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In the build-up of an investment decision, the existence of both active and passive investment vehicles triggers a puzzle for investors. Indeed the confrontation between active and index replication equity funds in terms of risk-adjusted performance and alpha generation has been a bone of contention since the inception of these investment structures. Accordingly, the objective of this chapter is to distinctly underscore whether an investor should be concerned in choosing between active and diverse passive investment structures.

The survivorship bias-free dataset consists of 776 equity funds which are domiciled either in America or Europe, and are likewise exposed to the equity markets of the same regions. In addition to geographical segmentation, equity funds are also categorised by structure and management type, specifically actively managed mutual funds, index mutual funds and passive exchange traded funds (‘ETFs’). This classification leads to the analysis of monthly net asset values (‘NAV’) of 12 distinct equally weighted portfolios, with a time horizon ranging from January 2004 to December 2014. Accordingly, the risk-adjusted performance of the equally weighted equity funds’ portfolios is examined by the application of mainstream single-factor and multi-factor asset pricing models namely Capital Asset Pricing Model (Fama, 1968; Fama & Macbeth, 1973; Lintner, 1965; Mossin, 1966; Sharpe, 1964; Treynor, 1961), Fama French Three-Factor (1993) and Carhart Four-Factor (1997).

Solely examination of monthly NAVs for a 10-year horizon suggests that active management is equivalent to index replication in terms of risk-adjusted returns. This prompts investors to be neutral gross of fees, yet when considering all transaction costs it is a distinct story. The relatively heftier fees charged by active management, predominantly initial fees, appear to revoke any outperformance in excess of the market portfolio, ensuing in a Fool’s Errand Hypothesis. Moreover, both active and index mutual funds’ performance may indeed be lower if financial advisors or distributors of equity funds charge additional fees over and above the fund houses’ expense ratios, putting the latter investment vehicles at a significant handicap vis-à-vis passive low-cost ETFs. This chapter urges investors to concentrate on expense ratios and other transaction costs rather than solely past returns, by accessing the cheapest available vehicle for each investment objective. Put simply, the general investor should retreat from portfolio management and instead access the market portfolio using low-cost index replication structures via an execution-only approach.

The battle among actively managed and index replication equity funds in terms of risk-adjusted performance and alpha generation has been a grey area since the inception of mutual funds. The interest in the subject constantly lightens up as fresh instruments infiltrate financial markets. Indeed the mutual fund puzzle (Gruber, 1996) together with the enhanced growth of ETFs has again rejuvenated the active versus passive debate, making it worth a detailed analysis especially for the benefit of investors who confront a dilemma in choosing between the two management styles.

Several studies have observed that stocks tend to drop by an amount that is less than the dividend on the ex-dividend day, the so-called ex-dividend day anomaly. However, there still remains a lack of consensus for a single explanation of this anomaly. Different from other studies, this dissertation attempts to answer the primary research question: how can investors make trading profits from the ex-dividend day anomaly and how much can they earn? With this goal, I examine the economic motivations of equity investors through four main hypotheses identified in the anomaly's literature: the tax differential hypothesis, the short-term trading hypothesis, the tick size hypothesis, and the leverage hypothesis.

While the U.S. ex-dividend anomaly is well studied, I examine a long data window (1975–2010) of Thailand data. The unique structure of the Thai stock market allows me to assess all four main hypotheses proposed in the literature simultaneously. Although I extract the sample data from two data sources, I demonstrate that the combined data are consistently sampled. I further construct three trading strategies – “daily return,” “lag one daily return,” and “weekly return” – to alleviate the potential effect of irregular data observation.

I find that the ex-dividend day anomaly exists in Thailand, is governed by the tax differential, and is driven by short-term trading activities. That is, investors trade heavily around the ex-dividend day to reap the benefits of the tax differential. I find mixed results for the predictions of the tick size hypothesis and results that are inconsistent with the predictions of the leverage hypothesis.

I conclude that, on the Stock Exchange of Thailand, juristic and foreign investors can profitably buy stocks cum-dividend and sell them ex-dividend while local investors should engage in short sale transactions. On average, investors who employ the daily return strategy have earned significant abnormal return up to 0.15% (45.66% annualized rate) and up to 0.17% (50.99% annualized rate) for the lag one daily return strategy. Investors can also make a trading profit by conducting the weekly return strategy and earn up to 0.59% (35.67% annualized rate), on average.

A systemic risk measure is proposed accounting for links and mutual dependencies between financial institutions utilizing tail event information. Financial Risk Meter (FRM) is based on least absolute shrinkage and selection operator quantile regression designed to capture tail event co-movements. The FRM focus lies on understanding active set data characteristics and the presentation of interdependencies in a network topology. Two FRM indices are presented, namely, FRM@Americas and FRM@Europe. The FRM indices detect systemic risk at selected areas and identify risk factors. In practice, FRM is applied to the return time series of selected financial institutions and macroeconomic risk factors. The authors identify companies exhibiting extreme “co-stress” as well as “activators” of stress. With the SRM@EuroArea, the authors extend to the government bond asset class, and to credit default swaps with FRM@iTraxx. FRM is a good predictor for recession probabilities, constituting the FRM-implied recession probabilities. Thereby, FRM indicates tail event behavior in a network of financial risk factors.

The utilization of artificial intelligence (AI) in the solution of many problems encountered in healthcare in recent years is rapidly becoming widespread. Understanding of the use and importance of efficiency, security and accessible healthcare to everyone and providing value-based services for healthcare decision-makers is essential. The special uses of machine learning, natural language processing and smart voice assistants, which have developed as sub-branches of AI, for healthcare services, the contributions of these techniques to the digital transformation of healthcare services and how all these will help decision-making processes in healthcare services, will be discussed in this chapter. And also, FDA-approved algorithms that are a kind of AI tool will be explained.

In this chapter we investigate the response of bond markets to macroeconomic news announcements in the euro area. Specifically, we analyze the impact of (un)expected changes in the interest rate, unemployment rate, consumer confidence index and industrial production index on the returns, volatility and correlations of European government bond markets. Overall, our results suggest that, bond return volatility strongly reacts to news announcements and that the response is asymmetric. However, the influence of macroeconomic news announcements appears insignificant for bond returns. Finally, our results paint a complex picture of the effect of macroeconomic news releases on correlations.

This chapter examines the impacts of scheduled announcements of 14 widely followed macroeconomic news on the corporate bond market from July 2002 to June 2017 and documents several new findings. First, good (bad) macroeconomic news tends to have a negative (positive) effect on IG bond returns and a positive (negative) effect on high-yield (HY) bond returns. Second, nonfarm payroll (NFP) appears to be the “King of announcements” for the corporate bond market. Third, while information about revisions of prior releases is incorporated into bond prices on announcement days, future revisions fail to be priced in. Fourth, the news information is thoroughly and quickly reflected in bond prices on the announcement day. Finally, corporate bond volatility increases on announcement days, whereas the Zero Lower Bound (ZLB) policy has little effect on conditional volatility.

This chapter aimed to uncover the gaps in the existing construction supply chain management (CSCM) models. Organisational culture and the fourth industrial revolution (4IR) components are the two gaps that were identified through reviewing existing CSCM models. The 4IR is driven by three components which are smart management, virtualisation and cyber-physical system. It was proposed in this chapter that the practice of CSCM should be in tandem with the components of 4IR. This chapter recommended that for the effective practice of the construction supply chain (CSC) in the 4IR era, construction stakeholders should adopt an innovative and collaborative organisational culture. The organisational culture adopted by a construction firm performs a crucial role in encouraging construction stakeholders in adopting 4IR components for CSCM. Each of the 4IR components is driven by technologies like autonomous robots, building information modelling (BIM), radio frequency identification (RFID), the internet of things (IoT) and others. Among all the technologies, it was discovered that RFID and BIM had gained prominence in most CSC literature. The chapter recommended that blockchain, digital twins and the cyber-physical system are the next trending technology for CSCM.

This chapter investigates the relative magnitude of the benefits of global diversification from the viewpoint of domestic investors in various countries by forming time-rolling efficient frontiers. To enhance feasibility of asset allocation strategies, the constraints of short-sales and over-weighting investments are taken into account. The empirical results suggest that local investors in less developed countries, particularly in Latin America, East Asia, and Southern Europe, comparatively benefit more from global diversification. Investors in the countries of civic-law origin tend to benefit more from global investment than the ones in the common-law states. Although the global market has become more integrated over the past decades, diversification benefits for domestic investors declined but did not vanish. The results of this chapter are useful for asset management professionals to determine target markets to promote the sales of international funds.