**2. Related Literature**

The literature in the area of algorithmic trading is vast, relative to its recent vintage (for example, see (Biais and Woolley 2011), for background and literature survey). In this section we briefly discuss some studies that are most closely related to ours.

In the finance literature there are a number of studies into market quality resulting from AT. Here are some that help to illustrate the diversity of data used and the existence of sometimes contradictory results. Hendershott and Riordan (2011) using 13 trading days data in January 2008 on AT orders submitted in the Deutsche Boerse, and Hendershott et al. (2011) using NYSE (New York Stock Exchange) electronic message tra ffic data over 2001–2005 concluded that as AT grows, liquidity improves. They also found that the initial price impact of an algorithmic trade is larger than that of human orders. Brogaard (2010) analyzed data from the financial crisis in 2008–2009 and concluded that AT reduces volatility and contributes significantly to the price discovery process. Kelejian and Mukerji (2016) analyzed the long-term impact of AT on non-AT traders with data from before the advent of AT to recent times (1985–2012) and found that AT transmits volatility based on short-term price correlations, moving the market away from fundamentals. In a study of minute-by-minute exchange rate trading data over the two years 2006–2007, Chaboud et al. (2014) concluded that non algorithmic traders cause most of the long run variance in the price of currencies, thus proving themselves to be better informed than algorithmic traders. They also found that AT is associated with lower liquidity immediately following important macroeconomic data releases. The research by Foucault and Menkveld (2008) is an example of a study of how upgrades to the trading system lead to di fferent impacts on the market outcomes such as liquidity.

As stated in the introduction, our paper's contribution is in seeking to bridge the gap between the empirical studies such as those we just outlined above and the actual working of AT. As our simulations of AT strategies reveal the associated market outcomes, we provide a cross-check for the conclusions that empirical studies have drawn from market data.

Our work also relates to asset market simulation studies; for example, Kearns et al. (2010) studied the profitability of AT. Using stock market data from 2008, they simulated an "omniscient" AT trader that e ffectively overestimates the maximum possible profit that could be earned from AT traders that year. They concluded that the total profit derived from AT trading is in general quite modest relative to the total trading volume. Arthur et al. (1996) created a stock market simulation environment called the Santa Fe Artificial Stock Market, which is an example of an agent-based financial market. Autonomous agents in this market learn and adapt their strategies over time. The focus is on the performance of the agents and their strategies and not on the market outcomes.

In contrast to these existing works that simulate asset markets, our novel contribution is that we study the impact of AT on market quality, while the existing literature is geared towards working out the profitability and performance of AT and market agents who use it. Thus, our line of research can ultimately aid regulatory interventions into market manipulations, by providing a platform capable of isolating and testing the market impact of specific strategies.
