Signing trades and an evaluation of the Lee–Ready algorithm

We use techniques developed to analyze the Supply Curve in liquidity models in order to analyze the accuracy of the Lee and Ready algorithm, both for highly liquid and relatively liquid stocks. Through the use of order book data combined with tick data, we are actually (somewhat tediously) able to tell whether or not a given trade is buyer or seller initiated. For those trades where such knowledge is certain, the accuracy of the Lee and Ready algorithm is not as accurate as has been assumed previously. We can essentially prove that the Lee and Ready algorithm is always at least 55% accurate, and is around 61% accurate for highly liquid stocks (i.e., the top 50 of the S&P 100).     

Power and causality

Quality & Quantity - The article first establishes the conceptual link between power and causality. It then attempts to review the literature on power from the causal modelling perspective. The...     

Can People Explain Their Own Vote? Introspective Questions as Indicators of Salience in the 1995 Quebec Referendum on Sovereignty

Most scholars doubt that voters are able to explain their own vote. We argue that introspective questions whereby respondents are invited to tell, in their own words, the reasons why they vote the way they do, provide useful information on which considerations are most salient in their voting decisions. We show that open-ended questions about reasons for voting Yes or No in the 1995 Quebec referendum on sovereignty help us to sort out subgroups of voters for whom a given consideration is more salient.     

Configurations of innovation: predictable and maverick modes

This study reports on an investigation of modes of innovation in 43 firms in six industrial sectors: (i) pulp and paper; (ii) computer. services; (iii) pharmaceucticals; (iv) apparel; (v) metallic products; and (vi) financial brokerage services. A model incorporating context, process, strategy, organization and performance variables was built to serve as a base to identify empirical conjfigurations of models of innovation. Configurations are gestalts of variables that describe particular modes of innovative behaviour. The model includes uariables representing context, process, strategy, organization and performance. Modes of innovations were found to he highly sector-specific. In fact, most firms innovate in predictable ways, in conformity with the logic of innovation in their industrial environment. A significant minority of firms, however, adopt maverick behaviours, similar to modes of innovation that prevail in other industries. Modes of innovation thus cut across industry lines as a result of firm's combetencies, competitive situationes or managerial preferences. A taxonomy of modes of innovation cutting across industry lines is presented. The configurations which emerged from the data analysis were: science-based product innovators; (ii) entrepreneurial fast-track experimenters; (iii) global cost leaders; (iv) lethargic reliance on information technology and (v) on process adaptation.     

Configurations of innovation: predictable and maverick modes

This study reports on an investigation of modes of innovation in 43 firms in six industrial sectors: (i) pulp and paper; (ii) computer. services; (iii) pharmaceucticals; (iv) apparel; (v) metallic products; and (vi) financial brokerage services. A model incorporating context, process, strategy, organization and performance variables was built to serve as a base to identify empirical conjfigurations of models of innovation. Configurations are gestalts of variables that describe particular modes of innovative behaviour. The model includes uariables representing context, process, strategy, organization and performance. Modes of innovations were found to he highly sector-specific. In fact, most firms innovate in predictable ways, in conformity with the logic of innovation in their industrial environment. A significant minority of firms, however, adopt maverick behaviours, similar to modes of innovation that prevail in other industries. Modes of innovation thus cut across industry lines as a result of firm's combetencies, competitive situationes or managerial preferences. A taxonomy of modes of innovation cutting across industry lines is presented. The configurations which emerged from the data analysis were: science-based product innovators; (ii) entrepreneurial fast-track experimenters; (iii) global cost leaders; (iv) lethargic reliance on information technology and (v) on process adaptation.     

The innovation galore,from classroom to the shop floor

Canada, and to a lesser extent the U.S.A., must look at technological innovation with a feeling of urgency. Although there is wide agreement about the pressing need for increased innovation in industry, especially in these times of decreasing productivity and growing imports of foreign high-technology products, there is no unanimity about the best ways to achieve a better posture in national and international markets for new technology. Yet, surely one of the keys lies in the upbringing of a larger flow of competent and highly imaginative technological innovators. This poses a singular challenge for universities whose pattern of higher education has followed traditional disciplinary lines, with little attention paid to the anatomy and the dynamics of the innovation process as it applies to the real world. To this date, four universities in the U.S.A. (M.I.T., Carnegie-Mellon, Oregon and Utah) and two in Canada (Waterloo and Ecole Polytechnique de Montréal) have tried to address this problem through the development of innovation centres situated on their campus and making use of university personnel and facilities to further their objectives. In one form or another, all of the centres strive to acquire deeper insight into the innovation process itself while attempting to develop a methodology to enhance innovation, both from an educational and managerial point of view.A methodology for innovation must improve both the effectiveness of new idea generation and innovation development and the yield of the dialogue between the innovator and the financial supporter of the project. When translated into education and training, this methodology systematizes the content of the innovator's subconscious and organizes his mental building blocks to produce a body of transferable knowledge, i.e. transforming intellectual property into a manageable and profitable asset. In contrast to traditional engineering or business education where knowledge is organized into disciplines and other logical formats to facilitate teaching, the structure of education in design and innovation is built on elemental physical configurations and system behaviours.Whereas the information flow in professional management is essentially financial in nature, in innovation management the information flow is more related to ideas than money, even though financial aspects underlie the whole structure as well. While the former operates in closed feedback loop fashion, the latter is an open-ended addendum to the usual company operations. It is aimed at future growth.Coaching innovators to excel in world market competition bears resemblance to the drilling of athletes for the Olympic Games. The Olympic contestant must have a tremendous amount of willpower, in addition to skills and physical fitness. The comparable behavioral element for innovation is motivation, with related entrepreneurial skills and technological competence. This motivation must be shared also by the other members of the innovation team. Thus, not only must there be a methodology to teach innovation but ways and means must be found to create the necessary motivation and nurture the entrepreneurship.