Heterogeneous real-time trading strategies in the foreign exchange market

The foreign exchange (FX) market is worldwide, but the dealers differ in their geographical locations (time zones), working hours, time horizons, home currencies, access to information,transaction costs, and other institutional constraints. The variety of time horizones is large: from intra-day dealers, who close their positions every evening, to long-term investors and central banks. Depending on the constraints, the different market participats need different strategies to reach their goal, which is usually maximizing the profit, or rather a utility function including risk. Different intra-day trading strategies can be studied only if high-density data are available. Oslen & Associates (O & A) has collected and analysed large amounts of FX quotes by market makers around the clock (up to 5000 non-equally spaced prices per day for the German mark against US$). Based on these data, a set of real-time intra-day trading models has been developed. These models give explicit trading recommendations under realistic constraints. They are allowed to trade only during the opening hours of a market, depending on the time zone and local holidays. The models have been running real-time for more than three years, thus leading to an ex ante test. The test results, obtained with a risk-sensitive performance measure, are presented. All these trading models are profitable, but they differ in their risk behaviour and dealing frequency. If a certain profitable intra-day algorithm is tested with different working hours, its success can considerably change. A systematic study shows that the best choice of working hours is usually when the most important markets for the particular FX rate are active. All the results demonstrate that the assumption of a homogeneous 24-hour FX market with identical dealers, following an identical ‘rational expectation’, is far from reality. To explain the market dynamics, a heterogeneous model of the market with different types of dealers is more appropriate.     

Managing strategic intellectual property assets in the fuzzy front end of new product development process

The strategic use of intellectual property (IP) is crucial for technology-based companies to gain competitive advantage. The recent transformation of the US patent system brings new challenges and opportunities in this arena. In this regard, this study attempts to identify techniques which can help with IP evaluation and selection in the fuzzy front end (FFE) of new product development (NPD) process. This study combines data collection methods such as mining the literature, conducting in-depth interviews, surveying questionnaires, and analyzing cases. This research serves as an analysis of modern literature and identifies a multicriteria weighted scoring model that can be employed to help with the patent decision process. The criterion to discern patent eligibility is a contended discussion. For this survey administration, 300 companies, as the targeted sample, were randomly selected to be reached from LexisNexis database. Consequently, this paper identifies the key decision criteria to incorporate into this model and obtains weights gathered from surveying IP professionals and R&D managers in US-based electronics manufacturing firms (SIC code: 36). This study proposes a structured approach to identify ideas that should be patented in the FFE of NPD process by way of an analysis of pertaining literature and case studies. The technique we present in this paper could be essential for many firms to achieve IP success as their strategic means. Moreover, this tool can help R&D managers not only speed up the FFE of NPD process but also make more informed and target-worthy decisions for IP filing.     

Wavelet timescales and conditional relationship between higher-order systematic co-moments and portfolio returns

This paper investigates the association between portfolio returns and higher-order systematic co-moments at different timescales obtained through wavelet multi-scaling, a technique that decomposes a given return series into timescales enabling investigation at different return intervals. In Australian industry portfolios, the relative risk positions indicated by systematic co-moments at some timescales are different from those revealed in daily returns. A strong positive (negative) linear association between beta and portfolio return and co-kurtosis and portfolio return in the up (down) market is observed in daily returns and at different timescales. The beta risk is priced in the up and down markets. Co-kurtosis is not priced when the beta is in the pricing model. Co-skewness appears to be priced at a relatively high timescale and this is observed only after the up and down separation of market returns.     

Nonparametric stochastic frontiers: A local maximum likelihood approach

This paper proposes a new approach to handle nonparametric stochastic frontier (SF) models. It is based on local maximum likelihood techniques. The model is presented as encompassing some anchorage parametric model in a nonparametric way. First, we derive asymptotic properties of the estimator for the general case (local linear approximations). Then the results are tailored to a SF model where the convoluted error term (efficiency plus noise) is the sum of a half normal and a normal random variable. The parametric anchorage model is a linear production function with a homoscedastic error term. The local approximation is linear for both the production function and the parameters of the error terms. The performance of our estimator is then established in finite samples using simulated data sets as well as with a cross-sectional data on US commercial banks. The methods appear to be robust, numerically stable and particularly useful for investigating a production process and the derived efficiency scores.     

An essay on the biological origin of producing surplus value by human labor

In this essay, we explore the origins of the ability to produce surplus value through human labor. The main hypothesis is that the safety factor for energy capacity in humans determines the surplus value of human labor in human societies. We estimate the safety factor for Homo sapiens to be around 2.78. This is based on the rise of the encephalization quotient from 2.5 in Australopithecus africanus to 5.8 in H. sapiens, and we assume this rise is matched by a rise in the safety factor from 1.2 to 2.78. Being a communal species compels human individuals to produce both their personal and their community’s energy need. The possible correlation between Dunbar’s number and the safety factor of energy production capacity provides a very essential feature of human labor, namely “surplus value”. The average labor activity of a modern human is sufficient to provide energy for two to three people’s energy burden in a community. Human labor not only produces the energy for survival of an individual but also produces a surplus due to the safety factor. Therefore, work activity of an individual enables them to supply more than they need for themselves as a value.     

Do search frictions matter for inflation dynamics?

We assess the empirical relevance for inflation dynamics of accounting for the presence of search frictions in the labor market. The new Keynesian Phillips curve explains inflation as being mainly driven by current and expected future marginal costs. Recent empirical research has emphasized different measures of real marginal costs to be consistent with observed inflation persistence. We argue that, allowing for search frictions in the labor market, real marginal cost should also incorporate the cost of generating and maintaining long-term employment relationships, along with conventional measures, such as real unit labor costs. In order to construct a synthetic measure of real marginal costs, we use newly available labor market data on worker finding and separation rates that reflect hiring and firing costs. We then estimate a new Keynesian Phillips curve by generalized method of moments (GMM) using the imputed marginal cost series as an observable and find that the contribution of labor market frictions in explaining inflation dynamics is small.     

Implementing technology roadmap process in the energy services sector: A case study of a government agency

Energy sector has become increasingly sensitive to emerging new technologies as our society is seeking alternative energy sources. Many utility companies and government agencies have started to implement technology planning processes for roadmapping their future technology portfolios. This paper focuses on technology planning in the government energy services sector. Through a case study research method, the paper documents how technology planning and specifically technology roadmaps were implemented at a federal agency tasked with managing power transmission in the Northwest United States. Three application areas are covered: transmission, renewables and energy efficiency. The paper provides details on the Energy Efficiency Roadmaps. Through the review of the case a technology planning methodology based on technology roadmaps is detailed. Key conclusions were reached on how to manage such process implementation in similar organizations. Some of these conclusions can be generalized to those that are implementing technology planning processes for the first time. We concluded that adoption of such methods would require a longer time than anticipated. Organizational changes to adopt the process will likely reduce the time it takes to deliver the required roadmaps. We also found that a typical sequence of events would be Technology Gap Analysis and Identification of Technology Candidates, Evaluation and Prioritization of Technologies, Roadmapping of Technologies and Allocation of Resources to the R&D Programs or to the direct acquisition of the technologies.