the trading system (Naidu & Rozeff, 2004).

The results also indicated a significant increase in the volume of trade after the transfer from the old to the automated system. The results yielded a mean trading volume of 0.62 before the transfer and 0.74 after the transfer of the system to the automated system. This indicates that the automated system yielded better liquidity on the stocks.

Nevertheless, the study did not find any significant impact on the degree of volatility. A number of factors can explain this phenomenon (Naidu & Rozeff, 2004). For instance, it is probable that an increase in the volume of transactions caused an increase in volatility and the temporal consolidation caused a reduction in volatility, thus making it stagnant (Black, 2001).

Conclusion

It is clear that automation of the trading system in the Saudi Arabian stock market has a significant effect on the volume of trade and stock market efficiency, which is marked by an increase in both parameters. In addition, it does not have an impact on the volatility of the trading system (Black, 2001). Moreover, the study concludes that the automated system may have an impact on a pricing error, which means that it might have an impact on improving the efficiency of the market. This study further finds that the automation of trade systems remains an important step towards the enhancement of transparency in trading (Black, 2001). For instance, it makes it impossible for dealers to take the advantage of their positions to use the information at their disposal in oppressing other investors. In addition, it has been noted that automation of the trading systems works best in centralized markets and poorly in fragmented markets. As such, transparency and automation seem to go hand in hand.

This study appraises the automation of the Saudi stock market as an example of the automation of modern trading systems. It shows that the use of technology enhances transparency, volume as well as efficiency of trading. Thus, it is recommended that the most updated technology be used in the automation of the stock market in Saudi Arabia. Thus, this study confirms the previous findings that automation of the trading systems in the modern context is contributing to the development and growth of stock markets specific economies in general (Naidu & Rozeff, 2004). In addition, the study proves right the hypothesis that “that the automation of the trading system in Saudi Arabia is expected to enhance trading in terms of volume of trade, transparency and efficiency on the long-term, but the short-term effect might be a disorganization of the system and reduction of efficiency, at least for a short time”.

References

Amihud Y., & Mandelson, H. (2007).Trading mechanisms and stock returns. Journal of Finance, 42(2), 533-553.

Biais, B. (2008). Price formation and equilibrium liquidity in fragmented and centralized markets. Journal of Finance, 48(1), 157-185.

Black, D. (2001). Toward a fully automated stock exchange. Financial analysts journal, 27(3), 27-32.

Domowitz, I., & Steil, B. (2009). Automation, trading costs, and the structure of the securities trading industry. Brookings-Wharton papers on financial services, 2, 33-92.

Naidu, G.N., & Rozeff, M. S. (2004). Volume, volatility, liquidity and efficiency of the Singapore Stock Exchange before and after automation. Pacific-basin Finance Journal, 2(1): 23-42.

Pagano M., & Roelle, A. (2006). Transparency and liquidity: A comparison of auction and dealer markets with informed trading. Journal of Finance, 51(1), 579-611.

Pirrong, C. (2006). Market liquidity and depth on computerized and open outcry trading systems: A comparison of DTB and LIFFE bund contracts. Journal of Futures Markets,16(2), 519-543.

Sato, M. (2010). Tokyo equity market – its development and policies. Pacific basin capital markets research, 3(1), 332-351.

Schwartz, R. (2009). Reshaping the equity markets: A guide for the 1990s. New York, NY: Harper Business.

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