Essay: The rise of Algorithmic Trading and its effects on

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Return Dispersion and Market Predictability

Technology is becoming more and more of an influential factor in the lives of ordinary people around the globe and the internet has expanded in such a way that living without it, has in some countries even become impossible. Currently, Artificial Intelligence and Quantum Computing are at the verge of break through and could potentially become as influential in society as the internet has become in our current daily lives. Correspondingly, in the world of finance the rise of the internet and its subsequent technological developments are greatly impacting financial markets. For instance, transactions have become electronic and the time that it takes to execute a trade has decreased to milliseconds, and even nanoseconds. In addition, a new custom-built chip which is able to execute trades within 740 nanoseconds is being launched by Fitnetix, a UK based company. According to Johnson et al. (2012) this technological race is likely to be pushed further until the physical limits of the speed of light are met. Amongst these technological developments in the financial markets, automated trading might be the most present-day and prominent revolution. An algorithm can be defined as a precise plan of steps that uses computations to transform the input values into an output value (Leshik & Cralle, 2011). Supply and demand on the stock markets is increasingly in the hands of these computational algorithms that fully autonomously decide to buy or sell a stock on the behalf of its “owner”. As presented in Figure 1 by Glantz & Kissel (2013, p. 258), the percentage of market volume that can be attributed to algorithmic trading has risen greatly in the past twenty years with asset managers, high frequency traders and hedge funds accounting for most of the volume (Glantz & Kissel, 2013). Our proxy for algorithmic trading based on CRSP data support findings and also shows a clear rise in algorithmic trading activity as can be observed in Figure 2.

Figure 1. Algorithmic trading as a percentage of market volume. Reprinted from: Multi-asset risk modeling: techniques for a global economy in an electronic and algorithmic trading era, by M. Glantz, & R. Kissel, 2013, p. 258, Copyright by Academic Press.

Figure 2. Proxy for Algorithmic Trading based on CRSP data

Nevertheless, algorithmic trading is still a new topic and even though its foundation can be traced back to 1949 it has only become widely spread in the last two decades (Leshik & Cralle, 2011). To give an example, if one searches algorithmic trading on Google Scholar (Date: 18/7/2017), only 500 results will appear that contain “algorithmic trading” in its title of which most are working papers and only 20 of these were written before 2005. When put into context, these 500 papers and books amount to only 0,08% of the 67000 articles which hold “financial crisis” in its name.

For this reason, many of the used sources remain books and working papers as information on algorithmic trading is still limited.

However, according to Kaya (2016), in 2014 high frequency trading already accounted for 49 percent of all the volume in U.S. equity markets, where one must keep in mind that high frequency trading is merely a subgroup of algorithmic trading. The connection between algorithmic trading and its effects on the human aspects are barely touched upon within existing financial literature. It is likely that algorithmic trading in combination with improved artificial intelligence and quantum computing will completely change the financial markets as they are known to us now. Its relevance is undeniable and yet still so little is known about how the automation revolution impacts financial markets. Quantum computing and artificial intelligence still lie in the future, nevertheless human traders are already being substituted by computers on a great scale and its effects should be measurable using quantitative data. Measuring the effects of algorithmic trading is likely to give insights in how financial markets will behave in the future. The rise of algorithmic trading imposes that a decline in direct human influence has manifested itself within the financial markets. It can therefore be reasoned that trading algorithms differ in trading behavior from human investors in the sense that trading algorithms are assumed to never deviate from their set of predefined rules unless stated within their rules. In other words, a trading algorithm will always behave within its programmed boundaries but accounting for all the information that is delivered to it. On the other hand, human traders are more likely to act based on their intuition and what is happening in their environment, with the tendency to value certain information above others.

These influences can be identified as behavioral biases which are recurring patterns in human behavior that simplify the predictability of their behavior (Heiner, 1983).

Humans are rational but only boundedly so and often are attracted to a majority opinion (Kahneman, 2003). In the world of finance, this pull of social gravity to the majority opinion, together with bounded rationality, cause the amplification of inefficiencies in the stock market as investors consistently keep overpricing popular stocks and underpricing less favored equities (Deman & Lufkin, 2000). Furthermore, Kim and Kim (2014) state that investor sentiment is affected by historical share price performance, which further strengthens the market inefficiencies. Considering that the stock market is already to a certain extent inefficient, it is likely that investor sentiment is often biased because of unrepresentative share prices which then again could lead to more inaccurate forecasts. Additionally, Chaboud, Chiquoine, Hjalmarsson & Vega (2014) find evidence that “algorithmic trading contributes to a more efficient price discovery process via the elimination of triangular arbitrage opportunities”. All in all, it can be assumed that the market is becoming more efficient with the increased influence of algorithms. Furthermore, according to the efficient market hypothesis developed by Fama (1995) this development should reinforce the random walk of stock prices and consequently its unpredictability. Research on price dispersion related to algorithmic trading has not been performed previously and the most connected literature is on transaction costs dispersion by Enge, Russel & Ferstenberg (2007) where only Morgan Stanly data instead of complete stock market data is used. Furthermore, the link between algorithmic trading and market predictability also knows no predecessors and will explore new terrain in the field of algorithmic trading, using the fundamental relationships between algorithmic trading, market quality and information previously researched by Hendershott, Jones & Menkveld (2011) and Lyle & Naughton (2015).

For this reason, the main theme within this study is to evaluate how increased algorithmic trading has affected analysts’ capabilities to predict future market movements. Removing emotional entities from the market is expected to improve the efficiency of the market and hence decrease the market predictability. Moreover, another sub question is used to develop an empirical foundation for answering the main question which sums up to: Does algorithmic trading lead to less price dispersion within the stock market? Chaboud et al. (2014) show that automated trading strategies are less diverse than strategies used by human investors and that humans are responsible for a larger part of the variance in returns than their algorithmic counterparts. It follows that as algorithms possess more similarities than human traders it leads to suspect that the size of the range of returns also known as dispersion has decreased with increased algorithmic trading. Moreover, when looking at our data graphically it can be observed that return dispe
rsion shows a clear downtrend over time, except for some extreme values during the financial crisis in 2008/2009, see Figure 3. Additionally, regressing dispersion against time confirms the downward slope resulting in a negative statistically significant coefficient on time with a p-value of 0.001. Considering that algorithmic trading increased over time it could imply a relation with dispersion.

Figure 3. Dispersion against time

The current study investigates the effects of algorithmic trading in more detail, by systematically performing fixed effects panel data regressions. This might enable us to see how increased algorithmic trading has affected return dispersion and market predictability.

The regression findings lead to the conclusion that dispersion is indeed reduced through increased algorithmic trading. Furthermore, it is found that more algorithmic trading led to smaller prediction errors and hence improved market predictability.

In the next chapter, the theoretical framework that was used to establish this research will be discussed, built on the following research questions:

Does increased algorithmic trading within the market affect analysts’ capabilities to predict future market movements?

Sub question:

Does algorithmic trading lead to less price dispersion in the stock market?

Theoretical Background

Current State of Literature

To determine the influence of algorithmic trading on dispersion and market predictability, first of all the origins of trading algorithms and the use of automated trading systems must be investigated. Additionally, to find how fewer human traders impact market predictability and dispersion, financial behavioral biases and market predictability should be examined as well.

Algorithmic Trading and Automated Trading Systems (ATS)

Leshik & Cralle (2011) explain that algorithms used for trading can be traced back to 1949 when Alfred Winslow Jones used an algorithm to balance between long and short positions on a hedge fund. An algorithm can be defined as a precise plan of steps that use computations to transform the input values into an output value. Fundamental to computer software and computations, algorithms have become a mainstream aid to the daily trader. It was not until the 1980’s when algorithmic or black box trading became hugely profitable due to the invention of Pair Trading. Decreased costs, improved control mechanisms with self-documenting trade record and speed of execution are some of the advantages that algorithmic trading can offer to increase the likelihood of a trade to turn out successful. First of all, in order to understand how exactly financial markets are affected by algorithmic trading it is of need to get to the very basis of how a trading algorithm works. For that reason, an example algorithm for a coke vending machine is introduced. The algorithm can be constructed as simple as:

if sum of COINS INSERTED > $1 then RETURN(sum of COINS INSERTED – 1)

if sum of COINS INSERTED = $1 then DROP CAN

if sum of COINS INSERTED < $1 then SHOW MESSAGE(Insufficient Amount)


In this example the amount of coins inserted is the main input, its total value instructs the vending machine to drop the coke can and return any change if necessary. The algorithm will simply follow the set of rules to transform input into output and never deviates from these rules during the process. Similarly, to the example algorithm, trading algorithms are merely the set of predefined rules that convert input into output. Hence, trading algorithms are implemented within Automated Trading Systems that facilitate data collection to obtain input values and to transform output values into an actual action. Automated Trading Systems, also known as ATS, are a combination of both hardware and software that, by using trading algorithms, manages orders and positions within a stock portfolio on a basis of real- time data feeds and historical data that is stored in a database. The data input usually is a combination of factors such as the share price, volume, number of trades, technical indicators, and even news events can serve as an input value for the more advanced learning algorithms (van Vliet, 2007). It follows that the Automated Trading System autonomously creates orders based on its input values and implements these on the exchange, all within milliseconds competing with human investors (van Vliet, 2007). Hence it can be argued that an ATS is to a trading algorithm what a physical coke vending machine can be considered to be to a coke vending algorithm.

To construct an ATS one has to be familiar with computer science, quantitative finance, trading strategy and quality management. As “data is the lifeblood of electronic markets” the basis of ATS lies in the underlying data which can be managed using Microsoft Visual C++ or .NET applications. Technological superiority through ATS can offer an enormous advantage against competitors, but still does not imply profitability (van Vliet, 2007). Leshik and Cralle (2011) consider the most popular and widely used algorithms to be: Volume Weighted Average Price (VWAP), Time Weighted Average Price (TWAP), Percentage of Volume (POV), Search for Liquidity (Black Lance), Stay Parallel with the Market (The PEG), Large Order Hiding (Iceberg), Pair Trading Strategy, Leshik-Cralle, Recursive, Serial, Parallel and Iterative. Whereas Izumi, Toriumi & Matsui (2009) evaluated a distinct set of automated trading strategies. Izumi et al. compare the risk and return of all strategies within their sample set and concluded the strategies to provide better information than conventional methods. Moreover, the research showed that the impact of automated trading strategies on markets does not merely depend on their code. Additionally, the way they are combined and influence each other can impact the market more so. The common factor amongst almost all popular trading algorithms seems to lie in technical analysis as the most popular trading algorithms are largely based on technical analysis related indicators such as moving average and the relative strength index as main indicators to create the buy or sell decision. Technical analysis pertains to predicting future stock prices by studying past stock price performance and several other trading statistics like trading volume and number of trades (Brock, Lakonishok & LeBaron, 1992).

Technical analysis is often considered as non-scientific due to its non-fundamental nature, nonetheless a survey study by Menkhoff (2010) proves that the vast majority of all fund managers rely on technical analysis. Additionally, Bessembinder & Chan (1997) demonstrate that even rather simple technical analysis holds statistically significant forecasting power within financial markets. Technical analysis is more related to psychology than fundamentals and the more inductive technical analysis is used, the more it reinforces its own predictive powers almost like a self-fulfilling prophecy.

In Figure 4 the risk and return outcome of the by Izumi et al. (2009, p. 3474) tested automated trading strategies agents are displayed. Partially to illustrate some available strategies other than the ones mentioned by Leshik & Cralle (2011). The results were achieved using back testing on several stock markets. For these trading strategies to work, several parameters for the input variables can be used, it is elementary that the parameters take on values that reflect the price level of fundamental information to the firm and economic conditions and preferably use adaptive agents. The parameters and code as used by Izumi et al. (2009) can be found in Apendix B. Moreover, from the parameters can be derived that actual trading algorithms are ver
y similar to the coke vending machine example algorithm illustrated above. For most of these algorithms, technical indicators based on price or volume information such as moving averages or upper and lower bands are used as input values.

Figure 4. Standard deviations versus Returns of ATS. Reprinted from “Evaluation of automated- trading strategies using an artificial market.” By K. Izumi, F. Toriumi & H. Matsui, 2009, 72(16), 3474.

Not only can ATS use price and volume information or technical indicators as input values. The algorithms can be integrated with machine learning to automatically read news feed and turn these into input values for the algorithm. According to Nuij et al. (2014) automating the incorporation of news feed into stock trading strategies can boost the returns of individual technical indicators compared to those without the incorporation of news messages. By means of extracting an event from a news feed text and pairing these with an impact based on historic stock price deviations for a specific event this news variable can be used in addition to existing technical indicators.

Subsequently the rules that are created through news associated events can be mutated within the trading algorithm by improved versions of the rules which have led to higher returns. Such automatic reprogramming on the basis of previous return outcomes is one example of how machine learning can be implemented in ATS.

Predictability & Biases in Behavioral Finance

Algorithmic trading is connected to behavioral finance in the sense that algorithms many times are programmed to trade on investor biases that exist because of individual or group behavior. The technical indicators incorporated in trading algorithms function through behavioral finance. Therefore, it could even be argued that technical economic indicators are actually socio-economic indicators. Behavioral finance often is contradictive to the efficient market theory suggesting that stock prices are actually to a certain extent predictable because of psychological and social concepts that cause inefficiencies on the stock market (Shiller, 2003).

There is polarity in human behavior that reflects how stocks oscillate between up and down trends similarly to state of mind and mood that a human or group of humans are in. All forms of emotion seem to exert forces on the stock market in one way or another. To name an example, even reaching physical new highs in the form a tall building reverbs on the stock market by leaving a peak in the graph followed by a fall. The Dubai stock market rose significantly after finishing the Burj Khalifa, world’s tallest building (Mitroi, 2014). Moreover, there are recursive patterns for some financial anomalies such as the day-of-the-week effect which are not yet understood. Evidence seems to suggest that these anomalies happen because of mass psychology (Shiller, 2003).

Vasiliou, Eriotis & Papathanasiou (2008) mention that moving averages stress where a trend is headed and flatten out fluctuations caused by the noise of irrational investors also known as noise traders. Additionally, Vasiliou et al. find that the utility of the technical trading rules used in their research improved over time.

Market Efficiency and Predictability

Litzenberger, Castura & Gorelick (2012) stated that market quality has improved in the past decades. A clear cause for this trend is increased competition through more automation and high frequency trading in the market which leads to decreases in bid and ask spreads and improved liquidity. This improved liquidity causes the orders in limit order books to be exercised in a faster pace. Moreover, when relating market quality to algorithmic trading, Lyle, Naughton and Weller (2015) discovered that algorithmic trading strategies which provide liquidity such as market making strategies increase market quality. Whereas liquidity taking, non-market maker algorithmic trading activity harms market quality. Bouchaud, Farmer & Lillo (2008) conclude prices in markets to sustain a close to perfect unpredictability in the short run. Firstly, considering that outstanding liquidity is always small meaning that prices do not immediately mirror all information available to the market. Secondly on electronic markets there is no possibility to distinguish informed and uninformed trades for all trades have the same impact. It follows that all informative aspects of a trade should be internal to the market meaning that trades, order flow and cancellations carry information.

Beja and Goldman (1980) rightfully state that a market constructed by humans can impossibly be so mechanically perfect and efficient that all information would directly be integrated in the prices before it can be observed.

Implying that price anomalies will always be present, leaving room for predictability. Moreover, Pesaran (2003) reinforces predictability by stating that “A large number of studies in the finance literature have confirmed that stock returns can be predicted to some degree by means of interest rates, dividend yields and a variety of macroeconomic variables exhibiting clear business cycle variations.” According to Pesaran market efficiency should be distanced from predictability.


Data Collection & Processing

Most of the data and queries used for the research have been obtained through Wharton University of Pennsylvania’s WRDS database & query tool (Wharton Research Data Services). In this research, three different datasets are used that exist within the WRDS database, named: CRSP – Daily Stock, IBES – Price Target and Federal Reserve Bank – Interest Rates. These sub-datasets eventually will be merged before the hypotheses can be tested and will be elaborated on in the following section. Further details on the datasets can be obtained from Table A1 where all query extraction specifications are denoted.

The chosen data period from 1999 to 2017 is a trade-off between covering a period as extensive as possible while at the same time trying to keep the data editable within Stata using the limited computing power that the research has to its disposal. Moreover, since IBES data is only available from 1999 onwards, this will automatically be the start of the period. Furthermore, it can be argued using Glantz & Kissel’s (2013, p. 258) Figure 1 that algorithmic trading before 1999 would have amounted to such a small percentage of the market volume that it is not of critical value in answering the research question.

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