The Value Relevance of Sentiment

It is generally accepted that excessive exuberance or gloom in investor sentiment contributes to booms and crashes in share prices. However, views differ on the merits of active policy intervention due to gaps in our understanding of the transmission mechanism. To fill this gap we apply a fully ex ante valuation model in which an index of investor sentiment is included along with earnings and growth fundamentals to explain value. The outcome is a precise indication of the value relevance of sentiment. We employ the investor sentiment indicator proposed by Baker and Wurgler (2007). Valuation, and implied permanent growth, based on the inclusion of standard fundamentals is compared with that obtained when sentiment is added. The resulting ratio produces an index of ’the valuation effects of sentiment’ that can be assessed with statistical significance. Out-of-sample fit is also examined. For the Dow index the valuation effects of sentiment are significant and as large as 40% of market value at the peak of the ’dot-com’ bubble. The index we propose identifies conditions, detectable in advance and under the control of policy makers, that are conducive to the creation of asset bubbles. It is easy to construct, timely, robust and can be used improve our understanding of what leads to bubbles and crashes and to inform policy.Bubbles, fundamental valuation, sentiment, early warning indicators

Sentiment in foreign exchange markets: Hidden fundamentals by the back door or just noise?

Foreign exchange markets have to deal next to hard facts with lots of expectations and emo-tions. One of the major puzzles in international finance remains the “exchange rate discon-nect puzzleâ€. Analyzing sentiment in foreign exchange markets, it appears in fact that senti-ment contains some forward looking information. Particularly due to the unknown economic relevance of sentiment in foreign exchange markets so far, we first analyze the relationship between fundamentals and sentiment in order to reveal underlying forces of the latter; sec-ond we accomplish our analysis by concentrating on popular expectation concepts and con-sidering threshold effects. Third, we evaluate sentiment by testing on accuracy and on for-ward looking elements of subsequent exchange rate returnsForeign exchange market, sentiment, bootstrap, threshold

Highly Relevant Routing Recommendation Systems for Handling Few Data Using MDL Principle and Embedded Relevance Boosting Factors

A route recommendation system can provide better recommendation if it also takes collected user reviews into account, e.g. places that generally get positive reviews may be preferred. However, to classify sentiment, many classification algorithms existing today suffer in handling small data items such as short written reviews. In this paper we propose a model for a strongly relevant route recommendation system that is based on an MDL-based (Minimum Description Length) sentiment classification and show that such a system is capable of handling small data items (short user reviews). Another highlight of the model is the inclusion of a set of boosting factors in the relevance calculation to improve the relevance in any recommendation system that implements the model.Comment: ACM SIGIR 2018 Workshop on Learning from Limited or Noisy Data for Information Retrieval (LND4IR'18), July 12, 2018, Ann Arbor, Michigan, USA, 8 pages, 9 figure

The differential impact of investor sentiment on the value relevance of book value versus earnings

Thesis (Ph.D.)--Boston UniversityThis study investigates the differential role of investor sentiment on the value relevance of book value versus earnings. I predict and find that the value relevance of book value is higher during low sentiment relative to high sentiment periods, and conversely that the value relevance of earnings is higher during high sentiment relative to low sentiment periods. These findings are consistent with investors-when optimistic-placing a higher weight on earnings, which represent an accounting proxy more indicative of future performance, whereas investors-when pessimistic-placing a higher weight on book value, which represents an accounting proxy (given historical cost conventions) that is more indicative of current value. Additional analyses suggest that this sentiment effect is more pronounced for book value components that are closely related to abandonment value, and for earnings components that have strong indication of future earnings (specifically, permanent earnings). Results are also robust to alternative measures of investor sentiment

Ranking and Selecting Multi-Hop Knowledge Paths to Better Predict Human Needs

To make machines better understand sentiments, research needs to move from polarity identification to understanding the reasons that underlie the expression of sentiment. Categorizing the goals or needs of humans is one way to explain the expression of sentiment in text. Humans are good at understanding situations described in natural language and can easily connect them to the character's psychological needs using commonsense knowledge. We present a novel method to extract, rank, filter and select multi-hop relation paths from a commonsense knowledge resource to interpret the expression of sentiment in terms of their underlying human needs. We efficiently integrate the acquired knowledge paths in a neural model that interfaces context representations with knowledge using a gated attention mechanism. We assess the model's performance on a recently published dataset for categorizing human needs. Selectively integrating knowledge paths boosts performance and establishes a new state-of-the-art. Our model offers interpretability through the learned attention map over commonsense knowledge paths. Human evaluation highlights the relevance of the encoded knowledge

Explaining Recurrent Neural Network Predictions in Sentiment Analysis

Recently, a technique called Layer-wise Relevance Propagation (LRP) was shown to deliver insightful explanations in the form of input space relevances for understanding feed-forward neural network classification decisions. In the present work, we extend the usage of LRP to recurrent neural networks. We propose a specific propagation rule applicable to multiplicative connections as they arise in recurrent network architectures such as LSTMs and GRUs. We apply our technique to a word-based bi-directional LSTM model on a five-class sentiment prediction task, and evaluate the resulting LRP relevances both qualitatively and quantitatively, obtaining better results than a gradient-based related method which was used in previous work.Comment: 9 pages, 4 figures, accepted for EMNLP'17 Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA

Investor sentiment in the theoretical field of behavioural finance

Investor sentiment is a research area in the theoretical field of behavioural finance that analyses the sentiment of investors and the way it influences stock market activity. Recently, there has been an increase in the number of publications in this area, which indicates its incremental relevance. To date, there is no consensus on the theoretical structure of behavioural finance nor on the investor sentiment research area. We have used co-citation, bibliographic coupling and co-occurrence analysis to provide an overview of the structure of investor sentiment. Therefore, this study contributes to defining the theoretical structure of investor sentiment by identifying the foundations of the research area and main journals, references, authors, or keywords, which represent the core of knowledge of this research area. The results obtained suggest that investor sentiment is related to efficient market theory and behavioural finance theories. Furthermore, investor sentiment is a relevant research field, especially since 2014. Advances in computer science or theories based on physics or mathematics can help to better define the influence of investor sentiment on stock markets. This study advances research on investor sentiment within the field of behavioural finance, thus showing its relevance