Investor sentiment and country exchange traded funds: Does economic freedom matter?

Using unbalanced panel data of 27 iShares MSCI country-specific exchange traded funds (ETFs) over the period 1996–2014, this paper applies quantile regression to examine the impacts of global, foreign, and U.S. investor sentiments on the returns of the ETFs traded in the U.S. markets. We further investigate whether a country’s economic freedom affects the relationship between investor sentiments and ETF returns. We find that ETF returns are strongly determined by investor sentiments and the ETF expense ratio. The quantile regression approach reveals that high-return ETFs are positively sensitive to changes in global sentiment (measured by market turnover, VIX, U.S. federal funds rate), foreign sentiment (measured by current account balance, inflation, market turnover, public debt), U.S. sentiment, currency exchange ratio, and expense ratio, while negatively influenced by economic freedom and Asian proxy. The effects of VIX and foreign inflation are a reversal; that is, returns from lower (higher) quantiles have a negative (positive) relation with VIX and foreign inflation. Not all components of economic freedom affect returns equally.     

Do natural disasters and geopolitical risks matter for cross-border country exchange-traded fund returns?

This paper examines whether U.S. and home country geopolitical risks (GPRs) and disasters matter for the returns from cross-border trading of country exchange-traded funds (ETFs) by employing a quantile regression approach. Using monthly returns of 125 country-specific ETFs traded in the U.S. from 38 countries over the period 2004–2018, we find that the highest averages of total deaths, total damage values, total affected, and GPRs are all in developing countries. United States disasters have comparatively more significant impacts than home country disasters on ETF returns as does the salient influences of U.S. investor sentiments, supporting the market sentiment hypothesis. Moreover, U.S. and home country GPRs and disasters also have predictive power on returns. The contemptuous effects and predictive powers of GPRs and disasters are asymmetric across quantiles. The influences of home country GPRs are more salient than the GRPS of U.S., implying that ETFs can be a safe haven during U.S. geopolitical risks. Additionally, our results show that the impacts of disasters on returns can be negative and/or positive, implying the possibility of disasters exuding an impetus and/or risk to country ETFs.     

Twitter’s daily happiness sentiment,economic policy uncertainty,and stock index fluctuations

This research applies quantile Granger causality and impulse-response analyses to evaluate the causal linkages among Twitter’s daily happiness sentiment, economic policy uncertainty (EPU), and S&P 500 indices across the U.S. stock market cycles. We present notable evidence of bi-directional causality among cyclical components of Twitter’s daily happiness sentiment, economic policy uncertainty, and S&P 500 indices for most quantiles. The causal linkage of Twitter’s daily happiness sentiment and S&P 500 indices identified in this study reconciles the so-called Easterlin Paradox and Easterlin Illusion arguments from previous studies on income-happiness relationship. Moreover, given a high (low) EPU level, the positive (negative) impulse-response effects between the Twitter’s daily happiness sentiment and the S&P 500 indices are justified during a stock market bust cycle, but the signs of these correlations change to negative (positive) during a stock market boom cycle. These findings imply that investors’ hedging strategies can be linked to the surveillance of the Twitter’s daily happiness sentiment index.     

Location of trade,return comovements,and diversification benefits: Evidence from Asian country ETFs

Using data from the Asian country ETFs and their MSCI indices, this paper examines whether trading location affects return comovements and diversification benefits. Our empirical results show that the magnitude of return comovements for the Asian country ETFs is higher than the corresponding MSCI indices. Moreover, our empirical findings indicate that the factors, including investor sentiment, market conditions, and economic fundamentals of the U.S. market, have greater effects on the return comovements for the Asian country ETFs than their underlying MSCI indices. Finally, the evidence presents a higher diversification benefit for the Asian MSCI indices than the Asian country ETFs.     

Overnight stock returns,intraday returns,and firm-specific investor sentiment

In this study, we propose a new index for measuring firm-specific investor sentiment using overnight and intraday stock returns. We use actual equity data to construct the firm-level investor sentiment index and find that the new index has characteristics expected of a sentiment measure. In addition, we propose a novel sentiment-weighted trading strategy and apply it to momentum and short-term reversal strategies. We find that the sentiment-weighted trading strategy generates better performance in momentum and short-term reversal strategies. The sentiment-weighted trading strategy’s superior performance is evidence that our firm-level investor sentiment index possesses predictive powers with regard to future returns.     

Does investor sentiment impact the returns and volatility of Islamic equities?

In this paper, we estimate generalized autoregressive conditional heteroskedasticity (GARCH) and vector autoregressive (VAR) models to examine whether investor sentiment impacts the returns and volatility of various U.S. Dow Jones Islamic equity indices. The results from GARCH estimations show that changes in investor sentiment are positively correlated with the returns of the Shari’ah-compliant market portfolio. In addition, we find similar results for the three Shari’ah-compliant firm-size portfolios (i.e., large-, medium-, and small-cap). However, this relationship is stronger for harder to arbitrage Shari’ah-compliant stocks; that is, investor sentiment has a greater influence on small-cap equities. Additionally, estimations from the vector autoregressive model confirm the aforementioned results. In terms of volatility, GARCH estimations suggest that bullish shifts in investor sentiment in the current period are accompanied by lower conditional volatility in the ensuing period. In general, our findings suggest that as noise traders create more risk the market seems to reward them with higher expected returns.     

Stock prices and the location of trade: Evidence from China-backed ADRs

This study examines whether the trading location affects equity returns of China-backed American Depository Receipts (ADRs) traded in the US. If International Financial Markets are integrated, stock prices should be affected only by their fundamentals; otherwise, stock prices may also be affected by their trading locations/investor sentiment. We find that China ADRs’ returns are affected more by the US market fluctuations than by Chinese market returns. We interpret the results as suggesting that International Financial Markets are at least partially segmented and country-specific investor sentiment affects stock prices.     

The contagion effects of volatility indices across the U.S. and Europe

This research adopts an autoregressive conditional jump intensity (ARJI) model by utilizing intraday data of overlapping trading hours to analyze the global contagion effects of sentimental responses and volatility dynamics through the volatility indices of the U.S. and three major European countries. The results show strong evidence of contagion effects among the volatility indices. For regional effects, compared to the other two European indices (VFTSE and VCAC), the volatility index of Germany (VDAX) generates greater impacts on the other indices, and we also observe bi-directional causalities among these three countries. The findings support the meteor shower hypothesis among the sample countries. Additionally, jump innovations in Germany affect investor sentiments with the fastest convergence speed. Finally, the jump convergence speed in the UK is slower, and the impacts of unexpected news could also last longer for UK investors.     

Factors affecting delinquency of household credit in the U.S.: Does consumer sentiment play a role?

In this study, we provide both theoretical and empirical evidence on the determinants of household loan delinquency for home ownership, credit card and auto loans for the U.S. states in a panel framework over a period from 2003 through 2017. In particular, we examine the impact of consumer sentiments on loan delinquency rates. We show that improved current consumer sentiment significantly induce lower mortgage, credit card and automobile loan defaults in the American states subdivided into four different regions. We also find that the higher overall and expected consumer sentiment raise loan delinquencies. Implicit in this finding is the apparently excessive and inappropriate expansion of loans in the U.S. economy in the face of consumers’ optimism, which in turn, provides an intuitive understanding of the circumstances that could precede a depression or outbreak of anomalies in the financial sector. Our general findings further exhibit significant positive effect of unemployment rate and mostly adverse effect of per capita income on mortgage and automobile loan delinquency rates. The results provide some compelling evidence with regard to the effect of consumer confidence on household credit delinquency rates across various states in the U.S. and are robust to alternative measures of income and mortgage rates.     

The impacts of investor sentiment on returns and conditional volatility of international stock markets

One of the main arguments of behavioral finance is that some properties of asset prices are most probably regarded as deviations from fundamental value and they are generated by the participation of traders who are not fully rational, thus called noise traders. Noise trader theory postulates that sentiment traders have greater impact during high-sentiment periods than during low-sentiment periods, and sentiment traders miscalculate the variance of returns undermining the mean-variance relation. The main objective of this research is to construct a model to evaluate the returns and conditional volatility of various stock market indexes considering the changes in the investor sentiment by measuring the effects of noise trader demand shocks on returns and volatility. EGARCH model is used to determine whether earning shocks have more influence on the conditional volatility in high sentiment periods weakening the mean–variance relation. This paper takes an international approach using weekly market index returns of U.S., Japan, Hong Kong, U.K., France, Germany, and Turkey. Weekly trading volumes of these indexes are regressed against a group of macroeconomic variables and the residuals are used as proxies for investor sentiment and significant evidence is found that there is asymmetric volatility in these market indexes and earning shocks have more influence on conditional volatility when the sentiment is high.     

Price effects after one-day abnormal returns in developed and emerging markets: ESG versus traditional indices

This paper examines price effects related to one day abnormal returns on the stock market indices of both developed and emerging countries while accounting for differences between environmental, social, governance (ESG), and conventional indices. Using daily data from MSCI family indices from 2007 to 2020 and various methods to avoid methodological bias, the following hypotheses are tested: after one-day abnormal returns, specific price effects (momentum/contrarian) appear (H1) in cases of positive (H1.1) and negative (H1.2) returns, price effects after one-day abnormal returns are stronger in the case of traditional indices as compared to ESG indices (H2), price effects after one-day abnormal returns are stronger during the crisis period (H3), a dynamic trigger approach is more appropriate for defining abnormal returns than a static approach (H4), price effects after one-day abnormal returns are stronger in emerging markets as compared to developed ones (H5). The results are mixed in the case of H1 and provide no evidence in favor of H2-H5. They also show no significant differences between ESG and conventional indices. The types of detected price effects are the same for the cases of ESG and conventional indices; their power is different in some cases. Overall, a strong contrarian effect is observed in the US stock market after one-day abnormal returns; a trading strategy constructed based on this observation could generate profits from trading. The main results offer additional evidence against the Efficient Market Hypothesis and provide implications that can assist practitioners in beating the market.     

Scheduled macroeconomic news announcements and intraday market sentiment

We analyze the effects of scheduled macroeconomic news on intraday and daily market sentiment by comparing sentiment on news announcement dates with that on non-announcement dates. Announcements of macroeconomic indicators change neither intraday nor daily market sentiment. However, the directions of the announced values have asymmetric effects on intraday market sentiment, although they do not affect daily market sentiment. For example, an announcement of an increase in the gross domestic product (GDP) reduces short-term intraday market sentiment, whereas an announcement of a decrease in GDP does not significantly affect intraday market sentiment. We also find that the effect of intraday market sentiment on short-term market returns is greater following announcements of macroeconomic indicators that significantly affect intraday market sentiment.     

How does HFT activity impact market volatility and the bid-ask spread after an exogenous shock? An empirical analysis on S&P 500 ETF

In this paper, we empirically analyse infra-second datasets of the SPDR S&P 500 ETF (specifically, the ETF of the S&P 500 exchanged on BATS, named SPY.Z) in order to explain how high-frequency trading (HFT) activities (aggressive and passive) impact market volatility and the bid-ask spread before and after an exogenous shock (i.e., the 2016 US presidential election). Using SPDR S&P 500 ETF datasets as a proxy for the market on regular volume trading days (November 3, 2016) and on high-volume trading days (November 9, 2016), we show that HFT, on average, has a disturbing action mainly on regular volume trading days, whereas on high-volume trading days, it appears to have a stabilizing effect by balancing both the volatility and bid-ask spread. That is, HFT as a whole has a more neutral impact on the market’s volatility and bid-ask spread than the single aggressive and passive components. In fact, aggressive HFT has a consistent negative effect that increases, on average, both the volatility and bid-ask spread, whereas passive HFT displays a positive effect that decreases, on average, the volatility and bid-ask spread.     

Currency depreciation effects on ADR returns: evidence from Latin America

We use seemingly unrelated regressions (SUR) and multivariate regression models (MVRM) in a panel sample of 74 American depository receipts (ADR) programs from Argentina, Brazil, Chile, and Mexico during the period May 1994 to May 2009 to analyze the behavior of ADR returns during the 300-day period surrounding the currency crises breakdown in the originator??s country. Controlling for the underlying stock and local and host country equity indices, we find that ADRs generate significant negative abnormal returns during currency crises, due to translation exposure. Abnormal returns remain statistically significant even in crises triggered by currency depreciations as small as 3.6%. The results persist after including exchange rate returns as a control variable and after an orthogonalization procedure of exchange rate against local country indices. In agreement with ADR literature, our results show that ADR prices are determined primarily by the underlying stock, exchange rates, and host country index, in that order. Moreover, we observe how market integration has become evident in more recent times as the coefficients for the U.S. stock market have increased its contribution to ADR price discovery.     

Spillovers of currency carry trade returns,market risk sentiment,and U.S. market returns

This paper examines the link between spillovers of currency carry trade returns and U.S. market returns. Following Tse and Zhao (2012), this paper hypothesizes that the magnitude of spillovers of currency carry trade returns is positively correlated with market risk sentiment and, therefore, has an impact on market returns. Using the G10 currencies and S&P 500 index futures, the empirical results present a high magnitude of spillover effects of currency carry trade markets. The empirical findings also show a significantly positive relationship between spillovers of currency carry trade returns and subsequent market returns. Furthermore, the results indicate that this relationship is stronger in bear markets than in bull markets. Finally, our findings show that spillovers of currency carry trade returns significantly affect the subsequent transition probabilities of market returns.     

上证180指数流动性效应的实证研究

本文从实证的角度探讨了上证180指数创立事件对其成份股流动性的影响以及进一步改善的方法。上证180指数的成立事件一方面促进了成份股价差水平和交易信息对称程度的改善,另一方面又促使交易深度水平和交易活跃性的降低。这些流动性效应是由于机构投资者对180成份股偏好所造成的:机构投资者增加了180成份股的需求同时减少了供给,就导致了交易深度和活跃性的降低;机构投资者作为相对知情交易者,它们的偏好增加了市场的需求竞争程度,从而降低了交易者之间的信息不对称程度以及改善了市场价差水平。根据国外的经验,ETF可以有效地增强市场交易深度,因此本文建议创立上证180指数ETF弥补180指数对深度方面的负面影响。     

President’s Tweets,US-China economic conflict and stock market Volatility: Evidence from China and G5 countries

This study provides empirical evidence that the tweets from US President Donald J. Trump influence the trading decisions of investors worldwide. We examine the effects of Trump’s tweets related to China on stock market volatility in China and the G5 countries. Our results show that Trump’s original tweets related to the US-China economic conflict expand volatility in stock markets worldwide, and the US-China trade friction intensifies this effect. Furthermore, Trump’s tweets with different sentiments have different impacts on the returns of global stock markets. Our findings confirm that international investors may make their investment decisions based on information conveyed in these tweets.     

Impact of network investor sentiment and news arrival on jumps

This paper studied the influence of news announcements and network investor sentiment on Chinese stock index and index futures market jumps. A machine learning text analysis algorithm was employed to measure investor forum sentiment. It was found that news arrivals were an important reason for jump occurrences, jumps were significantly associated with network investor sentiment, and while occasionally the news and network investor sentiment resulted in simultaneous market jumps, they appeared to be relatively independent. The network investor sentiment time-lag and asymmetric effects were also tested, from which it was found that network investor sentiment had a significant asymmetric effect on the jumps, but time-lag effects had little influence. News announcements and the top 25% of the extreme network sentiments were found to explain more than 50% of the jumps, with extreme sentiments tending to increase the volatility of the news-related jumps and persistently influencing returns after the news-related jumps.     

Pair trading based on quantile forecasting of smooth transition GARCH models

Pair trading is a statistical arbitrage strategy used on similar assets with dissimilar valuations. We utilize smooth transition heteroskedastic models with a second-order logistic function to generate trading entry and exit signals and suggest two pair trading strategies: the first uses the upper and lower threshold values in the proposed model as trading entry and exit signals, while the second strategy instead takes one-step-ahead quantile forecasts obtained from the same model. We employ Bayesian Markov chain Monte Carlo sampling methods for updating the estimates and quantile forecasts. As an illustration, we conduct a simulation study and empirical analysis of the daily stock returns of 36 stocks from U.S. stock markets. We use the minimum square distance method to select ten stock pairs, choose additional five pairs consisting of two companies in the same industrial sector, and then finally consider pair trading profits for two out-of-sample periods in 2014 within a six-month time frame as well as for the entire year. The proposed strategies yield average annualized returns of at least 35.5% without a transaction cost and at least 18.4% with a transaction cost.     

Hard to arbitrage,hard for analysts to forecast

This paper examines whether limits to arbitrage (LA) affect analysts' earnings forecast accuracy. Using the LA index, which is constructed from unique trading constraints in the Chinese stock market and other commonly used measures, we find that forecast accuracy is much lower for stocks with high LA. Moreover, our results are more suited to explanations of cognitive bias that turn to investor sentiment or limited attention and cannot be fully explained by more objective factors, including analyst ability, broker size, broker experience, and commission pressure. We also find that LA amplifies analyst forecast dispersion. Such results indicate that LA distorts analysts’ earnings expectations and provides new insight into how LA affects anomaly returns.