It is in our nature to measure the intensity of an unseen event by observing its impact on the affected entity. Since reaction of the trader crowd to a financial event in form of stock price movement very closely reflects the severity of that event, words used in the news to describe that event also exhibit similar degree of emotion. Thus, we can assign emotional valence rating to the most relevant words in the news using this reversed relationship of causality instead of assigning assumed emotional valence to words, like it has been the case in many previous works. We have gathered data for financial events of the past from stock exchange and have mathematically analyzed it to quantify the impact of those events. These results are then applied to assign ratings to more than 7000 PoS-tagged English stems extracted from financial news articles for the corresponding events using Natural Language Processing techniques. In this way, a domain-specific word-emotion lexicon has been created.