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The perspective of behavioural finance is that anomalies in the cross-section of returns are driven by mispricing that arises from investor irrationality that cannot be easily arbitraged away. In this study, we examine the implications of this for international government bond markets. Using data for 25 countries for the years 1992–2015, we replicate multiple factor strategies that represent four major return drivers: defensive (low-risk), carry, value and momentum. We investigate the relationships between the performance of these strategies and market-wide measures of limits to arbitrage and investor sentiment. We find that the defensive strategy performs best during tight arbitrage conditions whereas severe limits to arbitrage negatively affect momentum profits. ; The National Science Centre of Poland ; https://www.tandfonline.com/loi/rero20 ; am2019 ; Financial Management

The perspective of behavioural finance is that anomalies in the cross-section of returns are driven by mispricing that arises from investor irrationality that cannot be easily arbitraged away. In this study, we examine the implications of this for international government bond markets. Using data for 25 countries for the years 1992–2015, we replicate multiple factor strategies that represent four major return drivers: defensive (low-risk), carry, value and momentum. We investigate the relationships between the performance of these strategies and market-wide measures of limits to arbitrage and investor sentiment. We find that the defensive strategy performs best during tight arbitrage conditions whereas severe limits to arbitrage negatively affect momentum profits

In the interests of the environment, many countries set limits on the use of non-renewable energy sources and promote renewable energy sources through policy and legislation. Consequently, the demand for components for renewable energy systems exhibits an upward trend. For this reason, managers, investors, and banks are interested in knowing whether investing in a business associated with the semiconductor and related device manufacturing sector, especially the photovoltaic (PV) systems manufacturers, is worthy of a penny. Using a sample for the period of 2015–2018, we apply a new approach to panel data, extending existing research using Classification Trees with the k-Nearest Neighbor and Altman model. Our aim is to analyze the financial conditions of enterprises to identify key indicators that distinguish companies producing PV system components (labeled "green, G") from companies that do not manufacture PV components ("red, R"). Our results show that green companies can be distinguished from red companies at classification accuracies of 86% and 90% for CRT and CHAID algorithms in Classification Trees method and 93% for k-Nearest Neighbor method, respectively. Based on the Altman model and the analysis of crucial ratios, we also find that green businesses are characterized by lower financial performance although future ratio values may equal or exceed the values for the red companies if current upward trends are sustained. Therefore, investing in green companies presents a viable alternative. ; Wrocław University of Science and Technology ; https://www.mdpi.com/journal/energies ; am2022 ; Financial Management