Showing papers on "Stock (geology) published in 2018"

Measuring the Frequency Dynamics of Financial Connectedness and Systemic Risk

Abstract: We propose a new framework for measuring connectedness among financial variables that arise due to heterogeneous frequency responses to shocks. To estimate connectedness in short-, medium-, and long-term financial cycles, we introduce a framework based on the spectral representation of variance decompositions. In an empirical application, we document the rich time-frequency dynamics of volatility connectedness in U.S. financial institutions. Economically, periods in which connectedness is created at high frequencies are periods when stock markets seem to process information rapidly and calmly, and a shock to one asset in the system will have an impact mainly in the short term. When the connectedness is created at lower frequencies, it suggests that shocks are persistent and are being transmitted for longer periods.

Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction

Abstract: Stock trend prediction plays a critical role in seeking maximized profit from the stock investment. However, precise trend prediction is very difficult since the highly volatile and non-stationary nature of the stock market. Exploding information on the Internet together with the advancing development of natural language processing and text mining techniques have enabled investors to unveil market trends and volatility from online content. Unfortunately, the quality, trustworthiness, and comprehensiveness of online content related to stock market vary drastically, and a large portion consists of the low-quality news, comments, or even rumors. To address this challenge, we imitate the learning process of human beings facing such chaotic online news, driven by three principles: sequential content dependency, diverse influence, and effective and efficient learning. In this paper, to capture the first two principles, we designed a Hybrid Attention Networks(HAN) to predict the stock trend based on the sequence of recent related news. Moreover, we apply the self-paced learning mechanism to imitate the third principle. Extensive experiments on real-world stock market data demonstrate the effectiveness of our framework. A further simulation illustrates that a straightforward trading strategy based on our proposed framework can significantly increase the annualized return.

Can Twitter Help Predict Firm-Level Earnings and Stock Returns?

Abstract: Prior research has examined how companies exploit Twitter in communicating with investors, and whether Twitter activity predicts the stock market as a whole. We test whether opinions of individuals tweeted just prior to a firm's earnings announcement predict its earnings and announcement returns. Using a broad sample from 2009 to 2012, we find that the aggregate opinion from individual tweets successfully predicts a firm's forthcoming quarterly earnings and announcement returns. These results hold for tweets that convey original information, as well as tweets that disseminate existing information, and are stronger for tweets providing information directly related to firm fundamentals and stock trading. Importantly, our results hold even after controlling for concurrent information or opinion from traditional media sources, and are stronger for firms in weaker information environments. Our findings highlight the importance of considering the aggregate opinion from individual tweets when assessing...

Do Shareholders Benefit from Green Bonds

Abstract: The green bond market has been growing rapidly worldwide since its debut in 2007. We present the first empirical study on the announcement returns and real effects of green bond issuance by firms in 28 countries during 2007-2017. After compiling a comprehensive international green bond dataset, we document that stock prices positively respond to green bond issuance. However, we do not find a significant premium for green bonds, suggesting that the positive stock returns are not driven by the lower cost of debt. Nevertheless, we show that institutional ownership, especially from domestic institutions, increases after the firm issues green bonds. Moreover, stock liquidity significantly improves upon the issuance of green bonds. Overall, our findings suggest that the firm’s issuance of green bonds is beneficial to its existing shareholders.

Forecasting stock market crisis events using deep and statistical machine learning techniques

Abstract: This work contributes to this ongoing debate on the nature and the characteristics of propagation channels of crash events in international stock markets. Specifically, we investigate transmission mechanisms across stock markets along with effects from bond and currency markets. Our approach comprises a solid forecasting mechanism of the probability of a stock market crash event in various time frames. The developed approach combines different machine learning algorithms which are presented with daily stock, bond and currency data from 39 countries that cover a large spectrum of economies. Specifically, we leverage the merits of a series of techniques including Classification Trees, Support Vector Machines, Random Forests, Neural Networks, Extreme Gradient Boosting, and Deep Neural Networks. To the best of our knowledge, this is the first time that Deep Learning and Boosting approaches are considered in the literature as a means of predicting stock market crisis episodes. The independent variables included in our data contain information regarding both the two fundamental linkage channels through which financial contagion can be initiated: returns and volatility. We apply a suite of machine learning algorithms for selecting the most relevant variables out of a large set of proposed ones. Finally, we employ bootstrap sampling for adjusting the imbalanced nature of the available fitting dataset. Our experimental results provide strong evidence that stock market crises tend to exhibit persistence. We also find significant evidence of interdependence and cross-contagion effects among stock, bond and currency markets. Finally, we show that the use of Deep Neural Networks significantly increases the classification accuracy, while offering a robust way to create a global systemic early warning tool that is more efficient and risk-sensitive than the currently established ones. Thus, central banks may use these tools to early adjust their monetary policy, so as to ensure financial stability.

Oil Prices and the Stock Market

Abstract: This paper develops a novel method for classifying oil price changes as supply or demand driven and documents several new facts about the relation between oil prices and stock returns. Demand shocks are strongly positively correlated with market returns, while supply shocks have a strong negative correlation. The negative eects of supply shocks are concentrated in rms which produce consumer goods, and are also strongest for oil importing countries. Demand shocks are identied as returns to an index of oil producing rms which are orthogonal to unexpected changes in the VIX index. Supply shocks are oil price changes which are orthogonal to demand shocks and changes in the VIX. Theoretical and empirical evidence are presented in support of this strategy.

Correlation Structure and Evolution of World Stock Markets: Evidence from Pearson and Partial Correlation-Based Networks

Abstract: We construct a Pearson correlation-based network and a partial correlation-based network, i.e., two minimum spanning trees (MST-Pearson and MST-Partial), to analyze the correlation structure and evolution of world stock markets. We propose a new method for constructing the MST-Partial. We use daily price indices of 57 stock markets from 2005 to 2014 and find (i) that the distributions of the Pearson correlation coefficient and the partial correlation coefficient differ completely, which implies that the correlation between pairs of stock markets is greatly affected by other markets, and (ii) that both MSTs are scale-free networks and that the MST-Pearson network is more compact than the MST-Partial. Depending on the geographical locations of the stock markets, two large clusters (i.e., European and Asia-Pacific) are formed in the MST-Pearson, but in the MST-Partial the European cluster splits into two subgroups bridged by the American cluster with the USA at its center. We also find (iii) that the centrality structure indicates that outcomes obtained from the MST-Partial are more reasonable and useful than those from the MST-Pearson, e.g., in the MST-Partial, markets of the USA, Germany, and Japan clearly serve as hubs or connectors in world stock markets, (iv) that during the 2008 financial crisis the time-varying topological measures of the two MSTs formed a valley, implying that during a crisis stock markets are tightly correlated and information (e.g., about price fluctuations) is transmitted quickly, and (v) that the presence of multi-step survival ratios indicates that network stability decreases as step length increases. From these findings we conclude that the MST-Partial is an effective new tool for use by international investors and hedge-fund operators.

Migration, Knowledge Diffusion and the Comparative Advantage of Nations

Abstract: Do migrants shape the dynamic comparative advantage of their sending and receiving countries? To answer this question we study the drivers of knowledge diffusion by looking at the dynamics of the export basket of countries, with particular focus on migration. The fact that knowledge diffusion requires direct human interaction implies that the international diffusion of knowledge should follow the pattern of international migration. This is what this paper documents. Our main finding is that migration, and particularly skilled immigration, is a strong and robust driver of productive knowledge diffusion as measured by the appearance and growth of tradable goods in the migrants' receiving and sending countries. We find that a 10% increase in the stock of immigrants from countries exporters of a given product is associated with a 2% increase in the likelihood that the host country will start exporting that good "from scratch" in the following 10-year period. In terms of ability to expand the export basket of countries, a migrant with college education or above is about ten times more "effective" than an unskilled migrant. The results are robust to accounting for shifts in product-specific global demand, to excluding bilateral trade possibly generated by network effects, as well as to instrumenting for migration using a gravity model.

Oil Prices and Stock Markets: A Review of the Theory and Empirical Evidence

Abstract: Do oil prices and stock markets move in tandem or in opposite directions? The complex and time varying relationship between oil prices and stock markets has caught the attention of the financial press, investors, policymakers, researchers, and the general public in recent years. In light of such attention, this paper reviews research on the oil price and stock market relationship. The majority of papers we survey study the impacts of oil markets on stock markets, whereas, little research in the reverse direction exists. Our review finds that the causal effects between oil and stock markets depend heavily on whether research is performed using aggregate stock market indices, sectorial indices, or firm-level data and whether stock markets operate in net oil-importing or net oil-exporting countries. Additionally, conclusions vary depending on whether studies use symmetric or asymmetric changes in the price of oil, or whether they focus on unexpected changes in oil prices. Finally, we find that most studies show oil price volatility transmits to stock market volatility, and that including measures of stock market performance improves forecasts of oil prices and oil price volatility. Several important avenues for further research are identified.

Temporal Relational Ranking for Stock Prediction

Abstract: Stock prediction aims to predict the future trends of a stock in order to help investors to make good investment decisions. Traditional solutions for stock prediction are based on time-series models. With the recent success of deep neural networks in modeling sequential data, deep learning has become a promising choice for stock prediction. However, most existing deep learning solutions are not optimized towards the target of investment, i.e., selecting the best stock with the highest expected revenue. Specifically, they typically formulate stock prediction as a classification (to predict stock trend) or a regression problem (to predict stock price). More importantly, they largely treat the stocks as independent of each other. The valuable signal in the rich relations between stocks (or companies), such as two stocks are in the same sector and two companies have a supplier-customer relation, is not considered. In this work, we contribute a new deep learning solution, named Relational Stock Ranking (RSR), for stock prediction. Our RSR method advances existing solutions in two major aspects: 1) tailoring the deep learning models for stock ranking, and 2) capturing the stock relations in a time-sensitive manner. The key novelty of our work is the proposal of a new component in neural network modeling, named Temporal Graph Convolution, which jointly models the temporal evolution and relation network of stocks. To validate our method, we perform back-testing on the historical data of two stock markets, NYSE and NASDAQ. Extensive experiments demonstrate the superiority of our RSR method. It outperforms state-of-the-art stock prediction solutions achieving an average return ratio of 98% and 71% on NYSE and NASDAQ, respectively.

Short selling risk

Abstract: Short sellers face unique risks, such as the risk that stock loans become expensive and the risk that stock loans are recalled. We show that these short selling risks affect prices among the cross-section of stocks. Stocks with more short selling risk have lower returns, less price efficiency, and less short selling.

Did the S.A.R.S. epidemic weaken the integration of Asian stock markets? Evidence from smooth time-varying cointegration analysis

Abstract: The purpose of this study is to examine the effect of the Severe Acute Respiratory Syndrome (S.A.R.S.) epidemic on the long-run relationship between China and four Asian stock markets. To t...

Indigenous versus foreign innovation and energy intensity in China

Abstract: This paper empirically investigates the roles of indigenous and foreign innovations in the development of technology spillovers originating from foreign direct investments, exports and imports on the energy intensity across China's 30 provinces for the period 2000–2013. The Driscoll-Kraay standard error estimator is first used to tackle the problems of heteroscedasticity and serial correlations in the models, and further discussion with a panel cointegration analysis is employed to confirm the estimates. The results indicate that indigenous innovations play a more important effect on energy intensity than foreign innovations. However, the panel threshold analysis indicates that the effects of foreign innovations on the energy intensity across China depend on the technological absorptive capacity affecting factors such as local research and development investment and human capital stock.

Estimates of Global Bilateral Migration Flows by Gender between 1960 and 2015

Abstract: An indirect estimation method is used to derive country to country migration flows from changes in global bilateral stock data. Estimates are obtained over five- and 10-year periods between 1960 an...

Exchange traded funds and asset return correlations

Abstract: We provide novel evidence supporting the notion that arbitrageurs can contribute to return comovement via exchange trade funds (ETF) arbitrage. Using a large sample of US equity ETF holdings, we document the link between measures of ETF activity and return comovement at both the fund and the stock levels, after controlling for a host of variables and fixed effects and by exploiting the ‘discontinuity’ between stock indices. The effect is also stronger among small and illiquid stocks. An examination of ETF return autocorrelations and stock lagged beta provides evidence for price reversal, suggesting that some ETF-driven return comovement may be excessive.

Stock market efficiency: A comparative analysis of Islamic and conventional stock markets

Abstract: In this paper, we examine the comparative efficiency of 12 Islamic and conventional stock markets counterparts using multifractal de-trended fluctuation analysis (MF-DFA). The full sample results indicate that developed markets are relatively more efficient, followed by the BRICS’ stock markets. The comparative efficiency analysis shows that almost all the Islamic stock markets excluding Russia, Jordan and Pakistan are more efficient than their conventional counterparts. Implying that Islamic stock markets are new, however the peculiar nature, shari’ah compliant laws and good governance and disclosure mechanisms make them more efficient. Further, our results indicate that the Islamic stock markets’ adjustment to speculative activity is, in fact, higher than their conventional counterparts. The findings of the study may help regulators and policy makers to reduce economic distortions through more effective resource allocation.

Going Green or Going Away? A Spatial Empirical Examination of the Relationship between Environmental Regulations, Biased Technological Progress, and Green Total Factor Productivity.

Abstract: China's economic development has resulted in significant resource consumption and environmental damage However, technological progress is important for achieving coordinated economic development and environmental protection Appropriate environmental regulation policies are also important Although green total factor productivity, environmental regulations, and technological progress vary by location, few studies have been conducted from a spatial perspective However, spatial spillover effects should be taken into consideration This study used energy consumption, the sum of physical capital stock and ecological service value as total capital stock, the number of employed people as inputs, sulfur dioxide emissions as undesired outputs, and green GDP as total output to obtain green TFP through a slacks-based measure (SBM) global Malmquist-Luenberger Index This study also estimated China's biased technological progress under environmental constraints from 2004 to 2015 based on relevant data (eg, green GDP, total capital stock, and employment figures) The relationship between green total factor productivity (GTFP), technological progress, and environmental regulation was then examined using a spatial Durbin model Results were as follows: (1) Based on the complementary elements, although the labor costs gradually increase, the rapid accumulation of capital leads to technological progress that is biased toward capital However, technological progress in the labor bias can significantly increase GTFP (2) There is a u-shaped relationship between existing environmental regulations and GTFP Technological progress can significantly promote GTFP in the surrounding areas through existing environmental regulations (3) Under spatial weight, the secondary industry coefficient was negative while human capital stock and FDID had positive effects on GTFP Technological progress is the source of economic growth It is therefore necessary to promote biased technological development and improve labor-force skills while implementing effective environmental regulation policies

Web Media and Stock Markets : A Survey and Future Directions from a Big Data Perspective

Abstract: Stock market volatility is influenced by information release, dissemination, and public acceptance. With the increasing volume and speed of social media, the effects of Web information on stock markets are becoming increasingly salient. However, studies of the effects of Web media on stock markets lack both depth and breadth due to the challenges in automatically acquiring and analyzing massive amounts of relevant information. In this study, we systematically reviewed 229 research articles on quantifying the interplay between Web media and stock markets from the fields of Finance, Management Information Systems, and Computer Science. In particular, we first categorized the representative works in terms of media type and then summarized the core techniques for converting textual information into machine-friendly forms. Finally, we compared the analysis models used to capture the hidden relationships between Web media and stock movements. Our goal is to clarify current cutting-edge research and its possible future directions to fully understand the mechanisms of Web information percolation and its impact on stock markets from the perspectives of investors cognitive behaviors, corporate governance, and stock market regulation.