High-Frequency Trading and Price Discovery

High Frequency Trading and Price Discovery

High Frequency Trading and Price Discovery

High frequency traders and the price process

Over the past few decades, financial markets have undergone remarkable reforms as a result of developments in computer technology and changing regulations, which have dramatically altered the structures and the properties of financial markets. The advances in technology have largely increased the speed of communication and trading. This has given birth to the development of algorithmic trading (AT) and high-frequency trading (HFT). The proliferation of AT and HFT has raised many issues regarding their impacts on the market. This paper proposes a framework characterized by an agent-based artificial stock market where market phenomena result from the interaction between many heterogeneous non-HFTs and HFTs. In comparison with the existing literature on the agent-based modeling of HFT, the traders in our model adopt a genetic programming (GP) learning algorithm. Since they are more adaptive and heuristic, they can form quite diverse trading strategies, rather than zero-intelligence strategies or pre-specified fundamentalist or chartist strategies. Based on this framework, this paper examines the effects of HFT on price discovery, market stability, volume, and allocative efficiency loss.

Technical Analysis, Liquidity, and Price Discovery

ABSTRACTWe analyze the contribution to price discovery of market and limit orders by high‐frequency traders (HFTs) and non‐HFTs. While market orders have a larger individual price impact, limit orders are far more numerous. This results in price discovery occurring predominantly through limit orders. HFTs submit the bulk of limit orders and these limit orders provide most of the price discovery. Submissions of limit orders and their contribution to price discovery fall with volatility due to changes in HFTs’ behavior. Consistent with adverse selection arising from faster reactions to public information, HFTs’ informational advantage is partially explained by public information.

Implications of High-Frequency Trading for Security Markets

High frequency trading (HFT) has grown substantially in recent years, due to fast-paced technological developments and their rapid uptake, particularly in equity markets. This paper investigates how HFT could evolve and, by developing a robust understanding of its effects, to identify potential risks and opportunities that it could present in terms of financial stability and other market outcomes such as volatility, liquidity, price efficiency and price discovery. Despite commonly held negative perceptions, the available evidence indicates that HFT and algorithmic trading (AT) may have several beneficial effects on markets. However, they may cause instabilities in financial markets in specific circumstances. Carefully chosen regulatory measures are needed to address concerns in the shorter term. However, further work is needed to inform policies in the longer term, particularly in view of likely uncertainties and lack of data. This will be vital to support evidence-based regulation in this controversial and rapidly evolving field.

We examine price discovery in sequential markets for the 10‐year US Treasury note, German bund, and UK gilt futures over the period 2010–2017. We find that price discovery increases after the opening of the US stock market. Order flows in the bond futures markets are more informative for permanent price changes in the 30‐min period after the US stock market opens. A placebo test using US statutory holidays confirms our findings. A cross‐market analysis suggests that the increased price discovery in the bond futures is related to returns and net order flows of the US stock market.

High frequency trading, price discovery and market efficiency in the FTSE100

High-frequency trading (HFT) has grown substantially in recent years due to fast-paced technological developments and their rapid uptake, particularly in equity markets. This review investigates how HFT could evolve and, by developing a robust understanding of its effects, identifies potential risks and opportunities that HFT could present in terms of financial stability and other market outcomes such as volatility, liquidity, price efficiency, and price discovery. Despite commonly held negative perceptions, the available evidence indicates that HFT and algorithmic trading may have several beneficial effects on markets. However, these types of trading may cause instabilities in financial markets in specific circumstances. Carefully chosen regulatory measures are needed to address concerns in the shorter term. However, further work is needed to inform policies in the longer term, particularly in view of likely uncertainties and lack of data. This work will be vital in supporting evidence-based regulation in this controversial and rapidly evolving field.

The growing prevalence of High-Frequency Trading (HFT) in financial markets has sparked intense debate over its impact on market stability. This paper aims to research the adverse effects that come along with HFT's enhancement of liquidity and price discovery efficiency. It contrasts the hardware infrastructure and execution speeds of high-frequency automated trading against manual trading and analyzes how the liquidity bubbles provided by HFT contribute to market volatility. This paper then concludes with three main points. First, HFT can pose a threat to market stability. Second, specific HFT strategies may mislead other market participants and lead to price distortions. Lastly, HFT may exacerbate market participation inequality, posing a market fairness challenge. Based on these findings, this paper recommends that regulatory authorities and policymakers pay closer attention to the potential risks of HFT and implement measures to maintain market stability and fairness.

High-Frequency Trading, Stock Volatility, and Price Discovery

FREQUENTLY the most important determinant of the rate of return in equity investments is the rate and direction of price change.' Success in investments, if it not be due to chance, is largely a function of the ability to predict price changes. The importance of predicting price changes has led to a number of studies aimed at discovering the determinants of price change. These studies may be conveniently classified into three groups: first, those studies that sought to predict future price changes from past price changes; second, those investigations that attempted to predict price changes from price ratios, such as the price/ earnings ratio; and third, those studies which sought to predict price changes from past changes in other variables, such as earnings. In the last decade a score of studies were made which attempted to discover whether future price changes could be predicted from past price changes. The results were generally disappointing. It was found that successive price changes tended to be independent; the price change of a stock in one period had little bearing on the price change in the next period. Moreover, the relative price change of a stock (relative to other stocks) in one period was not indicative of the relative price change of that stock in the next period.2 Although the full import of the results of these studies is not yet clear, the studies certainly have important implications for financial analysis. Some have concluded that the results call into question the very utility of fundamental financial analysis.3 This conclusion was certainly premature and may be questioned, as will be shown below. The results of the second and third groups of studies were only partly encouraging. The correlation between price ratios and future price changes, though promising, was frequently neither significant nor positive and price changes in one period tended to be independent of earnings changes in the preceding period.4 None of the studies were devoted to the question of the relation between relative earnings changes and relative price changes in the same period. This question should probably have been examined first. Even though previous e arnings growth was unrelated to present changes in prices, perhaps percentage changes in prices and earnings in the same period were highly correlated. Per share earnings growth could still have a substantial influence on simultaneous price changes. If there were little connection between relative earnings changes and p r i c e changes in the same period, then the ability to predict relative earnings changes might be of limited value. If, on the contrary, there were high correlation between relative earnings changes and relative price changes in the same period, then the ability to predict relative earnings changes would be extremely important.5 The purpose of this article is to report the results of a study of the influence of rates of growth of per share earnings on percentage changes in stock prices in the same period.

We examine the relative efficiency of price discovery between the new market (KOSDAQ) and the main board (KOSPI) in the Korean stock markets that have the same trading mechanism (i.e. electronic limit‐order book), focusing on the comparisons of each market’s efficiency of price discovery in three aspects: speed, degree, and accuracy. We find that, for our entire firm sample, price discovery on KOSDAQ is less efficient than on KOSPI. However, the price discovery of the most liquid group (top 40 stocks) on KOSDAQ turns out to be as efficient as the lowest group (top 160th–200th stocks) among the top 200 liquid stocks on KOSPI. These two quintiles are comparable in terms of their firm characteristics, so it appears that the greater overall efficiency of price discovery on KOSPI is due to the characteristics of its listed firms, rather than any inherent difference between a main board and a new market. We also find evidence that the speed of price discovery is mainly determined by turnover, whereas the accuracy of price discovery is mainly determined by turnover and intraday volatility. All together, our results provide some policy implications for developing or even developed countries eager to establish a viable new market. First, price discovery in a successful or viable new market in an emerging economy behaves as predicted in the market microstructure literature, even though that literature is based primarily on main boards in advanced stock markets. Second, price discovery in the most liquid group in a new market is more accurate, although slower, than in the lowest group among the liquid stocks on a main board; on balance, the main board and new market are comparable. Finally, the accuracy of price discovery is more (less) impacted by turnover (intraday volatility) on the new market than on the main board.