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Given the maturation of the internet and virtual communities, an important emerging issue in the humanities and social sciences is how to accurately analyze the vast quantity of documents on public and social network websites. Therefore, this chapter integrates political blogs and news articles to develop a public mood dynamic prediction model for the stock market, while referencing the behavioral finance perspective and online political community characteristics. The goal of this chapter is to apply a big data and opinion mining approach to a sentiment analysis for the relationship between political status and economic development in Taiwan. The proposed model is verified using experimental datasets collected from ChinaTimes.com, cnYES.com, Yahoo stock market news, and Google stock market news, covering the period from January 1, 2016 to June 30, 2017. The empirical results indicate the accuracy rate with which the proposed model forecasts stock prices.

Myopic going concern practice refers to the current audit going concern opinion that a firm is rewarded a favorable going concern opinion as long as it has the capability to satisfy its debt obligation in the following year. We show, via a structural agency problem we develop in the paper, that such a practice has a potential economic cost to the firm. We study Lucent Technologies Inc. in detail for its loss in economic value and also measure the magnitude of this impact with 500 companies. We find that Lucent should have lost its going concern status in 2002 as it had to sell off its assets to meet debt obligations and nearly 18% of the 500 firms suffer some degree of economic loss due to the agency problem.

A systemic risk measure is proposed accounting for links and mutual dependencies between financial institutions utilizing tail event information. Financial Risk Meter (FRM) is based on least absolute shrinkage and selection operator quantile regression designed to capture tail event co-movements. The FRM focus lies on understanding active set data characteristics and the presentation of interdependencies in a network topology. Two FRM indices are presented, namely, FRM@Americas and FRM@Europe. The FRM indices detect systemic risk at selected areas and identify risk factors. In practice, FRM is applied to the return time series of selected financial institutions and macroeconomic risk factors. The authors identify companies exhibiting extreme “co-stress” as well as “activators” of stress. With the SRM@EuroArea, the authors extend to the government bond asset class, and to credit default swaps with FRM@iTraxx. FRM is a good predictor for recession probabilities, constituting the FRM-implied recession probabilities. Thereby, FRM indicates tail event behavior in a network of financial risk factors.

Real estate investment trusts (REITs) provide a mechanism through which investors can participate in the real estate market with liquidity and transparency. In this study, we benchmark the performance of 11 residential REITs for the period 2009–2013. The study tracks the performance of residential REITs through the economic crisis period. The data envelopment analysis (DEA) model uses well-performing units (efficiency of 1% or 100%) that are closest to the underperforming unit on the efficiency frontier as a “role model” (peer units) for the underperforming unit. In addition, the DEA model also calculates by how much a nonperforming unit should increase the output level or decrease the inputs level to be on the efficiency frontier (100%) (slack values). Thus, the DEA model identifies the underperforming units and the most feasible path to move to efficiency frontier. The DEA model identifies the peer units that are closely related to these units and calculates the value of the slack variables required to achieve the same efficiency level as their peers.

This chapter derives sentiment indicators (implied volatility and implied skewness) from the option pricing models of Corrado and Su (1996), Bakshi, Kapadia, and Madan (2003), and Zhang, Zhen, Sun, and Zhao (2017), and then integrates these sentiment indicators with artificial intelligence deep neural network (AIDNN) for developing the behavioral finance AIDNN (BFAIDNN) algorithms. We apply the BFAIDNN algorithms to daily derivatives data of Taiwan Futures and Options markets from 2015 to 2017. Our results demonstrate that the trading strategies established by the BFAIDNN algorithms can generate positive rewards.

Purpose: This paper aims to study the impact of adoption eXtensible Business Reporting Language (XBRL) on earnings management.

Design/methodology/approach: This study uses a sample of all firms listed on the Indonesian stock exchange, except for finance and real-estate sectors from 2012 to 2019, with a total of 2,560 firms–years with panel data analysis.

Findings: Four findings in this study are listed as follow. First, the surprising result is that accrual earnings management increase after the adoption of XBRL. Second, after the adoption of XBRL, there was an increase in real earnings management. Third, the results of the study prove that the use of Big 4 auditors will weaken the increase in real earnings management after the adoption of XBRL. Finally, this study shows that after the adoption of XBRL, it turns out that both accrual and real earnings management experienced an increase.

Originality/value: This study contributes to providing an evaluation note to IDX regulators that the goals they want to achieve have not been achieved. This study provides empirical evidence for the debate over whether the adoption of XBRL is beneficial.