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The ‘managed stock’ market in Taiwan is neglected by the authorities and general investors. In this paper, we explore the link between financial trait and stock price changes in this special market.

Overall, we analyze and discuss managerial implications for institutional investors, general investors and the authorities as well.

Historical stock prices have long been used to evaluate a stock’s future returns as well as the risks associated with those returns. Similarly, historical dividends have been used to evaluate the intrinsic value of a stock using, among other methods, a dividend discount model. In this chapter, we propose an alternate use of the dividend discount model to enable an investor to assess the risks associated with a particular stock based on its dividend history. In traditional applications of the dividend discount model for stock valuation, the value of a stock is the net present value of its future cash dividends. We propose an alternative approach in which we calculate the internal rate of return for a stream of future cash dividends assuming the current stock price. We use a bootstrapping approach to generate a stream of future cash dividends, and use a Monte Carlo simulation approach to run multiple trials of the model. The probability distribution of the internal rates of return obtained from the simulation model provides an investor with an expected percentage return and the standard deviation of the return for the stock. This allows an investor to not only compare the expected internal rates of return for a group of stocks but to also evaluate the associated risks. We illustrate this internal rate of return approach using stocks that make up the Dow Jones Industrial Average.

This study aims to measure the degree of volatility comovement between stock market liquidity and informational efficiency across Asia, Europe, North-South America, Africa, and the Pacific Ocean over three decades. In particular, the authors analyze the extent of the time-varying nexus between different aspects of stock market liquidity and multifractal scaling properties of the stock return series across various regions and diversified market conditions. This study further investigates several factors altering the degree of dynamic conditional correlations (DCCs) between the efficiency and liquidity of the domestic stock markets.

The study measures five aspects of stock market liquidity – tightness, depth, breadth, immediacy, and adjusted immediacy. The authors evaluate the multifractal scaling properties of the stock return series to measure the level of stock market efficiency across the regions and diversified market conditions. The study uses the dynamic conditional correlation-multivariate generalized autoregressive conditional heteroscedasticity framework to quantify the degree of volatility comovement between liquidity and efficiency over the period.

The study finds the presence of stronger volatility comovement between inefficiency and illiquidity due to the price impact characteristics of the stock markets irrespective of different regions and diversified market conditions. The extent of time-variation increased following the shock periods, indicating the significant role of the financial crisis in increasing the volatility comovement between inefficiency and illiquidity. The highest degree of time-varying correlation is observed in the developed stock markets of Northwestern and Northern Europe compared to the regional and emerging counterparts. On the other hand, weak DCCs are observed in the emerging stock markets of Europe.

The output of the present study assists investors in identifying diversification opportunities across the regions. Additionally, the study has significant implications for market regulators, aiding in predicting future troughs and peaks. The prediction, in turn, helps formulate capital market development plans during dynamic economic situations.

Capital markets are the backbone of an economy. COVID-19 has created an unacceptable and unpredictable economic environment worldwide, resulting in a significant setback for securities exchanges. India also experienced two waves of this pandemic, which led to a significant downturn in the capital market.

Researchers have endeavored to study the impact of the first and second waves of COVID-19 on the performance of various sectoral stocks in India. The performance of selected sectoral indices of the Bombay Stock Exchange was compared with the market performance of the S&P BSE 100. An event study was conducted to analyze the normal return, abnormal return (A.R.), and t-statistics of A.R. for various sectoral stocks. In addition, the abnormal returns of sectoral stocks between the first and second waves of COVID-19 in India were compared

The results of the tests showed heterogeneous A.R. between different sectors in both the first and second waves of COVID-19 in India. Positive investor outlook and government financial support programs for various sectors helped them recover from the second wave of COVID-19.

The study analyzed the impact of the peak of the first and second waves of COVID-19 on selected sectoral indices. There may be several reasons for the performance of this particular stock index. However, we have tried to analyze the best possible reasons for this turbulence in the performance of stocks of selected sectoral indices. The study can be further analyzed to examine the long-term impact of such a pandemic on other sectors.

The study is based on the panic behavior of investors during such a pandemic. No one was prepared for this and expected this pandemic to last this long. This pandemic has taught so many lessons to everyone involved. Investors need to be prepared and cautious for such unforeseen disasters before making any investment decision. They need to analyze which industry can survive under such circumstances, and then they should invest there. Industries and enterprises must adapt and improve by honestly looking at their weaknesses and trying to meet investors' expectations.

Given the systemic nature of climate change, there are many interdependencies between its primary components and feedback loops, emphasising the need to simultaneously consider the stock market implications of physical and transitional climate-related risks. More importantly, carbon emissions are expected to be reduced through various transition pathways. However, transitional climate risks have been validated as capable of predicting stock market behaviour, hence the motivation for the role of technology shocks.

We use a GARCH-MIDAS model to examine the relationship between climate change and stock return volatility since it enables data analysis at various frequencies within the same framework. We employ a novel dataset to track technology shocks, and the study spans decades of data from 1880 to 2018.

We find that the relationship between climate change and stock return volatility is episodic and varies with different degrees of intensity of high-temperature anomalies and technology shocks. Our results suggest that policy actions should include investing in climate technologies to reduce greenhouse gas emissions and encouraging investment in eco-friendly assets.

There has been little or no consideration for the probable complementary effects of physical and transition climate-related risks on stock markets. Hence, the novelty in the context of this study is the hypothesis that transitional risks, if explored from the point of view of technological innovations, can moderate the stock market’s vulnerability to physical climate risks.

Stock markets are essential for households for wealth creation and for firms for raising financial resources for capacity expansion and growth. Market participants, therefore, need an understanding of stock price movements. Stock market indices and individual stock prices reflect the macroeconomic environment and are subject to external and internal shocks. It is important to disentangle the impact of macroeconomic shocks, market uncertainty and speculative elements and examine them separately for prediction. To aid households, firms and policymakers, the paper proposes a granular decomposition-based prediction framework for different time periods in India, characterized by different market states with varying degrees of uncertainty.

Ensemble empirical mode decomposition (EEMD) and fuzzy-C-means (FCM) clustering algorithms are used to decompose stock prices into short, medium and long-run components. Multiverse optimization (MVO) is used to combine extreme gradient boosting regression (XGBR), Facebook Prophet and support vector regression (SVR) for forecasting. Application of explainable artificial intelligence (XAI) helps identify feature contributions.

We find that historic volatility, expected market uncertainty, oscillators and macroeconomic variables explain different components of stock prices and their impact varies with the industry and the market state. The proposed framework yields efficient predictions even during the COVID-19 pandemic and the Russia–Ukraine war period. Efficiency measures indicate the robustness of the approach. Findings suggest that large-cap stocks are relatively more predictable.

The paper is on Indian stock markets. Future work will extend it to other stock markets and other financial products.

The proposed methodology will be of practical use for traders, fund managers and financial advisors. Policymakers may find it useful for assessing the impact of macroeconomic shocks and reducing market volatility.

Development of a granular decomposition-based forecasting framework and separating the effects of explanatory variables in different time scales and macroeconomic periods.

The primary objective of this study is to determine how concentrated ownership affects stock returns by country and scale (by market capitalization), like large, medium, and small-cap firms in selected developed economies of the world.

Using a dataset comprising 12,751 annual observations from 850 listed companies from developed economies from 2004 to 2018, the study employs panel data models and instrumental variable estimation to mitigate endogeneity bias.

The findings reveal a significant and positive correlation between ownership concentration and expected returns on corporate equities in developed economies. Furthermore, the study categorizes firms into distinct size categories and finds nuanced differences in the relationship between ownership concentration and stock returns across large, medium, and small-cap enterprises. The results of the study reveal that ownership concentration (by country) and scale (Large, medium, and small) have a significant and positive impact on the stock returns of firms in developed economies.

the practical implications of this study extend to investors, firms, policymakers, regulators, and other stakeholders involved in the financial markets. By considering these implications, stakeholders can make informed decisions to enhance market efficiency, investor protection, and overall market integrity.

To the authors' understanding, this study is the first to examine the impact of concentrated ownership on excessive stock returns across countries and scales, with an explicit focus on large, medium, and small companies in select developed economies worldwide.

This study aims to examine whether mandatory audit partner rotation is associated with future stock price crash risk.

This study makes use of a regulatory change from the Ministry of Finance of China and the China Securities Regulation Commission, which requires mandatory rotation of audit partners since 2004, as a natural experiment to establish causality and applies a difference-in-difference research design.

Audit partner rotation leads to a significant decrease in future stock price crash risk in the departing partner’s final year of tenure preceding mandatory rotation, consistent with peer monitoring argument of mandatory rotation. Inconsistent with other arguments, including client-specific knowledge, fresh perspective and auditor independence, no significant effect takes a place in the incoming partner’s first year of tenure following mandatory rotation. Mechanism analysis documents that mandatory audit partner rotation reduces stock price crash risk by improving audit quality and constraining managerial empire building.

The results shed new light on the capital market consequence of mandatory audit partner rotation and the cause of stock price crash risk.

This study aims to investigate the conditional volatility of the Asian stock market concerning Bitcoin and global crude oil price movement.

This study uses the newest Dynamic Conditional Correlation (DCC)-Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to examine the conditional volatility of the stock market for Bitcoin and crude oil prices in the Asian perspective. The sample stock market includes Chinese, Indian, Japanese, Malaysian, Pakistani, Singaporean, South Korean and Turkish stock exchanges, with daily time series data ranging from 4 April 2015−31 July 2023.

The outcome reveals the presence of volatility clustering on the return series of crude oil, Bitcoin and all selected stock exchanges of the current study. Secondly, the outcome of DCC, manifests that there is no short-run volatility spillover from crude oil to the Malaysian, Pakistani and South Korean and Turkish stock markets, whereas Chinese, Indian, Japanese, Singapore stock exchanges show the short-run volatility spillover from crude oil in the short run. On the other hand, in the long run, there is a volatility spillover effect from crude oil to all the stock exchanges. Thirdly, the findings suggest that there is no immediate spillover of volatility from Bitcoin to the stock markets return volatility of China, India, Malaysia, Pakistan, South Korea and Singapore. In contrast, both the Japanese and Turkish stock exchanges exhibit a short-term volatility spillover from Bitcoin. In the long term, a volatility spillover effect from Bitcoin is observed in all stock exchanges except for Malaysia. Lastly, based on the outcome of conditional variance, it can be concluded that there was increase in the return volatility of stock exchanges during the period of the COVID-19 pandemic.

The analysis below does not account for the bias induced due to certain small sample properties of DCC-GARCH model. There exists a huge literature that suggests other methodologies for small sample corrections such as the DCC connectedness approach. On the other hand, decisive corollaries of the conclusions drawn above have been made purely based on a comprehensive investigation of eight Asian stock exchange economies. However, there is scope for inclusive examination by considering other Nordic and Western financial markets with panel data approach to get more robust inferences about the reality.

Most of the empirical analysis in this perspective skewed towards the Nordic and Western countries. In addition to that many empirical investigations examine either the impact of crude oil price movement or Bitcoin performance on the stock market return volatility. However, none of the examinations quests the crude oil and Bitcoin together to unearth their implication on the stock market return volatility in a single study, especially in the Asian context. Hence, current investigation endeavours to examine the ramifications of Bitcoin and crude oil price movement on the stock market return volatility from an Asian perspective, which has significant implications for the investors of the Asian financial market.