Existing fault localization techniques typically analyze static information and run-time profiles of faulty software programs, and subsequently calculate suspiciousness values for each program entity. Such strategies typically have overbroad information to be analyzed and lead to unsatisfactory results. Exception is a widely-used programming language feature. It is closely related to the execution status during the execution of programs, and thus can be incorporated into automatic fault localization techniques for better effectiveness. Based on this intuition, we propose EXPECT, a novel fault localization technique that makes use of exception information, a valuable source of data for fault localization while being often ignored in previous research. Specifically, EXPECT first constructs exception trigger streams (including exception trigger information and execution traces), and then localizes faults by tracing bifurcation points between different exception trigger streams. Moreover, the tie-breaking problem can be also benefited from the use of exception trigger streams. Experimental results demonstrate the advantages of EXPECT: it achieves as high as 38.26% improvements in localizing faults regarding the Exam metric in comparison to the state-of-the-art fault localization technique, and it reduces the scales of ties in existing FL methods by up to 99.08%.