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The purpose of this paper is to investigate whether the interaction between sentiments and past prices can lead to higher abnormal profit in futures markets. Such examinations allow the authors to relate the paper to the debate that focuses on examining the behavior of different types of traders in futures market, and who among these traders destabilize the markets.

First, the authors develop new dynamic strategies in US futures market that combine sentiment by type of traders based on trader position provided by the Disaggregated Commitments of Traders with short-term contrarian signals. Next, the authors adjust the abnormal profits to the CAPM model and Miffre and Rallis’s (2007) model. Finally, the authors use the Du (2012) decomposition methodology.

The main findings are that the abnormal profit is more pronounced when the authors combine past returns with lagged high producer/merchant/processor/user or low managed money sentiment. The results from swap dealer or other reportable groups show that there is no pervasive directional relation between their sentiment and contrarian profit. A further investigation of the sources of abnormal profits demonstrates that these profits survive even after the adjustment of obtained return to risk. Instead, these profits are mainly due to the overreaction to the news by irrational traders.

Based on behavioral finance theories, the authors conclude that producer, merchant, processor and user behave like irrational traders, while managed money traders behave like rational ones. Given that current regulatory proposes the limitation of speculation, the policy implications of these results are important. Therefore, these findings suggest that policy distinctions on trading motives may be more challenging to construct than ever.

The purpose of this paper is to comprehensively review a large and heterogeneous body of academic literature on investors' feedback trading, one of the most popular trading patterns observed historically in financial markets. Specifically, the authors aim to synthesize the diverse theoretical approaches to feedback trading in order to provide a detailed discussion of its various determinants, and to systematically review the empirical literature across various asset classes to gauge whether their feedback trading entails discernible patterns and the determinants that motivate them.

Given the high degree of heterogeneity of both theoretical and empirical approaches, the authors adopt a semi-systematic type of approach to review the feedback trading literature, inspired by the RAMESES protocol for meta-narrative reviews. The final sample consists of 243 papers covering diverse asset classes, investor types and geographies.

The authors find feedback trading to be very widely observed over time and across markets internationally. Institutional investors engage in feedback trading in a herd-like manner, and most noticeably in small domestic stocks and emerging markets. Regulatory changes and financial crises affect the intensity of their feedback trades. Retail investors are mostly contrarian and underperform their institutional counterparts, while the latter's trades can be often motivated by market sentiment.

The authors provide a detailed overview of various possible theoretical determinants, both behavioural and non-behavioural, of feedback trading, as well as a comprehensive overview and synthesis of the empirical literature. The authors also propose a series of possible directions for future research.

Corporate Social Responsibility (CSR) has been in practice in India even before it was mandated by the Companies Act, 2013. While the objectives of CSR varied from philanthropy, being socially responsible to improving the corporate image, the relationship between financial performance and CSR has not been established. Also only a few companies are aligning their CSR activities with their corporate goals. This chapter builds a framework for integrating business with its CSR activities. The first part of the chapter describes how the concept of CSR evolved over years in general and specifically in India. It also discusses the current status of CSR in India. The second part of the chapter uses a well-known CSR model of e-Choupal to build a framework to integrate CSR with business.

The purpose of the paper is to improve the rating prediction accuracy in recommender systems (RSs) by metric learning (ML) method. The similarity metric of user and item is calculated with gray relational analysis.

First, the potential features of users and items are captured by exploiting ML, such that the rating prediction can be performed. In metric space, the user and item positions can be learned by training their embedding vectors. Second, instead of the traditional distance measurements, the gray relational analysis is employed in the evaluation of the position similarity between user and item, because the latter can reduce the impact of data sparsity and further explore the rating data correlation. On the basis of the above improvements, a new rating prediction algorithm is proposed. Experiments are implemented to validate the effectiveness of the algorithm.

The novel algorithm is evaluated by the extensive experiments on two real-world datasets. Experimental results demonstrate that the proposed model achieves remarkable performance on the rating prediction task.

The rating prediction algorithm is adopted to predict the users' preference, and then, it provides personalized recommendations for users. In fact, this method can expand to the field of classification and provide potentials for this domain.

The algorithm can uncover the finer grained preference by ML. Furthermore, the similarity can be measured using gray relational analysis, which can mitigate the limitation of data sparsity.