A piecewise linear model for trade sign inference

We use transaction level data for twelve stocks with large market capitalization on the Australian Stock Exchange to develop an empirical model for trade sign (trade initiator) inference. The new model is a piecewise linear parameterization of the model proposed recently in Ref. [1]. The space of the predictor variables is partitioned into six regions. Signs of individual trades within the regions are inferred according to simple and interpretable rules. Across the 12 stocks the new model achieves an average out-of-sample classification accuracy of 74.38% (SD=4.25%), which is 2.98% above the corresponding accuracy reported in Ref. [1]. Two of the model's regions, together accounting for 16.79% of the total number of daily trades, have each an average classification accuracy exceeding 91.50%. The results indicate a strong dependence between the predictor variables and the trade sign, and provide evidence for an endogenous component in the order flow. An interpretation of the trade sign classification accuracy within the model's regions offers new insights into a relationship between two regularities observed in the markets with a limit order book, competition for order execution and transaction cost minimization.Order submission, Trade classification, Piecewise linear, Competition for order execution, Transaction cost minimization

A local non-parametric model for trade sign inference

We investigate a regularity in market order submission strategies for twelve stocks with large market capitalization on the Australian Stock Exchange. The regularity is evidenced by a predictable relationship between the trade sign (trade initiator), size of the trade, and the contents of the limit order book before the trade. We demonstrate this predictability by developing an empirical inference model to classify trades into buyer-initiated and seller-initiated. The model employs a local non-parametric method, k-nearest-neighbor, which in the past was used successfully for chaotic time series prediction. The k-nearest- neighbor with three predictor variables achieves an average out-of- sample classification accuracy of 71.40%, compared to 63.32% for the linear logistic regression with seven predictor variables. The result suggests that a non-linear approach may produce a more parsimonious trade sign inference model with a higher out-of-sample classification accuracy. Furthermore, for most of our stocks the observed regularity in market order submissions seems to have a memory of at least 30 trading days.Order submission, Trade classification, K-nearest-neighbor, Non-linear, Memory

The LGBT Special Emphasis Program of the US Department of Agriculture’s Natural Resources Conservation Service

The Natural Resource Conservation Service (NRCS) is a branch of the US Department of Agriculture (USDA). The NRCS assists farmers and forest landowners to protect the soil, water, and other natural resources on their land. Located in almost every county within the United States, the NRCS employs almost 12,000 people. The NRCS is an equal opportunity employer and provider. Within the NRCS and other agencies of the USDA, Special Emphasis Programs (SEPs) have become established as a component of the Equal Employment Opportunity Program. SEPs were set up to address the unique concerns of members of certain groups in achieving diversity, inclusion, and equal opportunity. An LGBT SEP has been established to address the concerns of LGBT employees within the NRCS, as well as the concerns of LGBT individuals applying for NRCS programs. The specific purpose of the LGBT program is to provide LGBT awareness and education to NRCS employees and partners while focusing on such issues as employment, retention, promotion, training, career development, and advancement opportunities affecting NRCS employees and program applicants. Some goals of the program are to ensure LGBT individuals receive equal treatment in all aspects of employment, to encourage the participation of LGBT populations in all NRCS programs, and to educate all NRCS employees by raising the level of awareness of LGBT workplace issues and concerns

Computational Models for Stock Market Order Submissions

The motivation for the research presented in this thesis stems from the recent availability of high frequency limit order book data, relative scarcity of studies employing such data, economic significance of transaction costs management, and a perceived potential of data mining for uncovering patterns and relationships not identified by the traditional top-down modelling approach. We analyse and build computational models for order submissions on the Australian Stock Exchange, an order-driven market with a public electronic limit order book. The focus of the thesis is on the trade implementation problem faced by a trader who wants to transact a buy or sell order of a certain size. We use two approaches to build our models, top-down and bottom-up. The traditional, top-down approach is applied to develop an optimal order submission plan for an order which is too large to be traded immediately without a prohibitive price impact. We present an optimisation framework and some solutions for non-stationary and non-linear price impact and price impact risk. We find that our proposed transaction costs model produces fairly good forecasts of the variance of the execution shortfall. The second, bottom-up, or data mining, approach is employed for trade sign inference, where trade sign is defined as the side which initiates both a trade and the market order that triggered the trade. We are interested in an endogenous component of the order flow, as evidenced by the predictable relationship between trade sign and the variables used to infer it. We want to discover the rules which govern the trade sign, and establish a connection between them and two empirically observed regularities in market order submissions, competition for order execution and transaction cost minimisation. To achieve the above aims we first use exploratory analysis of trade and limit order book data. In particular, we conduct unsupervised clustering with the self-organising map technique. The visualisation of the transformed data reveals that buyer-initiated and seller-initiated trades form two distinct clusters. We then propose a local non-parametric trade sign inference model based on the k-nearest-neighbour classifier. The best k-nearest-neighbour classifier constructed by us requires only three predictor variables and achieves an average out-of-sample accuracy of 71.40% (SD=4.01%)1, across all of the tested stocks. The best set of predictor variables found for the non-parametric model is subsequently used to develop a piecewise linear trade sign model. That model proves superior to the k-nearest-neighbour classifier, and achieves an average out-of-sample classification accuracy of 74.38% (SD=4.25%). The result is statistically significant, after adjusting for multiple comparisons. The overall classification performance of the piecewise linear model indicates a strong dependence between trade sign and the three predictor variables, and provides evidence for the endogenous component in the order flow. Moreover, the rules for trade sign classification derived from the structure of the piecewise linear model reflect the two regularities observed in market order submissions, competition for order execution and transaction cost minimisation, and offer new insights into the relationship between them. The obtained results confirm the applicability and relevance of data mining for the analysis and modelling of stock market order submissions

Corporate Cultures in Global Interaction: A Management Guide

Over the past few years, “globalization” has become a catchword. It reflects a widening horizon, particularly in the business world. Transactions are taking on international dimensions, the networks of relationships in business, politics and society are growing ever more complex. The problem that emerges is this: heterogeneity leads to complexity, and complexity leads to heterogeneity

The rise of micromobilities at tourism destinations

Purpose This paper aims to identify the need for research that focuses on micromobilities at tourist destinations, charting their recent expansion and exploring development challenges. Design/methodology/approach This discussion draws together recent evidence and studies that are directly and indirectly related to the rise of micromobilities. It identifies and critically analyses the trend going forward, its potential benefits and challenges, and offers several areas of future study. Findings Micromobilities relates to a new umbrella term that includes, but is not limited to, walking, cycling (both existing modes), e-bikes and e-scooters (new modes). The proliferation of new micro-modes in urban zones at destinations can be viewed positively in terms of their potential to increase sustainable urban mobility and therefore destination attractiveness; but also negatively in terms of potential space issues, accessibility and sustainable implementation. Destination developers and stakeholders should therefore consider carefully how to successfully integrate micromobilities into sustainable transport systems. Originality/value This paper addresses a trend that is extremely prominent at many destinations but largely absent from academic study and that is also being described by commentators as key to sustainable futures at destinations

Mid-term assessment of the National Peace Corps Association Ebola Relief Fund: determining effectiveness and future direction

Public Health Relevance: This evaluation seeks to analyze and discuss the effectiveness of a novel model of fundraising and grant management to aid in the resolution of a large epidemic. On March 23, 2014 the World Health Organization (WHO) announced the outbreak of Ebolavirus in Guinea, which continued to spread and overwhelm the neighboring countries Liberia and Sierra Leone. Ebolavirus is a hemorrhagic virus with a case fatality rate of 50-70%. In September of 2015, the National Peace Corps Association (NPCA) formed the Ebola Relief Fund (ERF) in response to members’ desires to participate in the international relief effort. Between October and February, the ERF collected 100 proposals and awarded 25 grants, totaling approximately $75,000. Presently, the ERF is midway through its operations having completed Round 1 programs, Round 2 programs are nearing completion, and funds were recently disbursed for Round 3. The objectives of this report are broadly to 1.) Assess the effectiveness of the ERF at soliciting high quality program proposals, as well as the impact of selected programs and 2.) To determine the future of ERF as the outbreak is being rapidly controlled. Qualitative reviews of participating organizations’ initial proposal critiques, mid-term reports, and final reports were conducted to assess overall quality of grants submitted, compliance with proposed funding requests, and success of funded programs. A comprehensive review of news articles published between the dates of February 1, 2015 and April 1, 2015 was conducted to make recommendations regarding the future direction of the ERF. The evaluation found that ERF had been able to elicit proposals of sufficient quality to warrant funding and the organizations were highly compliant and successful in the delivery of their programs. Moving forward the ERF should consider changing the criteria used to select grants. If grants will continue to be awarded in the future to assist in the Ebola effort, applicants should only be limited to parts of Guinea and Sierra Leone still fighting the outbreak. A better use of funds may be to invest in longer term development efforts in the three countries to assist with recovery from the epidemic

Nurse Perspectives of Trauma-Informed Care

There is growing interest in trauma-informed nursing methods to better respond to the needs of patients with histories of adverse childhood experiences and other traumatic events. Recent advances in the understanding of how trauma can negatively affect long-term health outcomes have fostered a shift towards trauma-informed care as a method to decrease patient retraumatization in nursing practice. With the implementation of trauma-informed care in many areas of healthcare and public health, several challenges have been exposed. The purpose of this study was to examine nurses’ lived experience of implementing trauma-informed care into nursing practice for the care of patients with physical disabilities, known or unknown histories of adverse childhood or traumatic experiences, and secondary maladaptive coping behaviors at a skilled-nursing facility in a midsized city in the state of Michigan. A Gadamerian hermeneutic approach was used to collect and analyze data from 15 licensed nurses via in-depth interviews and reflexive methods. The belief-based model of the theory of planned behavior was used to elicit nurse participants’ salient beliefs. Results from the interpreted coded textual data revealed four primary themes: nurses feeling empowered to avoid inadvertent patient retraumatization, enhanced empathy towards patients, uncertainty about referents’ use of trauma-informed care, and the essential importance of being equipped and prepared. The results of this study may be used to improve nurses use of trauma-informed care, thereby decreasing patient retraumatization and potentially improving individual and community health outcomes

From Mobike to no bike in Greater Manchester: Using the capabilities approach to explore Europe's first wave of dockless bike share

Globally, bike share schemes are an element of a rapidly changing urban transport landscape. Whilst many docked schemes are now embedded in cities around the world, the recent explosion of dockless systems provides an opportunity to evaluate claims that this form of shared mobility has the potential to alleviate common barriers to cycling, relieve congestion, boost low carbon travel, get people active, and reduce social exclusion. Drawing on a mixed methods study of 2270 online survey respondents and 27 interviews, all living in, working in or visiting Greater Manchester during a trial of dockless bike share, we explore the ways in which the technological, spatial and practical configuration of bike share schemes relate to a city's infrastructure and existing cycling practices. We question assertions that bike share provision necessarily results in increased rates of cycling and enhanced social inclusion. By using a capabilities approach and utlilising the concept of ‘conversion factors’ to describe the differing capacities or opportunities that people have to convert resources at their disposal into ‘capabilities’ or ‘functionings’, we show how the practice of bike sharing can influence a population's propensity to cycle, as well as how bike share interacts with established barriers to cycling. We find that many established barriers to cycling remain relevant, especially environmental factors, and that bike share creates its own additional challenges. We conclude that bike share operators must recognise the role of personal and social conversion factors more explicitly and be sensitive to the social and physical geography of cities, rather than assuming that a ‘one size fits all’ approach is adequate. To do this they should engage more closely with existing bodies, including transport authorities and local authorities, in co-creating bike share systems. Using the capabilities approach enables us to identify ways in which it could be made relevant and accessible to a more diverse population