News Sentiment: A New Yield Curve Factor

The authors show that sentiments from newspaper articles can explain and predict movements in the term structure of U.S. government bonds. This effect is stronger at the short end of the curve, coinciding with greater volatility and investors' need to continually reassess the Fed's reaction function. Facing such uncertainty, market participants rely on news and sentiment as a central element in their decision-making process. Considering this dependence, the authors propose a new yield curve factor—news sentiment—that is distinct from the 3 established yield curve factors (level, slope, and curvature) as well as from fundamental macroeconomic variables.     

Predicting Equity Markets with Digital Online Media Sentiment: Evidence from Markov-switching Models

The authors examine the predictive capabilities of online investor sentiment for the returns and volatility of MSCI U.S. Equity Sector Indices by including exogenous variables in the mean and volatility specifications of a Markov-switching model. As predicted by the semistrong efficient market hypothesis, they find that the Thomson Reuters Marketpsych Indices (TRMI) predict volatility to a greater extent than they do returns. The TRMI derived from equity specific digital news are better predictors than similar sentiment from social media. In the two-regime setting, there is evidence supporting the hypothesis of emotions playing a more important role during stressed markets compared to calm periods. The authors also find differences in sentiment sensitivity between different industries: it is greatest for financials, whereas the energy and information technology sectors are scarcely affected by sentiment. Results are obtained with the R programming language. Code is available from the authors upon request.     

Daily happiness and stock returns: The case of Chinese company listed in the United States

Existing literature exclusively focuses on the association between local investor sentiment and local stock market performance. In this paper, we investigate the contemporaneous and the lead-lag relationship between local daily happiness sentiment extracted from Twitter and stock returns of cross-listed companies, i.e., the Chinese companies listed in the United States. The empirical results show that: 1) by respectively controlling for the firm capitalization, liquidity and volatility, there exists the largest skewness on the Most-happiness subgroup. (2) There exist bi-directional relationships between daily happiness sentiment and market variables, i.e., the stock return, range-based volatility and excess trading volume. (3) There are significantly positive stock returns, higher excess trading volume and higher range-based volatility around the daily happiness sentiment spike days. These findings not only suggest that there exists significant interdependence between online activities and stock market dynamics, but also provide evidence for the existence of “home bias”.     

Investor Pessimism and the German Stock Market: Exploring Google Search Queries

We analyze the relationship of retail investor sentiment and the German stock market by introducing four distinct investor pessimism indices (IPIs) based on selected aggregate Google search queries. We assess the predictive power of weekly changes in sentiment captured by the IPIs for contemporaneous and future DAX returns, volatility and trading volume. The indices are found to have individually varying, but overall remarkably high explanatory power. An increase in retail investor pessimism is accompanied by decreasing contemporaneous market returns and an increase in volatility and trading volume. Future returns tend to increase while future volatility and trading volume decrease. The outcome is in line with the conjecture of correction effects. Overall, the results are well in line with modern investor sentiment theory.     

Where does return and volatility come from? The case of Asian ETFs

We analyze return and volatility of Asian iShares traded in the U.S. The difference in trading schedules between the U.S. and Asia offers a unique market setting that allows us to distinguish various return and volatility sources. We find Asian ETFs have higher overnight volatility than daytime volatility, explained by public information released during each local market's trading session. Local Asian markets also play an important role in determining each Asian ETF return. Nonetheless, returns for these funds are highly correlated with U.S. markets, indicative of the effects of investor sentiment and location of trade. Finally, returns in the U.S. market Granger-cause returns in all six Asian markets are analyzed.     

Relationships among U.S. oil prices and oil industry equity indices

The cointegration analysis suggests that the pure oil industry equity system and the mixed oil price/equity index system offers more opportunities for long-run portfolio diversification and less market integration than the pure oil price systems. On a daily basis, in the oil price systems all oil prices with the exception of the 3-month futures can explain the future movements of each other. In the mixed system, none of the daily oil industry stock indices can explain the daily future movements of the New York Mercantile Exchange (NYMEX) futures prices, whereas these prices can explain the movements of independent companies engaged in exploration, refining, and marketing. The spillover analysis of oil volatility transmission suggests that the oil futures market has a matching or echoing volatility effect on the stocks of some oil sectors and a volatility-dampening effect on the stocks of others. The policy implication is that, during times of high oil volatility, traders should choose the S&P oil sector stocks that match their tolerance for volatility and use the right financial derivative to hedge against or profit from this volatility. The day effect for volatility transmission suggests that Friday has a calming effect on the volatility of oil stocks in general. The effect for Monday is not significant.     

Stock return autocorrelations and predictability in the Chinese stock market—Evidence from threshold quantile autoregressive models

This paper applies the threshold quantile autoregressive model to study stock return autocorrelations and predictability in the Chinese stock market from 2005 to 2014. The results show that the Shanghai A-share stock index has significant negative autocorrelations in the lower regime and has significant positive autocorrelations in the higher regime. It attributes that Chinese investors overreact and underreact in two different states. These results are similar when we employ individual stocks. Besides, we investigate stock return autocorrelations by different stock characteristics, including liquidity, volatility, market to book ratio and investor sentiment. The results show autocorrelations are significantly large in the middle and higher regimes of market to book ratio and volatility. Psychological biases can result into return autocorrelations by using investor sentiment proxy since autocorrelations are significantly larger in the middle and higher regime of investor sentiment. The empirical results show that predictability exists in the Chinese stock market.     

Time-varying relationship of news sentiment,implied volatility and stock returns

I examine the relationship between aggregate news sentiment, S&P 500 index (SPX) returns, and changes in the implied volatility index (VIX). I find a significant negative contemporaneous relationship between changes in VIX and both news sentiment and stock returns. This relationship is asymmetric whereby changes in VIX are larger following negative news and/or stock market declines. Vector autoregression (VAR) analysis of the dynamics and cross-dependencies between variables reveals a strong positive relationship between previous and current period changes in implied volatility and stock returns, while current period and lagged news sentiment has a significant positive (negative) relationship with stock returns (changes in VIX). I develop a simple trading strategy whereby high (low) levels of implied volatility signal attractive opportunities to take short (long) positions in the underlying index, while extremely negative (positive) news sentiment signals opportunities to enter short (long) index positions. The investor fear gauge (VIX) appears to perform better than news sentiment measures in forecasting future returns.     

The effect of political volatility on capital markets in EU accession and neighborhood countries

Political instability has the potential to disrupt financial markets. But how do political institutions affect financial movements in an environment where all institutions are in flux? This paper looks at the effects of formal and informal political volatility in the new EU countries of central and eastern Europe, in the Eastern Neighborhood, and farther afield in Central Asia to answer this question. Using asymmetric GARCH modeling on monthly data, I find that informal political volatility has a significant negative effect on stock returns, while formal political institutions generate much higher financial volatility than changes in monetary policy.     

Effects of volatility shocks on the dynamic linkages between exchange rate,interest rate and the stock market: The case of Turkey

This study analyzes the dynamic relationship between exchange rate (against US dollar), interest rate and the stock market (both in local currency) of Turkey from January 2003 to September 2013. In particular, the paper tries to answer if the correlations between these important variables change abruptly in high volatile periods and if they do, is this change temporary or permanent? In that manner, we first estimate the dynamic correlations between these variables using the VAR(p)–FIAPARCH(1,d,1)–cDCC(1,1) approach. Then, we endogenously detect the volatility shift dates by a novel method of penalized contrast functions and investigate the relation between the dynamic correlations and the high volatile periods. Results reveal that volatility shocks create abrupt changes in the dynamic correlations, however this effect is only short term and do not sustain between consecutive high volatility regimes. Thus, policymakers and investors do not need to be concerned about long run contagion effects.     

The importance of fear: investor sentiment and stock market returns

The presence of investor sentiment pushes asset prices away from the equilibrium level justified by underlying fundamentals. While sentiment is not directly observable, identifying appropriate proxies and, quantifying the impact of sentiment on asset prices is an important topic. Asset prices that do not appropriately reflect fundamental values may result in inefficient allocation of capital – impacting portfolio allocation decisions and the cost of capital. Utilizing a number of sentiment proxies, over the period 1990–2015, we demonstrate a strong relationship between investor sentiment and stock returns that is consistent with theoretical explanations of sentiment. We determine that implied volatility index (VIX) is the preferred measure of sentiment in terms of improving model fit and adding explanatory power. Causality tests suggest that investor fear (VIX) drives returns across firm-size and value, and also across industry. We also illustrate that firms that are more subjective to value, or face limits to arbitrage, such as small-cap stocks, or those in the business equipment (technology) or telecoms industry, are most responsive to changes investor sentiment. Finally, we demonstrate that sentiment has a greater influence on market returns during recession, when sentiment is at its lowest ebb, and this is particularly true for those stocks most susceptible to speculative demand.     

Price limits on a call auction market: Evidence from the Warsaw Stock Exchange

We empirically investigate the impact of price limits on volatility and autocorrelation in the call auction segment of the Warsaw Stock Exchange (WSE). Because call auctions offer time-out periods to investors, we do not expect price limits to counter overreaction and panic in this market structure. Indeed, our empirical findings show that price limits result in excess volatility on the next trading day and strong continuation of price movements, which indicates that price limits only delay the adjustment of prices to equilibrium levels. Our results question the necessity of price limits in the call auction system of the WSE.     

宏观经济状态对资产定价因子的影响

为刻画资产定价因子随宏观经济状态时变性,本文通过经济增长和通货膨胀两个指标定义经济状态,建立宏观经济状态下的条件资产定价模型。研究发现在中国资本市场上此模型能够显著改进CAPM模型的效力,其定价效力甚至优于多因子模型。进一步研究表明,宏观经济通过两个维度影响资产价格：第一,多数资产在衰退时期对市场风险更敏感,其代表市场因子的贝塔系数大于复苏与过热阶段;第二,小市值公司的贝塔系数均值及波动性大于大公司,在经济衰退时承担更高的风险收益。     

Does local and Euro area sentiment matter for sovereign debt markets? Evidence from a bailout country

Does sentiment impact the sovereign debt markets? This article investigates whether lagged domestic and Euro area irrational sentiment (optimism or pessimism unwarranted by fundamentals) predicts future sovereign bond spreads, in Portugal, between January 2000 and December 2013. We find that domestic and Euro area sentiment negatively forecasts total return spreads and that this effect is stronger during the bailout period. Also, we find that the business sentiment is even most noticed. Therefore, Portuguese sovereign debt market is prone to the influence of investors’ sentiment.     

Investor sentiment,variance risk premium and delta-hedged gains

Delta-hedged gains are supposed to be negative and represent a volatility risk premium. Using a sample of Standard & Poor 500 index options from 2006 to 2009, this study documents two anomalies that cannot be explained by the volatility risk premium. First, delta-hedged gains are more negative for out-of-money options than for at-the-money options. Second, delta-hedged gains are significantly positive during financial crisis period. We propose a behavioural explanation in which both option prices and stock prices are affected by investor’s sentiment, but pessimistic sentiment has a greater impact on stock market than option market. This asymmetric response to pessimistic mood in turn affects the relative expensiveness of option prices.     

Information contagion within small worlds and changes in kurtosis and volatility in financial prices

An agent based artificial market is developed to determine the impact of the interaction between investors on prices. It consists of sentiment investors, a single fundamental investor and a market maker. Sentiment investors live in a small world network and have limited liquidity. They trade based on their assessment of the future direction of the market. Consistent with the social learning literature, there are two types of sentiment investors; social learners and experts. Experts only consider private information while social learners also consider the views of neighbours. It is found that the interaction between the agents generate kurtosis and persistence characteristics of volatility in returns. In addition, the level of kurtosis and volatility depends on the inter-connectedness of the network as well as the number of experts and the number of connections from these experts to social learners. Cluster coefficient and characteristic path length analysis show that kurtosis and volatility are lowest within the small world region of the network. This effect is negated as the number of experts increases beyond a threshold.     

Beauty contest,bounded rationality,and sentiment pricing dynamics

We present a dynamic asset pricing model that incorporates investor sentiment, bounded rationality and higher-order expectations to study how these factors affect asset pricing equilibrium. In the model, we utilize a two-period trading market and investors make decisions based on the heterogeneous expectations principle and the “sparsity-based bounded rational” sentiment. We find that bounded rationality results in mispricing and reduces it in next period. Investor sentiment produces more significant effects than private signals, optimistic investor sentiment increases hedging demand, thus causing prices to soar. Higher-order investors are more rational and attentive to the strategies of other participants rather than private signals. This model also derives the dampening effect of higher-order expectations to price volatility and the heterogeneity expectation depicts inconsistent investor behavior in financial markets. In the model, investors' expectations about future price is distorted by their sentiment and bounded rationality, so they obtain a biased mean from the signal extraction.     

The impacts of regime-switching structures and fat-tailed characteristics on the relationship between inflation and inflation uncertainty

A great of deal of study has explored the relationship between inflation and inflation uncertainty under the assumptions of normal distribution and no regime shift. This paper attempts to investigate whether changes in the specification of distribution specification and regime shifts will affect the inflation-uncertainty relationship. Empirical results show that these two factors have a vital effect on the inflation-uncertainty relationship. A specification with four states and the Student’s t distributed error terms can successfully describe the dynamics of the inflation rate. After taking the non-normal density and independent regime shifts into account, this paper finds that inflation uncertainty has no impact on inflation, regardless of inflation pressure. Inflation has a negative impact on inflation uncertainty during periods of high inflation volatility, while the impact of inflation on inflation uncertainty is insignificant during periods of low inflation volatility.     

How integrated are real estate markets with the world market? Evidence from case-wise bootstrap analysis

We investigate the extent by which real estate markets are integrated with the world market. We apply a case-wise bootstrap analysis — a method that is robust to non-normality and increased volatility that characterises financial markets, especially during periods of distress. We also take into account the effect of the global financial crisis. Our investigation is conducted in relation to five most important and highly internationalised real estate markets, namely, the US, UK, Japan, Australia and the United Arab Emirates (UAE). We find that the first four markets are integrated with the world market — with Japan, the US, and the UK being the most integrated, but the last one is not. Our results also show that the US real estate market crisis affected the five markets differently. It made the UAE, Australia and the US real estate markets more integrated internationally but resulted in the Japanese market becoming less globally integrated. In the case of the UK, the crisis did not affect at all its level of integration with the world market.