欧洲央行-财务回报、情绪和市场波动。动态评估（英）

Working Paper Series Financial returns, sentiment and market volatility. A dynamic assessment. Stefano Borgioli, Giampiero M. Gallo, Chiara Ongari Disclaimer: This paper should not be reported as representing the views of the European Central Bank (ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB. No 2999 AbstractIn 1936, John Maynard Keynes proposed that emotions and instincts are pivotal indecision-making, particularly for investors. Both positive and negative moods can influencejudgments and decisions, extending to economic and financial choices. Intuitions, emotionalstates, and biases significantly shape how people think and act. Measuring mood or sen-timent is challenging, but surveys and data collection methods, such as confidence indicesand consensus forecasts, offer some solutions. Recently, the availability of web data, includ-ing search engine queries and social media activity, has provided high-frequency sentimentmeasures. For example, the Italian National Statistical Institute’s Social Mood on EconomyIndex (SMEI) uses Twitter data to assess economic sentiment in Italy. The relationship be-tween SMEI and financial market activity, specifically the FTSE MIB index and its volatility,is examined using a trivariate Vector Autoregressive model, taking into account the impactof the COVID-19 pandemic.Keywords: VAR, Granger Causality, sentiment analysis, financial market, forecastingJEL Codes: C1, C32, C53, G4ECB Working Paper Series No 299911Non-technical summaryBoth positive and negative emotions can impact judgments and economic decisions. Althoughmeasuring mood or sentiment is challenging, tools like surveys and confidence indices help. Infact online data, such as search engine queries and social media activity, provide more frequentsentiment measurements.For instance, the Italian National Statistical Institute created theSocial Mood on Economy Index (SMEI) using Twitter data to gauge economic sentiment inItaly. This study examines the relationship between the SMEI and financial market activity,particularly the FTSE MIB index, using a model that considers also possible effects of theCOVID-19 pandemic.Two main data sources are used: the SMEI, which measures daily Italian economic sentimentthrough tweets, and the FTSE MIB, which represents the performance of 40 major Italian stocksand includes volatility data. The analysis uses Vector Auto-regressive Models (VAR) to studythe relationships between the SMEI, FTSE MIB returns, and volatility from February 10, 2016,to March 8, 2020. Granger causality tests are then conducted to determine if past values of onevariable can predict current values of another, revealing potential bidirectional influences.“Even apart from the instability due to speculation, there is the instability due to the charac-teristic of human nature that a large proportion of our positive activities depend on spontaneousoptimism rather than on a mathematical expectation, whethe