Jerry C. Ho

Self-help interventions may offer a scalable adjunct to traditional care, but their effectiveness in relapse prevention is not well-established. Objectives: This review aimed to assess their effectiveness in preventing relapses among individuals with mood disorders. We systematically reviewed the pertinent trial literature in Web of Science, EMBASE, PubMed, PsycINFO, and Cochrane databases until May 2024. Randomized controlled trials that examined the self-help interventions among individuals diagnosed with major depressive disorder (MDD) or bipolar disorder (BD) were included. The random-effects model computed the pooled risk ratios of relapse, with subgroup analyses and meta-regression analyses to explore heterogeneity sources. Fifteen papers and 16 comparisons of randomized trials involving 2735 patients with mood disorders were eligible for this meta-analysis. Adjunct self-help interventions had a small but significant effect on reducing the relapse rates of major depressive disorder (pooled risk ratio: 0.78, 95% confidence interval (CI): 0.66–0.92, P = 0.0032, NNT = 11), and were marginally better in bipolar disorder (pooled risk ratio: 0.62, 95% CI: 0.40–0.97, P = .0344, NNT = 12), as compared to treatment as usual (TAU). No subgroup difference was found based on intervention components, settings, delivery method, or guidance levels. The average dropout rate for self-help interventions (18.9%) did not significantly differ from TAU dropout rates. The examination of treatment adherence was highly variable, precluding definitive conclusions. Self-help interventions demonstrate a modest preventative effect on relapse in mood disorders, despite low to very low certainty. Future research is essential to identify which elements of self-help interventions are most effective.

Purpose: The authors investigate the association of the constructed corporate social responsibility (CSR) measures with the banks’ profitability, social contributions and CSR spending as well as the market reaction to CSR spending.Design/methodology/approach: Using textual analysis of the CSR reports of banks listed on the Chinese market, the authors construct CSR measures in six domains: business, environment, human rights, corporate governance, charity and social capital. Our textual-based CSR measures contain substantial and valuable information beyond what Rankins CSR ratings offer.Findings: The findings suggest that banks with stronger engagements and interests in the business-related CSR domain experience higher profitability, while those that are more committed to the corporate governance and charity-related domains create larger social contributions. Banks tend to incur higher CSR spending when they are more active in corporate governance. Although the stock market reacts positively to CSR expenditures, the reaction is less favorable for banks with CSR expenditures above the industry norm.Practical implications: This study offers insights to policymakers of the regulatory bodies and the banks in China. To enhance the financial safety and soundness of the banking system, the regulatory bodies should encourage banks to strategically allocate corporate resources to achieve higher CSR ratings and engage more business-related CSR activities. To create larger social values, bank management should invest more in philanthropic CSR initiatives such as corporate governance and charity activities. To pursue higher corporate profits, they should engage more in self-centered business-related CSR activities. However, according to the reaction of the market, they should not over-invest in CSR activities.Originality/value: While the use of textual analysis to evaluate CSR disclosure has recently emerged in the literature, few studies focus on banks in China. Using the term frequency–inverse data frequency (TF-IDF) method, the authors constructed a score for each of the six CSR domains: business (BUS), environment (ENV), human rights (HR), corporate governance (GOV), charity (CHY) and social capital (SCAP). To the best of our knowledge, no studies have adopted the textual approach to evaluate social reporting quality and CSR activities in the context of the banking industry in China.

We test the predictive ability of investor sentiment on the return and volatility at the aggregate market level in the U.S., four largest European countries and

We investigate the role of the degree of fatalities in the impact of an aviation disaster on the stock prices of the crash airline and its rival airlines. Our results show that aviation disasters are followed by deeper negative rates of return for the crash airlines as the degree of fatalities increase. The rival airlines may benefit from the disasters if the number of deaths is single-digit due to the switching effect. However, the rival airlines may also suffer negative rates of return on their stocks after a larger scale aviation disaster that results in two-digit or three-digit fatalities because of the spillover effect. Overall, aviation disaster cause higher market value losses for the crash airlines than for the non-crash airlines.

This paper investigates the role of investor sentiment as a risk factor. To this end, we first estimate the sensitivity of individual stocks to the shift in the market-wide sentiment on a monthly rolling basis, and then construct a factor mimicking portfolio of sentiment, SMN (sensitive minus non-sensitive). The sentiment factor, representing the payoff on the long-short spreads between stocks with high and low sentiment betas, has an average return of 1% per month. We next test the pricing ability of the SMN factor for individual stocks in the U.S. markets, and present evidence that this sentiment factor has significant explanatory power in the cross-section of individual stock returns. The evidence indicates that stocks with high exposures to the sentiment risk earn high expected returns, suggesting that investors demand positive compensation for bearing this risk. Furthermore, we investigate whether sentiment-augmented models help explain asset pricing anomalies. We adopt the two-pass regression framework of Avramov and Chordia (2006) in which we obtain the risk-adjusted stock returns in the first-pass regression using conventional risk factors and the sentiment factor. In the second-pass, we run the regression of the risk-adjusted returns on the variables of asset-pricing anomalies. Consistent with the theory that sentiment creates a risk in addition to fundamental risk, we find that asset pricing models augmented by this sentiment factor always explain the size effect, and that conditional pricing models often capture the size, value and momentum effects. Our results are robust to different measures of investor sentiment.

This paper assesses whether incorporating investor sentiment as conditioning information in asset pricing models helps capture the impacts of the size, value, liquidity and momentum effects on risk-adjusted returns of individual stocks. We use survey sentiment measures and a composite index as proxies for investor sentiment. In our conditional framework, the size effect becomes less important in the conditional CAPM and is no longer significant in all the other models examined. Furthermore, the conditional models often capture the value, liquidity and momentum effects.

This paper examines the change in returns, trading activities, and liquidity of 43 firms that moved from the Over-The-Counter market to Taiwan Stock Exchange du