Abstract
In this work we simulate algorithmic trading (AT) in asset markets to clarify its impact. Our markets consist of human and algorithmic counterparts of traders that trade based on technical and fundamental analysis, and statistical arbitrage strategies. Our specific contributions are: (1) directly analyze AT behavior to connect AT trading strategies to specific outcomes in the market; (2) measure the impact of AT on market quality; and (3) test the sensitivity of our findings to variations in market conditions and possible future events of interest. Examples of such variations and future events are the level of market uncertainty and the degree of algorithmic versus human trading. Our results show that liquidity increases initially as AT rises to about 10% share of the market; beyond this point, liquidity increases only marginally. Statistical arbitrage appears to lead to significant deviation from fundamentals. Our results can facilitate market oversight and provide hypotheses for future empirical work charting the path for developing countries where AT is still at a nascent stage.
Highlights
Algorithmic trading (AT) in US stock markets has grown at a blistering pace, starting from the mid-1990s when it accounted for only 3 percent of the market, to recent times when it has reached almost 85% of dollar trade volumes (Zhang 2010)
We use our simulated asset market to conduct two separate experiments to evaluate the impact of algorithmic trading (AT) on (1) market liquidity; and (2) in the presence of statistical arbitrage/pairs trading, the relations between the price and the fundamental value of the stock
We evaluate the market quality of interest at each level of market presence of AT, studying how the market quality might change with the advent and progression of AT
Summary
Algorithmic trading (AT) in US stock markets has grown at a blistering pace, starting from the mid-1990s when it accounted for only 3 percent of the market, to recent times when it has reached almost 85% of dollar trade volumes (Zhang 2010). The aim of this paper is to bridge the gap between researchers’ empirical results and the real-world attempts (e.g., by regulators) to understand how each algorithmic trading strategy works. We do this by using simulations to systematically demonstrate how the behavior of various algorithms result in the observed impact on market quality (liquidity, price spreads, trade-related price discovery, and correlation of asset prices). Our simulated markets give us the flexibility to start from the AT strategies of our choosing and simulate the resulting market outcomes. Our line of research can aid market regulators by providing a platform capable of isolating and testing the market impact of specific AT strategies
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