Structure and dynamics of financial networks by feature ranking method

Much research has been done on time series of financial market in last two decades using linear and non-linear correlation of the returns of stocks. In this paper, we design a method of network reconstruction for the financial market by using the insights from machine learning tool. To do so, we analyze the time series of financial indices of S&P 500 around some financial crises from 1998 to 2012 by using feature ranking approach where we use the returns of stocks in a certain day to predict the feature ranks of the next day. We use two different feature ranking approaches—Random Forest and Gradient Boosting—to rank the importance of each node for predicting the returns of each other node, which produces the feature ranking matrix. To construct threshold network, we assign a threshold which is equal to mean of the feature ranking matrix. The dynamics of network topology in threshold networks constructed by new approach can identify the financial crises covered by the monitored time series. We observe that the most influential companies during global financial crisis were in the sector of energy and financial services while during European debt crisis, the companies are in the communication services. The Shannon entropy is calculated from the feature ranking which is seen to increase over time before market crash. The rise of entropy implies the influences of stocks to each other are becoming equal, can be used as a precursor of market crash. The technique of feature ranking can be an alternative way to infer more accurate network structure for financial market than existing methods, can be used for the development of the market.

Focus on management and leadership in language centres: The needs and challenges of a language centre director

The organisational status and the main tasks of a university language centre are given different interpretations in different universities. Some language centres find themselves in a challenging situation where the centre’s existence seems to be at stake, whereas others prosper and find positively encouraging opportunities for the future. All this is reflected in the work of language centre directors across Europe. To discuss these challenges and to further develop the cooperation of directors, a Focus Group on Management and Leadership has been established by CercleS. The aim of the focus group is to create a low threshold-network for directors for sharing challenges and questions and by doing this to provide collegial support in management and leadership issues. The focus group conducted a survey in 2015 to learn more about the management and leadership challenges and the support needs of directors. In this paper, we will discuss the results of the focus group survey. The target is to discern common themes and develop recommendations for directors’ future professional cooperation. There will be a particular focus on sharing experiences and ideas for strategy work, staff development and funding, by creating a supportive professional network. Such a network includes a coaching and mentoring system for directors within the CercleS member language centres. The common theme is empowerment: supporting directors in a collegial framework to share experiences, develop their centres further, and increase their own personal well-being at work.

Asynchronous threshold networks with multisorted signals

An asynchronous threshold network is a network of threshold elements (agents) that operate in continuous time. The agents can be in one of two states: active or passive. An active agent generates a signal of certain sort (color) and power. This signal is received by all agents that have inputs of the same color. An agent has a potential that changes under exciting or inhibiting effects of signals; it is active only if its potential exceeds a threshold. Changes in agent activity are events that divide a continuous timeline into discrete time steps. The dependence of the behavior of an autonomous network on the values of its parameters is studied.