ESG Scores and Bank Performance During COVID-19

This chapter examines the impact of ESG scores on bank stock returns as a response to the COVID-19 pandemic. The authors use a sample of 73 publicly listed banks from 15 developed European countries. They perform the analysis using two different periods that cover the pandemic: the first major wave period of COVID-19 (February-April 2020) and an extended period (February 2020-April 2021). The findings reveal the negative influence of the COVID-19 pandemic on bank stock returns during the first wave of the pandemic. They further find that, during the first wave, stock returns of banks with higher ESG scores were more resilient to the pandemic. However, when they use the extended time period (from February 2020-April 2021), the influence of both COVID-19 and ESG scores becomes insignificant. The chapter's findings have important policy implications during unprecedented crisis times such as COVID-19.

Detecting bearish and bullish markets in financial time series using hierarchical hidden Markov models

Financial markets exhibit alternating periods of rising and falling prices. Stock traders seeking to make profitable investment decisions have to account for those trends, where the goal is to accurately predict switches from bullish to bearish markets and vice versa. Popular tools for modelling financial time series are hidden Markov models, where a latent state process is used to explicitly model switches among different market regimes. In their basic form, however, hidden Markov models are not capable of capturing both short- and long-term trends, which can lead to a misinterpretation of short-term price fluctuations as changes in the long-term trend. In this article, we demonstrate how hierarchical hidden Markov models can be used to draw a comprehensive picture of market behaviour, which can contribute to the development of more sophisticated trading strategies. The feasibility of the suggested approach is illustrated in two real-data applications, where we model data from the Deutscher Aktienindex and the Deutsche Bank stock. The proposed methodology is implemented in the R package fHMM, which is available on CRAN.

Performance Evaluation of Selected Banking Stocks Listed on Bombay Stock Exchange During Pre & Post Covid-19 Crisis

Investing in the stock market has always been regarded as risky. Market sentiment is a factor that influences stock prices. The purpose of this study is to assess the performance of selected banking stocks based on risk and excess return generated by them during the study period. The study also determines the effect of certain financial variables on sample banking stocks during the time crisis of Covid’19. Economic variables such as the BSE Sensex, rate of exchange, variation in FII (Foreign Institutional Investors), and coupon rate of Government Sector (G-Sec) were analysed in conjunction with the analysis of banking stocks. The regression and correlation tests are used to determine the significance of variables using SPSS. Following the BSE’s performance provides insight into the future modifications throughout the price levels of bank shares. Following a sharp decline in the market, private sector bank stock prices are correct, but not public sector bank stock prices. Throughout the first part of the research, there is a direct relationship between the BSE, Sensex, and the selected stocks, but only a weak correlation with FII, G-Sec coupon rate, and the exchange rate. Along the second part of the research, the relationship between stock prices and economic variables varies widely between banks.

Research on the Stock Correlation Network Entropy and Its Influencing Factors in the Chinese Interbank Market

The stability of the interbank market is an essential guarantee for the sustainable development of society. Network entropy theory provides a critical research paradigm for the study of the stability of the interbank market. Considering that few scholars have conducted in-depth analysis on the influencing factors of the network entropy’s evolution process, this paper constructs the stock correlation network entropy model in the Chinese interbank market based on the correlation of stock price fluctuation. It focuses on analyzing the influencing factors in the dynamic evolution process of the stock correlation network entropy. In the light of empirical research, we can obtain the following results. First, the interbank market network’s aggregation coefficient, the interbank market network’s centrality, and the bank stock’s return rate play a positive role in the dynamic evolution of stock correlation network entropy in the Chinese interbank market. Second, the bank stock return’s volatility is negatively correlated with its network entropy. To maintain the stability of the financial market, the supervision department can monitor the stability of the interbank market by constructing the stock correlation network entropy.

PENGARUH PENERAPAN MANAJEMEN RISIKO TERHADAP KINERJA KEUANGAN (STUDI PADA BANK UMUM KELOMPOK USAHA)

Risk management is part of a comprehensive business strategy with the aim of contributing to protecting and increasing shareholder value. An increase in stock value indicates an increase in stock returns obtained by investors. This study examines the effect of risk management implementation on bank stock returns as seen from the bank book group, namely bank book group 1, bank book group 2, bank book group 3 and bank book group 4. The application of risk management is seen from credit risk, liquidity risk, risk. operational and market risk. The research was conducted on all commercial banks that went public and were active from 2015 to 2019, as many as 44 banks. The results of the study state that: overall (for all bank book groups) the application of risk management, namely credit risk, liquidity risk, operational risk and market risk does not affect stock returns, except for bank book group 1, credit risk and operational risk and market risk for book group 4 has a significant effect on stock returns.