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The Effect of High Frequency Trading on Stock Price Volatility

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The Effect of High Frequency Trading on Stock Price Volatility The effect of High Frequency Trading on stock price volatility Junhao Fu 10466584 Programme : Economics and Business Track: Finance and Organization Supervisor: Drs. P.V. (Pepijn) Trietsch Date: 20/2 /2016 1 Abstract. This paper investigates the relationship between High Frequency tradings(HFTs) and stock price volatility. As the huge increasing awareness of HFT activity in recent years, it has posed the crucial question of whether HFTs are beneficial for financial markets to both investors and firms. There are some existing researches have studied the impact of HFT on different measures of market efficiency, such as liquidity, price discovery and latency, but most of the results are not conclusive. Therefore, built on pervious researches, this study focuses on the impact of High frequency tradings on stock price volatility over the period 2014-2015 for a sample of 30 selected companies in U.S stock market. The amount of High frequency tradings (HFTs) are calculated by a formula built up based on Frank Zhang’s formula in 2010. Data is collected from CRSP and Thomason one until September 2015. The data of positions of Institutions investors and individual investors will be used to calculate HFT, and together with other common factors that affect volatility such as the past 12 months return, turnover rate, market value on equity, stock price level, interest rate and inflation rate, a model will be built up to analyze the contribution of HFT on stock price volatility. The result is significant, even though there is no sufficient evident to show that the HFT has impact on the stock price for those securities served in financial sector, but in general the study shows there are negative impact on the HFTs in the price volatility among these 30 companies. However, the impacts are smaller compare to other factors that included into the model. Therefore, this paper concluded that HFT has negative impact on stock price volatility, but the impacts are relatively small. 1. Introduction 1.1 problem and central question With an advanced and well developed computer science today, algorithmic trading system has become the most popular trading platform around the world. It saved a lot of time during the transaction in the market. It allows investor establish specific strategy beforehand both for buying and selling a certain security. Once the strategy has been programmed, it will be automatically executed by the computer. In general, there are some advantages for High frequency trading, for example, it helps investor to minimize emotional investment. Due to the trading was set in program and it will execute tradings automatically, it’s so a rational investment behavior can be ensured. Secondly the trading strategy can be backtested, which means HFT can be applied into historical market data to determine the viability of the strategy. And there is no room for interpretation 2 after the program started. Therefore it helps the investor to test the expectancy of the market from computers and so as to adjust their strategy into real world. Moreover, HFT helps to improve the trading efficiency. Which investors are benefited from the immediate responds of computers so they would not have any delays in trading. As soon as the position is set, orders are automatically traded to prevent over losses. Also the trading system allows investor to diversify their interest in stocks. Therefore they can open severe accounts and trade with different strategy and securities at the same time. It also helps investors to hedge risks and prevent any unexpected losses. However, some drawbacks also come along with HFTs. Which it is highly rely on computers therefore if something goes wrong with the systems or any bugs in the software it will lead to a costly loss. Also investors who highly rely on HFT are always over-optimization. When they employ backtesting techniques the systems always provide the optimal outcomes and didn’t take into account with unexpected losses. Therefore when HFTs are implemented into real trading, the target price are always not set to be a most profitable level and sometimes even made a loss because of over expectation on the market. There are some motivations for starting this research. Due to its characters of short holding period and high amount of trading, HFT are always seemed to be a major factor that interfere the market risk. One of examples is the black flash . which started at 2.30 pm on 6th may, the US stock market indexes, such as the S&P 500, Dow Jones Industrial index and Nasdaq, collapsed and rebounded very rapidly within 36 minutes. The Dow Jones Industrial index had 9% dropped just within a very short time period. It was commonly thought that High-Frequency Tradings is one of the main reasons that caused the sudden crash on stock market. Data also shows that there are huge amount of HFT activities during the trading day and most investor start to blame the sudden price drop to HFT. Another example is lots of traditional traders complained that HFT has always push up or lower down the price level just before their deal done, which they use a high speed computers to finish the deal faster than traditional traders, so that they have to accept a price level of a deal that they did not expect for. And thus it increases the price level volatility. However, according to some post analysis of the incident, supporters of HFT defended that HFT did not cause the crash. CME Group(2010) stated that, according to their investigation by using statistical data, there is no significant evidence to support that the huge number of HFT activities was related to the crash, in fact the report shows that HFT improved the stability of market price. The statement leads to another round of debate regarding to the effect of HFT to market price level. Hence, I was motivated to analyze to see if HFT has impact on stock price volatility, the central question is: Does High-frequency contribute impact on stock price volatility? An empirical methodology will be applied in this research and it will be explained in the later paragraphs. 3 1.2 Historical background of High Frequency Trading As one of largest subsets in algorithmic trading, High-frequency tradings (HFTs) is also highly rely on the strategy, but the price will not be set up either too high nor too low, therefore the program was expected to executed a deal within a very short time(normally not overnight). HFT is introduced in 1999 after the new authorization on E- stock exchange by the U.S. government, with characterized of short holding period, speedy trade and high turnover ratio, its proportion of market share was soon to begin rapid growth and start to dominate U.S stock market in early 21st century. According to data from the NYSE(2009), the HFT volume has been grew for 149% during the year of 2005 to 2008. There is approximately $141bn of hedge fund assets were implemented HFT strategies in the first quarter of year 2009, which accounted for more than 79% market shares in the hedge fund market. The case of Renaissance Technologies is the first case that HFT has successfully implemented their investment strategies, which HFT investors are the market makers and they increased Renaissance Technologies’ liquidity but also lower the volatility and helped narrow bid-offer spreads, the risk for the stock itself was minimized and that makes trading and investing cheaper for other market participants. The trading volume for HFTs is also a huge number, some market leaders in HFT industry are Chicago Trading, Timber Hill and Virtu Financial. Nowadays HFT firms account for 3/4 of stock trading volume with only 2% number of total operating intermediaries (Frank, 2010) .The Bank of England (2013) also estimates that in Europe HFT accounts for approximately 40% of equity orders volume and for Asia the number is about 5-10%, with potential for rapid growth. After HFTs strategy is now being implemented widely, it gets more difficult to deploy their profitability. According to an estimation from Frederi Viens of Purdue University(2014), the operating income for HFTs has been declined from about $5 billion in 2010 , to about $1.25 billion in 2012. It means that even though the HFTs still account for a majority of trading in stock market nowadays, the increasing uncertainty and the intense competition within the market makes them more difficult to make profit. 2.Literature Review 4 2.1 Autotrading As mentioned above , HFT is a subset from autotrading , which is also called Algorithmic trading . There have been some previous studies shows that the increasing number of auto trading did not bring negative effect on stock market, which it did not enhance the price volatility. Hendershott and Riordan( 2009) , compared automated trading and other trades in German equity market. The paper found out that the automated trading accounted for more than half of the total trading volume in top 30 stocks, and in fact the study showed that automate trading contributes more on price efficiency and no evidence shows that the volatility has been increased. A similar study is done by Chaboud, Hjalmarsson, Vega and Chiquoine(2008). They used a database that identify auto trading and classical trading separately, and the result shows that in the EBS forex market, an increase in auto trades tend to produce more liquidity in the market but also associated decreasing volatility. 2.2 Price volatility Stock price volatility is a main factor in a sufficient equity market, it was used to measure different kind of characteristics such as asset allocation, market efficiency and risk management within an equity market.
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