Market Impact in Trader-Agents:Adding Multi-Level Order-Flow Imbalance-Sensitivity to Automated Trading Systems

Financial markets populated by human traders often exhibit "market impact", where the traders' quote-prices move in the direction of anticipated change, before any transaction has taken place, as an immediate reaction to the arrival of a large (i.e., "block") buy or sell order in the market: e.g., traders in the market know that a block buy order will push the price up, and so they immediately adjust their quote-prices upwards. Most major financial markets now involve many "robot traders", autonomous adaptive software agents, rather than humans. This paper explores how to give such trader-agents a reliable anticipatory sensitivity to block orders, such that markets populated entirely by robot traders also show market-impact effects. In a 2019 publication Church & Cliff presented initial results from a simple deterministic robot trader, ISHV, which exhibits this market impact effect via monitoring a metric of imbalance between supply and demand in the market. The novel contributions of our paper are: (a) we critique the methods used by Church & Cliff, revealing them to be weak, and argue that a more robust measure of imbalance is required; (b) we argue for the use of multi-level order-flow imbalance (MLOFI: Xu et al., 2019) as a better basis for imbalance-sensitive robot trader-agents; and (c) we demonstrate the use of the more robust MLOFI measure in extending ISHV, and also the well-known AA and ZIP trading-agent algorithms (which have both been previously shown to consistently outperform human traders). We demonstrate that the new imbalance-sensitive trader-agents introduced here do exhibit market impact effects, and hence are better-suited to operating in markets where impact is a factor of concern or interest, but do not suffer the weaknesses of the methods used by Church & Cliff. The source-code for our work reported here is freely available on GitHub.Comment: To be presented at the 13th International Conference on Agents and Artificial Intelligence (ICAART2021), Vienna, 4th--6th February 2021. 15 pages; 9 figure

Exploring Narrative Economics:A Novel Agent-Based-Modeling Platform that Integrates Automated Traders with Opinion Dynamics

In seeking to explain aspects of real-world economies that defy easy understanding when analysed via conventional means, Nobel Laureate Robert Shiller has since 2017 introduced and developed the idea of Narrative Economics, where observable economic factors such as the dynamics of prices in asset markets are explained largely as a consequence of the narratives (i.e., the stories) heard, told, and believed by participants in those markets. Shiller argues that otherwise irrational and difficult-to-explain behaviors, such as investors participating in highly volatile cryptocurrency markets, are best explained and understood in narrative terms: people invest because they believe, because they have a heartfelt opinions, about the future prospects of the asset, and they tell to themselves and others stories (narratives) about those beliefs and opinions. In this paper we describe what is, to the best of our knowledge, the first ever agent-based modelling platform that allows for the study of issues in narrative economics. We have created this by integrating and synthesizing research in two previously separate fields: opinion dynamics (OD), and agent-based computational economics (ACE) in the form of minimally-intelligent trader-agents operating in accurately modelled financial markets. We show here for the first time how long-established models in OD and in ACE can be brought together to enable the experimental study of issues in narrative economics, and we present initial results from our system. The program-code for our simulation platform has been released as freely-available open-source software on GitHub, to enable other researchers to replicate and extend our workComment: To be presented at the 13th International Conference on Agents and Artificial Intelligence (ICAART2021), Vienna, 4th--6th February 2021. 18 pages; 14 figure

Fluid evolution in an Oceanic Core Complex: a fluid inclusion study from IODP hole U1309 D - Atlantis Massif, 30°N, Mid-Atlantic Ridge

In the detachment mode of slow seafloor spreading, convex-upward detachment faults take up a high proportion of the plate separation velocity exposing gabbro and serpentinized peridotite on the seafloor. Large, long-lived hydrothermal systems such as TAG are situated off axis and may be controlled by fluid flow up a detachment fault, with the source of magmatic heat being as deep as 7 kmbsf. The consequences of such deep circulation for the evolution of fluid temperature and salinity have not previously been investigated. Microthermometry on fluid inclusions trapped in diabase, gabbro, and trondjhemite, recovered at the Atlantis Massif Oceanic Core Complex (30°N, Mid-Atlantic Ridge), reveals evidence for magmatic exsolution, phase separation, and mixing between hydrothermal fluids and previously phase-separated fluids. Four types of fluid inclusions were identified, ranging in salinity from 1.4 to 35 wt % NaCl, although the most common inclusions have salinities close to seawater (3.4 wt % NaCl). Homogenization temperatures range from 160 to >400°C, with the highest temperatures in hypersaline inclusions trapped in trondjhemite and the lowest temperatures in low-salinity inclusions trapped in quartz veins. The fluid history of the Atlantis Massif is interpreted in the context of published thermochronometric data from the Massif, and a comparison with the inferred circulation pattern beneath the TAG hydrothermal field, to better constrain the pressure temperature conditions of trapping and when in the history of exhumation of the rocks sampled by IODP Hole U1309D fluids have been trapped

Wind measurement system

A system for remotely measuring vertical and horizontal winds present in discrete volumes of air at selected locations above the ground is described. A laser beam is optically focused in range by a telescope, and the output beam is conically scanned at an angle about a vertical axis. The backscatter, or reflected light, from the ambient particulates in a volume of air, the focal volume, is detected for shifts in wavelength, and from these, horizontal and vertical wind components are computed

AMCIS 2002 Panels and Workshops II: Spreadsheet-Based DSS Curriculum Issues

When challenged to justify the value of information systems (IS) research, decision support systems (DSS) is usually cited as one the most compelling examples of where IS research made the transition successfully from theoretical academic journals into the real-world . In light of this assessment, it is somewhat surprising that offerings of DSS courses waned over the years. This paper identifies several possible reasons for the decline in DSS course offerings and suggests innovative approaches using spreadsheets for breathing new-life into this cornerstone of the IS field

Learning Needs and Quality Care Among Family Caregivers and Elderly Patients of Guadalupe, Cebu City, Central Philippines

This study assessed the learning needs of the family caregivers based on knowledge, skills, attitude and values (KSAV), their quality of care services and the elderly patients’ assessment on the quality of the provided care. The study utilised a descriptive-normative method employing survey research design utilizing a randomized cluster sampling technique. Primary data were collected using a standardised modified interview guide. Face-toface interviews were carried out to family caregivers and elderly patients in Guadalupe Village, Cebu City, Central Philippines. Findings revealed that caregiving jobs are mostly handled by women. Likewise, there were more female than male elderly patients, almost three-fourths of them attained high school or lower level education. Family caregivers’ possessed very good attitude and values. They were also good in terms of knowledge and skills toward the care of elderly patients. Similarly, elderly patients view their caregivers to be very good in attitude and values and good in terms of knowledge and skills. In conclusion, the learning needs among family care givers dealt more on understanding the nature, medical conditions, medication administration, caregiving techniques, diet and nutrition, ambulation techniques and strategies, financial supports from peers and total care management of the elderly patients. The provision of these learning needs anchored on the knowledge, skills, attitude and values ensures the caregivers from experiencing depressions, desperations, and self-isolation

An evidence-based management framework for business analytics

It is said that knowledge is power, yet often, decision makers ignore information that ought to be considered. The phenomenon known as Semmelweis reflex occurs when new knowledge is rejected because it contradicts established norms. The goal of evidence-based management (EBMgt) is to help overcome Semmelweis reflex by integrating evaluated external evidence with stakeholder preference, practitioner experiences, and context. This evaluated external evidence is the product of scientific research. In this paper, we demonstrate an EBMgt business analytics model that uses computer simulation to provide scientific evidence to help decision makers evaluate equipment replacement problems, specifically the parallel machine replacement problem. The business analytics application is demonstrated in the form of a fleet management problem for a state transportation agency. The resulting analysis uses real-world data allowing decision makers to unfreeze their current system, move to a new state, and re-freeze a new system

Decentralised Monte Carlo Tree Search for Active Perception

We propose a decentralised variant of Monte Carlo tree search (MCTS) that is suitable for a variety of tasks in multi-robot active perception. Our algorithm allows each robot to optimise its own individual action space by maintaining a probability distribution over plans in the joint-action space. Robots periodically communicate a compressed form of these search trees, which are used to update the locally-stored joint distributions using an optimisation approach inspired by variational methods. Our method admits any objective function defined over robot actions, assumes intermittent communication, and is anytime. We extend the analysis of the standard MCTS for our algorithm and characterise asymptotic convergence under reasonable assumptions. We evaluate the practical performance of our method for generalised team orienteering and active object recognition using real data, and show that it compares favourably to centralised MCTS even with severely degraded communication. These examples support the relevance of our algorithm for real-world active perception with multi-robot systems