Narrow Results

A deterministic model is introduced in terms of conservation principles to describe the (qualitative) price dynamics of a stock market. It is shown how the fundamentalist and chartist patterns of the trader behavior affect the price dynamics. The model can display complex oscillatory behavior with transient erratic oscillations, which resembles the behavior found in actual price dynamics. By means of numerical simulations, it is shown that the model can produce price time-series with statistics similar to that found in real financial data.

Weak internal controls should increase risk perception among various contracting parties, e.g., institutional investors. This study examines whether the penalty firms pay for weak internal controls is associated with ownership decisions made by institutional investors in Taiwan and whether such decisions differ from those made by qualified foreign institutional investors (denoted as QFIIs) and local institutional investors. Empirical results indicate that weak internal controls are negatively associated with changes to institutional investor ownership, particularly for QFIIs. Further evidence shows that this negative association is more pronounced for firms with high divergence of control and cash-flow rights. This suggests that, faced with weak internal controls, institutions passively vote with their feet rather than actively monitor their portfolio firms. We demonstrate several diagnostic tests and show that the results are robust in various specifications.

In distributed systems, traders mediate between clients and service providers. This paper introduces a trading model, which supports multiagent systems (MAS) and goes beyond simple trading in three ways: (a) Service composition — The trader composes complex services of the current service offers. During the composition, it checks the availability of the service offers. (b) Use of group agents — Group agents represent a group of agents with their individual policies and other context information. The trader can use the group agent's information for a pre-selection of service offers. (c) Adaptability — The trading model uses the notion of clients' trust into services and adapts to the clients' preferences and system policies. The trading model is used in a Computer-Supported Cooperative Work (CSCW) application, in which the trader finds adequate communication services for project teams with geographically distributed members.

About 140 countries have announced or are considering net zero targets. To explore the implications of such targets, we apply an integrated earth system–economic model to investigate illustrative net zero emissions scenarios. Given the technologies as characterized in our modeling framework, we find that with net zero targets afforestation in earlier years and biomass energy with carbon capture and storage (BECCS) technology in later years are important negative emissions technologies, allowing continued emissions from hard-to-reduce sectors and sources. With the entire world achieving net zero by 2050 a very rapid scale-up of BECCS is required, increasing mitigation costs through mid-century substantially, compared with a scenario where some countries achieve net zero by 2050 while others continue some emissions in the latter half of the century. The scenarios slightly overshoot 1.5^∘C at mid-century but are at or below 1.5^∘C by 2100 with median climate response. Accounting for climate uncertainty, global achievement of net zero by 2050 essentially guarantees that the 1.5^∘C target will be achieved, compared to having a 50–50 chance in the scenario without net zero. This indicates a tradeoff between policy costs and likelihood of achieving 1.5^∘C.

This paper describes the changes taking place in derivatives markets as a result of the 2007–2009 credit crisis. It discusses the developments of new platforms for trading, the use of central counterparties for clearing, the role of trade repositories, and the requirements for the posting of collateral. It explains the way in which the over-the-counter and exchange-traded derivatives markets are converging and argues that liquidity is becoming as important as capital to banks in their decision making.

Using a positive mathematical programming (PMP) model with improved ‘wide-scope’ calibration, this study demonstrates how allocative efficiency of scarce water could be improved in the Bow- and Oldman River Sub Basins (BRSB and ORSB) of Southern Alberta, where 12 irrigation districts, two cities and three major industrial/commercial users withdraw bulk of the surface water for irrigation, municipal, industrial and commercial needs. Earlier studies ironically neglected the larger ORSB even though it is subject to the same water licensing and regulation policies as the BRSB. The inclusion of nine irrigation districts and non-irrigation users of ORSB enables this model to estimate allocative efficiency gains in a more comprehensive manner than before. Results indicate that ORSB has a relatively less elastic water demand curve primarily due to its more reliance on irrigation and less water saving/supply options. It is also less responsive to allocations with alternative policies as reflected in net returns, land use and cropping pattern changes due to its less elastic water demand.

Interval forecasting tasks are commonly used to test for forecast-overconfidence. Pointing at deficiencies of the methodology, we advance a modified assignment, where subjects provide point predictions and assess the likelihood of return falling within small intervals around their estimates. The difference between the subjective likelihood assessments and the realized hit rates is advanced as an improved forecast-overprecision measure. Over three incentivized studies, 163 of 195 participants overestimate their hit rates, and a closer look at the data illustrates that inaccuracy and excessive certainty act as distinct sources of overprecision. Applications where the adapted task may prove more powerful than standard interval forecasting are discussed.

Real-world economic and business applications are in general demanding information processing tasks, whereby high volumes of incomplete, noisy data and high information costs are often the most remarkable features. Hence the need for advanced information analysis methods, such as those developed in the pattern recognition literature. This chapter shows real-world applications of a number of methods, most of them still quite new to the economic and financial analysts. The focus is not on the full coverage of all relevant information analysis approaches (a challenging task indeed), rather on the understanding of how the underlying data structures and data representations influence the interpretation activity. To this purpose, the presentation is organized by application domains, where we address broadly different areas including as examples real-time trading, financial analysis and long-term economic analysis, as well as the related data and interpretation goals. Correspondingly, several different methods used in these application domains are described and shown at work, including: statistics, grammars, neural networks, and qualitative modeling.