Investor attention, analyst recommendation revisions, and stock prices

We provide evidence that investors underreact after analysts' recommendation upgrades; however, price reactions are faster after downgrades. We measure individual investors' attention using Google's search volume index. Our findings indicate that, after upgrades, stocks that enjoy greater individual investors' attention underreact significantly more compared to stocks that receive high level of attention from institutional investors. On the other hand, after recommendation downgrades, stocks with higher levels of prior attention from individual investors overreact and show a significantly greater price reversal compared to stocks that received high level of attention from institutional investors. Our results suggest that attentive individual investors may not be rational; hence investor attention and investor sophistication are important for price discovery in the market.

The Post-revision drift, analyst characteristics and investor attention

The aim of this dissertation is to provide a coherent explanation for the post-analyst recommendation drift. First, I find that the post-analyst recommendation drift is explained by the degree of attention paid by individual investors. Second I find that the extremeness and the credibility of information leads to changes in the degree of attention and a post-analyst recommendation drift. Finally, I find that the diffusion of private information contained in the analyst recommendation interacts with attention related biases leading to a post-recommendation drift.

Analysts’ recommendations in Brazil: do they add value?

This paper examines the value of analysts’ recommendations in Brazilian Stock Market. We studied a sample of 294 weeks of recommendations make public by the best seller newspaper in Brazil with six different investment strategies and time horizons. The main conclusion is that it is possible to beat the Brazilian market indexes Ibovespa and IBrX following the analysts’ stock recommendations. The best strategies are buying only the recommended stocks, buying the recommended stocks whose target and market prices difference is bigger than 25% and lesser or equal than 50%. The performance of the six strategies is analyzed through the use of bootstrap and Monte Carlo techniques.

How do analysts deal with bad news? Going-concern opinions and analyst behaviour

Tese dout., University of Edinburg, 2008

Marx as critical discourse analyst : the genesis of a critical method and its relevance to the critique of global capital.

In this paper we identify elements in Marx´s economic and political writings that are relevant to contemporary critical discourse analysis (CDA). We argue that Marx can be seen to be engaging in a form of discourse analysis. We identify the elements in Marx´s historical materialist method that support such a perspective, and exemplify these in a longitudinal comparison of Marx´s texts.

The effect of national governance codes on firm disclosure practices : evidence from analyst earnings forecasts

This study examines whether voluntary national governance codes have a significant effect on company disclosure practices. Two direct effects of the codes are expected: 1) an overall improvement in company disclosure practices, which is greater when the codes have a greater emphasis on disclosure; and 2) a leveling out of disclosure practices across companies (i.e., larger improvements in companies that were previously poorer disclosers) due to the codes new comply-or-explain requirements. The codes are also expected to have an indirect effect on disclosure practices through their effect on company governance practices. The results show that the introduction of the codes in eight East Asian countries has been associated with lower analyst forecast error and a leveling out of disclosure practices across companies. The codes are also found to have an indirect effect on company disclosure practices through their effect on board independence. This study shows that a regulatory approach to improving disclosure practices is not always necessary. Voluntary national governance codes are found to have both a significant direct effect and a significant indirect effect on company disclosure practices. In addition, the results indicate that analysts in Asia do react to changes in disclosure practices, so there is an incentive for small companies and family-owned companies to further improve their disclosure practices.

Developing trust networks based on user tagging information for recommendation making

Recommender systems are one of the recent inventions to deal with ever growing information overload. Collaborative filtering seems to be the most popular technique in recommender systems. With sufficient background information of item ratings, its performance is promising enough. But research shows that it performs very poor in a cold start situation where previous rating data is sparse. As an alternative, trust can be used for neighbor formation to generate automated recommendation. User assigned explicit trust rating such as how much they trust each other is used for this purpose. However, reliable explicit trust data is not always available. In this paper we propose a new method of developing trust networks based on user’s interest similarity in the absence of explicit trust data. To identify the interest similarity, we have used user’s personalized tagging information. This trust network can be used to find the neighbors to make automated recommendations. Our experiment result shows that the proposed trust based method outperforms the traditional collaborative filtering approach which uses users rating data. Its performance improves even further when we utilize trust propagation techniques to broaden the range of neighborhood.

A restrike waveform predictive model for shunt capacitor bank switching with point-on-wave recommendation

Circuit-breakers (CBs) are subject to electrical stresses with restrikes during capacitor bank operation. Stresses are caused by the overvoltages across CBs, the interrupting currents and the rate of rise of recovery voltage (RRRV). Such electrical stresses also depend on the types of system grounding and the types of dielectric strength curves. The aim of this study is to demonstrate a restrike waveform predictive model for a SF6 CB that considered the types of system grounding: grounded and non-grounded and the computation accuracy comparison on the application of the cold withstand dielectric strength and the hot recovery dielectric strength curve including the POW (point-on-wave) recommendations to make an assessment of increasing the CB remaining life. The simulation of SF6 CB stresses in a typical 400 kV system was undertaken and the results in the applications are presented. The simulated restrike waveforms produced with the identified features using wavelet transform can be used for restrike diagnostic algorithm development with wavelet transform to locate a substation with breaker restrikes. This study found that the hot withstand dielectric strength curve has less magnitude than the cold withstand dielectric strength curve for restrike simulation results. Computation accuracy improved with the hot withstand dielectric strength and POW controlled switching can increase the life for a SF6 CB.

Learning domain ontology for tag recommendation

Recently, user tagging systems have grown in popularity on the web. The tagging process is quite simple for ordinary users, which contributes to its popularity. However, free vocabulary has lack of standardization and semantic ambiguity. It is possible to capture the semantics from user tagging and represent those in a form of ontology, but the application of the learned ontology for recommendation making has not been that flourishing. In this paper we discuss our approach to learn domain ontology from user tagging information and apply the extracted tag ontology in a pilot tag recommendation experiment. The initial result shows that by using the tag ontology to re-rank the recommended tags, the accuracy of the tag recommendation can be improved.

Ontology learning from user tagging for recommendation making

Recently, user tagging systems have grown in popularity on the web. The tagging process is quite simple for ordinary users, which contributes to its popularity. However, free vocabulary has lack of standardization and semantic ambiguity. It is possible to capture the semantics from user tagging into some form of ontology, but the application of the resulted ontology for recommendation making has not been that flourishing. In this paper we discuss our approach to learn domain ontology from user tagging information and apply the extracted tag ontology in a pilot tag recommendation experiment. The initial result shows that by using the tag ontology to re-rank the recommended tags, the accuracy of the tag recommendation can be improved.

Effective hybrid recommendation combining users-searches correlations using tensors

Most recommendation methods employ item-item similarity measures or use ratings data to generate recommendations. These methods use traditional two dimensional models to find inter relationships between alike users and products. This paper proposes a novel recommendation method using the multi-dimensional model, tensor, to group similar users based on common search behaviour, and then finding associations within such groups for making effective inter group recommendations. Web log data is multi-dimensional data. Unlike vector based methods, tensors have the ability to highly correlate and find latent relationships between such similar instances, consisting of users and searches. Non redundant rules from such associations of user-searches are then used for making recommendations to the users.

Individual user behaviour modelling for effective web recommendation

With the growth of the Web, E-commerce activities are also becoming popular. Product recommendation is an effective way of marketing a product to potential customers. Based on a user’s previous searches, most recommendation methods employ two dimensional models to find relevant items. Such items are then recommended to a user. Further too many irrelevant recommendations worsen the information overload problem for a user. This happens because such models based on vectors and matrices are unable to find the latent relationships that exist between users and searches. Identifying user behaviour is a complex process, and usually involves comparing searches made by him. In most of the cases traditional vector and matrix based methods are used to find prominent features as searched by a user. In this research we employ tensors to find relevant features as searched by users. Such relevant features are then used for making recommendations. Evaluation on real datasets show the effectiveness of such recommendations over vector and matrix based methods.

A people-to-people recommendation system using Tensor Space Models

Existing recommendation systems often recommend products to users by capturing the item-to-item and user-to-user similarity measures. These types of recommendation systems become inefficient in people-to-people networks for people to people recommendation that require two way relationship. Also, existing recommendation methods use traditional two dimensional models to find inter relationships between alike users and items. It is not efficient enough to model the people-to-people network with two-dimensional models as the latent correlations between the people and their attributes are not utilized. In this paper, we propose a novel tensor decomposition-based recommendation method for recommending people-to-people based on users profiles and their interactions. The people-to-people network data is multi-dimensional data which when modeled using vector based methods tend to result in information loss as they capture either the interactions or the attributes of the users but not both the information. This paper utilizes tensor models that have the ability to correlate and find latent relationships between similar users based on both information, user interactions and user attributes, in order to generate recommendations. Empirical analysis is conducted on a real-life online dating dataset. As demonstrated in results, the use of tensor modeling and decomposition has enabled the identification of latent correlations between people based on their attributes and interactions in the network and quality recommendations have been derived using the 'alike' users concept.