Markov chains

Call number: LD2668 .R4 1964 H63

CEO overconfidence and Australian Real Estate Investment Trusts : trading activity and performance

Behavioural finance has been the focal point of discussion and attention in the last three decades, thus having a crucial role in explaining the reasons behind irrational investing. As a result, it has revealed that all stakeholders, including investors, analysts and managers are prone to irrational investment behaviour, regardless of one's experience and the level of education. Overconfidence is one of the most prominent factors that can lead investors to make irrational decisions in the financial markets, including the Australian Real Estate Investment Trust (A-REIT) market. The A-REIT market is one of the most successful REIT markets in the world. As publicly quoted companies, A-REITs may be exposed to the implications of corporate overconfidence and its influence on the investment decision-making process. This research contributes to the behavioural finance literature by investigating the degree of managerial overconfidence amongst A-REITs, as well as providing a comprehensive insight into the behavioural biases in A-REITs, with an emphasis towards the need to avoid illusions that can harm corporate or individual wealth. Whilst a similar study was conducted in the United States REIT (US-REIT) market (Eichholtz & Yönder, 2015), the scope of the research was confined to US-REITs and no other study has reported its effects in any other global REIT markets such as the A-REIT market; this being the primary contribution of the research. Using various information and secondary data, covering 92 CEOs across 46 A-REITs, the findings showed that overconfident CEOs were neither overinvesting in property nor they were selling fewer properties than their non-confident counterparts. The results also indicated that CEOs’ overconfidence did not have a significant impact on A-REITs’ investments, a finding that somewhat contradicts past overconfidence studies. These findings, alongside the Australian management literature and ESG scores, suggest that corporate governance may have played a major role in mitigating corporate overconfidence

An Exploratory Analysis of Father Involvement in Low-Income Families

Using data from the Fragile Families study, this paper explores factors that influence paternal involvement in low-income families. 4873 fathers from the Fragile Families study were classified using CART (Classification and Regression Tree Analysis). CART is a nonparametric technique that allows many different factors to be combined in order to classify homogeneous subgroups within a sample. The CART analysis distinguished between residential and non-residential fathers. In addition, among residential fathers, race emerged as the distinguishing factor. For White men, residential status was the only factor to affect involvement. For African American and Hispanic men however, interactions among several sociodemographic characteristics revealed that both contextual and individual factors affect paternal involvement. Results suggest that an ecological approach is necessary in the investigation of paternal involvement.

Shaping the Breast in Aesthetic and Reconstructive Breast Surgery: An Easy Three-Step Principle. Part II - Breast Reconstruction after Total Mastectomy

This is Part II of four parts describing the three-step principle being applied in reconstructive and aesthetic breast surgery. Part I explains how to analyze a problematic breast by understanding the main anatomical features of a breast and how they interact: the footprint, the conus of the breast, and the skin envelope. This part describes how one can optimize results with breast reconstructions after complete mastectomy. For both primary and secondary reconstructions, the authors explain how to analyze the mastectomized breast and the deformed chest wall, before giving step-by-step guidelines for rebuilding the entire breast with either autologous tissue or implants. The differences in shaping unilateral or bilateral breast reconstructions with autologous tissue are clarified. Regardless of timing or method of reconstruction, it is shown that by breaking down the surgical strategy into three easy (anatomical) steps, the reconstructive surgeon will be able to provide more aesthetically pleasing and reproducible results. Throughout these four parts, the three-step principle will be the red line on which to fall back to define the problem and to propose a solution

Comparison of multiple machine learning algorithms for urban air quality forecasting

Environmental air pollution has become one of the major threats to human lives nowadays in developed and developing countries. Due to its importance, there exist various air pollution forecasting models, however, machine learning models proved one of the most efficient methods for prediction. In this paper, we assessed the ability of machine learning techniques to forecast NO2, SO2, and PM10 in Amman, Jordan. We compared multiple machine learning methods like artificial neural networks, support vector regression, decision tree regression, and extreme gradient boosting. We also investigated the effect of the pollution station and the meteorological station distance on the prediction result as well as explored the most relevant seasonal variables and the most important minimal set of features required for prediction to improve the prediction time. The experiments showed promising results for predicting air pollution in Amman with artificial neural network outperforming the other algorithms and scoring RMSE of 0.949 ppb, 0.451 ppb, and 5.570 µg/m3 for NO2, SO2, and PM10 respectively. Our results indicated that when the meteorological variables were obtained from the same pollution station the results were better. We were also able to reduce the time by reducing the set of variables required for prediction from 11 down to 3 and achieved major time improvement by about 80% for NO2, 92% for SO2, and 90% for PM10. The most important variables required for predicting NO2 were the previous day values of NO2, humidity and wind direction. While for SO2 they were the previous day values of SO2, temperature, and wind direction values of the previous day. Finally, for PM10 they were the previous day values of PM10, humidity, and day of the year