Algorithmic trading engines versus human traders – do they behave different in securities markets?

After exchanges and alternative trading venues have introduced electronic execution mechanisms worldwide, the focus of the securities trading industry shifted to the use of fully electronic trading engines by banks, brokers and their institutional customers. These Algorithmic Trading engines enable order submissions without human intervention based on quantitative models applying historical and real-time market data. Although there is a widespread discussion on the pros and cons of Algorithmic Trading and on its impact on market volatility and market quality, little is known on how algorithms actually place their orders in the market and whether and in which respect this differs form other order submissions. Based on a dataset that – for the first time – includes a specific flag to enable the identification of orders submitted by Algorithmic Trading engines, the paper investigates the extent of Algorithmic Trading activity and specifically their order placement strategies in comparison to human traders in the Xetra trading system. It is shown that Algorithmic Trading has become a relevant part of overall market activity and that Algorithmic Trading engines fundamentally differ from human traders in their order submission, modification and deletion behavior as they exploit real-time market data and latest market movements

Assessing the impact of algorithmic trading on markets: a simulation approach

Innovative automated execution strategies like Algorithmic Trading gain significant market share on electronic market venues worldwide, although their impact on market outcome has not been investigated in depth yet. In order to assess the impact of such concepts, e.g. effects on the price formation or the volatility of prices, a simulation environment is presented that provides stylized implementations of algorithmic trading behavior and allows for modeling latency. As simulations allow for reproducing exactly the same basic situation, an assessment of the impact of algorithmic trading models can be conducted by comparing different simulation runs including and excluding a trader constituting an algorithmic trading model in its trading behavior. By this means the impact of Algorithmic Trading on different characteristics of market outcome can be assessed. The results indicate that large volumes to execute by the algorithmic trader have an increasing impact on market prices. On the other hand, lower latency appears to lower market volatility

Does algorithmic trading improve liquidity?

Algorithmic trading has sharply increased over the past decade. Equity market liquidity has improved as well. Are the two trends related? For a recent five-year panel of New York Stock Exchange (NYSE) stocks, we use a normalized measure of electronic message traffic (order submissions, cancellations, and executions) as a proxy for algorithmic trading, and we trace the associations between liquidity and message traffic. Based on within-stock variation, we find that algorithmic trading and liquidity are positively related. To sort out causality, we use the start of autoquoting on the NYSE as an exogenous instrument for algorithmic trading. Previously, specialists were responsible for manually disseminating the inside quote. As stocks were phased in gradually during early 2003, the manual quote was replaced by a new automated quote whenever there was a change to the NYSE limit order book. This market structure change provides quicker feedback to traders and algorithms and results in more message traffic. For large-cap stocks in particular, quoted and effective spreads narrow under autoquote and adverse selection declines, indicating that algorithmic trading does causally improve liquidity

Analysis of binary trading patterns in Xetra

This paper proposes the Shannon entropy as an appropriate one-dimensional measure of behavioural trading patterns in financial markets. The concept is applied to the illustrative example of algorithmic vs. non-algorithmic trading and empirical data from Deutsche Börse's electronic cash equity trading system, Xetra. The results reveal pronounced differences between algorithmic and non-algorithmic traders. In particular, trading patterns of algorithmic traders exhibit a medium degree of regularity while non-algorithmic trading tends towards either very regular or very irregular trading patterns. JEL Classification: C40, D0, G14, G15, G2

Liquidity cycles and make/take fees in electronic markets

In this paper, the authors develop a dynamic model of trading with two specialized sides: traders posting quotes (“market makers”) and traders hitting quotes (“market takers”). Traders monitor the market to seize profit opportunities, generating high frequency make/take liquidity cycles. Monitoring decisions by market-makers and market-takers are self-reinforcing, generating multiple equilibria with differing liquidity levels and duration clustering. The trading rate is typically maximized when makers and takers are charged different fees or even paid rebates, as observed in reality. The model yields several empirical implications regarding the determinants of make/take fees, the trading rate, the bid-ask spread, and the effect of algorithmic trading on these variables. Finally, algorithmic trading can improve welfare because it increases the rate at which gains from trade are realized.liquidity; monitoring; make/take fees; duration clustering; algorithmic trading; two-sided markets

Modeling asset prices for algorithmic and high frequency trading.

Algorithmic Trading (AT) and High Frequency (HF) trading, which are responsible for over 70% of US stocks trading volume, have greatly changed the microstructure dynamics of tick-by-tick stock data. In this paper we employ a hidden Markov model to examine how the intra-day dynamics of the stock market have changed, and how to use this information to develop trading strategies at ultra-high frequencies. In particular, we show how to employ our model to submit limit-orders to profit from the bid-ask spread and we also provide evidence of how HF traders may profit from liquidity incentives (liquidity rebates). We use data from February 2001 and February 2008 to show that while in 2001 the intra-day states with shortest average durations were also the ones with very few trades, in 2008 the vast majority of trades took place in the states with shortest average durations. Moreover, in 2008 the fastest states have the smallest price impact as measured by the volatility of price innovationsHigh frequency traders; Algorithmic trading; Durations; Hidden Markov model;

Controlling risk in a lightning-speed trading environment

A small group of high-frequency algorithmic trading firms have invested heavily in technology to leverage the nexus of high-speed communications, mathematical advances, trading and high-speed computing. By doing so, they are able to complete trades at lightning speeds. High-frequency algorithmic trading strategies rely on computerized quantitative models that identify which type of financial instruments to buy or sell (e.g., stocks, options or futures), as well as the quantity, price, timing and location of the trades. These so-called black boxes are capable of reading market data, transmitting thousands of order messages per second to an exchange, cancelling and replacing orders based on changing market conditions and capturing price discrepancies with little or no human intervention.Counterfeits and counterfeiting

Social signals and algorithmic trading of Bitcoin

The availability of data on digital traces is growing to unprecedented sizes, but inferring actionable knowledge from large-scale data is far from being trivial. This is especially important for computational finance, where digital traces of human behavior offer a great potential to drive trading strategies. We contribute to this by providing a consistent approach that integrates various datasources in the design of algorithmic traders. This allows us to derive insights into the principles behind the profitability of our trading strategies. We illustrate our approach through the analysis of Bitcoin, a cryptocurrency known for its large price fluctuations. In our analysis, we include economic signals of volume and price of exchange for USD, adoption of the Bitcoin technology, and transaction volume of Bitcoin. We add social signals related to information search, word of mouth volume, emotional valence, and opinion polarization as expressed in tweets related to Bitcoin for more than 3 years. Our analysis reveals that increases in opinion polarization and exchange volume precede rising Bitcoin prices, and that emotional valence precedes opinion polarization and rising exchange volumes. We apply these insights to design algorithmic trading strategies for Bitcoin, reaching very high profits in less than a year. We verify this high profitability with robust statistical methods that take into account risk and trading costs, confirming the long-standing hypothesis that trading based social media sentiment has the potential to yield positive returns on investment.Comment: http://rsos.royalsocietypublishing.org/content/2/9/15028

Optimal Execution Trajectories. Linear Market Impact with Exponential Decay

Optimal execution of portfolio transactions is the essential part of algorithmic trading. In this paper we present in simple analytical form the optimal trajectory for risk-averse trader with the assumption of exponential market recovery and short-time investment horizon