JOURNAL OF ECONOMICS AND BUSINESS最新文献

This study investigates the effects of fake news on stock returns of targeted firms. Fake news is information which is presented as true but which is in fact fabricated and meant to mislead readers. On the basis of disagreement models, we argue that the fact that some investors might not be able to discern whether a piece of news is true or fabricated can cause disagreement among investors on the true value of the firm. This will cause the stock prices of targeted firms to respond to the fake news, even if its informational content is non-existent. Using event study methodology and OLS regressions, we analyse a sample of fake news initiated by outsiders and announced in the US and Europe during the period 2007–2019. We find that negative false news items have negative and significant short-term effects on returns, while positive and neutral news items do not have a clear impact on stock returns. Moreover, we find no significant difference between traditional media outlets and social media. Our results thus provide new evidence on the information-based manipulations of financial markets.

The 2007–2009 global financial crisis which originated in the U.S. subprime mortgage market and then spread to the whole financial system, drew attention to the impact that subprime lending and subprime mortgage securitization can have on the real economy. This paper explores the dynamics of the economy with a subprime mortgage market and the financial innovation in that sector when housing shocks arise. I develop a DSGE model with different types of borrowers and a shadow banking sector that is involved in subprime lending and securitization activities. The model shows that the process of securitizing risky credit amplifies the propagation of housing shocks in the economy through creating a link between the financial sector and the real sector.

Lending activities, especially for small and medium enterprises (SMEs), are increasingly based on financial technologies, facilitated by the availability of advanced machine learning (ML) methods that can accurately predict the financial performance of a company from the available data sources. However, despite their high predictive accuracy, ML models may not give users sufficient interpretation of the results. Therefore, it may not be adequate for informed decision-making, as stated, for example, in the recently proposed artificial intelligence (AI) regulations. To fill the gap, we employed Shapley values in the context of model selection. Thus, we propose a model selection method based on predictive accuracy that can be employed for all types of ML models, those with a probabilistic background, as in the current state-of-the-art. We applied our proposal to a credit-scoring database with more than 100,000 SMEs. The empirical findings indicate that the risk of investing in a specific SME can be predicted and interpreted well using a machine-learning model which is both predictively accurate and explainable.

University endowments differ from other institutions in that donation income and spending rates are of equal importance to asset allocation decisions. Our paper develops an optimal endowment framework with endogenous donations income through a feedback dependence based on investment performance. We investigate both substitution and wealth effects due to incomplete markets and nonlinear donation risk. Looking at empirical data on actual US endowment practices, we validate our donations processes. Large endowments take riskier positions. However, this effect weakens with increasing donation risk, documenting another form of the substitution effect empirically. Spending rates are time varying and determined by relative investment opportunities and the corresponding interaction with endowment size.