Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review

With the advent of the fourth industrial revolution, data-driven decision making has also become an integral part of decision making. At the same time, deep learning is one of the core technologies of the fourth industrial revolution that have become vital in decision making. However, in the era of epidemics and big data, the volume of data has increased dramatically while the sources have become progressively more complex, making data distribution highly susceptible to change. These situations can easily lead to concept drift, which directly affects the effectiveness of prediction models. How to cope with such complex situations and make timely and accurate decisions from multiple perspectives is a challenging research issue. To address this challenge, we summarize concept drift adaptation methods under the deep learning framework, which is beneficial to help decision makers make better decisions and analyze the causes of concept drift. First, we provide an overall introduction to concept drift, including the definition, causes, types, and process of concept drift adaptation methods under the deep learning framework. Second, we summarize concept drift adaptation methods in terms of discriminative learning, generative learning, hybrid learning, and others. For each aspect, we elaborate on the update modes, detection modes, and adaptation drift types of concept drift adaptation methods. In addition, we briefly describe the characteristics and application fields of deep learning algorithms using concept drift adaptation methods. Finally, we summarize common datasets and evaluation metrics and present future directions.

Modelling volatility effects between stock, oil, gold and forex markets: Evidence from India

Although several studies on the integration of diverse stock markets have been conducted in the financial literature, most of them have focused on the integration and volatility spillovers across established stock markets. The present study explores the dynamics of integration and volatility spillover across gold, oil, forex, and stock markets during four significant events in India: the pre-changed government regime, the post-changed government regime, the post-Brexit referendum date, and the COVID era. Daily data from 2010 to 2022 is divided into four categories using the Chow test. This is done to examine if these events' financial turmoil affects market interconnectivity. The unit root test determines data stationarity. The ARCH LM test examines series volatility clustering, and the BEKK GARCH test examines market volatility spillover. Results indicate that gold cannot be considered a hedge or safe haven. Secondly, market interconnectedness increased during the crisis period. Third, domestic political and geopolitical conditions globally do not increase the scale of spillover amongst financial assets, though they impact the spillover's magnitude. The results of this study have several important implications for portfolio diversification and risk management. © Varsha Ingalhalli, Prachi Kolamker, 2023.

Taiwan's Food Resiliency - or Not - in a Conflict with China

The US military, intelligence, and diplomatic communities have overlooked a key vulnerability in their assessment of a potential military conflict between China and Taiwan- Taiwan's growing reliance on agricultural imports and its food stocks (except for rice) that could endure trade disruptions for only six months. This article assesses Taiwan's agricultural sector and its ability to feed the country's population if food imports and production are disrupted;identifies the food products that should be prioritized in resupply operations, based on Taiwan's nutritional needs and domestic food production;and outlines the required logistical assets. These findings underscore the urgency for US military planners to develop long-term logistical solutions for this complex strategic issue.

Hedging potentials of green investments against climate and oil market risks

PurposeThis study aims to examine the ability of clean energy stocks to provide cover for investors against market risks related to climate change and disturbances in the oil market.Design/methodology/approachThe study adopts the feasible quasi generalized least squares technique to estimate a predictive model based on Westerlund and Narayan's (2015) approach to evaluating the hedging effectiveness of clean energy stocks. The out-of-sample forecast evaluations of the oil risk-based and climate risk-based clean energy predictive models are explored using Clark and West's model (2007) and a modified Diebold & Mariano forecast evaluation test for nested and non-nested models, respectively.FindingsThe study finds ample evidence that clean energy stocks may hedge against oil market risks. This result is robust to alternative measures of oil risk and holds when applied to data from the COVID-19 pandemic. In contrast, the hedging effectiveness of clean energy against climate risks is limited to 4 of the 6 clean energy indices and restricted to climate risk measured with climate policy uncertainty.Originality/valueThe study contributes to the literature by providing extensive analysis of hedging effectiveness of several clean energy indices (global, the United States (US), Europe and Asia) and sectoral clean energy indices (solar and wind) against oil market and climate risks using various measures of oil risk (WTI (West Texas intermediate) and Brent volatility) and climate risk (climate policy uncertainty and energy and environmental regulation) as predictors. It also conducts forecast evaluations of the clean energy predictive models for nested and non-nested models.

Diversification and hedging strategies of green bonds in financial asset portfolios during the COVID-19 pandemic

In this paper, we investigate whether investors can reap potential diversification or hedging benefits from holding green bonds in a portfolio containing a conventional financial asset during the COVID-19 pandemic. Using data from 6 November 2014 to 5 November 2020, we estimate corrected dynamic conditional correlation between between green bonds and four major asset classes: stocks, corporate bonds, commodities, and clean energy. We extend our analysis by using these correlations to examine hedging, optimal portfolio weights, and naïve strategies and evaluate their implications for investors by calculating hedging effectiveness and utility gain improvement. Results reveal that across the full sample, pre-COVID-19, and during-COVID-19 periods, optimal portfolio weights represent an ideal strategy to realize the greatest risk reduction and risk-adjusted return. Further, green bonds could add substantial diversification benefits for investors holding assets in clean energy, global stocks, and commodities.

Portfolio performance implications of investment in renewable energy equities: Green versus gray

The Sustainable Development Goals of the United Nation and interest by investors in Environmental, Social and Governance (ESG) investment strategies have caused a rapid shift to the green or renewable energy sector, from traditional or gray (oil, gas, and coal) energy companies. In this study, we examine whether and to what extent, financially speaking, there is a price to pay for investing in renewable energy sector equity. Moreover, we seek to determine whether green investments can be considered a hedge during times of financial stress. We find that alphas from investments in a portfolio of gray (overall energy sector) stocks and versus a portfolio of renewable energy equities during an exogenous, non-financial shock-the COVID-19 pandemic-and during non-crisis periods did not differ statistically. However, the renewable energy index showed higher idiosyncratic volatility than the energy index, as expected. The results are robust to alternative model specifications. From a practical perspective, our results are informative in that they provide insights into the tradeoffs associated with renewable energy investments. In particular, risk-adjusted returns to a renewable energy portfolio may be affected by greater idiosyncratic risk.

Extreme spillovers between insurance tokens and insurance stocks: Evidence from the quantile connectedness approach

This study examines potential tail spillovers between insurance tokens and conventional stocks using the quantile connectedness approach by Ando et al. (2018).  In particular, this study explores static and dynamic spillovers at lower and upper tails of the return distribution. In line with previous studies, tokens and conventional stocks within the insurance market may show positive but low connectedness levels. Furthermore, our findings confirm a higher sensitivity of the insurance system at both tails of the distribution in comparison with the median (Q = 0.50). As expected, dynamic connectedness measures change over time, intensifying at the extremes of the distribution. This finding is confirmed by the robustness test that consists of analyzing the RTD (Relative Tail Dependence) measure, as we reject the symmetric response, since its values are clearly different from zero in most of the sample period. These results are of interest to portfolio managers, as the findings will allow them to suggest adjustments to investment portfolios according to the evolution of the dynamic spillovers found.

Gambling-Motivated Market Attention and Stock Market Volatility

Retail investors show gambling preferences and pay greater attention to the market than individual stocks. Previous studies report a positive and significant relationship between market attention and volatility. This relationship results from the joint effects of attention to investment-motivated and gambling-motivated components. However, the separate roles of these two components have not yet been examined. Hence, this study applied principal component analysis to identify the gambling-motivated component from market attention and gambling-related variables. The investment-motivated component is the regression residual of the market's attention paid to the gambling-motivated component. This study linearly relates these two components to volatility. The generalized method of moments regression was used to resolve endogeneity problems and biased estimates. The Google search volume index is a proxy for unobserved retail investors' market attention. Using a daily sample of the Thai market from August 6, 2008, to September 30, 2022 (a total of 3,450 observations), this study found a positive relationship between market attention and stock market volatility. This relationship results from the positive effects of both investment-motivated and gambling-motivated components. Attention to gambling is more influential than attention to investment. The explanatory powers of gambling-attention and investment-attention for volatility were 81.33% and 18.67%, respectively. These effects were less pronounced during the COVID-19 pandemic.

Reevaluating the risk minimization utility of Islamic stocks and bonds (Sukuk) in international financial markets

We examine the risk minimization utility of Islamic stock and Sukuk (bond) indices by studying their linkages against traditional global counterparts. We first employ an asymmetric power ARCH-based ADCC model on an extended dataset employed by Kenourgios et al. (2016). Our sample ranges from July 2007 to June 2021 covering the Global Financial Crisis (GFC), the European Sovereign Debt Crisis (ESDC), and the COVID-19 pandemic. Econometric tests suggest strong evidence of coupling in the bulk of Islamic equity indices. A handful of emerging market indices constitute exceptions. Qualitatively similar results emerge from time–frequency analysis via wavelet tools, revealing pervasive coupling in both returns and volatility series. The linkages are scale-dependent in only a few pairs. In contrast, Sukuk indices are uncoupled from their global fixed income counterparts and relevant risky debt portfolios. In sum, the risk-return characteristics of Islamic equities (especially in developed economies) remain coupled to major global benchmarks and therefore are unlikely to appeal as safe haven candidates. The converse applies to Sukuk, which promises potential portfolio diversification benefits and safe haven status in ‘normal' and crisis periods.

Emojis and stock returns

Purpose: The authors explore how the sentiment expressed by emojis in comments on stocks is associated with the stocks' subsequent returns. Design/methodology/approach: By applying our own analyzer, the authors find a sentiment effect of emojis on stocks returns separately to the plain text-expressed sentiment in Reddit posts about meme stocks such as Gamestop during the Covid-19 pandemic. Findings: The authors document that a one-standard deviation change in emoji sentiment magnitude measured as the quantity of positive emoji sentiment posts over the previous hour is associated with an 0.06% (annualized: 109.2%) one-hour abnormal stock return compared to a mean of 0.03% (annualized: 54.6%). If the stock exhibits a higher intra-hour volatility, a proxy for uninformed noise trading, this relation is more pronounced and even stronger compared to stock return's relation to plain text sentiment. Research limitations/implications: The authors are not able to show causation that is open to future research. It also remains an open question how emojis impact market price efficiency. Practical implications: Emojis are positively related to stock returns in addition to plain text-expressed content if they are discussed heavily by retail investors in Internet boards such as Reddit. Social implications: Shared emotions expressed by emojis might have an influence on how disconnected individuals make homogeneous decisions. This argument might explain our found relation of emojis and stock returns. Originality/value: So, the study findings provide empirical evidence that emojis in Reddit posts convey information on future short-term stocks returns distinct from information expressed in plain text, in the case of volatile stocks, with a higher magnitude. © 2023, Emerald Publishing Limited.

An Analytical Study on Volatility and Impact of Selected Sectoral Indices on National Stock Exchange during Covid-19

The main objective of this paper is to analyze the COVID-19 effects in selected companies in NSE. Ten companies based on market capitalization for the sectoral i ndices analyses are studied. The company's growth in sectoral Indices was analyzed for the pre and post COVID-19 period by collecting one and half a year data from July 2019 to October 2020 using two hypotheses. The unit test, Co-integration test, serial correlation, heteroscedastic tests, and GARCH tests were performed to perform this hypothesis. Generally, regression tests are used for analysing the company's growth. Based on the results, the cointegration results reveal moderate Volatility during the pandemic period from February 2020 to October 2020, whereas during the pre-period, it shows high Volatility. This result is identical to both the volatile and regression tests. © Indian Institute of Finance.

Executive Summary

[...]it was determined that the herd investment was valid for BioNTech and Moderna when the highest index value belonged to BioNTech company. The following study is also related to the financial sector, focused on the Turkish Banking Sector and examines the relationship between financial innovation and economic growth in banks on a regional basis. According to the study results, it was understood that subjective norms and perceived behaviour control had a positive and significant effect on students' recycling behaviours. The last study of this issue, digital household technology ownership analysis, investigated the effects of preferred technology applications in the household and socio-economic and socio-demographic factors by using the generalised sequential logit method with TURKSTAT 2021 Household Information Technologies Usage Research Microdata.

Safe havens in Islamic financial markets: COVID-19 versus GFC

Using a bivariate dynamic conditional correlation (DCC) generalized autoregressive conditional heteroskedasticity (GARCH) model, this study compares the safe-haven properties of various assets against the major Gulf Cooperation Council (GCC) stock indexes during two periods of financial turmoil, the COVID-19 pandemic and the 2008 Global Financial Crisis (GFC). Sovereign bonds offered the highest hedging benefits under both crises. The traditional safe assets, gold and silver, which were reasonably productive under the GFC, have been less so during the pandemic. The Japanese yen emerged as a very safe choice for investors holding GCC stock indexes. Both sector indexes and stock indexes failed to safeguard investors most of the time during each crisis.

Predictors of Clean Energy Stock Returns: An Analysis with Best Subset Regressions

We investigate the determinants of clean energy stock returns by considering a large set of variables. We focus on the Covid-19 period and use a novel statistical technique, best subset regressions with non-Gaussian errors, for variable selection. Our examination shows that clean energy stocks are significantly exposed to small company and emerging market equities, a new finding to the literature. Moreover, we find no influence from the oil market, contrary to conclusions of a large part of the prior work.

System Dynamics Modeling for Assessing the Impact of COVID-19 on Food Supply Chains: A Case Study of Kenya and Rwanda

Disruptions in the food supply chains caused by the COVID-19 pandemic have destabilized the balance between production, supply, transport, distribution, and consumption. Consequently, these disruptions have affected food and nutritional security all over the world. This study proposes a framework for investigating the impact of COVID-19 on food supply chains, considering Eastern Africa as a focus region with Kenya and Rwanda as case studies. A systems thinking approach with three systemic components (food and nutrition, COVID-19 contagion, and human health) was applied. The contagion component was characterized by the susceptible, exposed, infected, recovered, and deceased (SEIRD) epidemiological modeling method. We then applied a causal loop diagram and stock and flow diagrams to map the links and interactions between variables from the contagion, health, and food supply chain components of the whole system. The results reveal that COVID-19 has adversely affected food and nutritional security in Eastern African countries. Key response measures to COVID-19 such as lockdowns, closure of borders, isolation, and quarantining have resulted in labor shortages, increased unemployment rates, loss of income, and the subsequent contraction of economies. The disruption of the food supply chain has negatively impacted the main pillars of food and nutrition security, which are availability, accessibility, utilization, and stability. We suggest direct food supply from local producers to the consuming communities to shorten the food supply chain and therefore enhance food self-sufficiency to reduce the severe effects of COVID-19 on food and nutrition security. Overall, our study provides a useful framework to help design better policies and build more resilient and inclusive food systems during COVID-19 and similar pandemics in the future.

What do we know about the idiosyncratic risk of clean energy equities?

The COVID-19 pandemic stimulated the need to invest in clean energy firms for better returns and climate risk mitigation. This study provides a detailed overview of the impact of idiosyncratic risk (IVOL) on excess returns of 95 clean energy stocks. Overall, investors in clean energy stocks are guided by the pessimist group of investors who underprice the high IVOL stocks and demand high-risk premiums to diversify the firm-specific risk. Further, during the COVID-19 period, there is no significant relationship between clean energy excess stock returns and IVOL. During this period, clean energy stocks were exposed to higher information asymmetry, limiting the arbitrage opportunities and producing a weaker return-IVOL relation indicating that clean energy stocks reflect the properties of technology stocks. IVOL has a low level of persistence which may be helpful in forecasting. This study offers valuable insights for regulators and investors from the investment decisions, asset pricing, and diversification perspective.

Upside/Downside spillovers between oil and Chinese stock sectors: From the global financial crisis to global pandemic

This paper examines the effects of the COVID-19 outbreak, recent oil price fall, and both global and European financial crises on dependence structure and asymmetric risk spillovers between crude oil and Chinese stock sectors. Using time-varying symmetric and asymmetric copula functions and the conditional Value at Risk measure, we provide evidence of positive tail dependence in most sectors using copula and conditional Value-at-Risk techniques. We can see the average dependence between oil and industries during the oil crisis. Moreover, we find strong evidence of bidirectional risk spillovers for all oil-sector pairs. The intensity of risk spillovers from oil to all stock sectors varies across sectors. The risk spillovers from sectors to oil are substantially larger than those from oil to sectors during COVID-19. Furthermore, the return spillover is time varying and sensitive to external shocks. The spillover strengths are higher during COVID-19 than financial and oil crises. Finally, oil do not exhibit neither hedge nor safe-haven characteristics irrespective of crisis periods.

Asymmetric volatility spillover among global oil, gold, and Chinese sectors in the presence of major emergencies

The volatility of international crude oil and gold markets has affected stock markets through several economic channels, and the impact tends to be more evident with the appearance of emergencies. However, the volatility linkages between commodities and Chinese sector stocks in the presence of emergencies are understudied. To examine the asymmetric relationship and time-varying connectedness between commodities and Chinese sector stocks, this paper first employs GJR-GARCH to capture the realized volatility of international oil, gold, and Chinese sector stocks. Secondly, we decompose the realized volatility of international oil and gold into bad and good volatility and then employ the TVP-VAR-DY approach to obtain the connectedness index. The final result shows asymmetric volatility spillover among oil, gold, and Chinese sector stocks. During the COVID-19 outbreak, the gold good volatility transmission is intenser than bad volatility. Thirdly, the analysis is also carried out under different subperiods. They include three international events: the global financial crisis and the European debt crisis, the oil crisis, and COVID-19. The result reveals heterogeneity exists in the impact of international oil and gold on the Chinese sector stocks under different emergencies. These findings are of great significance for policymakers to improve the sector management under the impact of different emergencies and for investors to design diversified portfolios according to the commodity-sector risk spillover effects. © 2023 Elsevier Ltd

Dividend policy, its asymmetric behavior and stock liquidity

Purpose: This paper examines the impact of dividend policy on stock market liquidity, and whether the dividend payouts has an asymmetric effect on stock liquidity. Design/methodology/approach: A multivariate panel-data regression analysis is conducted for a sample of the largest 411 nonfinancial US firms. Three main hypothesis are tested: (1) whether dividend payouts impact affect stock liquidity, (2) whether low and high dividend payments can asymmetrically effect on stock liquidity and (3) whether the presence of the GFC has an impact the relationship between dividend payments and stock liquidity. Findings: The study finds that dividend policy is adversely associated with stock liquidity. This supports the prediction of the liquidity-dividend hypothesis. The authors also report that stock liquidity asymmetrically responds to changes in dividend payouts, confirming the prediction of the dividend-signaling approach. More specifically, higher dividend payments decrease stock liquidity by a lower magnitude than the increase in stock liquidity resulting from lower dividend payments. Finally, the presence of the GFC weakened the relationship between dividend payments and stock liquidity. Research limitations/implications: The paper can help in performing future research by using different dataset covering the COVID-19 crisis. Practical implications: The paper allows market participants to better understand the impact of dividend policy and its asymmetric effects on stock liquidity. The authors' analyses can direct investors and regulators to adopt new supervisory devices to create an appropriate level of dividend payouts that helps to effectively support the level of stock liquidity. Social implications: The paper intends to support the business community and to make strong contributions to the economic development and the welfare of the community. Originality/value: The originality comes from its new evidence as it can help in assessing the importance of dividend policy and its asymmetric impact on stock liquidity in the full sample and during the GFC. The paper is helpful in performing future analyses using a new sample period for another set of data as well as accounting for COVID-19 pandemic crisis. [ FROM AUTHOR] Copyright of Journal of Economic Studies is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Covid‐19 outbreak and stocks return on the West African Economic and Monetary Union's stock market: An empirical analysis of the relationship through the event study approach

This study uses the Wilcoxon's signed ranks test to identify the effect of the Covid‐19 outbreak on the stocks returns of companies listed on the West African Economic and Monetary Union's (WAEMU) stock market by considering two event dates (January 23, 2020 and March 2, 2020). To account for the temporal volatility in the event approach, the study resort to a GARCH model. Empirical findings suggest that January 23, 2020 event (first case of death due to Covid‐19 in China) have had a minor impact on the WAEMU stock market while the event on March 2, 2020 (first case of Covid‐19 in the WAEMU) strongly affected the financial market. This negative impact is much more pronounced for the distribution sectors (−34.16%). Robustness analysis reveals that the main information leading to disruption on the market is the weekly death cases and not the confirmed cases. In addition, government anti‐Covid‐19 measures such as social distancing and governance positively affect the stock return whereas lockdown, public health measures and movement restrictions contribute to a decline in the stock's price.