The influence of the COVID-19 pandemic on asset-price discovery: Testing the case of Chinese informational asymmetry

Random forest model in tax risk identification of real estate enterprise income tax

The text describes improvements made to the random forest model to enhance its distinctiveness in addressing tax risks within the real estate industry, thereby tackling issues related to tax losses. Firstly, the paper introduces the potential application of the random forest model in identifying tax risks. Subsequently, the experimental analysis focuses on the selection of indicators for tax risk. Finally, the paper develops and utilizes actual taxpayer data to test a risk identification model, confirming its effectiveness. The experimental results indicate that the model's output report includes basic taxpayer information, a summary of tax compliance risks, value-added tax refund situations, directions of suspicious items, and detailed information on common indicators. This paper comprehensively presents detailed taxpayer data, providing an intuitive understanding of tax-related risks. Additionally, the paper reveals the level of enterprise risk registration assessment, risk probability, risk value, and risk assessment ranking. Further analysis shows that enterprise risk points primarily exist in operating income, selling expenses, financial expenses, and total profit. Additionally, the results indicate significant differences between the model's judgment values and declared values, especially in the high-risk probability of total operating income and profit. This implies a significant underreporting issue concerning corporate income tax for real estate enterprises. Therefore, this paper contributes to enhancing the identification of tax risks for real estate enterprises. Using the optimized random forest model makes it possible to accurately assess enterprises' tax compliance risks and identify specific risk points.

Heterogeneously informed trading and the stock market efficiency during the COVID-19 pandemic

This study investigates the U.S. stock market efficiency from the symmetric and asymmetric perspectives during the COVID-19 pandemic. We explore that the pandemic boosts (hurts) the information role of symmetrically (asymmetrically) informed trading. Specifically, we find that the epidemic outbreak and infection scale strengthen (weaken) the stock return reaction to symmetrically (asymmetrically) informed trading. Evidence also indicates that the effect of symmetrically (asymmetrically) informed trading on stocks' permanent price shocks and price informational efficiency is enhanced (impaired) during the pandemic. Moreover, all these effects are consistently more intensive to informed buys.

Did COVID-19 tourism sector supports alleviate investor fear?

The COVID-19 pandemic presented a dynamic black-swan event to which governments implemented support programmes to reduce sectoral probability of default. This research analyses investor response to such assistance, designed to mitigate the effects of the pandemic upon international aviation and tourism. Investor confidence in such support schemes is estimated through short-term abnormal returns. Results indicate significant differential behaviour, with fiscal policy found to be a dominant and largely effective mechanism generating median abnormal returns of 2.17 %. Specific assistance programmes relating to COVID-19 loan facilities, and the provision of pandemic relief packages significantly alleviated short-term investor concerns with median abnormal returns estimated between 2.87 % and 3.89 % respectively. Our empirical results offer investors and policymakers an additional layer of information.

Cryptocurrency liquidity and volatility interrelationships during the COVID-19 pandemic

We examine the interactions between cryptocurrency price volatility and liquidity during the outbreak of the COVID-19 pandemic. Evidence suggests that these developing digital products have played a new role as a potential safe-haven during periods of substantial financial market panic. Results suggest that cryptocurrency market liquidity increased significantly after the WHO identification of a worldwide pandemic. Significant and substantial interactions between cryptocurrency price and liquidity effects are identified. These results add further support to the argument that substantial flows of investment entered cryptocurrency markets in search of an investment safe-haven during this exceptional black-swan event.

Time-Varying Causalities in Prices and Volatilities between the Cross-Listed Stocks in Chinese Mainland and Hong Kong Stock Markets

Due to the heterogeneity of investor structure between the Chinese mainland stock market (A-share market) and the Hong Kong stock market (H-share market) as well as the limitations on arbitrage activities, most cross-listed stocks in the two markets (AH stocks) have the characteristics of “one asset, two prices”, in which AH stocks with the same vote rights and dividend streams are traded at different prices in different markets. Based on the VAR (LA-VAR as well) model and a four-variable system including AH stock indices (AHXA, AHXH), the China Securities Index 300 (CSI 300), and the Hang Seng Index (HSI), this paper applies a new time-varying causality test to examine the causalities in prices and volatilities for two pairings (AXHA-AHXH pairing and CSI 300-HSI pairing) during the sample period spanning from 4 January 2010 to 21 May 2021. The empirical results exhibit statistically significant time-varying causalities of the two pairings. Specifically, at the price level, AHXH has a significant negative causal effect on AHXA from October 2017 to February 2020 except for several months in 2018, while AHXA merely has a negative impact on AHXA during a short period from March 2017 to May 2017. Of note, the direction of causalities in volatilities between AHXA and AHXH reverses. A positive causality is found from AHXA to AHXH at the 5% significance level during the period of April 2014 through May 2021, while no causality is detected in the opposite direction during the whole sample period. Meanwhile, the volatilities of CSI 300 significantly Granger cause those of HSI over the whole sample period, but not vice versa. Implications of our results are discussed.

The influence of the COVID-19 pandemic on the hedging functionality of Chinese financial markets

In this paper, we investigate both constant and time-varying hedge ratios in terms of the effectiveness of CSI300 index futures during the COVID-19 crisis. Using naïve, OLS and EC/ROLS strategies to estimate constant hedge ratios, results indicate that the CSI300 spot index presents decreased effectiveness using the naïve hedging strategy; however, increased effectiveness of OLS and EC hedge ratios are identified. Differential behaviour is identified when considering five newly introduced COVID-19 concept-based stock indices. Time-varying hedge ratios indicate the weakened effectiveness, ranging between 20% and 40% variance reduction. Evidence suggests that the capability of the CSI300 index futures to hedge against the risks of the COVID-19 is impaired, regardless of whether constant or time-varying hedge ratios are used. Such results provide important implications to both local and foreign investors in the Chinese stock market.

How COVID-19 has affected stock market persistence? Evidence from the G7's

This paper examines how COVID-19 pandemic has affected volatility persistence in the G7's stock markets. Based on daily data we divided the whole sample into two sub-samples according to its breakpoints and found that they occurred right after the declaration of COVID-19 pandemic by the World Health Organization - WHO (2020). This approach allows us to assess the main differences between these two distinct phases. Thus, while the first sub-period is relatively calm, the second one, which coincides with the pandemic outbreak, shows higher levels of volatility. Considering this, we rely on GARCH-type models to assess the degree of persistence of volatility and to evaluate how it has evolved across sub-samples. Our results show that the FIGARCH(1,d,1) is the best model to describe the data and that the degree of persistence is very different from the first to the second sub-sample. Thus, while the pre-pandemic period exhibits lower levels of persistence it has greatly increased with the COVID-19 outbreak. In particular, S&P 500 and FTSE/MIB became the most persistent indices in contrast to NIKKEI 225 and FTSE 100, which were amongst the least persistent.

FX markets’ reactions to COVID-19: Are they different?

In this paper, we empirically investigate the impact of the COVID-19 pandemic on FX markets. We find important differences between COVID-19 and previous high-risk episodes: the Global Financial Crisis, the Swiss National Bank's removal of the Swiss franc/euro floor, and Brexit. Contrary to these episodes, the USD did not show any safe haven characteristics during the pandemic. Furthermore, the estimated volatility and non-parametric value-at-risk of three currency portfolios indicate that COVID-19 was not as risky as previous stressful events. We provide evidence that investors could minimize COVID-19 risk by investing in the Canadian dollar and the Japanese yen, and by reducing their exposure to European currencies.

Price-switching spillovers between gold, oil, and stock markets: Evidence from the USA and China during the COVID-19 pandemic

This paper examines price-switching spillovers between the US and Chinese stock, crude oil, and gold futures markets before and during the COVID-19 pandemic. Using a Markov-switching vector autoregressive model, we show that stock markets were mainly influenced by their own shocks, with effects that were sensitive to regime shifts. Connectedness network analysis reveals that gold and stock markets were net contributors (receivers) of spillovers in the low-volatility regime (high-volatility regime), while oil was a major receiver (contributor) of spillovers in the low-volatility regime (high volatility regime). Regimes were mainly low-volatility from January 2019 to February 2020 and high-volatility from March 2020 to May 2020. We conclude that the COVID-19 pandemic intensified spillovers from commodity markets to the US and Chinese stock markets.

Forecasting mid-price movement of Bitcoin futures using machine learning

In the aftermath of the global financial crisis and ongoing COVID-19 pandemic, investors face challenges in understanding price dynamics across assets. This paper explores the performance of the various type of machine learning algorithms (MLAs) to predict mid-price movement for Bitcoin futures prices. We use high-frequency intraday data to evaluate the relative forecasting performances across various time frequencies, ranging between 5 and 60-min. Our findings show that the average classification accuracy for five out of the six MLAs is consistently above the 50% threshold, indicating that MLAs outperform benchmark models such as ARIMA and random walk in forecasting Bitcoin futures prices. This highlights the importance and relevance of MLAs to produce accurate forecasts for bitcoin futures prices during the COVID-19 turmoil.

The impact of the COVID-19 outbreak on Chinese-listed tourism stocks

This research explored the effects of the coronavirus disease (COVID-19) outbreak on stock price movements of China's tourism industry by using an event study method. The results showed that the crisis negatively impacted tourism sector stocks. Further quantile regression analyses supported the non-linear relationship between the government's responses and stock returns. The results present that the resurgence of the virus in Beijing did bring about a short-term negative impact on the tourism industry. The empirical results can be used for future researchers to conduct a comparative study of cultural differences concerning government responses to the COVID-19.

Co-movement of energy prices and stock market return: environmental wavelet nexus of COVID-19 pandemic from the USA, Europe, and China

This work aims to study the time-frequency relationship between the recent COVID-19 pandemic and instabilities in oil price and the stock market, geopolitical risks, and uncertainty in the economic policy in the USA, Europe, and China. The coherence wavelet method and the wavelet-based Granger causality tests are applied to the data (31st December 2019 to 1st August 2020) based on daily COVID-19 observations, oil prices, US-EPU, the US geopolitical risk index, and the US stock price index. The short- and long-term COVID-19 consequences are depicted differently and may initially be viewed as an economic crisis. The results illustrate the reduced industrial productivity, which intensifies with the increase in the pandemic's severeness (i.e., a 10.57% decrease in the productivity index with a 1% increase in the pandemic severeness). Similarly, indices for oil demand, stock market, GDP growth, and electricity demand decrease significantly with an increase in the pandemic severeness index (i.e., a 1% increase in the pandemic severeness results in a 0.9%, 0.67%, 1.12%, and 0.65% decrease, respectively). However, the oil market shows low co-movement with the stock exchange, exchange rate, and gold markets. Therefore, investors and the government are recommended to invest in the oil market to generate revenue during the sanctions period.