Optimal Execution with Dynamic Order Flow Imbalance

We examine optimal execution models that take into account both market microstructure impact and informational costs. Informational footprint is related to order flow and is represented by the trader's influence on the flow imbalance process, while microstructure influence is captured by instantaneous price impact. We propose a continuous-time stochastic control problem that balances between these two costs. Incorporating order flow imbalance leads to the consideration of the current market state and specifically whether one's orders lean with or against the prevailing order flow, key components often ignored by execution models in the literature. In particular, to react to changing order flow, we endogenize the trading horizon $T$. After developing the general indefinite-horizon formulation, we investigate several tractable approximations that sequentially optimize over price impact and over $T$. These approximations, especially a dynamic version based on receding horizon control, are shown to be very accurate and connect to the prevailing Almgren-Chriss framework. We also discuss features of empirical order flow and links between our model and "Optimal Execution Horizon" by Easley et al (Mathematical Finance, 2013).Comment: 31 pages, 8 figure

The boomerang returns? Accounting for the impact of uncertainties on the dynamics of remanufacturing systems

Recent years have witnessed companies abandon traditional open-loop supply chain structures in favour of closed-loop variants, in a bid to mitigate environmental impacts and exploit economic opportunities. Central to the closed-loop paradigm is remanufacturing: the restoration of used products to useful life. While this operational model has huge potential to extend product life-cycles, the collection and recovery processes diminish the effectiveness of existing control mechanisms for open-loop systems. We systematically review the literature in the field of closed-loop supply chain dynamics, which explores the time-varying interactions of material and information flows in the different elements of remanufacturing supply chains. We supplement this with further reviews of what we call the three ‘pillars’ of such systems, i.e. forecasting, collection, and inventory and production control. This provides us with an interdisciplinary lens to investigate how a ‘boomerang’ effect (i.e. sale, consumption, and return processes) impacts on the behaviour of the closed-loop system and to understand how it can be controlled. To facilitate this, we contrast closed-loop supply chain dynamics research to the well-developed research in each pillar; explore how different disciplines have accommodated the supply, process, demand, and control uncertainties; and provide insights for future research on the dynamics of remanufacturing systems

Price pressures

We study price pressures in stock prices—price deviations from fundamental value due to a risk-averse intermediary supplying liquidity to asynchronously arriving investors. Empirically, twelve years of daily New York Stock Exchange intermediary data reveal economically large price pressures. A $100,000 inventory shock causes an average price pressure of 0.28% with a half-life of 0.92 days. Price pressure causes average transitory volatility in daily stock returns of 0.49%. Price pressure effects are substantially larger with longer durations in smaller stocks. Theoretically, in a simple dynamic inventory model the ‘representative’ intermediary uses price pressure to control risk through inventory mean reversion. She trades off the revenue loss due to price pressure against the price risk associated with remaining in a nonzero inventory state. The model’s closed-form solution identifies the intermediary’s relative risk aversion and the distribution of investors’ private values for trading from the observed time series patterns. These allow us to estimate the social costs—deviations from constrained Pareto efficiency—due to price pressure which average 0.35 basis points of the value traded. JEL Classification: G12, G14, D53, D6

New perspectives on realism, tractability, and complexity in economics

Fuzzy logic and genetic algorithms are used to rework more realistic (and more complex) models of competitive markets. The resulting equilibria are significantly different from the ones predicted from the usual static analysis; the methodology solves the Walrasian problem of how markets can reach equilibrium, starting with firms trading at disparate prices. The modified equilibria found in these complex market models involve some mutual self-restraint on the part of the agents involved, relative to economically rational behaviour. Research (using similar techniques) into the evolution of collaborative behaviours in economics, and of altruism generally, is summarized; and the joint significance of these two bodies of work for public policy is reviewed. The possible extension of the fuzzy/ genetic methodology to other technical aspects of economics (including international trade theory, and development) is also discussed, as are the limitations to the usefulness of any type of theory in political domains. For the latter purpose, a more differentiated concept of rationality, appropriate to ill-structured choices, is developed. The philosophical case for laissez-faire policies is considered briefly; and the prospects for change in the way we ‘do economics’ are analysed

A taxonomy of supply chain innovations

In this paper, a taxonomy of supply chain and logistics innovations was developed and presented. The taxonomy was based on an extensive literature survey of both theoretical research and case studies. The primary goals are to provide guidelines for choosing the most appropriate innovations for a company, and help companies in positioning themselves in the supply of chain innovations landscape. To this end, the three dimensions of supply chain innovations, namely the goals, supply chain attributes, and innovation attributes were identified and classified. The taxonomy allows for the efficient representation of critical supply chain innovations information, and serves the mentioned goals, which are fundamental to companies in a multitude of industries