禽流感危机下肉鸡产业市场状态分析*——基于互联网大数据和MS-VAR模型

[目的]为了有效应对禽流感冲击、维持肉鸡产业稳定健康发展。[方法]基于互联网大数据构建了禽流感舆情指数,选取2012年1月至2017年3月周度数据,通过MS-VAR模型分析了禽流感危机下肉鸡市场状态的转换特征以及不同市场状态下产业链价格传导关系。[结果]2012年以来可将肉鸡产业市场状态划分为危机状态和正常状态,肉鸡产业市场在两个市场状态下转换频繁;肉鸡产业危机市场状态和正常市场状态平均持续期分别为4.65周和9.05周;肉鸡市场在区制转移概率上存在明显的非对称特征;肉雏鸡、活鸡和白条鸡价格之间在危机状态下相关性小于正常市场状态下相关性;不同市场状态下肉鸡产业链价格传导存在差异;肉鸡产业市场在危机状态下不稳定程度明显增加。[结论]提出了加大对禽流感疫情的监测以及疫情处理能力,密切关注公众禽流感舆情动态变动情况,做好舆情引导,加强禽流感疫情科学知识的宣传,增强公众对禽流感的认知等对策建议。     

Positive Feedback Trading and Investor Sentiment

This article examines how investor sentiment affects positive feedback trading behavior. By analyzing the daily closing total return of CSI 300 index and its individual returns of stocks, we find that relatively high or low sentiment induces active positive feedback trading. With a specific indicator of sentiment, we explain the microstructure setting of the relationship between positive feedback trading and sentiment. We adopt the classical feedback model from Sentana and Wadhwani (1992) to measure positive feedback trading behavior. By adding sentiment factor to the model, we successfully explain how sentiment influences the behavior of both feedback traders and rational investors. The empirical findings suggest that positive feedback traders are more likely to trade when the prices of most securities move forward together. When the sentiment of feedback traders is at an intermediate level, the feedback trading behavior is insignificant.     

Market Response to Investor Sentiment

This paper reconsiders the effect of investor sentiment on stock prices. Our main contribution is that, in addition to the intermediate term return predictability, we also analyze the immediate price reaction to the publication of survey‐based investor sentiment indicators. We find that the sign of the immediate market response is the same as that of the predictability at intermediate time horizons. This is consistent with underreaction to cash flow news or with investor sentiment being related to mispricing. It is inconsistent with the alternative explanations of a rational response to cash flow news or sentiment indicators providing information about future expected returns.     

Investor Sentiment,Trading Behavior and Informational Efficiency in Index Futures Markets

This paper shows that traders in index futures markets are positive feedback traders—they buy when prices increase and sell when prices decline. Positive feedback trading appears to be more active in periods of high investor sentiment. This finding is consistent with the notion that feedback trading is driven by expectations of noise traders. Consistent with the noise trading hypothesis, order flow in index futures markets is less informative when investors are optimistic. Transitory volatility measured at high frequencies also appears to decline in periods of bullish sentiment, suggesting that sentiment‐driven trading increases market liquidity.     

The short of it: Investor sentiment and anomalies

This study explores the role of investor sentiment in a broad set of anomalies in cross-sectional stock returns. We consider a setting in which the presence of market-wide sentiment is combined with the argument that overpricing should be more prevalent than underpricing, due to short-sale impediments. Long-short strategies that exploit the anomalies exhibit profits consistent with this setting. First, each anomaly is stronger (its long-short strategy is more profitable) following high levels of sentiment. Second, the short leg of each strategy is more profitable following high sentiment. Finally, sentiment exhibits no relation to returns on the long legs of the strategies.     

The impact of German-speaking online media on tourist arrivals in popular tourist destinations for Europeans

This empirical study analyzes the relationship between the sentiments in online media with regard to travel destinations and corresponding tourist arrivals. We expect the media reports on political and economic instability and turmoil to enhance tourist arrival nowcasts and forecasts, as they can probably complement them with information on disruptions and shocks. Therefore, we believe this research will help to build better models for tourism demand nowcasting and forecasting. We use the sentiment in the German-speaking online media because the German-speaking region is the most populated in Europe and has the largest group of travelers visiting destinations in and around Europe.

An artificial neural network is used to analyze the mood of the media. The software classifies news items regarding potential tourist destinations with either positive or negative labels. The number of positive and negative news items is used to build sentiment indices for popular tourist destinations for Europeans.

Our results show strong correlations between the mood concerning tourist destinations and tourist arrivals in these countries. Indeed, disruptions and shocks prevalent in the news are reflected in similar ratios in both tourist arrivals and sentiment indices. These results can be used as a new explanatory variable for tourism demand modelling.     

A behavioural model of investor sentiment in limit order markets

By incorporating behavioural sentiment in a model of a limit order market, we show that behavioural sentiment not only helps to replicate most of the stylized facts in limit order markets simultaneously, but it also plays a unique role in explaining those stylized facts that cannot be explained by noise trading, such as fat tails in the return distribution, long memory in the trading volume, an increasing and non-linear relationship between trade imbalance and mid-price returns, as well as the diagonal effect, or event clustering, in order submission types. The results show that behavioural sentiment is an important driving force behind many of the well-documented stylized facts in limit order markets.     

Media,sentiment and market performance in the long run

This paper investigates the impact of media pessimism on financial market returns and volatility in the long run. We hypothesize that media sentiment translates into investor sentiment. Based on the underreaction and overreaction hypotheses [Barberis, N., A. Shleifer, and R. Vishny. 1998. “A Model of Investor Sentiment.” Journal of Empirical Economics 49 (3): 307–343], we suggest that media pessimism has an effect on market performance after a lag of several months. We construct a monthly media pessimism indicator by taking the ratio of the number of newspaper articles that contain predetermined negative words to the number of newspaper articles that contain predetermined positive words in the headline and in the lead paragraph. Our results indicate that media pessimism is associated with negative (positive) market returns 14–17 (24–25) months in advance and positive market volatilities 1–20 months in advance. Our results are statistically and economically significant. We find evidence for Granger causality of media pessimism on market performance. Our media pessimism indicator possesses additional predictive power for the Baker and Wurgler [2006. “Investor Sentiment and the Cross-section of Stock Returns.” Journal of Finance 61 (4): 1645–1680] investor sentiment index and the Chicago Board Options Exchange Market Volatility Index.     

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.     

A non-linear approach for predicting stock returns and volatility with the use of investor sentiment indices

The popular sentiment-based investor index S^BW introduced by Baker and Wurgler (2006, 2007) is shown to have no predictive ability for stock returns. However, Huang et al. (2015) developed a new investor sentiment index, S^PLS, which can predict monthly stock returns based on a linear framework. However, the linear model may lead to misspecification and lack of robustness. We provide statistical evidence that the relationship between stock returns, S^BW and S^PLS is characterized by structural instability and inherent nonlinearity. Given this, using a nonparametric causality approach, we show that neither S^BW nor S^PLS predicts stock market returns or even its volatility, as opposed to previous empirical evidence.