Dynamics of investor spanning trees around dot-com bubble

Inference of monopartite networks from bipartite systems with different link types

Many of the real-world data sets can be portrayed as bipartite networks. Since connections between nodes of the same type are lacking, they need to be inferred. The standard way to do this is by converting the bipartite networks to their monopartite projection. However, this simple approach renders an incomplete representation of all the information in the original network. To this end, we propose a new statistical method to identify the most critical links in the bipartite network projection. Our method takes into account the heterogeneity of node connections. Moreover, it can handle situations where links of different types are present. We compare our method against the state-of-the-art and illustrate the findings with synthetic data and empirical examples of investor and political data.

Research on multiple bubbles in China's multi-level stock market

Financial bubbles have always been a topic of long-term concern for economists. Understanding bubble phenomenon and dating the period of bubbles in real time can provide an early warning diagnosis for financial bubbles and help regulatory authorities to control it and maintain market order. The generalized sup ADF (GSADF) and backward sup ADF (BSADF) tests with flexible window width can effectively detect and date periodically collapsing bubbles in real time. Based on the financial present value model, this paper applies right-tail recursive ADF test to test multiple bubbles in China's multi-level stock market. Unlike the other researches in China, the ratios of the real stock prices' natural logarithm to the real dividends' natural logarithm are used for our testing instead of stock price index. Empirical results show that there are 8 bubbles in the Main-Board Market, 6 bubbles in the Small and Medium Enterprises Board (SMEs), and 4 bubbles in the Growth Enterprise Market (GEM). These bubbles are liquidity-driven and presuppose a loose credit cycle, with the exception of bubbles in 2014-2015. The frequent emergence of bubbles in a short time indicates that China's stock market is still emerging market. In addition, frequent fluctuations imply there is a serious "herd effect" and a lack of monitoring mechanism for bubble risk. This study not only enrich the real-time dynamic research on periodical bubbles of China's stock market, but also provide an empirical reference for investors' investment choices, financial decisions of listed companies and warning mechanism of regulatory authorities.

Perception and experiences of sexual harassment among women working in hospitality workplaces of Bahir Dar city, Northwest Ethiopia: a qualitative study

BACKGROUND

Workplace sexual harassment is a public health problem that depends on gender, context, and perceived ideology. Although studies have documented the prevalence and consequences of workplace sexual harassment worldwide, victims' perceptions and experiences are still poorly understood in low and middle-income countries, particularly Ethiopia. Female workers in the hospitality industry, including hotels, bars, restaurants, fast-food restaurants, and cafeterias, are particularly affected. Hence, this study aimed to explore sexual harassment perceptions and experiences among women working in these workplaces.

METHODS

An exploratory qualitative study was conducted from 1 January to 30 August 2019. Data were collected from female employees and key informants from several hospitality workplaces in Bahir Dar City. Data were collected through focus group discussions, in-depth interviews, and key-informant interviews. Women who experienced sexual harassment were selected using the snowball method, and key informants were recruited purposefully. Six focus group discussions, ten in-depth interviews, and thirteen key informant interviews were conducted. Data were analysed using the ATLAS ti version 8.4.24.

RESULTS

In this study, most participants perceived that sexual harassment is pressuring, threatening, touching, abducting sexual advances, and experiencing verbal, physical, and non-verbal types. Similarly, the perceived risk factors were related to the organisations, the customers, and the victims, with the consequences being work-related, health-related, financial-related, and family-related.

CONCLUSIONS

Workplace sexual harassment in hospitality workplaces is poorly understood, but many women experience it. A variety of factors also caused it, and it influenced both organisations and people. Public awareness programs, pre-service preparation, in-service training, prevention, and psychosocial support are needed. Similarly, policies and strategies for the organisations should be developed and implemented.

Structure of Investor Networks and Financial Crises

In this paper, we ask whether the structure of investor networks, estimated using shareholder registration data, is abnormal during a financial crises. We answer this question by analyzing the structure of investor networks through several most prominent global network features. The networks are estimated from data on marketplace transactions of all publicly traded securities executed in the Helsinki Stock Exchange by Finnish stock shareholders between 1995 and 2016. We observe that most of the feature distributions were abnormal during the 2008–2009 financial crisis, with statistical significance. This paper provides evidence that the financial crisis was associated with a structural change in investors’ trade time synchronization. This indicates that the way how investors use their private information channels changes depending on the market conditions.

Multilayer Aggregation with Statistical Validation: Application to Investor Networks

Multilayer networks are attracting growing attention in many fields, including finance. In this paper, we develop a new tractable procedure for multilayer aggregation based on statistical validation, which we apply to investor networks. Moreover, we propose two other improvements to their analysis: transaction bootstrapping and investor categorization. The aggregation procedure can be used to integrate security-wise and time-wise information about investor trading networks, but it is not limited to finance. In fact, it can be used for different applications, such as gene, transportation, and social networks, were they inferred or observable. Additionally, in the investor network inference, we use transaction bootstrapping for better statistical validation. Investor categorization allows for constant size networks and having more observations for each node, which is important in the inference especially for less liquid securities. Furthermore, we observe that the window size used for averaging has a substantial effect on the number of inferred relationships. We apply this procedure by analyzing a unique data set of Finnish shareholders during the period 2004-2009. We find that households in the capital have high centrality in investor networks, which, under the theory of information channels in investor networks suggests that they are well-informed investors.