The Relation Between Trading Volume Concentration and Stock Returns

The Taiwan Stock Exchange discloses data on daily trading volume across brokerage firms for each listed stock. Market practitioners suggest that the concentration of trading volume contains information on the trading behaviors of big players. We use the Gini Coefficient to measure the degree of concentration, upon which a trading strategy is proposed. We conduct an event study to examine whether such a strategy will yield abnormal returns. Our sample contains 375 listed companies with events identified during the sample period from February 2020 to August 2020. The empirical results show that the trading signal based on the Gini coefficient is informative and that most of the average abnormal returns after the event date are significantly positive with the cumulative average abnormal returns increasing almost monotonically up to the end day of the event window. Consistent with prior studies in which different measures of concentration are utilized, our findings provide additional evidence that the Gini Coefficient could help investors to develop profitable stock selection and market timing strategies.

DPP: Deep predictor for price movement from candlestick charts

Forecasting the stock market prices is complicated and challenging since the price movement is affected by many factors such as releasing market news about earnings and profits, international and domestic economic situation, political events, monetary policy, major abrupt affairs, etc. In this work, a novel framework: deep predictor for price movement (DPP) using candlestick charts in the stock historical data is proposed. This framework comprises three steps: 1. decomposing a given candlestick chart into sub-charts; 2. using CNN-autoencoder to acquire the best representation of sub-charts; 3. applying RNN to predict the price movements from a collection of sub-chart representations. An extensive study is operated to assess the performance of the DPP based models using the trading data of Taiwan Stock Exchange Capitalization Weighted Stock Index and a stock market index, Nikkei 225, for the Tokyo Stock Exchange. Three baseline models based on IEM, Prophet, and LSTM approaches are compared with the DPP based models.

Are the shareholding and trading behaviors of diverse investors affected by the relaxation of day trading?

The day trading in Taiwanese stock market expands considerably at the beginning of 2016, which increases the transactions of stocks consequently and sparks our interest in exploring the issue of day trading. In this study, we use the data of Taiwan Stock Exchange listed firms to investigate whether the day trading volume over total trading volume (hereinafter referred to as the day trading ratio) and the turnover ratio enhanced by the increase of day trading volume would affect the shareholding and trading behaviors of diverse institutional and individual investors. Unquestionably, we bring out several impressive findings. First, foreign institutional investors would not prefer holding or trading the stocks with high day trading ratios, whereas individual investors would prefer holding these kinds of stocks. We infer that this finding might result from the fundamental and the speculative concerns of these various investors. Second, domestic institutional investors and security dealers would prefer trading the stocks with high turnover ratios, but foreign institutional investors still lack of interest in trading these stocks, implying that the investment strategies would be dissimilar among various institutional investors. Since foreign institutional investors are regarded as the successful institutional investors in Taiwan, we argue that our revealed results may help market participants trace the behaviors of diverse investors, especially the foreign institutional investors, after day trading relaxation in Taiwan.

Using Deep Learning Algorithms for CPAs’ Going Concern Prediction

Certified public accounts’ (CPAs) audit opinions of going concern are the important basis for evaluating whether enterprises can achieve normal operations and sustainable development. This study aims to construct going concern prediction models to help CPAs and auditors to make more effective/correct judgments on going concern opinion decisions by deep learning algorithms, and using the following methods: deep neural networks (DNN), recurrent neural network (RNN), and classification and regression tree (CART). The samples of this study are companies listed on the Taiwan Stock Exchange and the Taipei Exchange, a total of 352 companies, including 88 companies with going concern doubt and 264 normal companies (with no going concern doubt). The data from 2002 to 2019 are taken from the Taiwan Economic Journal (TEJ) Database. According to the empirical results, with the important variables selected by CART and modeling by RNN, the CART-RNN model has the highest going concern prediction accuracy (the accuracy of the test dataset is 95.28%, and the average accuracy is 93.92%).

Mandatory CSR Disclosure, CSR Assurance, and the Cost of Debt Capital: Evidence from Taiwan

This study examined the impact of mandatory corporate social responsibility (CSR) disclosure, CSR assurance and the reputation of assurance providers (accounting firms) on the cost of debt capital. Our difference-in-difference research design in conjunction with univariate and multiple regression analysis was assessed using a large sample of firms listed on the Taiwan Stock Exchange and the Taipei Exchange. Our empirical results revealed that mandatory CSR assurance on CSR disclosure provided by accounting firms tended to reduce the cost of debt capital. However, contrary to expectations, the reputation of the accounting firm (Big 4 accounting firms vs. non-Big 4 accounting firms) tasked with providing CSR assurance did not have a significant effect on the cost of debt capital. These results have implications for firms seeking an assurance provider as well as for Big 4 accounting firms. These results also provide specific evidence relevant to government agencies seeking to update policies and extend the scope of mandatory CSR assurance to other environmentally sensitive industries.

The Role of Financial Flexibility on Enterprise Sustainable Development during the COVID-19 Crisis—A Consideration of Tangible Assets

Financial flexibility refers to the ability of a firm to respond effectively to unanticipated shocks to its cash flows or its investment opportunities and is a key factor in the sustainable development of enterprise. This article explores the effect of financial flexibility on the enterprise performance of Taiwan’s manufacturing industry during the COVID-19 pandemic. Data for the first and second quarter of 2020 from companies listed on the Taiwan Stock Exchange were collected and analyzed. The results indicate that for listed manufacturing companies on the Taiwan Stock Exchange, financial flexibility has a significant and positive effect on enterprise performance (return on assets, ROA), particularly in the asset-heavy manufacturing industry. However, financial flexibility has no significant effect on the enterprise performance of the asset-light manufacturing industry or the semiconductor industry. This study also show evidence that Taiwan’s asset-light manufacturing industry suffered the most from the COVID-19 crisis, which is not conducive to its sustainable development. In summary, the results show that Taiwan’s manufacturing industry has poor financial flexibility and one of the worst ROA during the COVID-19 pandemic. Based on the results of this research, effective suggestions to rationally retain financial flexibility and pay more attention to liquidity risk management for sustainable development are proposed for Taiwan’s manufacturing industry.

Stock Characteristics and Individual Herding

Individuals who invest stocks in a market with excess volatility generally end up selling or holding the stocks at losses. The purpose of this study was to examine individual herding as it related to three comprehensible stock characteristics, market capitalization, price-to-book ratio, and industry affiliation. The target population was the individual investors who traded in Taiwan Stock Exchange in 2016. Data were collected through subscription. Based on Lakonishok, Shleifer, and Vishny's measure, individual herding was significant. The three stock characteristics were separately and as a whole related to individual herding. The findings confirmed sell-herding higher than buy-herding, more serious herding in high market capitalization stocks, and broad industry herding. The findings also extended knowledge to comparable herding levels with 8 to 10 years ago, more linearity between log market capitalization and log odds of herd occurrence, and less herding in P/B ratio stocks with other independent variables controlled.

An Effective Enterprise Earnings Management Detection Model for Capital Market Development

This study focuses on accrual-based earnings management. The purpose of this study is to establish an innovative and high-accuracy model for detecting earnings management using hybrid machine learning methods integrating stepwise regression, elastic net, logistic regression (Logit regression), and decision tree C5.0. Samples of this study are the electronic companies listed on the Taiwan Stock Exchange, and data are derived from the Taiwan Economic Journal (TEJ) for a period of ten years from 2008 to 2017. Results show that the earnings management detection model, as established by elastic net and C5.0, provides the best classification performance, and its average accuracy reaches 97.32%.