Does high frequency algorithmic trading matter for non-AT investors?

Kelejian, Harry H.; Mukerji, Purba

doi:10.1016/j.ribaf.2015.10.014

Cited by 25 publications

(10 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 2 shows that the correlation of prices is significantly higher than the correlation in fundamental values of the two stocks. This is in line with empirical findings and anecdotal evidence that indicate AT leading to co-movements in market prices of assets (e.g., Zweig 2010), and price spillovers across assets (e.g., Kelejian and Mukerji 2016), which are above and beyond what fundamentals alone can explain.…”

Section: Experiments 2: Statistical Arbitrage/pairs Tradingsupporting

confidence: 89%

“…Thus, our findings match the real world where AT has changed the landscape of market transactions and has had profound implications for market participants (e.g., Kelejian and Mukerji 2016), and for those charged with market oversight (e.g., U.S. Commodities Futures Trading Commission and U.S. Securities and Exchange Commission 2010). The reason for the changes being experienced is the important differences that set human and algorithmic traders apart, even though their strategies might seem similar.…”

Section: Introductionsupporting

confidence: 65%

“…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 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 2006years -2007years , 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.…”

Section: Related Literaturementioning

confidence: 99%

“…Empirical analysis of algorithmic trading have estimated its impact on various aspects of market quality (see for example, Brogaard 2010;Brogaard et al 2014;Chaboud et al 2014;Hasbrouck and Saar 2013;Hendershott and Riordan 2011;Kelejian and Mukerji 2016;Menkveld 2013;Riordan and Storkenmaier 2011;Zhang 2010). However, many of the findings are contradictory.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The Impact of Algorithmic Trading in a Simulated Asset Market

Mukerji

Chung

Walsh

et al. 2019

JRFM

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Experiments 2: Statistical Arbitrage/pairs Tradingsupporting

confidence: 89%

Section: Introductionsupporting

confidence: 65%

Section: Related Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Impact of Algorithmic Trading in a Simulated Asset Market

Mukerji

Chung

Walsh

et al. 2019

JRFM

Self Cite

View full text Add to dashboard Cite

show abstract

“…In practice, trading firms monitor market conditions and integrate pauses in their algorithms (Kelejian and Mukerji (2016) Within the model of Ait-Sahalia and Saglam (2014), the quantity of each order is fixed at one lot.…”

Section: Empirical Investigationmentioning

confidence: 99%

Profitability and Market Quality of High Frequency Market-Makers: An Empirical Investigation

Yergeau

2016

SSRN Journal

View full text Add to dashboard Cite

Financial markets in contemporary regulatory settings require the presence of high-frequency liquidity providers. We present an applied study of the profitability and the impact on market quality of an individual high-frequency trader acting as a market-maker. Using a sample of sixty stocks over a six-month period, we implement the optimal quoting policy (OQP) of liquidity provision from Ait-Sahalia and Saglam (2014) dynamic inventory management model. The OQP allows the high-frequency trader to extract a constant annuity from the market but its profitability is insufficient to cover the costs of market-making activities. The OQP is embedded in a trading strategy that relaxes the model's constraint on the quantity traded. Circuit-breakers are implemented and market imperfections are considered. Profits excluding maker-fees and considering transaction fees are economically significant. We propose a methodology to adjust the returns for asynchronous trading and varying leverage levels associated with dynamic inventory management. This allows us to qualify high trade volume as a proxy of informed trading. The high-frequency trader behaves as a constant liquidity provider and has a positive effect on market quality even in periods of market stress.

show abstract