Chapter 1 sets the stage for the Novelty-Narrative Hypothesis as applied to stock market outcomes throughout the book. The importance of nonrepetitive events, narrative dynamics, and investor emotion for understanding market instability is introduced through insights from great early thinkers of modern-day financial markets – think Knight and Keynes – but also through extant evidence from other disciplines such as cognitive psychology and sociology. Benefits of textual news analytics in assessing the role of novelty and narratives under uncertainty are introduced with brief descriptions of the Bloomberg and RavenPack data employed throughout. The main analytical features of Chapters 5 through 12 are previewed emphasizing the methodological benefits of big data textual analysis applied to millions of financial news reports. The chapter highlights key findings from empirical investigations connecting Knightian uncertainty indices derived from novel corporate events and narrative proxies to periods of temporal instability in stock returns, volatility, trading volume, and fund flow relationships. The chapter closes with foreshadowing a Kuhnian paradigm shift in macro-finance.