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The primary objective of this research is to provide evidence that there are two distinct layers of investor sentiments that can affect asset valuation models. The first is general market-wide sentiments, while the second is biased approaches toward specific assets.

To achieve the goal, the authors conducted a multi-step analysis of stock returns and constructed complex sentiment indices that reflect the optimism or pessimism of stock market participants. The authors used panel regression with fixed effects and a sample of the US stock market to improve the explanatory power of the three-factor models.

The analysis showed that both market-level and stock-level sentiments have significant contributions, although they are not equal. The impact of stock-level sentiments is more profound than market-level sentiments, suggesting that neglecting the stock-level sentiment proxies in asset valuation models may lead to severe deficiencies.

In contrast to previous studies, the authors propose that investor sentiments should be measured using a multi-level factor approach rather than a single-factor approach. The authors identified two distinct levels of investor sentiment: general market-wide sentiments and individual stock-specific sentiments.

As digital technologies expand access to new forms of legalized gambling, including sports betting and online gaming, it is important to assess the impact of macroeconomic and equity market outcomes on fund flows into gambling. The authors’ findings will be of interest to policymakers and the gambling industry, as various forms of gambling, including day trading, gain broad public acceptance.

The authors examine the impact of macroeconomic forces, business cycles, and financial market wealth on gambling. The authors propose a nonlinear model linking aggregate gambling expenditures to macroeconomic, stock market, and gambling industry variables. The authors estimate the proposed model using nonlinear estimation procedures.

The authors find that price of wagering, incomes, and supply of gambling opportunities are the primary determinants of wagering demand. Aggregate wagering is negatively impacted by realized stock returns and market volatility, but rises during recessions.

To the best of the authors’ knowledge, the questions posed and addressed in this manuscript have not been addressed in prior literature.

To investigate whether additional information of the permanent news flow, especially reporting intensity, can help to increase transparency in housing markets, this study aims to examine the relationship between news coverage or news sentiment and residential real estate prices in Germany at a regional level.

Using methods in the field of natural language processing, in particular word embeddings and dictionary-based sentiment analyses, the authors derive five different sentiment measures from almost 320,000 news articles of two professional German real estate news providers. These sentiment indicators are used as covariates in a first difference fixed effects regression to investigate the relationship between news coverage or news sentiment and residential real estate prices.

The empirical results suggest that the ascertained news-based indicators have a significant positive relationship with residential real estate prices. It appears that the combination of news coverage and news sentiment proves to be a reliable indicator. Furthermore, the extracted sentiment measures lead residential real estate prices up to two quarters. Finally, the explanatory power increases when regressing on prices for condominiums compared with houses, implying that the indicators may rather reflect investor sentiment.

To the best of the authors’ knowledge, this is the first paper to extract both the news coverage and news sentiment from real estate-related news for regional German housing markets. The approach presented in this study to quantify additional qualitative data from texts is replicable and can be applied to many further research areas on real estate topics.

This paper attempts to investigate the effect of corruption on the real and accrual earnings management of target firms in the process of mergers and acquisitions.

The sample includes target firms from the European area that participate in mergers or acquisitions announced during 2010–2020. The preliminary empirical part estimates the level of earnings management during the period two years before the deal's announcement to identify whether the sample follows the manipulation behavior that the literature suggests for target firms. The primary empirical analysis focuses on the impact of corruption on real and accrual-based earnings management proxies, employing regression models and two alternative proxies for corruption. The existing literature points out that the combination of low levels of corruption and an integrated legal system reduces earnings manipulation.

The findings provide strong evidence for systematic downwards accounting manipulation practices, whereas the findings for real earnings management are not significant. The findings of the main empirical part show that corruption is positively associated with accrual-based manipulation and negatively related to real earnings management. In essence, in economies with a high level of transparency, managers adopt the manipulation of operating activities as a less detectable practice of earnings management instead of engaging in accounting procedures.

This study contributes to the literature highlighting the diversification of these firms' manipulation strategies according to the national level's corruption status.