Forex Algorithmic Trading RSS

Algorithmic Trading in FOREX Market


The use of algorithmic trading, where computer algorithms directly manage the trading process at high frequency, has become common in major … nancial markets in recent years, beginning in the U.S. equity market more than 15 years ago. There has been widespread interest in understanding the potential impact of algorithmic trading on market dynamics, as some analysts have highlighted the potential for improved liquidity and more e¢ cient price discovery while others have expressed concern that it may be a source of increased volatility and reduced liquidity, particularly in times of market stress. A number of articles and opinion pieces on the topic have recently appeared in the press, with most decrying practices used by some algorithmic traders in the equity market, and there have been calls for regulatory agencies in the United States and Europe to begin investigations. Despite this interest, there has been very little formal empirical research on algorithmic trading, primarily because of a lack of data where algorithmic trades are clearly identi… ed. A notable exception is a recent paper by Hendershott, Jones, and Menkveld (2007), who get around the data constraint by using the flow of electronic messages on the NYSE as a proxy for algorithmic trading. They conclude that algorithmic trading on the NYSE, contrary to the pessimists' concerns, likely causes an improvement in market liquidity. In the foreign exchange market, there has been no formal empirical research on the subject. The adoption of algorithmic trading in the foreign exchange market is a far more recent phenomenon than in the equity market, as the two major interdealer electronic trading platforms only began to allow algorithmic trades a few years ago. Growth in algorithmic trading has been very rapid, however, and a majority of foreign exchange transactions in the interdealer market currently involve at least one algorithmic counterparty.

In algorithmic trading (AT), computers directly interface with trading platforms, placing orders without immediate human intervention. The computers observe market data and possibly other information at very high frequency, and, based on a built-in algorithm, send back trading instructions, often within milliseconds. A variety of algorithms are used: for example, some look for arbitrage opportunities, including small dis- crepancies in the exchange rates between three currencies; some seek optimal execution of large orders at the minimum cost; and some seek to implement longer-term trading strategies in search of pro… ts. Among the most recent developments in algorithmic trading, some algorithms now automatically read and interpret economic data releases, generating trading orders before economists have begun to read the … rst line.

The extreme speed of execution that AT allows and the potential that algorithmic trades may be highly correlated, perhaps as many institutions use similar algorithms, have been cited as reasons for concerns that AT may generate large price swings and market instability. On the other hand, the fact that some algorithms aim for optimal execution at a minimal price impact may be expected to lower volatility. In this paper, we investigate whether algorithmic ('computer') trades and non-algorithmic ('human') trades have di¤erent e¤ects on the foreign exchange market. We … rst ask whether the presence of computer trades causes higher or lower volatility and whether computers increase or reduce liquidity during periods of market stress. We then study the relative importance of human and computer trades in the process of price discovery and re-visit the assumption that liquidity providers are 'uninformed'.

We formally investigate these issues using a novel dataset consisting of two years (2006 and 2007) of minute-by-minute trading data from EBS in three currency pairs: the euro-dollar, dollar-yen, and euro-yen. The data represent the vast majority of global spot interdealer transactions in these exchange rates. An important feature of the data is that the volume and direction of human and computer trades each minute are explicitly identi…fied, allowing us to measure their respective impacts.

We first show some evidence that computer trades are more highly correlated with each other than human trades, suggesting that the strategies used by computers are not as diverse as those used by humans. But the high correlation of computer trades does not necessarily translate into higher volatility. In fact, we … nd next that there is no evident causal relationship between AT and increased market volatility. If anything, the presence of more algorithmic trading appears to lead to lower market volatility, although the economic magnitude of the e¤ect is small. In order to account for the potential endogeneity of algorithmic trading with regards to volatility, we instrument for the actual level of algorithmic trading with the installed capacity for algorithmic trading in the EBS system at a given time.

Next, we study the relative provision of market liquidity by computers and humans at the times of the most influential U.S. macroeconomic data release, the nonfarm payroll report. We … find that, as a share of total market-making activity, computers tend to pull back slightly at the precise time of the release but then increase their presence in the following hour. This result suggests that computers do provide liquidity during periods of market stress.

Finally, we estimate return-order flow dynamics using a structural VAR framework in the tradition of Hasbrouck (1991a). The VAR estimation provides two important insights. First, we find that human order flow accounts for much of the long-run variance in exchange rate returns in the euro-dollar and dollar-yen exchange rate markets, i.e., humans appear to be the 'informed' traders in these markets. In contrast, in the euro-yen exchange rate market, computers and humans appear to be equally 'informed'. In this cross-rate, we believe that computers have a clear advantage over humans in detecting and reacting more quickly to triangular arbitrage opportunities, where the euro-yen price is brie‡y out of line with prices in the euro-dollar and dollar-yen markets. Second, we find that, on average, computers or humans that trade on a price posted by a computer do not impact prices quite as much as they do when they trade on a price posted by a human. One possible interpretation of this result is that computers tend to place limit orders more strategically than humans do. This empirical evidence supports the literature that proposes to depart from the prevalent assumption that liquidity providers in limit order books are passive.

Using highly-detailed high-frequency trading data for three major exchange rates over 2006 and 2007, we analyze the impact of the growth of algorithmic trading on the spot interdealer foreign exchange market. We focus on the following questions: (i) Are the algorithms underlying the computer-generated trades similar enough to result in highly correlated strategies, which some fear may cause disruptive market behavior? (ii) Does algorithmic trading increase volatility in the market, perhaps as a result of the previous concern? (iii) Do algorithmic traders improve or reduce market liquidity at times of market stress? (iv) Are human or computer traders the more 'informed' traders in the market, i.e. who has the most impact on price discovery? (v) Is there evidence in this market that the liquidity providers (the 'makers') and not just the liquidity 'takers', are informed, and do computer makers post orders more strategically than human makers?

By counterparty, the expansion in turnover in the interbank market was comparable to growth over the previous three years, but was outpaced by the increase recorded in the non-financial customer and non-reporting financial institution segments, which more than doubled in size. The currency composition of foreign exchange turnover became a little more dispersed, with the combined share of the US dollar, the euro and the yen in overall turnover falling.

The first three questions directly address concerns that have been raised recently in the financial press, especially in conjunction with the current crisis, while the last two questions relate more to the empirical market microstructure literature on price discovery and order placement. Together, the analysis of these questions brings new and interesting results to the table, both from a practical and academic perspective, in an area where almost no formal research has been available.

Our empirical results provide evidence that algorithmic trades are more correlated than non-algorithmic trades, suggesting that the trading strategies used by the computer traders are less diverse than those of their human counterparts. Although this may cause some concerns regarding the disruptive potential of computer-generated trades, we do not … find evidence of a positive causal relationship between the proportion of algorithmic trading in the market and the level of volatility; if anything, the evidence points towards a negative relationship, suggesting that the presence of algorithmic trading reduces volatility. As for the provision of market liquidity, we … nd evidence that, compared to non-algorithmic traders, algorithmic traders reduce their share of liquidity provision in the minute following major data announcements, when the probability of a price jump is very high. However, they increase their share of liquidity provision to the market over the entire hour following these announcements, which is almost always a period of elevated volatility. This empirical evidence thus suggests that computers do provide liquidity during periods of market stress.

To address the last two questions (price discovery and order placement), we use a high-frequency VAR framework in the tradition of Hasbrouck (1991a). We … find that non-algorithmic trades account for a sub- stantially larger share of the price movements in the euro-dollar and yen-dollar exchange rate markets than would be expected given the sizable fraction of algorithmic trades. Non-algorithmic traders are the 'in- formed' traders in these two markets, driving price discovery. In the cross-rate, the euro-yen exchange rate market, we … find that computers and humans are equally 'informed', likely because of the large proportion of algorithmic trades dedicated to search for triangular arbitrage opportunities. Finally, we … find that, on average, computer takers or human takers that trade on prices posted by computers do not impact prices as much as they do when they trade on prices posted by humans. One interpretation of this result is that computers place limit orders more strategically than humans do. This … finding dovetails with the literature on limit order books that relaxes the common modeling assumption that liquidity providers are passive.

Overall, this study therefore provides essentially no evidence to bolster the widespread concerns about the effect of algorithmic trading on the functioning of … financial markets. The lesson we take from our analysis of algorithmic trading in the interdealer foreign exchange market is that it is more how algorithmic trading is used and what it is predominantly designed to achieve that determines its impact on the market, and not primarily whether or not the order flow reaching the market is generated at high frequency by computers. In the global interdealer foreign exchange market, the rapid growth of algorithmic trading has not come at the cost of lower market quality, at least not in the data we have seen so far. Given the constant search for execution speed in … financial markets and the increasing availability of algorithmic trading technology, it is likely that, absent regulatory intervention, the share of algorithmic trading across most … financial markets will continue to grow. Our study o¤ers hope that the growing presence of algorithmic trading will not have a negative impact on global … financial markets.

This Report was publishled by United States Federal Reserve.