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This chapter examines how the breakthrough of Zhang Ziyi's depiction of a female kung fu master in The Grandmaster (2013) transforms the figure of the heroine in Chinese action films. Zhang is well known for her acting in action films conducted by renowned directors, such as Ang Lee, Zhang Yimou and Wong Kar-wai. After winning 12 different Best Actress awards for her portrayal of Gong Ruomei in The Grandmaster, Zhang announced that she would no longer perform in any action films to show her highest respect for the superlative character Gong. Tracing Zhang's transformational portrait of a heroine in The Grandmaster alongside her other action roles, this analysis demonstrates how her performance projects the directors' distinctive gender viewpoints. I argue that Zhang's characterisation of Gong remodels heroine-hood in Chinese action films. Inheriting the typical plot of a daughter's use of martial arts for revenge for her father's death, Gong breaks from conventional Chinese action films that highlight romantic love during a woman's adventure and the decisive final battle scene. Beyond the propensity for sensory stimulation, Gong's characterisation enables Zhang to determine that women can really act in action films – demonstrating their inner power and ability to create multi-layered characters – not merely relying upon physical action. This chapter offers a relational perspective of how women transform the action film genre not merely as gender spectacles but as embodied figures that represent emerging female subjectivity.

Nonperforming assets in any banking system have stressed the economic health of nations. Resultantly, literature has given considerable impetus to predict failures and bankruptcy. Past studies have focused on the outcome of failures, while, there is a dearth of studies focusing on ongoing firms in bad shape. We plug this gap and attempt to identify underlying communication patterns for firms witnessing prolonged underperformance. Using text mining, we extract and analyze semantic, linguistic, emotional, and sentiment-based features in non-numeric communication channels of these poor-performing firms and their peers. These uncovered patterns highlight the use of vocabulary and tone of communication, in correspondence to their financial well-being. Furthermore, using such patterns, we deploy various Machine Learning algorithms to identify loser firm(s) way ahead in time. We observe promising accuracy over a time window of five years. Such early warning signals can be of critical importance to various stakeholders of a firm. Exploration of writing style-related features for any firm would help its investors, lending agencies to assess the likelihood of future underperformance. Firm management can use them to take suitable precautionary measures and preempt the future possibility of distress. While investors and lenders can be benefitted from this incremental information to identify the likelihood of future failures.