Moore's Law versus Murphy's Law: Algorithmic Trading and Its Discontents

Abstract

Financial markets have undergone a remarkable transformation over the past two decades due to advances in technology. These advances include faster and cheaper computers, greater connectivity among market participants, and perhaps most important of all, more sophisticated trading algorithms. The benefits of such financial technology are evident: lower transactions costs, faster executions, and greater volume of trades. However, like any technology, trading technology has unintended consequences. In this paper, we review key innovations in trading technology starting with portfolio optimization in the 1950s and ending with high-frequency trading in the late 2000s, as well as opportunities, challenges, and economic incentives that accompanied these developments. We also discuss potential threats to financial stability created or facilitated by algorithmic trading and propose “Financial Regulation 2.0,” a set of design principles for bringing the current financial regulatory framework into the Digital Age.

Dynamical Models of Stock Prices Based on Technical Trading Rules---Part II: Analysis of the Model

“…If we want to use (25) to check the correlation of the returns generated by model (1), the first question is how to compute the expectation in (25). Since our price dynamical model (1) is deterministic, the returns generated by the model are not random, so what does the expectation of a non-random variable mean?…”

“…More specifically, we perform Monte Carlo simulations for the price dynamical model (1) with initial condition , where are constants and is a zero-mean unit-variance Gaussian random variable (same as we did for the simulations in Figs. 6 and 7), and then use the average over the different simulation runs for the expectation in (25). Let be the price of the j'th simulation run and S is the total number of the Monte Carlo simulations, define the drift of log price in time t as Then we say the returns are uncorrelated if is equal to .…”

Market stability vs. market resilience: Regulatory policies experiments in an agent-based model with low- and high-frequency trading

“…Indeed, on the one hand, some studies have highlighted the benefits of HFT as a source of an almost continuous flow of liquidity (see e.g., Brogaard, 2010;Menkveld, 2013). On the other hand, other works (see e.g., SEC, 2010;Angel, Harris, and Spatt, 2011;Lin, 2012;Kirilenko and Lo, 2013) have pointed to HFT as a source of higher volatility in markets and as a key driver in the generation of extreme events like flash crashes, whose incidence has grown in the last decades (Johnson, Zhao, Hunsader, Meng, Ravindar, Carran, and Tivnan, 2012;Golub, Keane, and Poon, 2012). The regulatory framework is complicated by the fact that -although many explanations have so far been proposed for flash crashes -no consensus has yet emerged about the fundamental causes of these extreme phenomena (see Haldane, 2011).…”

Bubbles in hybrid markets: How expectations about algorithmic trading affect human trading

“…It includes simple support of human traders in e.g. the scheduling of sales of assets without influencing the asset price on the market, but also includes sophisticated algorithmic traders which can learn and autonomously decide which assets they sell or buy (Kirilenko and Lo, 2013).…”