'Seeing the market': Technical analysis in trading styles

Whilst financial markets are not strangers to academic and professional scrutiny, they still remain epistemologically contested. For individuals trying to profit by trading shares, this uncertainty is manifested in the varying trading styles which they are able to utilize. This paper examines one trading style commonly used by non-professional share traders-technical analysis. Using research data obtained from individuals who identify themselves as technical analysts, this paper seeks to explain the ways in which individuals understand and use the technique in an attempt to make trading profits. In particular, four distinct subcategories or ideal types of technical analysis can be identified, each providing an alternative perceptual form for participating in financial markets. Each of these types relies upon a particular method for seeing the market, these visualization techniques highlighting the existence of forms of professional vision (as originally identified by Goodwin (1994)) in the way the trading styles are comprehended and acted upon.

The evaluation of the spatial and temporal stability of sugarcane farm performance based on yield and commercial cane sugar

In broader catchment scale investigations, there is a need to understand and ultimately exploit the spatial variation of agricultural crops for an improved economic return. In many instances, this spatial variation is temporally unstable and may be different for various crop attributes and crop species. In the Australian sugar industry, the opportunity arose to evaluate the performance of 231 farms in the Tully Mill area in far north Queensland using production information on cane yield (t/ha) and CCS ( a fresh weight measure of sucrose content in the cane) accumulated over a 12-year period. Such an arrangement of data can be expressed as a 3-way array where a farm x attribute x year matrix can be evaluated and interactions considered. Two multivariate techniques, the 3-way mixture method of clustering and the 3-mode principal component analysis, were employed to identify meaningful relationships between farms that performed similarly for both cane yield and CCS. In this context, farm has a spatial component and the aim of this analysis was to determine if systematic patterns in farm performance expressed by cane yield and CCS persisted over time. There was no spatial relationship between cane yield and CCS. However, the analysis revealed that the relationship between farms was remarkably stable from one year to the next for both attributes and there was some spatial aggregation of farm performance in parts of the mill area. This finding is important, since temporally consistent spatial variation may be exploited to improve regional production. Alternatively, the putative causes of the spatial variation may be explored to enhance the understanding of sugarcane production in the wet tropics of Australia.

Stability criteria for unsupervised temporal association networks

A biologically realizable, unsupervised learning rule is described for the online extraction of object features, suitable for solving a range of object recognition tasks. Alterations to the basic learning rule are proposed which allow the rule to better suit the parameters of a given input space. One negative consequence of such modifications is the potential for learning instability. The criteria for such instability are modeled using digital filtering techniques and predicted regions of stability and instability tested. The result is a family of learning rules which can be tailored to the specific environment, improving both convergence times and accuracy over the standard learning rule, while simultaneously insuring learning stability.

Temporal variation in arthropod sampling effectiveness: the case for using the beat sheet method in cotton

Predatory insects and spiders are key elements of integrated pest management (IPM) programmes in agricultural crops such as cotton. Management decisions in IPM programmes should to be based on a reliable and efficient method for counting both predators and pests. Knowledge of the temporal constraints that influence sampling is required because arthropod abundance estimates are likely to vary over a growing season and within a day. Few studies have adequately quantified this effect using the beat sheet, a potentially important sampling method. We compared the commonly used methods of suction and visual sampling to the beat sheet, with reference to an absolute cage clamp method for determining the abundance of various arthropod taxa over 5 weeks. There were significantly more entomophagous arthropods recorded using the beat sheet and cage clamp methods than by using suction or visual sampling, and these differences were more pronounced as the plants grew. In a second trial, relative estimates of entomophagous and phytophagous arthropod abundance were made using beat sheet samples collected over a day. Beat sheet estimates of the abundance of only eight of the 43 taxa examined were found to vary significantly over a day. Beat sheet sampling is recommended in further studies of arthropod abundance in cotton, but researchers and pest management advisors should bear in mind the time of season and time of day effects.

Finding event-oriented patterns in long temporal sequences

A major task of traditional temporal event sequence mining is to find all frequent event patterns from a long temporal sequence. In many real applications, however, events are often grouped into different types, and not all types are of equal importance. In this paper, we consider the problem of efficient mining of temporal event sequences which lead to an instance of a specific type of event. Temporal constraints are used to ensure sensibility of the mining results. We will first generalise and formalise the problem of event-oriented temporal sequence data mining. After discussing some unique issues in this new problem, we give a set of criteria, which are adapted from traditional data mining techniques, to measure the quality of patterns to be discovered. Finally we present an algorithm to discover potentially interesting patterns.