Attention, search, and information diffusion : study of stock network dynamics and returns

textThere is growing literature on search behavior and using search for prediction of market share or macroeconomic indicators. This research explores investors' stock search behaviors and investigates whether there are patterns in stock returns using those for return prediction. Stock search behaviors may reveal common interest among investors. In the first study, we use graph theory to find investment habitats (or search clusters) formed by users who search common set of stocks frequently. We study stock returns of stocks within the clusters and across the clusters to provide theoretical arguments that drive returns among search clusters. In the second study, we analyze return comovement and cross-predictability among economically related stocks searched frequently by investors. As search requires a considerable amount of cognitive resources of investors, they only search a few stocks and pay high attention to them. According to attention theory, the speed of information diffusion is associated with the level of attention. Quick information diffusion allows investors to receive relevant information immediately and take instantaneous trading action. This immediate action may lead to correlated return comovement. Slow information diffusion creates latency between the occurrence of an event and the action of investors. The slower response may lead to cross-predictability. Making use of the discrepancy in information diffusion, we implement a trading strategy to establish arbitrage opportunities among stocks due to difference in user attention. This research enriches the growing IS literature on information search by (1) identifying new investment habitats based on user search behaviors, (2) showing that varying degrees of co-attention and economic linkages may lead to different speed of information diffusion (3) developing a stock forecasting model based on real-time co-attention intensity of a group economically linked stocks and (4) embarking a new research area on search attention in stock market. The methods in handling complex search data may also contribute to big data research.Information, Risk, and Operations Management (IROM

Two Essays on Customer-Supplier Network and Trade Secrets

This dissertation investigates two important topics: idiosyncratic shock aggregation in customer-supplier network and impacts of trade secret litigations on stock performance. The first essay studies the underlying factors for stock returns comovement between a customer and supplier firm. The investigation further explores the idiosyncratic shocks propagation and aggregation in the network. The second essay documents the stock market reactions to trade secret lawsuit outcomes and its economic meanings to the industry. The first essay, Idiosyncratic Shocks Aggregation in Customer-Supplier Network, is inspired by Acemoglu, et al. (2012)’s theoretical work and Cohen and Frazzini (2008)’s empirical study. Traditional theory regarding idiosyncratic shocks suggests diversification effect averages out microeconomic shocks within each sector of an interconnected network. However, more recent studies show that idiosyncratic shocks may translate into aggregate shocks if the interconnected system is asymmetric. Empirical research in customer-supplier network shows that the stock returns of a customer and a supplier firms comove strongly. Idiosyncratic information and earnings news are the key drivers of the stock return comovement induced by the establishment of the customer-supplier relationship. By studying the return connections between customer and supplier firms, I find idiosyncratic shocks propagate and aggregate in this network. A new risk factor formed by aggregating idiosyncratic returns of customer firms is evidently priced in suppliers’ returns. This study builds on existing customer-supplier network research and contributes to the literature by pinpointing the information channels and contents that drive stock return comovement and document a new risk factor in the customer-supplier network. The second essay, Economic Outcomes of Corporate Espionage, uses a unique hand-collected trade secret lawsuit dataset, and documents strong stock market reactions to trade secret lawsuit outcomes. Trade secret lawsuit data including file date, plaintiffs and defendants, and court rulings are manually collected from the Lexis-Nexis database and carefully screened to determine the directions of court rulings. The empirical results indicate the stock market reacts to court outcomes not only at the firm level but also at the industry level. Further regression and difference-in-differences analysis suggest strong intellectual properties protection system encourages firms’ R&D investment and future growth opportunities

Global Trade and GDP Co-Movement

We revisit the association between trade and GDP comovement for 135 countries from 1970 to 2009. Guided by a simple theory, we introduce two notions of trade linkages: (i) the usual direct bilateral trade index and (ii) new indexes of common exposure to third countries capturing the role of similarity in trade networks. Both measures are economically and statistically associated with GDP correlation, suggesting an additional channel through which GDP fluctuations propagate through trade linkages. Moreover, high income countries become more synchronized when the content of their trade is tilted toward inputs while trade in final goods is key for low income countries. Finally, we present evidence that the density of the international trade network is associated with an amplification of the association between global trade flows and bilateral GDP comovement, leading to a significant evolution of the trade comovement slope over the last two decades

Essays on Systemic Risk in European Banking

This thesis makes a contribution to systemic risk literature in the European banking system. The intimate interdependence between the European banking industries and the fragile GIIPS debt market has jeopardized the banking sector in Europe. The threats of unfavourable financial conditions in European bank- ing sufficiently highlight the importance of the dissertation’s distinct focus on systemic risk measurement and on the risk drivers. The outcomes of the three included papers give support to the European authorities to enact comprehen- sive macroprudential regulation schemes.The first paper estimates the systemic risk contributions of GIIPS-block bank- ing on 14 major banking systems in Europe. The CoVaR measure further eval- uates the magnitude of risk using two methods; quantile regression and DCC. Our results indicate a substantial spillover effect of GIIPS banking on the exam- ined banking systems. In other words, the countries’ banking sectors are in part driven by systemic risk in the GIIPS banking system. We also find supporting ev- idence of amplified spillover effects from the GIIPS-block banking sector during the financial crises.The second paper firstly quantifies the sovereign debt spillovers based on daily returns of GIIPS and individual banks’ CDSs over the period of 2007-2015. Then, it examines banks’ financial features and financial markets’ circumstances that determine variations in the banks’ sovereign risk exposures. We find those banks that hold higher assets in times of crisis or work in markets with unfa- vorable profiles, i.e. low returns and high idiosyncratic risks tend to be further susceptible to sovereign risk. However, we do not observe that variations in the risk exposures have been driven by dissimilarities in individual fundamentals such as leverage, debt-to-cash, and market-to-book value of equity ratios.The third paper analyzes the main determinants of systemic contagion from an individual country’s banking sector to the whole banking industry of Europe in 1999-2013. The results show that differences in systemic risk contribution are driven by a combination of balance-sheet characteristics and macroeconomic conditions such as the country-level VaR, crisis episodes, size or total asset, bi- lateral loan, market-to-book ratio, stock market returns, and industry produc- tion index (IPI).Keywords: Systemic Risk, CoVaR, GIIPS, Quantile Regression, DCC, CDS JEL Classification: G01, G21, E43, N24, H63, F30

Securities trading in multiple markets: the Chinese perspective

This thesis studies the trading of the Chinese American Depositories Receipts (ADRs) and their respective underlying H shares issued in Hong Kong. The primary intention of this work is to investigate the arbitrage opportunity between the Chinese ADRs and their underlying H shares. This intention is motivated by the market observation that hedge funds are often in the top 10 shareholders of these Chinese ADRs. We start our study from the origin place of the Chinese ADRs, China’s stock market. We pay particular attention to the ownership structure of the Chinese listed firms, because part of the Chinese ADRs also listed A shares (exclusively owned by the Chinese citizens) in Shanghai. We also pay attention to the market microstructures and trading costs of the three China-related stock exchanges. We then proceed to empirical study on the Chinese ADRs arbitrage possibility by comparing the return distribution of two securities; we find these two securities are different in their return distributions, and which is due to the inequality in the higher moments, such as skewness, and kurtosis. Based on the law of one price and the weak-form efficient markets, the prices of identical securities that are traded in different markets should be similar, as any deviation in their prices will be arbitraged away. Given the intrinsic property of the ADRs that a convenient transferable mechanism exists between the ADRs and their underlying shares which makes arbitrage easy; the different return distributions of the ADRs and the underlying shares address the question that if arbitrage is costly that the equilibrium price of the security achieved in each market is affected mainly by its local market where the Chinese ADRs/the underlying Hong Kong shares are traded, such as the demand for and the supply of the stock in each market, the different market microstructures and market mechanisms which produce different trading costs in each market, and different noise trading arose from asymmetric information across multi-markets. And because of these trading costs, noise trading risk, and liquidity risk, the arbitrage opportunity between the two markets would not be exploited promptly. This concern then leads to the second intention of this work that how noise trading and trading cost comes into playing the role of determining asset prices, which makes us to empirically investigate the comovement effect, as well as liquidity risk. With regards to these issues, we progress into two strands, firstly, we test the relationship between the price differentials of the Chinese ADRs and the market return of the US and Hong Kong market. This test is to examine the comovement effect which is caused by asynchronous noise trading. We find the US market impact dominant over Hong Kong market impact, though both markets display significant impact on the ADRs’ price differentials. Secondly, we analyze the liquidity effect on the Chinese ADRs and their underlying Hong Kong shares by using two proxies to measure illiquidity cost and liquidity risk. We find significant positive relation between return and trading volume which is used to capture liquidity risk. This finding leads to a deeper study on the relationship between trading volume and return volatility from market microstructure perspective. In order to verify a proper model to describe return volatility, we carry out test to examine the heteroscedasticity condition, and proceed to use two asymmetric GARCH models to capture leverage effect. We find the Chinese ADRs and their underlying Hong Kong shares have different patterns in the leverage effect as modeled by these two asymmetric GARCH models, and this finding from another angle explains why these two securities are unequal in the higher moments of their return distribution. We then test two opposite hypotheses about volume-volatility relation. The Mixture of Distributions Hypothesis suggests a positive relation between contemporaneous volume and volatility, while the Sequential Information Arrival Hypothesis indicates a causality relationship between lead-lag volume and volatility. We find supportive evidence for the Sequential Information Arrival Hypothesis but not for the Mixture of Distributions Hypothesis

A Wavelet Analysis of the Bitcoin- Hashrate Nexus Accounting for the Effects of Energy Commodities

This study investigates the relationship between the growth rates of Bitcoin and Bitcoin hashrate while controlling for the effect of energy commodities, specifically two-month futures on Brent crude oil, coal, and natural gas. Based on daily data from January 2013 until December 2022, we utilize the wavelet methodology to analyze dynamics both in time and frequency. Building on the previous work of Rehman and Kang (2021), this study extends the sample period and improves the replicability of their findings. Controlling for the effect of energy commodities, our analysis reveals several interesting results, highlighting the temporal and dynamic nature of these relationships. Our most significant observation that was discovered in both bi- and multivariate forms of the wavelet methodology is the low-frequency in-phase coherence between bitcoin's returns and hashrate growth rates, which persists from the beginning of 2020 until the end of our sample period in 2023, with hashrate growth rates leading bitcoin returns. These findings suggest that the link between the returns on bitcoin and hashrate growth rates while considering the impact of the energy commodities is complex and context-dependent, and further research is needed to fully understand the underlying mechanisms driving these relationships. Our study contributes to the existing literature on the Bitcoin-hashrate nexus by providing a more comprehensive analysis that accounts for the dynamic nature of these relationships, and by improving the replicability of previous research

A Wavelet Analysis of the Bitcoin-Hashrate Nexus Accounting for the Effects of Energy Commodities

This study investigates the relationship between the growth rates of Bitcoin and Bitcoin hashrate while controlling for the effect of energy commodities, specifically two-month futures on Brent crude oil, coal, and natural gas. Based on daily data from January 2013 until December 2022, we utilize the wavelet methodology to analyze dynamics both in time and frequency. Building on the previous work of Rehman and Kang (2021), this study extends the sample period and improves the replicability of their findings. Controlling for the effect of energy commodities, our analysis reveals several interesting results, highlighting the temporal and dynamic nature of these relationships. Our most significant observation that was discovered in both bi- and multivariate forms of the wavelet methodology is the low-frequency in-phase coherence between bitcoin's returns and hashrate growth rates, which persists from the beginning of 2020 until the end of our sample period in 2023, with hashrate growth rates leading bitcoin returns. These findings suggest that the link between the returns on bitcoin and hashrate growth rates while considering the impact of the energy commodities is complex and context-dependent, and further research is needed to fully understand the underlying mechanisms driving these relationships. Our study contributes to the existing literature on the Bitcoin-hashrate nexus by providing a more comprehensive analysis that accounts for the dynamic nature of these relationships, and by improving the replicability of previous research

Assessing Interbank Connectedness Using Transmission Decomposition Techniques: an Application to Eurozone SIFIs

This paper aims to disentangle the impact of multiple transmission channels in interbank connectedness. We use the identification properties of a structural vector autoregression with a multivariate GARCH-in-mean structure (SVAR-MGARCH-M) to model the dynamics in equity returns of the Eurozone systemically important financial institutions (SIFIs). We can identify the impacts of multiple transmission channels such as asset communality, interbank deposits, and information contagion. Asset communality is both a factor in the transmission of shocks and a means of diversification: heightened connectedness results in an increase in shock spillovers, which are countered by risk sharing benefits. We show that connectedness increased during the financial crisis with a major role for common exposures. Institutions move from being a net recipient to a net transmitter of shocks when the asset communality channel is taken into account, suggesting that we need to evaluate the systemic importance of an institution using all transmission components

Essays in International Macroeconomics and Financial Crisis Forecasting

This thesis contributes long-run perspectives to the research on international macroeconomics and macro-finance. Chapters 2 and 3, analyze international financial linkages and their evolution over the past 150 years. Chapter 4 analyzes external adjustment under the pre-1914 Gold Standard – a fixed exchange rate regime in many ways reminiscent of today's euro area. Finally, chapter 5 uses the accumulated financial crisis experience since 1870 to evaluate the financial crisis forecasting performance of modern machine learning algorithms. Chapter 2, titled "Global risk-taking, exchange rates and monetary policy", revisits one of the core ideas in international macroeconomics, the idea that floating exchange rates help to decouple local interest rates from foreign rates. I find that this is only the case for safe rates, but not for risky rates. For risky rates, I find that their co-movement has increased over the 20th century, regardless of exchange rate regime. Why have floating exchange rates become less effective in decoupling risky rates? I argue that the growing role of leverage-constrained banks in global asset markets is key. More specifically, I introduce an international banking model in which banks' leverage constraints induce excessive volatility into risky rates, and their arbitrage activity spreads this volatility internationally, thus overwhelming floating exchange rates, which are already pinned down by safe rates. In chapter 3, which is joint work with Òscar Jordà, Alan M. Taylor and Moritz Schularick, we analyze the international co-movement of financial cycles and the effect of U.S. monetary policy on global asset prices. We show that the co-movement of financial variables has increased in the long run. The sharp increase in the co-movement of global equity markets in the past three decades is particularly notable. We demonstrate that fluctuations in risk premiums, and not risk-free rates and dividends, account for most of the observed equity price synchronization post-1980. We also show that U.S. monetary policy has come to play an important role as a source of fluctuations in risk appetite across global equity markets. Chapter 4, titled "When do fixed exchange rates work? Evidence from the Gold Standard" explores the circumstances under which a fixed exchange rate regime works. In joint work with Yao Chen, we empirically and theoretically analyze one of the world's largest and most durable fixed exchange rate regimes, the Gold Standard. External adjustment under the Gold Standard was associated with few, if any, output costs. In this chapter, we evaluate how flexible prices, international migration, and monetary policy contributed to this benign adjustment experience. For this purpose, we build and estimate an open economy model for the Gold Standard (1880-1913). We find that the output resilience of Gold Standard members that underwent external adjustment was primarily a consequence of flexible prices. When hit by a shock, quickly adjusting prices induced import- and export responses that stabilized incomes. Crucial in this regard was a historical contingency: namely large primary sectors, whose flexibly priced products drove the export booms that stabilized output during major external adjustments. Finally, chapter 5 contributes to the literature on financial crisis forecasting, using high dimensional data and modern machine learning algorithms. In this chapter, titled "Spotting the danger zone: Forecasting financial crises with classification tree ensembles and many predictors", I introduce classification tree ensembles (CTEs) to the banking crisis forecasting literature. I show that CTEs substantially improve out-of-sample forecasting performance over best practice early-warning systems. CTEs enable policymakers to correctly forecast 80% of crises with a 20% probability of incorrectly forecasting a crisis. These findings are based on a long-run sample (1870 - 2011), and two broad post-1970 samples which together cover almost all known systemic banking crises. More particular, I show that the marked improvement in forecasting performance over conventional best practice models results from the combination of many classification trees into an ensemble, and the use of many predictors