Machine Learning and Expert Judgement: Analyzing Emerging Topics in Accounting and Finance Research in the Asia–Pacific

In this paper, we focus on the question to what extent machine learning (ML) tools can be used to support systematic literature reviews. We apply a ML approach for topic detection to analyze emerging topics in the literature—our context is accounting and finance research in the Asia–Pacific region. To evaluate the robustness of the approach, we compare findings from the automated ML approach with the results from a manual analysis of the literature. The automated approach uses a keyword algorithm detection mechanism whereby the manual analysis uses common techniques for qualitative data analysis, that is, triangulation between researchers (expert judgement). From our paper, we conclude that both methods have strengths and weaknesses. The automated analysis works well for large corpora of text and provides a very standardized and non-biased way of analyzing the literature. However, the human researcher is potentially better equipped to evaluate current issues and future trends in the literature. Overall, the best results might be achieved when a variety of tools are used together.     

CEO internal experience and voluntary disclosure quality: Evidence from management forecasts

Internally‐promoted CEOs should have a deep understanding of their firm's products, supply chain, operations, business climate, corporate culture, and how to navigate among employees to get the information they need. Thus, we argue that internally‐promoted CEOs are likely to produce higher quality disclosure than outsider CEOs. Using a sample of US firms from the S&P1500 index from 2001 to 2011, we hand‐collect whether a CEO is hired from inside the firm and, if so, the number of years they worked at the firm before becoming CEO. We then examine whether managers with more internal experience issue higher quality disclosures and offer three main findings. First, CEOs with more internal experience are more likely to issue voluntary earnings forecasts than those managers with less internal experience as well as those managers hired from outside the firm. Second, CEOs with more internal experience issue more accurate earnings forecasts than those managers with less internal experience as well as those managers hired from outside the firm. Finally, investors react more strongly to forecasts issued by insider CEOs than to those issued by outsider CEOs. In additional analysis, we find no evidence that these results extend to mandatory reporting quality (i.e., accruals quality, restatements, or internal control weaknesses), perhaps because mandatory disclosure is subjected to heavy oversight by the board of directors, auditors, and regulators. Overall, our findings suggest that when managers have work experience with the firm prior to becoming the CEO, the firm's voluntary disclosure is of higher quality.     

Interventions in Markets with Adverse Selection: Implications for Discount Window Stigma

I study the implications for central bank discount window stigma of a workhorse model of adverse selection in financial markets. In the model, firms (banks) need to borrow to finance a productive project. There is limited liability and firms have private information about their ability to repay their debts, which gives rise to the possibility of adverse selection. The central bank can ameliorate the impact of adverse selection by lending to firms. Discount window borrowing is observable and it may be taken as a signal of firms' credit worthiness. Under some conditions, firms borrowing from the discount window may pay higher interest rates to borrow in the market, a phenomenon often associated with the presence of stigma. I discuss these and other outcomes in detail and what they suggest about the relevance of stigma as an empirical phenomenon.     

Lend me your wallets: the effect of charismatic leadership on external support for an organization

We argue that charismatic leadership can influence external support for the organization, particularly in making the company more attractive to outside investors. Two studies were conducted to test this general hypothesis. First, an archival study demonstrated that the stock of companies headed by charismatic leaders appreciated more than the stock of comparable companies, even after differences in corporate performance were controlled. It was also found that the effect of charismatic leadership was heightened under more difficult economic conditions. Second, an experiment was conducted in which the salience of charismatic leadership was manipulated, along with information about the prospects for an organization's turnaround. Results showed that appeals from a charismatic leader led to increased investment in the firm, and the leader's influence was greater when the prospects for an organizational turnaround were more difficult. It was also found that an endowment of stock enhanced the influence of charismatic appeals and that charismatic leadership may have affected the general risk propensities of followers. These findings were interpreted in terms of an external perspective on leadership, illustrating how leaders can manage the firm's economic and social environment. Copyright © 2004 John Wiley & Sons, Ltd.     

Predicting Household Poverty: A Methodological Note with a Kenyan Example

Household surveys provide data that is used for identifying and measuring the poverty status of households and individuals. However, carrying out such surveys is expensive, especially in poor developing countries. Thus it is important to make maximum use of the available survey data in developing countries, especially in sub‐Saharan Africa, where such data are expensive to collect and analyse. This paper develops a simple method for using poverty indices derived from survey data for a given year, to predict poverty rates for subsequent periods without having to conduct a new household survey. We illustrate the workings of the method with data from Kenyan household surveys for 1994 and 1997.     

The Local Whittle Estimator of Long-Memory Stochastic Volatility

We propose a new semiparametric estimator of the degree of persistencein volatility for long memory stochastic volatility (LMSV) models.The estimator uses the periodogram of the log squared returnsin a local Whittle criterion which explicitly accounts for thenoise term in the LMSV model. Finite-sample and asymptotic standarderrors for the estimator are provided. An extensive simulationstudy reveals that the local Whittle estimator is much lessbiased and that the finite-sample standard errors yield moreaccurate confidence intervals than the widely-used GPH estimator.The estimator is also found to be robust against possible leverageeffects. In an empirical analysis of the daily Deutsche Mark/USDollar exchange rate, the new estimator indicates stronger persistencein volatility than the GPH estimator, provided that a largenumber of frequencies is used.