Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation

Technical analysis, also known as "charting", has been a part of financial practice for many decades, yet little academic research has been devoted to a systematic evaluation of this discipline. One of the main obstacles is the highly subjective nature of technical analysis---the presence of geometric shapes in historical price charts is often in the eyes of the beholder. In this paper, we propose a systematic and automatic approach to technical pattern recognition using nonparametric kernel regression, and apply this method to a large number of US stocks from 1962 to 1996 to evaluate the effectiveness of technical analysis. By comparing the unconditional empirical distribution of daily stock returns to the conditional distribution---conditioned on specific technical indicators such as head-and-shoulders or double-bottoms---we find that over the 31-year sample period, several technical indicators do provide incremental information and may have some practical value.

Automation of the longwall mining system

Cost effective, safe, and technologically sound applications of automation technology to underground coal mining were identified. The longwall analysis commenced with a general search for government and industry experience of mining automation technology. A brief industry survey was conducted to identify longwall operational, safety, and design problems. The prime automation candidates resulting from the industry experience and survey were: (1) the shearer operation, (2) shield and conveyor pan line advance, (3) a management information system to allow improved mine logistics support, and (4) component fault isolation and diagnostics to reduce untimely maintenance delays. A system network analysis indicated that a 40% improvement in productivity was feasible if system delays associated with all of the above four areas were removed. A technology assessment and conceptual system design of each of the four automation candidate areas showed that state of the art digital computer, servomechanism, and actuator technologies could be applied to automate the longwall system

Information Technology in The Learning Economy -Challenges for Developing Countries

This paper inquires how the concept of the "learning economy" can be applied to the requirements of developing countries. The main purpose is to develop an analytical framework to better understand how learning and capability formation can foster industrial upgrading. Special emphasis is given to te spread of information technology (IT). We inquire under what conditions developing countries can use this set of generic technologies to improve their learning capabilities. We argue that information technology should not be regarded as a potential substitute for human skills and tacit knowledge. Instead, its main role should be to support the formation and use of tacit knowledge. In the paper we compare two stylised models of the learning economy, the Japanese versus the American model. The Japanese model is explicit in its promotion and exploitation of tacit knowledge, while the American model is driven by a permanent urge to reduce the importance of tacit knowledge and to transform it into information - that is into explicit, 4 well structured and codified knowledge. We show that each of these models has peculiar strengths and weaknesses. Developing countries need to develop their own hybrid forms of institutions that combine the advantages of both models in a way that is appropriate to their idiosyncratic needs and capabilities.information technology; learning; learning economy; knowledge; capabilities; networks; developing countries; economic development; industrial upgrading

Requirements: The Key to Sustainability

Software's critical role in society demands a paradigm shift in the software engineering mind-set. This shift's focus begins in requirements engineering. This article is part of a special issue on the Future of Software Engineering

Opening the Box: Information Technology, Work Practices, and Wages

There is substantial debate about the effects of technological change on wages. We argue that the relationship between technology and wages is context-dependent. To test this proposition, we use data gathered from 303 U.S. bank branches and examine empirically the association between different kinds of information technology (IT), work practices, and wages for the job of customer service representative in bank branches. We also test for interaction effects between IT and work practices. Our results suggest that context sometimes matters: the wage outcomes for IT that automates basic tasks are moderated by high-involvement work practices, while IT that improves the quality of organizational information is related positively to wage outcomes independently of context.

Automation of Government Processes

This rapid literature review examines the impact of, and lessons from, automating government processes in middle-income countries (MICs) and fragile and conflict affected environments (FCAEs). Automating government processes involves data collection and digitisation via information and communication technologies (ICTs), artificial intelligence (AI), and sometimes also machine learning (ML) (Paul, Jolley & Anthony, 2018, p.6). Some common examples of ML in international development include: strengthening early warning systems; situational awareness; supplementing development data; point-of-service diagnostics; market segmentation; and customer and citizen service interfaces (Paul, et al., 2018). Automation can improve the efficiency, quality and coverage of service delivery – e.g. automation of a medicine inventory management system in Pakistan led to control over theft, more transparency, better monitoring and evaluation, and more efficient service delivery in a pilot initiative (DFID, 2018). Automation of aspects of public sector staff recruitment, performance review, management, and monitoring can address nepotistic practices, can lead to efficiency savings on the salary bill, can improve staff and institutional performance, and can increase transparency and trust in institutions, among other impacts. Present ML and AI systems are unable to recognise if the decision made agrees with the context, like human analysis can, and thus ML can lead to suboptimal decisions (Paul, et. al., 2018). So while ML and AI systems can be statistically very good, they can fail for individual cases. This raises important dilemmas in regards to accountability

Cartesian genetic programming for trading: a preliminary investigation

In this paper, a preliminary investigation of Cartesian Genetic Programming (CGP) for algorithmic intraday trading is conducted. CGP is a recent new variant of genetic programming that differs from traditional approaches in a number of ways, including being able to evolve programs with limited size and with multiple outputs. CGP is used to evolve a predictor for intraday price movements, and trading strategies using the evolved predictors are evaluated along three dimensions (return, maximum drawdown and recovery factor) and against four different financial datasets (the Euro/US dollar exchange rate and the Dow Jones Industrial Average during periods from 2006 and 2010). We show that CGP is capable in many instances of evolving programs that, when used as trading strategies, lead to modest positive returns