新冠肺炎疫情对线上商品价格变化的影响

文章运用电商高频价格大数据,分析了新冠肺炎疫情对商品价格变化的影响。研究发现:根据公平定价理论,在突发疫情下商家会加入消费者情绪制定价格策略,商品价格经过调整后趋于稳定;并且,商品价格表现出较低的价格黏性,对称的交错调价使得新冠肺炎疫情对商品价格的冲击有限。因此,政府部门可利用线上商品价格编制大数据物价指标,及时反映市场供求变化,促进资源优化配置,为宏观政策制定提供依据;同时,加快推进消费数字化转型,优化线上交易环境,建立预警机制来应对突发情况。     

媒体情绪与房价波动

本文使用2000—2015年中国112个大中城市媒体报道和房价月度指数研究了媒体异质性对房价波动的影响。研究发现,纸媒情绪对房价波动的影响大于网媒情绪,中央媒体情绪对房价波动的影响大于地方媒体情绪,明星微博情绪对房价波动的影响大于普通微博情绪,媒体语气确定性和报道篇幅对房价波动有正影响,明星城市媒体情绪较非明星城市更易引起房价波动。据此,为防范非理性房价波动,应加强对不同媒体报道的监管。     

投资者情绪与沪深300股指期货定价偏差关系研究

股指期货已发展成为全球金融衍生品主流品种,股指期货定价问题事关市场套利行为,一直备受投资者关注。本文基于投资者情绪视角,运用VAR模型、脉冲响应、方差分解等方法,深入探讨沪深300股指期货定价偏差与投资者情绪的关系。研究发现:投资者情绪与沪深300股指期货定价偏差之间构成因果关系,且呈现正向影响;股指期货定价偏差受投资者情绪影响而长期存在,其对投资者情绪的影响短期效果显著。     

基于文本挖掘的西部城市旅游满意度研究 ——以携程西安和成都为例

利用携程网西安和成都的5万条旅游评论数据,构建旅游满意度指标体系,以评估不同城市旅游消费者的满意度。采用基于词云图、社会网络语义图以及LDA模型的特征分析来提取评价指标,构建适用于旅游评论的情感词典,并据此为评论情感赋值,结合层次分析法确定指标权重,分别计算满意度。结果显示：成都市的综合满意度高于西安市;游客对于成都旅游服务中的导游情况和行程安排的满意度高于西安;而西安携程旅游服务中地区游览特色更好,景点体验更佳。     

基于情感分析混合模型的用户评论主题分析 ——以vivo手机为例

为了帮助商家了解用户需求和产品问题,进一步提升产品和服务质量,对vivo手机评论数据进行基于关键词的词云图分析、社会语义网络分析、舆情时间序列可视化分析,挖掘用户关注焦点与手机特征的内在联系和用户情感倾向趋势;然后对评论数据进行基于LDA的主题特征分析,继而提出一种基于Word2vec和SVM、LDA的混合算法模型,挖掘用户正向和负向情感评论的潜在主题,得到不同情感倾向下用户对vivo手机不同方面的反映情况。分析结果表明,基于混合算法的挖掘结果比基于关键词的可视化分析、基于LDA的主题分析更清晰,更具准确性,为商家提供的建议更有意义。     

IPO first‐day returns: Skewness preference,investor sentiment and uncertainty underlying factors

In this paper, we investigate the initial public offering (IPO) first-day returns. Our focus is to examine the irrational component of the agent behavior towards IPO lotteries. Based on 234 French IPOs performed between 2002 and 2012, we find that IPOs with high initial returns have higher idiosyncratic skewness, turnover and momentum. This finding provides empirical evidence for investors' preference for stocks with lottery-like features and investor sentiment. In addition, we show that the skewness preference and the investor sentiment effect are stronger during periods of favorable market conditions. Our results are robust to the integration of uncertainty underlying factors.     

Rumor Clarification and Stock Returns: Do Bull Markets Behave Differently from Bear Markets?

This paper analyzes the effects of official rumor clarification on Chinese stock returns under different market conditions. The results show that the average cumulative abnormal return after the clarification event is significantly positive in a bull market, and significantly negative in a bear market. The results are robust across various types of rumors, including rumors of mergers and acquisitions, asset restructuring, and positive changes in a firm's operations. Moreover, in both bull and bear markets, investors are unable to distinguish between rumors that prove true and those that prove false, or between strong and weak rumor denial. Furthermore, investors are also unable to adjust their strategies accordingly.     

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.     

Aggregate insider trading and market returns: The role of transparency

Using a sample of countries that require timely disclosures of insider trades, I investigate the effect of country‐level institutions that promote transparency on the extent to which aggregate insider trades predict market returns. I find that financial information transparency mitigates the predictive content of aggregate insider trades when markets are more likely to deviate from fundamentals (i.e., during market fads), and when there is greater co‐movement in stock prices. In contrast, there is some evidence that governance and investor protection mitigate the association between aggregate insider trades and future earnings surprises. Hence, holding constant the timely disclosures of insider trades, other capital market institutions play complementary roles in mitigating the informational frictions that give rise to the predictive content of aggregate insider trades.     

Which consumer feedback metrics are the most valuable in driving consumer expenditure in the tourism industries? A view from macroeconomic perspective

The link between consumer feedback metrics and consumer expenditure at the microeconomic level has been extensively examined. However, little is known at the macroeconomic level about the influence of consumer feedback metrics on consumer expenditure. Relying on actual data concerning consumer feedback metrics and consumer expenditure, the present study examines the influence of various consumer feedback metrics on consumer expenditure in the tourism industries. Our study collected data about consumer feedback metrics and consumer expenditure over the period 2008–2017. The findings indicate that consumer satisfaction and consumer effort score (CES) are the best performing consumer feedback metrics in hotels, restaurants, and travel agencies industries. Furthermore, the top-2-box performs best for predicting consumer expenditure in online booking industry. The findings reflect the significance of consumer feedback metrics on the economy as a whole; therefore, efforts to boost consumer feedback metrics should consider a national agenda.