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Capital markets thrive on information, and the information revolution has transformed these markets all over the world. Investors can now keep track of the movements of capital markets in real-time and they react to the flow of information from around the world. One of the concerns of stock market investors is whether the markets operate efficiently, independently, and with sound fundamentals. However, real market movements tend to exhibit a link as is evident from recent market movements across the world.

The assessment of interdependence between stock markets is an important aspect of international portfolio management. The aim of this chapter is to examine the shock and volatility spillover between the Standard and Poor’s 500 (S&P500) index from the United States (US) Stock Exchange and the Istanbul Stock Exchange 100 (BIST100) index from the Stock Exchange Istanbul.

S&P500 index, which is the most important index representing US markets, and BIST100 index, which is the index representing the Turkish market, were used as variables in this study. In the analysis, the causality in variance test was applied to determine the volatility spillover between these two markets. Later, multivariate GARCH (MGARCH) models were used to measure the volatility spillover in the markets. VAR(1)-GARCH (1,1)-Diagonal BEKK model was applied to the daily data to determine the shock and volatility spillover in the markets.

As a result of the variance causality test, it was found that there is a bi-directional volatility spillover between S&P500 index and BIST100 index. When the return spillover between the markets is examined, a one-way spillover from the S&P500 index to the BIST100 index emerged. Diagonal BEKK model results show that each market is affected by its own news (unexpected shocks) and volatility. Furthermore, the volatility is persistent for both markets. These findings demonstrate that the US market and the Turkish market interact with each other.

The purpose of this study is to reveal the lead–lag structure between international crude oil price and stock markets.

The methods used for this study are as follows: empirical mode decomposition; shift-window-based Pearson coefficient and thermal causal path method.

The fluctuation characteristic of Chinese stock market before 2010 is very similar to international crude oil prices. After 2010, their fluctuation patterns are significantly different from each other. The two stock markets significantly led international crude oil prices, revealing varying lead–lag orders among stock markets. During 2000 and 2004, the stock markets significantly led international crude oil prices but they are less distinct from the lead–lag orders. After 2004, the effects changed so that the leading effect of Shanghai composite index remains no longer significant, and after 2012, S&P index just significantly lagged behind the international crude oil prices.

China and the US stock markets develop different pattens to handle the crude oil prices fluctuation after finance crisis in 1998.

The purpose of this study is to investigate the dynamic return volatility connectedness among S&P, Dow Jones (DJ) sustainability indices and their conventional counterparts.

This study uses time-series daily data for 10 S&P and DJ indices over the period of December 1, 2012 to December 8, 2021. The authors divide the data into three periods; over the whole sample, pre and during the Covid-19 pandemic. The study adopts the connectedness approach developed by Diebold and Yilmaz (2014).

The results reveal a high degree of connectedness between S&P and DJ indices and their relative sustainability indices over the whole sample, pre and during the Covid-19 pandemic, indicating that the sustainability indices converge toward their conventional peers. The results further show that the conventional S&P500, S&P Euro 50 and DJWI are the main transmitters of shocks, whereas the S&P400, S&P500 and S&P50 sustainability indices are the main receivers of shocks.

The study provides novel insights in terms of shock transmission of S&P and DJ sustainability indices and their conventional counterparts, where there is a lack of investigation of the connectedness between indices in this field.

The study has significant implications for investors and portfolio managers to devise portfolio strategies to minimize risk and trace the cause, the direction and the magnitude of risk transmission among different indices. Also, the results help policymakers to manage diverse types of risks associated with S&P and DJ indices. Finally, faith-based and ethical investors would be able to predict the pairwise spillover connectedness between these indices.

The primary and secondary market activity in collectible sportscards has evolved into that of a primitive, but organised financial market. This report reviews some aspects of the collectible sportscard market. The objective of the report is to introduce the sportscard investment medium to finance professionals, including those interested in the research potential of the market. The report includes an empirical analysis of the performance of some selected sportscard portfolio strategies for the period between March 1988 and December 1993. Sportscard collecting has evolved from an adolescent hobby of the 1950s into an active national market, estimated to involve approximately $5 billion and 3 million persons and served by a network of dealers and price information suppliers. The evolution of the sportscard market into its current state is described in this presentation. The description includes an empirical analysis of the performance of some selected sportscard portfolio strategies for the period between March 1988 and December 1993. The objective of the report is to provide information to those considering collectible sportscards as an investment medium and to those who might be interested in conducting financial research with collectible sportscard pricing data.

The purpose of this paper is to outline the problems encountered by a student-managed investment program (SMIP) when the pool of qualified finance majors is limited in number. Restructuring the program to a single-semester course and opening the class to motivated/intelligent non-finance majors increased the number of applicants, but resulted in alternative difficulties, particularly time constraints and inadequate student preparedness. A prerequisite exam and regimented classroom structure were the solutions.

The paper discusses the problems encountered and solutions devised to address the early year difficulties experienced by a newly developed SMIP at a relatively small university. The core of the paper chronicles the classroom approach to solving the main problem of a single-semester portfolio management course, the handling of an investment learning curve in a short period of time.

Though empirically limited due to the program’s infancy, portfolio performance has been encouraging and student feedback exceptional. Regarding the former, stocks purchased by the fund have created greater wealth in total than that of equal dollar investments in an S&P500 index fund.

Universities interested in running a student-managed fund should feel secure in a one-semester approach, regardless of talent pool size, as measured by the number of motivated, intelligent finance majors.

Aside from the uniqueness of requiring a mastery of entrance exam investing materials prior to the first class, this paper’s outline of core portfolio management activities includes several strategies and methods meant to streamline the process within a groupthink design.

This paper aims to investigate the use of the bias ratio as a possible early indicator of financial fraud – specifically in the reporting of hedge fund returns. In the wake of the 2008-2009 financial crisis, numerous hedge funds were liquidated and several cases of financial fraud exposed.

Risk-adjusted return metrics such as the Sharpe ratio and Value at Risk were used to raise suspicion for fraud. These metrics, however, assume distributional normality and thus have had limited success with hedge fund returns (a characteristic of which is highly skewed, non-normal return distributions).

Results indicate that potential fraud would have been detected in the early stages of the scheme’s life. Having demonstrated the credibility of the bias ratio, it was then applied to several indices and (anonymous) South African hedge funds. The results were used to demonstrate the ratio’s scope and robustness and draw attention to other metrics which could be used in conjunction with it. Results from these multiple sources could be used to justify further investigation.

The traditional metrics for performance evaluation (such as the Sharpe ratio), assume distributional normality and thus have had limited success with hedge fund returns (a characteristic of which is highly skewed, non-normal return distributions). The bias ratio, which does not rely on normally distributed returns, was applied to a known fraud case (Madoff’s Ponzi scheme).

The effectiveness of the bias ratio in demonstrating potential suspicious financial activity has been demonstrated.

The financial market has come under heightened scrutiny in the past decade (2005 – 2015) as a result of the fragile and uncertain economic milieu that still (2015) persists. Numerous risk and return measures have been used to evaluate hedge funds’ risk-adjusted performance, but many fail to account for non-normal return distributions exhibited by hedge funds. The bias ratio, however, has been demonstrated to effectively flag potentially fraudulent funds.

The purpose of this paper is to explore whether stock index volatility series exhibit real long memory.

The authors employ sequential procedure to test structural break in volatility series, and use DFA and 2ELW to estimate long memory parameter for the whole samples and subsamples, and further apply adaptive FIGARCH (AFIGARCH) to describe long memory and structural break.

The empirical results show that stock index volatility series are characterized by long memory and structural break, and therefore it is appropriate to use AFIGARCH to model stock index volatility process.

This study empirically investigates the properties of long memory and structural break in stock index volatility series. The conclusion has a certain reference value for understanding the properties of long memory and structural break in volatility series for academic researchers, market participants and policy makers, and for modeling and forecasting future volatility, testing market efficiency, pricing financial assets, constructing quantitative investment strategy and measuring market risk.

This study aims to assess the predictive performance of various factors on Bitcoin returns, used for the development of a robust forecasting support decision model using machine learning techniques, before and during the COVID-19 pandemic. More specifically, the authors investigate the impact of the investor's sentiment on forecasting the Bitcoin returns.

This method uses feature selection techniques to assess the predictive performance of the different factors on the Bitcoin returns. Subsequently, the authors developed a forecasting model for the Bitcoin returns by evaluating the accuracy of three machine learning models, namely the one-dimensional convolutional neural network (1D-CNN), the bidirectional deep learning long short-term memory (BLSTM) neural networks and the support vector machine model.

The findings shed light on the importance of the investor's sentiment in enhancing the accuracy of the return forecasts. Furthermore, the investor's sentiment, the economic policy uncertainty (EPU), gold and the financial stress index (FSI) are the top best determinants before the COVID-19 outbreak. However, there was a significant decrease in the importance of financial uncertainty (FSI and EPU) during the COVID-19 pandemic, proving that investors attach much more importance to the sentimental side than to the traditional uncertainty factors. Regarding the forecasting model accuracy, the authors found that the 1D-CNN model showed the lowest prediction error before and during the COVID-19 and outperformed the other models. Therefore, it represents the best-performing algorithm among its tested counterparts, while the BLSTM is the least accurate model.

Moreover, this study contributes to a better understanding relevant for investors and policymakers to better forecast the returns based on a forecasting model, which can be used as a decision-making support tool. Therefore, the obtained results can drive the investors to uncover potential determinants, which forecast the Bitcoin returns. It actually gives more weight to the sentiment rather than financial uncertainties factors during the pandemic crisis.

To the authors’ knowledge, this is the first study to have attempted to construct a novel crypto sentiment measure and use it to develop a Bitcoin forecasting model. In fact, the development of a robust forecasting model, using machine learning techniques, offers a practical value as a decision-making support tool for investment strategies and policy formulation.

This paper aims to provide the necessity to activate long-term exchange-traded derivatives (ETD) in Korea. In the era of aging, low interest rates and low economic growth, the investment demand for long-term financial products, and its hedging demand have steadily increased. Unfortunately, long-term ETD do not trade in Korea, and this study presents political suggestions to invigorate long-term ETD based on overseas cases and empirical analysis. Specifically, this study suggests the necessity to activate exchange traded funds (ETFs) options, long-term Korea treasury bond futures and options and long-term Volatility Index of Korea Composite Stock Price Index future and options. The introduction of those long-term ETD not only contributes to providing long-term investment and hedging vehicles but also reduces market inefficiencies in the Korean industry of ETFs, bonds and structured products.

The study investigates supply chain leaders’ initiatives to support their partners in the early stages of the coronavirus disease-2019 (COVID-19) pandemic, identifying measures taken to increase supply chain resilience and their impact on the quality of supply chain relationships.

Two complementary phases are employed. First, an exploratory approach is adopted, with the method of discourse analysis being employed in the identification of the supplier crisis response strategies by S&P500’s top 30 firms. Second, two scenario-based experiments with 983 participants evaluated the impact of such strategies in two dimensions of supply chain relationships’ quality (supplier satisfaction and supplier commitment).

Phase one revealed five initiatives’ groups adopted: safety measures, innovative tools, information and knowledge sharing, supply chain finance and supply chain continuity. Phase two results indicate that supplier crisis response strategies have positive effects on both supplier satisfaction and commitment. Data also suggest that safety measures, innovative tools, and information and knowledge sharing strategies negatively impacted supplier satisfaction and commitment, when compared with strategies adopted by other buying firms competing for the same supplier. Supply chain continuity was negatively associated with both dimensions when other buying firms implemented innovative tools and information and knowledge sharing strategies with their suppliers, while supply chain finance yielded in no differences in comparison to strategies adopted by competing buying firms.

The authors offer a theoretical typology for supply chain resilience (i.e. natural and artificial), providing support for buying firms’ decisions regarding supplier crisis response strategies through the strengthening of artificial supply chain resilience to increase the likelihood of vulnerable key suppliers’ survival.