A Framework for Enhancing Stock Investment Performance by Predicting Important Trading Points with Return-Adaptive Piecewise Linear Representation and Batch Attention Multi-Scale Convolutional Recurrent Neural Network

Efficient stock status analysis and forecasting are important for stock market participants to be able to improve returns and reduce associated risks. However, stock market data are replete with noise and randomness, rendering the task of attaining precise price predictions arduous. Moreover, the lagging phenomenon of price prediction makes it hard for the corresponding trading strategy to capture the turning points, resulting in lower investment returns. To address this issue, we propose a framework for Important Trading Point (ITP) prediction based on Return-Adaptive Piecewise Linear Representation (RA-PLR) and a Batch Attention Multi-Scale Convolution Recurrent Neural Network (Batch-MCRNN) with the starting point of improving stock investment returns. Firstly, a novel RA-PLR method is adopted to detect historical ITPs in the stock market. Then, we apply the Batch-MCRNN model to integrate the information of the data across space, time, and sample dimensions for predicting future ITPs. Finally, we design a trading strategy that combines the Relative Strength Index (RSI) and the Double Check (DC) method to match ITP predictions. We conducted a comprehensive and systematic comparison with several state-of-the-art benchmark models on real-world datasets regarding prediction accuracy, risk, return, and other indicators. Our proposed method significantly outperformed the comparative methods on all indicators and has significant reference value for stock investment.

Symbolic Encoding Methods with Entropy-Based Applications to Financial Time Series Analyses

Symbolic encoding of information is the foundation of Shannon's mathematical theory of communication. The concept of the informational efficiency of capital markets is closely related to the issue of information processing by equity market participants. Therefore, the aim of this comprehensive research is to examine and compare a battery of methods based on symbolic coding with thresholds and the modified Shannon entropy in the context of stock market efficiency. As these methods are especially useful in assessing the market efficiency in terms of sequential regularity in financial time series during extreme events, two turbulent periods are analyzed: (1) the COVID-19 pandemic outbreak and (2) the period of war in Ukraine. Selected European equity markets are investigated. The findings of empirical experiments document that the encoding method with two 5% and 95% quantile thresholds seems to be the most effective and precise procedure in recognizing the dynamic patterns in time series of stock market indices. Moreover, the Shannon entropy results obtained with the use of this symbolic encoding method are homogenous for all investigated markets and unambiguously confirm that the market informational efficiency measured by the entropy of index returns decreases during extreme event periods. Therefore, we can recommend the use of this STSA method for financial time series analyses.

Laplacian Spectra of Persistent Structures in Taiwan, Singapore, and US Stock Markets

An important challenge in the study of complex systems is to identify appropriate effective variables at different times. In this paper, we explain why structures that are persistent with respect to changes in length and time scales are proper effective variables, and illustrate how persistent structures can be identified from the spectra and Fiedler vector of the graph Laplacian at different stages of the topological data analysis (TDA) filtration process for twelve toy models. We then investigated four market crashes, three of which were related to the COVID-19 pandemic. In all four crashes, a persistent gap opens up in the Laplacian spectra when we go from a normal phase to a crash phase. In the crash phase, the persistent structure associated with the gap remains distinguishable up to a characteristic length scale ϵ* where the first non-zero Laplacian eigenvalue changes most rapidly. Before ϵ*, the distribution of components in the Fiedler vector is predominantly bi-modal, and this distribution becomes uni-modal after ϵ*. Our findings hint at the possibility of understanding market crashs in terms of both continuous and discontinuous changes. Beyond the graph Laplacian, we can also employ Hodge Laplacians of higher order for future research.

COVID-19 and stock market performance: Evidence from the RCEP countries☆

As the world's largest trading bloc, the agreement of RCEP, which was formalized in September 2020, is believed to play a non-neglectable role in the post-pandemic recovery. Real economies and the capital markets of the participating countries will have greater interactions due to tariff reduction and negative lists. By looking into the shocks in early 2020 that affect the stock markets of RCEP participating countries, we measure the stock market reaction to common risks just before the RCEP agreement was formalized. Following return-based, volume-based and liquidity-based event-study approaches, we use daily data from 11 Asia-Pacific countries to examine the stock market reactions. We find that RCEP economies for which the agreement took effect on January 1st, 2022 showed better risk resistance in response to COVID-19 shocks. In the long run, trading benefits brought by the RCEP agreement are expected to form and strengthen a system of circular flow of international trading activities among the participating countries, which will in turn increase the risk resistance ability of their stock markets.

Research on the dynamic spillover of stock markets under COVID-19-Taking the stock markets of China, Japan, and South Korea as an example

Examining stock market interactions between China (mainland China and Hong Kong), Japan, and South Korea, this study employs a framework that includes 239 economic variables to identify the spillover effects among these three countries, and empirically simulates the dynamic time-varying non-linear relationship between the stock markets of different countries. The findings are that in recent decades, China's stock market relied on Hong Kong's as a window to the exchange of price information with Japan and South Korea. More recently, the China stock market's spillover effect on East Asia has expanded. The spread of the crisis has strengthened co-movement between the stock markets of China, Japan, and South Korea.

The COVID-19 pandemic uncertainty, investor sentiment, and global equity markets: Evidence from the time-frequency co-movements

We use daily data of the Google search engine volume index (GSVI) to capture the pandemic uncertainty and examine its effect on stock market activity (return, volatility, and illiquidity) of major world economies while controlling the effect of the Financial and Economic Attitudes Revealed by Search (FEARS) sentiment index. We use a time–frequency based wavelet approach comprising wavelet coherence and phase difference for our empirical assessment. During the early spread of the COVID-19, our results suggest that pandemic uncertainty, and FEARS sentiment strongly co-move, and increased pandemic uncertainty leads to pessimistic investor sentiment. Furthermore, our partial wavelet analysis results indicate a synchronization relationship between pandemic uncertainty and stock market activities across G7 countries and the world market. Our results are robust to the inclusion of alternative pandemic fear measure in the form of equity market volatility infectious disease tracker. The pandemic uncertainty and associated sentiment implications could be one plausible reason for increased volatility and illiquidity in the market, and hence, policymakers should look upon this issue for the financial market stability perspective.

Do cryptocurrencies provide better hedging? Evidence from major equity markets during COVID-19 pandemic

Using the five-minute interval price data of two cryptocurrencies and eight stock market indices, we examine the risk spillover and hedging effectiveness between these two assets. Our approach provides a comparative assessment encompassing the pre-COVID-19 and COVID-19 sample periods. We employ copula models to assess the dependence and risk spillover from Bitcoin and Ethereum to stock market returns during both the pre-COVID-19 and COVID-19 periods. Notably, the COVID-19 pandemic has increased the risk spillover from Bitcoin and Ethereum to stock market returns. The findings vis-à-vis portfolio weights and hedge effectiveness highlight hedging gains; however, optimal investments in Bitcoin and Ethereum have reduced during the COVID-19 pandemic, while the cost of hedging has increased during this period. The findings also confirm that cryptocurrencies cannot provide incremental gains by hedging stock market risk during the COVID-19 pandemic.

Dynamics and Co-movements Between the COVID-19 Outbreak and the Stock Market in Latin American Countries: An Evaluation Based on the Wavelet-Partial Wavelet Coherence Model

The COVID-19 outbreak and the global uncertainty it causes produce an apparent panic in stock markets. Efforts to explain the economic spillover effects of COVID-19 can guide authorities to design a control policy against the financial impacts of pandemics. The paper examines the effects of the COVID-19 cases on the stock markets in the emerging Latin American countries of Argentina, Brazil, Chile, Colombia, Mexico, and Peru. The paper employs a continuous partial wavelet methodology to observe lead-lag relations between the daily variables of new COVID-19 cases and the stock market index for each Latin American country. Brazilian new COVID-19 cases led the Bovespa (BVSP) index to decline during the whole period, except February and June 2020, at one month-two month-frequency band. The wavelet and phase difference analyses indicate that, except for Brazil, COVID-19 cases did not affect the stock market indexes adversely during the whole sample period but did affect the stock exchange markets negatively during some sub-sample periods of the entire sample of each country. Dynamics of Latin American stock exchange markets in the short and long run can be explained by some other parameters of real and financial sectors and COVID-19 cases.

The impact of COVID-19 on cultural industries: An empirical research based on stock market returns

The COVID-19 virus has challenged the development of the cultural industries seriously, so far, however few studies have used empirical methods to analyze the impact of the pandemic on the overall cultural industries. Based on the panel data of listed companies, this paper explores the impact of COVID-19 on cultural industries from the perspective of stock market returns. The empirical results show that the pandemic has a significant negative impact on the stock market returns of cultural industries, but the degrees of impact on various creative sub-sectors are significantly different. The findings also indicate that digitalization can effectively reduce the negative impact of COVID-19 on cultural companies, and the epidemic has bigger negative impacts on small and newly-established cultural companies. Moreover, we find that the stock market returns of cultural industries have an inverted U-shaped relationship with the daily growth in total confirmed cases and in total cases of death caused by COVID-19, indicating that the negative marginal impact of COVID-19 on the cultural industries increases firstly and then gradually decreases. Finally, implications for companies and governments are presented respectively based on the findings.

The Impact of the COVID-19 Pandemic on Consumer Behavior-Evidence From China's Stock Market

The COVID-19 pandemic has dramatically reshaped consumers' grocery shopping behavior. Meanwhile, change in consumer shopping behavior might further exert a considerable and far-reaching impact on the food retail industry. Although the existing literature provides investigation on the impact of the pandemic on the retail industry, very few studies discuss the impact of changes in consumer shopping behavior on the stock market performance of the retail industry. This paper investigates selected food retailers listed in China's stock market. To overcome the problems of the Chow test, the Quandt-Andrews test was used to identify the dates of breakpoints of structural change in the stock price performance of those selected companies. The results suggest that there has indeed been an industry-wide structural change in the stock market performance during the pandemic. The study found that the dates of breakpoints for the selected companies were concentrated in the first half of 2020, when China was hit by the Covid-19 pandemic the most. Our survey shows that under strict epidemic prevention and control measures, consumers have gradually adapted to the new normal of epidemic prevention to a certain extent, established safety awareness, and changed their consumption behavior. Our study on stock price data implies that Chinese consumers experienced a shift from physical store offline purchases to online purchasing model.

Stock Market: From the 2008 Crisis to the COVID-19 Pandemic Period

The relationship between the Chinese market and the US market is widely concerned by researchers and investors. This paper uses transfer entropy and local random permutation (LRP) surrogates to detect the information flow dynamics between two markets. We provide a detailed analysis of the relationship between the two markets using long-term daily and weekly data. Calculations show that there is an asymmetric information flow between the two markets, in which the US market significantly affects the Chinese market. Dynamic analysis based on weekly data shows that the information flow evolves, and includes three significant periods between 2004 and 2021. We also used daily data to analyze the dynamics of information flow in detail over the three periods and found that changes in the intensity of information flow were accompanied by major events affecting the market, such as the 2008 financial crisis and the COVID-19 pandemic period. In particular, we analyzed the impact of the S&P500 index on different industry indices in the Chinese market and found that the dynamics of information flow exhibit multiple patterns. This study reveals the complex information flow between two markets from the perspective of nonlinear dynamics, thereby helping to analyze the impact of major events and providing quantitative analysis tools for investment practice.

Does It Pay to Issue Green? An Institutional Comparison of Mainland China and Hong Kong's Stock Markets Toward Green Bonds

The stock market is an indicator of investor sentiment when it comes to new information or innovative firm-level products. Green bonds are both innovative and unique in terms of their higher information disclosures and understanding the impact of sustainable finance on investor outlook for a company’s stock. Using the comparative case of Mainland China and Hong Kong’s stock market, we examine whether green bond announcements from 2016 to 2019 can create significant investor reactions. By employing the event study methodology, we confirm that both markets react in a positive way toward green bond announcements. This reinforces the reputational and financial benefits of green bonds. We find that issuers that are non-banks, environmentally friendly firms as well as those issuing non-general bonds, create a more positive reaction, whereas ownership aspects do not matter as much for investors. However, even among those issuers listed in both markets, certain institutional dynamics like strategic framing and source credibility tend to reinforce a firm’s institutional legitimacy and are seen as being more prominent for investor reaction. The policy implications of our study show that the stock market reaction among two connected economies, where previously varying institutional contexts have resulted in regional differences, are now equally supportive of sustainable financial markets like the green bond. As seen with the positive stock market sentiment, governments and listed issuers can now better align their policies and internal strategies, allowing the low-carbon transition to be a financially attractive opportunity for all investors.

ESG scores and the response of the S&P 1500 to monetary and fiscal policy during the Covid-19 pandemic

Examining the S&P 1500 stocks, the responses of the stocks to fiscal and monetary policy are found to differ due to E, S and G scores by the type of legislation. Non-Financial firms that manage environmental and governance risks better performed better over the pandemic. Part of this was due to their high environmental and governance scores allowing them to hedge the negative effects of the announcements of fiscal policies during the pandemic.

Linear and Nonlinear Effects in Connectedness Structure: Comparison between European Stock Markets

The purpose of this research is to compare the risk transfer structure in Central and Eastern European and Western European stock markets during the 2007–2009 financial crisis and the COVID-19 pandemic. Similar to the global financial crisis (GFC), the spread of coronavirus (COVID-19) created a significant level of risk, causing investors to suffer losses in a very short period of time. We use a variety of methods, including nonstandard like mutual information and transfer entropy. The results that we obtained indicate that there are significant nonlinear correlations in the capital markets that can be practically applied for investment portfolio optimization. From an investor perspective, our findings suggest that in the wake of global crisis and pandemic outbreak, the benefits of diversification will be limited by the transfer of funds between developed and developing country markets. Our study provides an insight into the risk transfer theory in developed and emerging markets as well as a cutting-edge methodology designed for analyzing the connectedness of markets. We contribute to the studies which have examined the different stock markets’ response to different turbulences. The study confirms that specific market effects can still play a significant role because of the interconnection of different sectors of the global economy.

How COVID-19 travels in- and outside of value chains and then affects the stock market: Evidence from China

The organisation of value chains within and between firms and even countries is an important reason for domestic as well as international travel. Hence, value chains create interdependencies which have to do with economic but also personal interactions between firms and places. The latter means value chains are a springboard for shocks-positive or negative-to travel and other related outcomes. This paper sheds light on how input-output relations in China as one human-interaction-intensive activity can help explain spreading patterns of COVID-19 in the first few months of 2020 in China. We document that COVID-19 at that time spread more intensively where input-output relations were stronger between cities in China, and this contributed to inducing direct and mediated, indirect effects on the stock market.

COVID-19 Vaccination Effect on Stock Market and Death Rate in India

The COVID-19 epidemic has brought attention to the vulnerability of new illnesses, and immunization remains a viable option for resuming normal life. This paper examines the influence of COVID-19 vaccination on the death rate and the performance of stock market in India. For this study, COVID-19 vaccination and death rate data is gathered from the Ministry of Health and Family Welfare (MoHFW) portal, and the data for the stock index is taken from the Bombay Stock Exchange (BSE), India. In order to achieve a precise representation of feature significance and distribution, EDA (Exploratory Data Analysis) is utilized in this study. The impact of COVID-19 immunization on the mortality rate and stock market index is investigated using both statistical analysis and Machine Learning Regression-based models. The models are remarkably accurate in reproducing actual result. The empirical study suggests that vaccination has a strong positive impact on the stock market and reducing the death rate. Furthermore, the policies recommended by government and monetary authorities coupled with COVID-19 vaccine supported the stock market recovery in pandemic.

Crash Diagnosis and Price Rebound Prediction in NYSE Composite Index Based on Visibility Graph and Time-Evolving Stock Correlation Network

This study proposes a framework to diagnose stock market crashes and predict the subsequent price rebounds. Based on the observation of anomalous changes in stock correlation networks during market crashes, we extend the log-periodic power-law model with a metric that is proposed to measure network anomalies. To calculate this metric, we design a prediction-guided anomaly detection algorithm based on the extreme value theory. Finally, we proposed a hybrid indicator to predict price rebounds of the stock index by combining the network anomaly metric and the visibility graph-based log-periodic power-law model. Experiments are conducted based on the New York Stock Exchange Composite Index from 4 January 1991 to 7 May 2021. It is shown that our proposed method outperforms the benchmark log-periodic power-law model on detecting the 12 major crashes and predicting the subsequent price rebounds by reducing the false alarm rate. This study sheds light on combining stock network analysis and financial time series modeling and highlights that anomalous changes of a stock network can be important criteria for detecting crashes and predicting recoveries of the stock market.

Stock Price Movement Prediction Using Sentiment Analysis and CandleStick Chart Representation

Determining the price movement of stocks is a challenging problem to solve because of factors such as industry performance, economic variables, investor sentiment, company news, company performance, and social media sentiment. People can predict the price movement of stocks by applying machine learning algorithms on information contained in historical data, stock candlestick-chart data, and social-media data. However, it is hard to predict stock movement based on a single classifier. In this study, we proposed a multichannel collaborative network by incorporating candlestick-chart and social-media data for stock trend predictions. We first extracted the social media sentiment features using the Natural Language Toolkit and sentiment analysis data from Twitter. We then transformed the stock’s historical time series data into a candlestick chart to elucidate patterns in the stock’s movement. Finally, we integrated the stock’s sentiment features and its candlestick chart to predict the stock price movement over 4-, 6-, 8-, and 10-day time periods. Our collaborative network consisted of two branches: the first branch contained a one-dimensional convolutional neural network (CNN) performing sentiment classification. The second branch included a two-dimensional (2D) CNN performing image classifications based on 2D candlestick chart data. We evaluated our model for five high-demand stocks (Apple, Tesla, IBM, Amazon, and Google) and determined that our collaborative network achieved promising results and compared favorably against single-network models using either sentiment data or candlestick charts alone. The proposed method obtained the most favorable performance with 75.38% accuracy for Apple stock. We also found that the stock price prediction achieved more favorable performance over longer periods of time compared with shorter periods of time.

Risk spillover from crude oil prices to GCC stock market returns: New evidence during the COVID-19 outbreak

In this study, we examine oil price extreme tail risk spillover to individual Gulf Cooperation Council (GCC) stock markets and quantify this spillover’s shift before and during the COVID-19 pandemic. A dynamic conditional correlation generalized autoregressive heteroscedastic (DCC- GARCH) model is employed to estimate three important measures of tail dependence risk: conditional value at risk (CoVaR), delta CoVaR (ΔCoVaR), and marginal expected shortfall (MES). Using daily data from January 2017 until May 2020, results point to significant systemic oil risk spillover in all GCC stock markets. In particular, the effect of oil price systemic risk on GCC stock market returns was significantly larger during COVID-19 than before the pandemic. Upon splitting COVID-19 into two phases based on severity, we identify Saudi Arabia as the only GCC market to have experienced significantly higher exposure to oil risk in Phase 1. Although all GCC stock markets received greater oil systemic risk spillover in Phase 2 of COVID-19, Saudi Arabia and the United Arab Emirates appeared more vulnerable to oil extreme risk than other countries. Our empirical findings reveal that investors should carefully consider the extreme oil risk effects on GCC stock markets when designing optimal portfolio strategies, minimizing portfolio risk, and adopting dynamic diversification process. Policymakers and regulators should also enact awareness, oversight, and action plans to minimize adverse oil risk effects.

The COVID-19 shocks on the stock markets of oil exploration and production enterprises

Using daily data from January 1, 2020 to March 31, 2021, this research explores COVID-19 shocks on the stock market of 15 representative oil exploration and production enterprises from 7 countries. We measure the COVID-19 epidemic from two levels, government response stringency index and number of confirmed cases, and employ stock prices and stock market returns to reflect the stock market. Our research results confirm that both the government response stringency index and the number of confirmed cases have a significantly negative influence on stock prices. We further find that the negative reaction of the stock market to the government response stringency index is greater than that from confirmed cases. Finally, we conclude that the government response stringency index have a significantly positive effect on stock market returns of oil exploration and production enterprises. Similar findings arise from analyzing specific enterprises. Overall, our conclusions provide some useful information for the decision-making of oil exploration and production enterprises’ investors and policy makers.

COVID-19 impact on stock market: Evidence from the Indian stock market

This paper has been empirically investigated the existence of the day-of-the-week effect by using closing daily data for Nifty 50, Nifty 50 Midcap, Nifty 100, Nifty 100 Midcap, Nifty 100 Smallcap, and Nifty 200 for before and during the COVID-19 health crisis. This study used secondary data for all indices over the period 1 April 2005-14 May 2020. The present study used both dummy variable regression and the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model. The total study period is divided into two sub-periods, that is, during and before the COVID-19 health crisis. A negative return is found for Mondays when the during-COVID-19 health crisis period is examined; in contrast, it was positive for the before COVID-19 period. Tuesday's effect on index return is found statistically significant and positive for all indices during the COVID-19 crisis.

The effect of COVID-19 pandemic on global stock markets: Return, volatility, and bad state probability dynamics

This study investigates the impact of COVID-19 pandemic on stock returns, conditional volatility, conditional skewness and bad state probability. This study utilizes an asymmetric exponential generalized autoregressive conditional heteroscedasticity model to capture the asymmetric effect of positive and negative shocks (news) on conditional volatility. Using a sample consisting of international stock market indices in Brazil, China, Italy, India, Germany, Russia, Spain, United Kingdom, and United States, over the period from January 1, 2013 to December 31, 2020, we find unprecedented increases in conditional volatilities and bad state probabilities across all the markets. However, this impact is not symmetric across markets. Furthermore, we find that the negative affect of deaths is more pronounced, compared to the positive impact of recovered cases.

Stock market reactions to the COVID-19 pandemic: The moderating role of corporate big data strategies based on Word2Vec☆

By developing a machine learning-based measure of corporate big data strategies, this study empirically explores how stock markets respond to the COVID-19 pandemic and whether corporate big data strategies make firms immune to the pandemic effect. We find that except for information technology and health care sectors, firms in most sectors in China are negatively affected by the COVID-19 outbreak. Among these firms, an increase in the number of daily new confirmed cases in the city of a firm's headquarters is associated with a decrease in its stock prices, however, such a decline is attenuated for firms with a high emphasis on big data strategies. Our results are robust when we use COVID-19 cases at the whole country level.

How to Promote the Performance of Parametric Volatility Forecasts in the Stock Market? A Neural Networks Approach

This study uses the fourteen stock indices as the sample and then utilizes eight parametric volatility forecasting models and eight composed volatility forecasting models to explore whether the neural network approach and the settings of leverage effect and non-normal return distribution can promote the performance of volatility forecasting, and which one of the sixteen models possesses the best volatility forecasting performance. The eight parametric volatility forecasts models are composed of the generalized autoregressive conditional heteroskedasticity (GARCH) or GJR-GARCH volatility specification combining with the normal, Student’s t, skewed Student’s t, and generalized skewed Student’s t distributions. Empirical results show that, the performance for the composed volatility forecasting approach is significantly superior to that for the parametric volatility forecasting approach. Furthermore, the GJR-GARCH volatility specification has better performance than the GARCH one. In addition, the non-normal distribution does not have better forecasting performance than the normal distribution. In addition, the GJR-GARCH model combined with both the normal distribution and a neural network approach has the best performance of volatility forecasting among sixteen models. Thus, a neural network approach significantly promotes the performance of volatility forecasting. On the other hand, the setting of leverage effect can encourage the performance of volatility forecasting whereas the setting of non-normal distribution cannot.

Data analytics to evaluate the impact of infectious disease on economy: Case study of COVID-19 pandemic

SARS-CoV-2 (COVID-19) is a new strain of coronavirus that is regarded as a respiratory disease and is transmittable among humans. At present, the disease has caused a pandemic, and COVID-19 cases are ballooning out of control. The impact of such turbulent situations can be controlled by tracking the patterns of infected and death cases through accurate prediction and by taking precautions accordingly. We collected worldwide COVID-19 case information and successfully predicted infected victims and possible death cases around the world and in the United States. In addition, we analyzed some leading stock market shares and successfully forecast their trends. We also scrutinized the share market price by proper reasoning and considered the state of affairs of COVID-19, including geographical dispersity. We publicly release our developed dashboard that presents statistical data of COVID-19 cases, shows predicted results, and reveals the impact of COVID-19 on leading companies and different countries' job markets.

The Effect of COVID-19 on Herding Behavior in Eastern European Stock Markets

Unlike past health crises that were more localized, the highly contagious coronavirus disease 2019 (COVID-19) crisis is impacting the world to an unprecedented extent. This is the first study examining how and whether the COVID-19 pandemic affects herding behavior in the Eastern European stock markets. Using samples from the stock markets of Russia, Poland, the Czech Republic, Hungary, Croatia, and Slovenia from January 1, 2010 to March 10, 2021, we demonstrate that the COVID-19 pandemic has increased herding behavior in all the sample stock markets. Our results show that the COVID-19 crisis reinforces the impact of global market returns on herding behavior in these specific stock markets. We find that COVID-19 strengthens the spillover effect of regional herding on herding behavior. Thus, financial authorities should monitor investors in the stock market to avoid the increase in herding behavior as well as the reinforcement of the global market returns and regional return dispersion on herding during the period of pandemic.

Network geometry and market instability

The complexity of financial markets arise from the strategic interactions among agents trading stocks, which manifest in the form of vibrant correlation patterns among stock prices. Over the past few decades, complex financial markets have often been represented as networks whose interacting pairs of nodes are stocks, connected by edges that signify the correlation strengths. However, we often have interactions that occur in groups of three or more nodes, and these cannot be described simply by pairwise interactions but we also need to take the relations between these interactions into account. Only recently, researchers have started devoting attention to the higher-order architecture of complex financial systems, that can significantly enhance our ability to estimate systemic risk as well as measure the robustness of financial systems in terms of market efficiency. Geometry-inspired network measures, such as the Ollivier-Ricci curvature and Forman-Ricci curvature, can be used to capture the network fragility and continuously monitor financial dynamics. Here, we explore the utility of such discrete Ricci curvatures in characterizing the structure of financial systems, and further, evaluate them as generic indicators of the market instability. For this purpose, we examine the daily returns from a set of stocks comprising the USA S&P-500 and the Japanese Nikkei-225 over a 32-year period, and monitor the changes in the edge-centric network curvatures. We find that the different geometric measures capture well the system-level features of the market and hence we can distinguish between the normal or 'business-as-usual' periods and all the major market crashes. This can be very useful in strategic designing of financial systems and regulating the markets in order to tackle financial instabilities.

On the effect of oil price in the context of Covid‐19

This article studies the impact of oil price change on the stock market, the exchange rate and the real estate market in the US over the last decade. To this end, we model the dynamics of the returns for these markets and test the effect of oil market volatility on their dynamics. Through different econometric investigations, we show, in the context of Covid‐19, that oil price has experienced significant effects on the US stock market and the US dollar exchange rate, while it has no significant impact on the US real estate market. In particular, we highlight, first, a positive and significant reaction of the stock market toward an oil price shock, which might be explained by the effect of high oil financialization over the last decade. Second, we show an adverse effect of the oil price change on the US dollar, suggesting a negative relationship between the oil price and the US dollar. Accordingly, the information provided by the oil sector might help to improve the forecast of the Dow Jones and the US$\€ exchange rate.

Investigating the relationship between volatilities of cryptocurrencies and other financial assets

This paper analyzes the relationships between volatilities of five cryptocurrencies, American indices (S&P500, Nasdaq, and VIX), oil, and gold. The results of the BEKK-GARCH model show evidence of a higher volatility spillover between cryptocurrencies and lower volatility spillover between cryptocurrencies and financial assets. The results of the DCC-GARCH model identify an important effect of the launch of Bitcoin futures. During the stability period, the overarching implications of the results are that there is a persistence of correlation between cryptocurrencies in high positive value and low dynamic conditional correlations between cryptocurrencies and financial assets. Also, we find that Bitcoin and gold are considered hedges for the US investors before the coronavirus crisis. Our results show that cryptocurrencies may offer diversification benefits for investors and are diversifiers during the stability period. At the beginning of 2020, we observe that the conditional correlation increased between cryptocurrencies, stock indexes, and oil which confirm the effect of the coronavirus contagion between them. Unlike gold, digital assets are not a safe haven for US investors during the coronavirus crisis.

Training Multilayer Perceptron with Genetic Algorithms and Particle Swarm Optimization for Modeling Stock Price Index Prediction

Predicting stock market (SM) trends is an issue of great interest among researchers, investors and traders since the successful prediction of SMs' direction may promise various benefits. Because of the fairly nonlinear nature of the historical data, accurate estimation of the SM direction is a rather challenging issue. The aim of this study is to present a novel machine learning (ML) model to forecast the movement of the Borsa Istanbul (BIST) 100 index. Modeling was performed by multilayer perceptron-genetic algorithms (MLP-GA) and multilayer perceptron-particle swarm optimization (MLP-PSO) in two scenarios considering Tanh (x) and the default Gaussian function as the output function. The historical financial time series data utilized in this research is from 1996 to 2020, consisting of nine technical indicators. Results are assessed using Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and correlation coefficient values to compare the accuracy and performance of the developed models. Based on the results, the involvement of the Tanh (x) as the output function, improved the accuracy of models compared with the default Gaussian function, significantly. MLP-PSO with population size 125, followed by MLP-GA with population size 50, provided higher accuracy for testing, reporting RMSE of 0.732583 and 0.733063, MAPE of 28.16%, 29.09% and correlation coefficient of 0.694 and 0.695, respectively. According to the results, using the hybrid ML method could successfully improve the prediction accuracy.

The impact of COVID-19 on emerging stock markets

The goal of this study is to investigate the impact of COVID-19 on emerging stock markets over the period March 10 - April 30, 2020. Findings reveal that the negative impact of pandemic on emerging stock markets has gradually fallen and begun to taper off by mid-April. In terms of regional classification, the impact of the outbreak has been the highest in Asian emerging markets whereas emerging markets in Europe have experienced the lowest. We also find that official response time and the size of stimulus package provided by the governments matter in offsetting the effects of the pandemic.

Stock Market Reactions to COVID-19 Pandemic Outbreak: Quantitative Evidence from ARDL Bounds Tests and Granger Causality Analysis

This paper examines the linkages in financial markets during coronavirus disease 2019 (COVID-19) pandemic outbreak. For this purpose, daily stock market returns were used over the period of December 31, 2019-April 20, 2020 for the following economies: USA, Spain, Italy, France, Germany, UK, China, and Romania. The study applied the autoregressive distributed lag (ARDL) model to explore whether the Romanian stock market is impacted by the crisis generated by novel coronavirus. Granger causality was employed to investigate the causalities among COVID-19 and stock market returns, as well as between pandemic measures and several commodities. The outcomes of the ARDL approach failed to find evidence towards the impact of Chinese COVID-19 records on the Romanian financial market, neither in the short-term, nor in the long-term. On the other hand, our quantitative approach reveals a negative effect of the new deaths' cases from Italy on the 10-year Romanian bond yield both in the short-run and long-run. The econometric research provide evidence that Romanian 10-year government bond is more sensitive to the news related to COVID-19 than the index of the Bucharest Stock Exchange. Granger causality analysis reveals causal associations between selected stock market returns and Philadelphia Gold/Silver Index.

Endangering China's environmental health security goals through negative environmental investor behaviours

China sees the need to maximise its environmental health security as a major priority in its sustainable development agenda. This is at the heart of China's "ecological civilisation" and "beautiful China" dream. One of the objectives of this dream is to sensitize investors to invest in health and environmental stocks to support environmental health goals. However, both the Shanghai and the Shenzhen stock markets continue to witness contemporaneous movement (herding behaviour) by investors from environmental stock to perceived safer stocks and this is stifling the growth of the environmental health sector due to capital deprivation. Our paper evaluates the significance and potential effect of this herding trend among environmental stocks using a collection of sophisticated econometric models namely, the state-space model, enhanced state-space model, the cross-sectional SD (CSSD) and the cross-sectional absolute deviation (CSAD). The models are used to evaluate firm-level data collected from the 80 environmental stocks indexed by the KGRM MSCI China IMI Environment 10/40 Index. Three of the models confirm the presence of endemic negative (herding) investor behaviour among environmental stocks in China and this threatens the sustainability of environmental stock capital to promote China's environmental health goals. We have proposed measures to ameliorate the risks posed by such negative contemporaneous investor behaviours.

A New Look on Financial Markets Co-Movement through Cooperative Dynamics in Many-Body Physics

One of the main contributions of the Capital Assets Pricing Model (CAPM) to portfolio theory was to explain the correlation between assets through its relationship with the market index. According to this approach, the market index is expected to explain the co-movement between two different stocks to a great extent. In this paper, we try to verify this hypothesis using a sample of 3.000 stocks of the USA market (attending to liquidity, capitalization, and free float criteria) by using some functions inspired by cooperative dynamics in physical particle systems. We will show that all of the co-movement among the stocks is completely explained by the market, even without considering the market beta of the stocks.

Randomness, Informational Entropy, and Volatility Interdependencies among the Major World Markets: The Role of the COVID-19 Pandemic

The main purpose of our paper is to evaluate the impact of the COVID-19 pandemic on randomness in volatility series of world major markets and to examine its effect on their interconnections. The data set includes equity (Bitcoin and Standard and Poor’s 500), precious metals (Gold and Silver), and energy markets (West Texas Instruments, Brent, and Gas). The generalized autoregressive conditional heteroskedasticity model is applied to the return series. The wavelet packet Shannon entropy is calculated from the estimated volatility series to assess randomness. Hierarchical clustering is employed to examine interconnections between volatilities. We found that (i) randomness in volatility of the S&P500 and in the volatility of precious metals were the most affected by the COVID-19 pandemic, while (ii) randomness in energy markets was less affected by the pandemic than equity and precious metal markets. Additionally, (iii) we showed an apparent emergence of three volatility clusters: precious metals (Gold and Silver), energy (Brent and Gas), and Bitcoin and WTI, and (iv) the S&P500 volatility represents a unique cluster, while (v) the S&P500 market volatility was not connected to the volatility of Bitcoin, energy, and precious metal markets before the pandemic. Moreover, (vi) the S&P500 market volatility became connected to volatility in energy markets and volatility in Bitcoin during the pandemic, and (vii) the volatility in precious metals is less connected to volatility in energy markets and to volatility in Bitcoin market during the pandemic. It is concluded that (i) investors may diversify their portfolios across single constituents of clusters, (ii) investing in energy markets during the pandemic period is appealing because of lower randomness in their respective volatilities, and that (iii) constructing a diversified portfolio would not be challenging as clustering structures are fairly stable across periods.

Deep Learning for Stock Market Prediction

The prediction of stock groups values has always been attractive and challenging for shareholders due to its inherent dynamics, non-linearity, and complex nature. This paper concentrates on the future prediction of stock market groups. Four groups named diversified financials, petroleum, non-metallic minerals, and basic metals from Tehran stock exchange were chosen for experimental evaluations. Data were collected for the groups based on 10 years of historical records. The value predictions are created for 1, 2, 5, 10, 15, 20, and 30 days in advance. Various machine learning algorithms were utilized for prediction of future values of stock market groups. We employed decision tree, bagging, random forest, adaptive boosting (Adaboost), gradient boosting, and eXtreme gradient boosting (XGBoost), and artificial neural networks (ANN), recurrent neural network (RNN) and long short-term memory (LSTM). Ten technical indicators were selected as the inputs into each of the prediction models. Finally, the results of the predictions were presented for each technique based on four metrics. Among all algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. In addition, for tree-based models, there is often an intense competition between Adaboost, Gradient Boosting, and XGBoost.

The Evolution Characteristics of Systemic Risk in China's Stock Market Based on a Dynamic Complex Network

The stock market is a complex system with unpredictable stock price fluctuations. When the positive feedback in the market amplifies, the systemic risk will increase rapidly. During the last 30 years of development, the mechanism and governance system of China’s stock market have been constantly improving, but irrational shocks have still appeared suddenly in the last decade, making investment decisions risky. Therefore, based on the daily return of all a-shares in China, this paper constructs a dynamic complex network of individual stocks, and represents the systemic risk of the market using the average weighting degree, as well as the adjusted structural entropy, of the network. In order to eliminate the influence of disturbance factors, empirical mode decomposition (EMD) and grey relational analysis (GRA) are used to decompose and reconstruct the sequences to obtain the evolution trend and periodic fluctuation of systemic risk. The results show that the systemic risk of China’s stock market as a whole shows a downward trend, and the periodic fluctuation of systemic risk has a long-term equilibrium relationship with the abnormal fluctuation of the stock market. Further, each rise of systemic risk corresponds to external factor shocks and internal structural problems.

Sovereign Credit Default Swap and Stock Markets in Central and Eastern European Countries: Are Feedback Effects at Work?

The purpose of the paper is to investigate the relationship between sovereign Credit Default Swap (CDS) and stock markets in nine emerging economies from Central and Eastern Europe (CEE), using daily data over the period January 2008–April 2018. The analysis deploys a Vector Autoregressive model, focusing on the direction of Granger causality between the credit and stock markets. We find evidence of the presence of bidirectional feedback between sovereign CDS and stock markets in CEE countries. The results highlight a transfer entropy of risk from the private to public sector over the whole period and respectively, from the public to private transfer entropy of risk during the European sovereign debt crisis only in Romania and Slovenia. Another finding that deserves particular attention is that the linkage between the CDS spreads and stock markets is time-varying and subject to regime shifts, depending on global financial conditions, such as the sovereign debt crisis. By providing insights on the inter-temporal causality of the comovements of the CDS–stock markets, the paper has significant practical implications for risk management practices and regulatory policies, under different market conditions of European emerging economies.

Building stock market resilience through digital transformation: using Google trends to analyze the impact of COVID-19 pandemic

As the 2019 novel coronavirus disease (COVID-19) pandemic rages globally, its impact has been felt in the stock markets around the world. Amidst the gloomy economic outlook, certain sectors seem to have survived better than others. This paper aims to investigate the sectors that have performed better even as market sentiment is affected by the pandemic. The daily closing stock prices of a total usable sample of 1,567 firms from 37 sectors are first analyzed using a combination of hierarchical clustering and shape-based distance (SBD) measures. Market sentiment is modeled from Google Trends on the COVID-19 pandemic. This is then analyzed against the time series of daily closing stock prices using augmented vector autoregression (VAR). The empirical results indicate that market sentiment towards the pandemic has significant effects on the stock prices of the sectors. Particularly, the stock price performance across sectors is differentiated by the level of the digital transformation of sectors, with those that are most digitally transformed, showing resilience towards negative market sentiment on the pandemic. This study contributes to the existing literature by incorporating search trends to analyze market sentiment, and by showing that digital transformation moderated the stock market resilience of firms against concern over the COVID-19 outbreak.

Intraday News Trading: The Reciprocal Relationships Between the Stock Market and Economic News

This study investigates the interdependent relationships between the stock market and economic news in the U.S. context. 2,440 economic tweets from Reuters and Bloomberg published in September 2015 were analyzed within short-term intervals (5 minutes, 20 minutes, and 1 hour) as well as 50 influential Bloomberg market coverage stories distributed via their terminals for the same period of time. Using Vector Auto Regression analyses, it was found that news volume, news relevance, and expert opinion in tweets seem to influence the fluctuation of the Dow Jones Industrial Average (DJI) positively, while economic news appears to respond to market fluctuation with less coverage, including fewer retweets, favorites, updates, or expert opinions conveyed. Inspecting the influential market stories by Bloomberg, the results imply that while Bloomberg terminals provide firsthand information on the market to professionals, tweets rather seem to offer follow-up reporting to the public. Furthermore, given that the effect of economic tweets on the DJI fluctuations was found to be strongest within longer time intervals (i.e., 1 hour), the findings imply that public traders need more time to evaluate information and to make a trading decision than professional investors.

Stock Market Fluctuations and Self-Harm among Children and Adolescents in Hong Kong

Although a few studies investigated the impact of stock market fluctuations on population health, the question of whether stock market fluctuations have an impact on self-harm in children and adolescents remain unanswered. This study therefore investigated the association between stock market fluctuations and self-harm among children and adolescents in Hong Kong. Daily self-harm attendance records were retrieved from all 18 local Accident and Emergency Departments (AED) from 2001 to 2012. 4931 children and adolescents who committed self-harm were included. The results indicated positive correlation between daily change in stock market index, Hang Seng Index (∇HSI, per 300 points), and daily self-harm incident risk of children and adolescents, without time lag between the two. The incident risk ratio for ∇HSI was 1.09 (p = 0.0339) in children and 1.06 (p = 0.0246) in adolescents. Importantly, non-trading days were found to impose significant protective effect in both groups against self-harm risk. Our results showed that stock market fluctuations were related to self-harm behaviors in children and adolescents. Parents and professionals should be educated about the potential harm of stock market fluctuations and the importance of effective parenting in reducing self-harm among children and adolescents.

Economic Stress and Mental Health: The Relationship Between the Stock Market and Neurotic Disorder Doctor Visits

This paper investigates the relationship between the stock market and the neurotic disorder doctor visits. We use aggregate data, partition the population by age and gender and examine the impact of changes in the stock market on neurotic disorders. Using doctor visits as a proxy measure of morbidity, we find evidence of some relationship between neurotic disorder morbidity and stock market variations. A stock market falling in a single day and the accumulation of daily stock market drops are both associated with more neurotic disorder doctor visits. We also observe more neurotic disorder doctor visits during periods of a low stock index for the elderly, regardless of gender. Copyright © 2016 John Wiley & Sons, Ltd.

Accurate, Traceable, and Verifiable Time Synchronization for World Financial Markets

The National Institute of Standards and Technology (NIST), through a collaboration with Perseus, a global provider of telecommunication services, is providing accurate, traceable, and verifiable time synchronization to stock exchanges in the United States, Europe, and Asia. The paper describes why accurate time is necessary for fair and equitable financial markets and summarizes current and proposed future synchronization requirements in the financial sector. We discuss reference time sources and provide a technical overview of how NIST transfers time to data center hosted stock exchange. We also discuss how Perseus distributes NIST time to financial market customers and describes how the time is verified. Measurement data are presented, along with a discussion of measurement uncertainty.

The Psychology and Neuroscience of Financial Decision Making

Financial decisions are among the most important life-shaping decisions that people make. We review facts about financial decisions and what cognitive and neural processes influence them. Because of cognitive constraints and a low average level of financial literacy, many household decisions violate sound financial principles. Households typically have underdiversified stock holdings and low retirement savings rates. Investors overextrapolate from past returns and trade too often. Even top corporate managers, who are typically highly educated, make decisions that are affected by overconfidence and personal history. Many of these behaviors can be explained by well-known principles from cognitive science. A boom in high-quality accumulated evidence-especially how practical, low-cost 'nudges' can improve financial decisions-is already giving clear guidance for balanced government regulation.

Relationship between suicide rate and economic growth and stock market in the People's Republic of China: 2004-2013

OBJECTIVES

The objective of this study was to estimate the features of suicide rate and its association with economic development and stock market during the past decade in the People's Republic of China.

METHODS

Official data were gathered and analyzed in the People's Republic of China during the period 2004-2013. Nationwide suicide rate was stratified by four year age-groups, sex, urban/rural areas, and regions (East, Central, and West). Annual economic indexes including gross domestic product (GDP) per capita and rural and urban income per capita were all adjusted for inflation. Variation coefficient of market index (VCMI) was also included as an economic index to measure the fluctuation of the stock market. Negative binomial regression was performed to examine the time trend of region-level suicide rates and effects of sex, age, urban/rural area, region, and economic index on the suicide rates.

RESULTS

Suicide rates of each age-group, sex, urban/rural area, and region were generally decreased from 2004 to 2013, while annual GDP per capita and rural and urban income per capita were generally increased by year. VCMI fluctuated largely, which peaked around 2009 and decreased after that time. Negative binomial regression showed that the decreased suicide rate in East and Central rural areas was the main cause of the decrease in suicide rate in the People's Republic of China. Suicide rate in the People's Republic of China for the study period increased with age and was higher in rural than in urban area, higher in males than in females, and the highest in the Central region. When GDP per capita increased by 2,787 RMB, the suicide rate decreased by 0.498 times. VCMI showed no significant relationship with suicide rate in the negative binomial regression.

CONCLUSION

Suicide rate decreased in 2004-2013; varied among different age-groups, sex, urban/rural areas, and regions; and was negatively associated with the economic growth in the People's Republic of China. Stock market showed no relationship with suicide rate, but this finding needs to be verified in a future study.

The Dow is Killing Me: Risky Health Behaviors and the Stock Market

We investigate how risky health behaviors and self-reported health vary with the Dow Jones Industrial Average (DJIA) and during stock market crashes. Because stock market indices are leading indicators of economic performance, this research contributes to our understanding of the macroeconomic determinants of health. Existing studies typically rely on the unemployment rate to proxy for economic performance, but this measure captures only one of many channels through which the economic environment may influence individual health decisions. We find that large, negative monthly DJIA returns, decreases in the level of the DJIA, and stock market crashes are widely associated with worsening self-reported mental health and more cigarette smoking, binge drinking, and fatal car accidents involving alcohol. These results are consistent with predictions from rational addiction models and have implications for research on the association between consumption and stock prices.

Cosmetic surgery growth and correlations with financial indices: a comparative study of the United Kingdom and United States from 2002-2011

BACKGROUND

Over the past 10 years, there has been significant fluctuation in the yearly growth rates for cosmetic surgery procedures in both the United States and the United Kingdom.

OBJECTIVES

The authors compare cosmetic surgical procedure rates in the United Kingdom and United States with the macroeconomic climate of each region to determine whether there is a direct relationship between cosmetic surgery rates and economic health.

METHODS

The authors analyzed annual cosmetic surgery statistics from the British Association of Aesthetic Plastic Surgeons and the American Society for Aesthetic Plastic Surgery for 2002-2011 against economic indices from both regions, including the gross domestic product (GDP), consumer prices indices (CPI), and stock market reports.

RESULTS

There was a 285.9% increase in the United Kingdom and a 1.1% increase in the United States in the number of procedures performed between 2002 and 2011. There were significant positive correlations between the number of cosmetic procedures performed in the United Kingdom and both the GDP (r = 0.986, P < .01) and CPI (r = 0.955, P < .01). Analysis of the US growth rates failed to show a significant relationship with any indices. UK interest rates showed a significant negative correlation (r = -0.668, P < .05) with procedures performed, whereas US interest rates showed a significant positive correlation.

CONCLUSIONS

Data from the United States and United Kingdom suggest 2 very different growth patterns in the number of cosmetic surgeries being performed as compared with the economy in each region. Economic indices are accurate indicators of numbers of procedures being performed in the United Kingdom, whereas rates in the United States seem independent of those factors.

Do Sell-Side Stock Analysts Exhibit Escalation of Commitment?

This paper presents evidence that when an analyst makes an out-of-consensus forecast of a company's quarterly earnings that turns out to be incorrect, she escalates her commitment to maintaining an out-of-consensus view on the company. Relative to an analyst who was close to the consensus, the out-of-consensus analyst adjusts her forecasts for the current fiscal year's earnings less in the direction of the quarterly earnings surprise. On average, this type of updating behavior reduces forecasting accuracy, so it does not seem to reflect superior private information. Further empirical results suggest that analysts do not have financial incentives to stand by extreme stock calls in the face of contradictory evidence. Managerial and financial market implications are discussed.