High-frequency trading | EPFL Graph Search

High-frequency trading (HFT) is a type of algorithmic trading in finance characterized by high speeds, high turnover rates, and high order-to-trade ratios that leverages high-frequency financial data and electronic trading tools. While there is no single definition of HFT, among its key attributes are highly sophisticated algorithms, co-location, and very short-term investment horizons in trading securities. HFT uses proprietary trading strategies carried out by computers to move in and out of positions in seconds or fractions of a second. In 2016, HFT on average initiated 10–40% of trading volume in equities, and 10–15% of volume in foreign exchange and commodities. High-frequency traders move in and out of short-term positions at high volumes and high speeds aiming to capture sometimes a fraction of a cent in profit on every trade. HFT firms do not consume significant amounts of capital, accumulate positions or hold their portfolios overnight

Essays in Financial Economics

Christopher Trevisan

This thesis is structured in three chapters, each pertaining to a specific problem in financial economics. The first chapter, titled 'High-Frequency Jump Analysis of the Bitcoin Market' and co-authored with Prof. Olivier Scaillet and Adrien Treccani of the University of Geneva, studies the market for bitcoin, a pioneer virtual currency, and its price dynamics. Using data for all the trades that took place on the exchange Mt. Gox during the period June 2011 to November 2013, we analyse the occurrence of jumps in the price process and conclude that they are frequent (one jump per week, on average) and that they tend to cluster in time. They are predicted by the order flow imbalance and the preponderance of aggressive traders. Jumps have a short-term positive impact on market activity and illiquidity and see a persistent change in the price. The second chapter, titled 'Price Formation in the Bitcoin Market', studies the same bitcoin market from the point of view of information and the way it is incorporated in the price. I analyse different measures of liquidity and price impact and find that bitcoin exhibits more adverse selection than comparable stocks. I examine the potential profits of liquidity provision and conclude that they are insufficient, which explains the absence of market makers and consequently the low market depth. I estimate a vector autoregressive model for the returns and a trade indicator and find that the effect of past returns and past trades on contemporaneous trades is significant and singular, indicating that agents infer considerable information which leads them to update their beliefs about the future evolution of the price, consistent with the view that bitcoin is a Keynesian beauty contest. The third chapter, titled 'Cash Holdings and Credit Risk', investigates the a priori surprising empirical phenomenon that firms with larger cash holdings appear to be riskier with regards to the valuation of their debt. It is explained by the endogeneity affecting the relation between a firm's cash holdings and credit spread. Indeed, a firm facing more risk (for instance, because its cash flows are more volatile) will optimally choose larger cash holdings as a hedge. I review the related literature and present a class of continuous-time capital structure models capable of capturing this phenomenon.

EPFL2016

High-Frequency Jump Analysis of the Bitcoin Market

Adrien Treccani, Christopher Trevisan

We use the database leak of Mt. Gox exchange to analyze the dynamics of the price of bitcoin from June 2011 to November 2013. This gives us a rare opportunity to study an emerging retail-focused, highly speculative and unregulated market with trader identifiers at a tick transaction level. Jumps are frequent events and they cluster in time. The order flow imbalance and the preponderance of aggressive traders, as well as a widening of the bid-ask spread predict them. Jumps have short-term positive impact on market activity and illiquidity and induce a persistent change in the price.

OXFORD UNIV PRESS2020

Slow Moving Capital and Trade Execution Costs: Evidence from a major trading Glitch

Vincent Jean Bogousslavsky, Pierre Collin Dufresne

We investigate the impact of an exogenous trading glitch at a high-frequency market-making firm on standard measures of stock liquidity (spreads, price impact, turnover, and depth) and institutional trading costs (implementation shortfall and VWAP slippage). Stocks in which the firm accumulates large long (short) positions increase (decrease) by about 4% during the glitch and become substantially more illiquid. It takes one day for prices and spread-based liquidity measures to revert. Institutional trading costs, however, remain significantly higher for more than one week. Both liquidity measures are also weakly correlated outside the glitch period, suggesting they capture different aspects of liquidity.

2020

Essays in Financial Economics

Christopher Trevisan

EPFL2016