An entropy-based analysis of the relationship between the DOW JONES Index and the TRNA Sentiment series

This article features an analysis of the relationship between the DOW JONES Industrial Average (DJIA) Index and a sentiment news series using daily data obtained from the Thomson Reuters News Analytics (TRNA) provided by SIRCA (The Securities Industry Research Centre of the Asia Pacific). The recent growth in the availability of on-line financial news sources, such as internet news and social media sources provides instantaneous access to financial news. Various commercial agencies have started developing their own filtered financial news feeds which are used by investors and traders to support their algorithmic trading strategies. TRNA is one such data set. In this study, we use the TRNA data set to construct a series of daily sentiment scores for DJIA stock index component companies. We use these daily DJIA market sentiment scores to study the relationship between financial news sentiment scores and the stock prices of these companies using entropy measures. The entropy and mutual information (MI) statistics permit an analysis of the amount of information within the sentiment series, its relationship to the DJIA and an indication of how the relationship changes over time.     

A Study of Social Network Effects on the Stock Market

Consumer confidence is an economic indicator that measures the degree of optimism that consumers feel about the overall state of the economy as well as their personal financial situation. The authors measure consumer sentiment via analysis of social networks and show that such sentiment affects stock prices; specifically, the S&P 500 and the Dow Jones Industrial Average. Shiller, Fischer and Freidman [1984], Fisher and Statman [2003], and Bremmer [2008] also examine the influence of consumer sentiment, measured from Conference Board data, on the stock market. The authors add to this literature by creating a measure of consumer confidence by utilizing Twitter data and by examining the relationship between our measure of consumer sentiment and the S&P 500 and the Dow. They implemented lexicographic analysis of Twitter data over a three-month period and found that talk intensity of economic issues not only causes shifts in the daily stock market prices, but also has a significant negative effect.     

Reuters Sentiment and Stock Returns

Sentiment from more than 3.6 million Reuters news articles is tested in a vector autoregression model framework on its ability to forecast returns of the Dow Jones Industrial Average stock index. We show that Reuters sentiment can explain and predict changes in stock returns better than macroeconomic factors. We further find that negative Reuters sentiment has more predictive power than positive Reuters sentiment. Trading strategies with Reuters sentiment achieve significant outperformance with high success rates as well as high Sharpe ratios.     

The impact of news,oil prices,and global market developments on Russian financial markets1

This paper analyses the impact of news, oil prices, and international financial market developments on daily returns on Russian bond and stock markets. First, regarding returns, energy news affects returns, while news from the war in Chechnya is not significant. Market volatility does not appear to be sensitive to either type of news. Second, a significant effect of the growth in oil prices on Russian stock returns is detected. Third, the international influence on Russian financial markets depends upon the degree of financial liberalization. The higher the degree of financial liberalization, the stronger is the impact of US stock returns on Russian financial markets. In addition, banking reform and interest rate liberalization efforts seem to dictate the globalization of Russian stock markets, while it is the progress in liberalizing securities markets and non‐bank financial institutions that matters more for the globalization of Russian bond markets.     

An empirical investigation of herding in the U.S. stock market

This paper proposes a new empirical testing method for detecting herding in stock markets. The traditional regression approach is extended to a vector autoregressive framework, in which the predictive power of squared index returns for the cross-sectional dispersion of equity returns is tested using a Granger causality test. Macroeconomic news announcements and the aggregate number of firm-level news items are treated as conditioning variables, while the average sentiment of firm-level news is treated as jointly determined. The testing algorithm allows the change points in the causal relationships between the cross-sectional dispersion of returns and squared index returns to be determined endogenously rather than being chosen arbitrarily a priori. Evidence of herding is detected in the constituent stocks of the Dow Jones Industrial Average at the onset of the subprime mortgage crisis, during the European debt and the U.S. debt-ceiling crises and the Chinese stock market crash of 2015. These results contrast with those obtained from the traditional methods where little evidence of herding is found in the US 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.     

Fiscal policy and stock market efficiency: An ARDL Bounds Testing approach

The aim of this paper is to investigate the semi-strong market efficiency hypothesis with respect to fiscal policy information, in the context of the Bucharest Stock Exchange. Taking into account that macroeconomic data series of emerging countries usually have a limited size and may be plagued by inconsistencies and structural breaks, this paper proposes an ARDL Bounds testing approach for studying the relationship between stock returns and lagged macroeconomic variables. Moreover, this approach allows us to examine both the long and short-term relationship between fiscal policy and stock returns. The results indicate that, in the long run, stock prices fully and efficiently reflect information on past fiscal policy. However, in the short run, the Romanian stock market reacts efficiently only to unexpected fiscal policy news, while anticipated fiscal policy information displays a significant lagged relationship with current stock returns. In addition, the results also showed that monetary policy information is not incorporated efficiently into stock prices, both in the short and the long run, and its impact on stock returns is larger than the one exerted by fiscal policy.     

Uncertainty assessment in socially responsible and Islamic stock markets in the short and long terms: an ARDL approach

This study measures financial uncertainty for two classes of alternative financial assets (Dow Jones Islamic and Dow Jones Sustainability Indexes) and the conventional US stock market (Dow Jones Industrial Index) for the period of 1999–2017, using an asymmetric exponential GARCH model. Using an ARDL model, we propose an intertemporal dynamic analysis of uncertainty for Islamic and socially responsible stock markets. Our findings show that, first, conventional and ethical investments present high comparable levels of uncertainty for which the dynamics is time-varying. Second, uncertainty in the conventional US stock market has a significant and positive effect on the uncertainty in alternative stock markets. Thus, uncertainty characterizes conventional and ethical stock markets both in the short and long terms. In particular, while the short-term uncertainty of ethical markets might be associated with their characteristics, the long-term aspect of uncertainty for ethical funds is rather associated with the effect of the conventional stock market environment. Although these findings show mean-reversion and uncertainty spillovers from the alternative stock markets to the conventional US one, they suggest lack of safety and certainty for investments in ethic markets, which remain fragile and closely dependent on the conventional market.     

Lunacy in the Stock Market—What is the Evidence?

Popular culture and folklore have long recognized the influence of the lunar cycle on plant, animal, and human behavior. Many of the effects have been validated in the physical and biological sciences. However, until recently such effects have been largely, if not completely ignored in the academic literature of financial economics. This study aims to contribute to answering whether there is, as some claim, a lunar influence on stock prices or volatility. The findings of this work support the Efficient Markets Hypothesis—no consistent, predictable lunar influence is found on either daily returns or daily price volatility in the Dow Jones Industrial Average, for either new or full moons. Some effects are found, but not consistent or predictable with lunar and calendar information alone.      

From Returns to Tweets and Back: An Investigation of the Stocks in the Dow Jones Industrial Average

A sizeable percentage of investors are using social media to obtain information about companies (Cogent Research [2008]). As a consequence, social media content about firms may have an impact on stock prices (Hachman [2011]). Various studies utilize social media content to forecast stock market-related factors such as returns, volatility, or trading volume. The objective of this article is to investigate whether a bidirectional intraday relationship between stock returns and volatility and tweets exists. The study analyzed 150,180 minute-by-minute stock price and tweet data for the 30 stocks in the Dow Jones Industrial Average over a random 13-day interval from June 2 to June 18, 2014 using a BEKK-MVGARCH methodology. Findings indicate that 87% of stock returns are influenced by lagged innovations of the tweets data, but there is little evidence to support that the direction is reciprocal, with only 7% of tweets being influenced by lagged innovations of the stock returns. Results further show that the lagged innovations from 40 percent of stock returns affect the current conditional volatility of the tweets, while 73 percent of tweets affect the current conditional volatility of stock returns. Moreover, there is strong evidence to suggest that the volatility originating from the returns to the tweets persists for 33 percent of stocks; the volatility originating from the tweets to the returns persists for 73 percent of stocks. Last, 53 percent of stocks exhibit both immediate and persistent impacts from returns to tweets, while 90 percent of stocks exhibit both immediate and persistent impacts from tweets to returns. These results may help traders achieve superior returns by buying and selling individual stocks or options. Also, asset and mutual fund managers may benefit by developing a social media strategy.     

Inflation news and stock returns: market direction and flow-through ability

Previous studies of the short-run response of daily stock prices to announcements of macroeconomic news could be biased when responses in different scenarios cancel each other out. In our analysis of inflation news in the Spanish stock market, we consider market direction arguments and implement our study based on the sector of activity to control for the ‘flow-through’ ability of the firms in each industry. In general, our results are quite consistent with the ‘market direction’ and the ‘flow-through’ hypotheses. Unanticipated inflation news implies abnormal returns depending on the direction of the news, the state of the economy and the flow-through ability of the sector. The impact of positive surprises affects the abnormal returns of many more sectors than does the impact of negative surprises, especially in the low states of economy. These significant effects are mainly observed in industries that are characterised by low flow-through ability.     

Evidence of Stock Returns and Abnormal Trading Volume: A Threshold Quantile Regression Approach

This paper presents a capital asset pricing model‐based threshold quantile regression model with a generalized autoregressive conditional heteroscedastic specification to examine relations between excess stock returns and “abnormal trading volume”. We employ an adaptive Bayesian Markov chain Monte Carlo method with asymmetric Laplace distribution to study six daily Dow Jones Industrial stocks. The proposed model captures asymmetric risk through market beta and volume coefficients, which change discretely between regimes. Moreover, they are driven by market information and various quantile levels. This study finds that abnormal volume has significantly negative effects on excess stock returns under low quantile levels; however, there are significantly positive effects under high quantile levels. The evidence indicates that each market beta varies with different quantile levels, capturing different states of market conditions.     

Investor sentiment and the prediction of stock returns: a quantile regression approach

We employ quantile regression to provide a detailed picture of the stock return forecasting ability of investor sentiment. We find that investor sentiment predicts aggregate stock returns at lower quantiles. However, the forecasting power is lost at upper quantiles. The results are robust after controlling for a comprehensive set of macroeconomic and financial predictors and for characteristic portfolios. We also show that investor sentiment consists mainly of cash flow news and contains little information about discount rate news. The ability to forecast cash flows increases gradually from the lower quantiles to upper quantiles. Our results do not support that the ability of investor sentiment to predict stock returns comes from a rational forecast of future cash flows.     

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”.     

The impact of foreign macroeconomic news on financial markets in the Czech Republic, Hungary, and Poland

In this paper, we investigate the effects of euro area and US macroeconomic news on financial markets in the Czech Republic, Hungary, and Poland (CEEC-3) from 1999 to 2006. Using a GARCH model, we examine the impact of news on daily returns of 3-month interest rates, stock market indices, exchange rates versus the euro, and the US dollar. First, both US and European macroeconomic news has a significant impact on CEEC-3 financial markets. Second, the process of European integration is accompanied by an increasing importance of euro area news relative to US news. Third, there are country-specific differences: for example, the Czech stock market is relatively more affected by foreign news since the Copenhagen Summit in December 2002. In general, our results support the hypothesis of a deepening euro area influence on the CEEC-3 over time and a corresponding reduction in the relative importance of US shocks.     

Stock returns and output growth in emerging and advanced economies

This paper studies the correlation between output growth and lagged stock returns in a panel of emerging market economies and advanced economies. It finds that the proportion of countries in which this correlation is significant is the same for emerging market economies as it is for advanced economies using yearly data, and somewhat lower using quarterly data. Asset prices therefore seem to contain valuable information to forecast output also in emerging market economies. Moreover, the paper finds that the strength of the correlation between output growth and lagged stock returns is significantly related to a number of stock market characteristics, such as a high market capitalization to GDP ratio and, less robustly, English legal origin and the number of listed domestic companies and initial public offerings.     

The cross-section and time-series effects of individual stock sentiment on stock prices

In this article, we construct an individual stock sentiment index by using the principal component analysis method. We empirically study the cross-section and time-series effects of investor sentiment on the stock prices based on the panel data model with dummy variable. The results indicate that individual stock sentiment has greater impact on small-firm stock prices than big-firm stock prices, which presents obvious cross-section effect. Moreover, individual stock sentiment leads to much sharper ?uctuations of stock prices in the stock market downturn than in the stock market expansion, which shows obvious time-series effect. Specifically, the individual stock sentiment has the greatest impact on small-firm stock prices under the stock market downturn, exerting significant dual asymmetric effect. Our results are helpful to understanding the micro-mechanism of sentiment effect.     

Impact of oil prices on firm stock return: industry-wise analysis

The study investigates the impact of oil prices on firm-level stock returns in case of Pakistan over the period 1998–2014, as this relationship is neglected by the previous literature. By using the panel data estimation, the results of full sample indicate significant positive effect of oil price changes on firm stock returns in the same period, whereas the lagged oil price changes have significant negative effect on firms’ stock return. Moreover, the industry-level analysis also confirms the similar findings; results indicate significant positive impact of oil price on firms’ stock return in full sample, textile, chemical and miscellaneous industry, while the lagged oil price changes negatively affect the stock returns of full sample and all the industries except tobacco, jute and vanaspati industries. The study confirms that rise in oil price transfers a positive signal in the stock market that boosts the firm-level stock returns in Pakistan. In contrast to the negative shocks, the stock returns are significantly affected by the positive oil price shocks.     

Macroeconomic News and Stock Returns in the United States and Germany

Abstract. Using daily data for the January 1997 to June 2002 period, we analyze similarities and differences in the impact of macroeconomic news on stock returns in the United States and Germany. We consider 27 different types of news for the United States and 12 different types of news for Germany. For the United States, we present evidence for asymmetric reactions of stock prices to news. In a boom (recession) period, bad (good) news on GDP growth and unemployment or lower (higher) than expected interest rates may be good news for stock prices. In the period under consideration there is little evidence for asymmetric effects in Germany. However, in the case of Germany, international news appears at least as important as domestic news. There is no evidence that US stock prices are influenced by German news. The analysis of bi-hourly data for Germany confirms these results.     

How index investment impacts commodities: A story about the financialization of agricultural commodities

This paper focuses on the impact of financial investors on agricultural prices, a phenomenon known as the financialization. In this aim, we check whether financial mechanisms drive extreme values and the mean of agricultural returns in the same way. Relying on the Threshold AutoRegressive Quantile (TQAR) methodology, we find evidence of reinforcement linkages between equity and agricultural markets since 2004, corresponding to the rise in inflows of institutional investors in commodity markets. These results show that agents impact more deeply commodity markets when the commodity index value is high. In addition, in extreme quantiles (0.75 and 0.90) of agricultural returns, the relationship between agricultural and stock returns is always significant when the commodity index return is in the higher regime. This finding suggests that, stock markets had a greater impact on agricultural price dynamics during the extreme movements which occurred during the 2007–2008 financial crisis, highlighting a potential influence of financial markets on the financialization of commodities.