Using Alternative Data to Enhance Factor-Based Portfolios

Abstract

Factor investing has emerged as a key pillar to contemporary portfolio management and it provides systematic exposure to a wide array of well-documented risk premia like value, momentum, quality, and low volatility. Nevertheless, the models usually lack power to predict and flexibility to suit dynamic markets due to their dependence on the traditional financial and accounting data. The broadening of the range of alternative data providers including consumer transaction histories and geo-location data, social media sentiment and satellite photos provides a new chance to fine tune factor signals and improve portfolio performance. This article investigates how factors-based portfolios incorporate alternative data, including describing the conceptual assumptions and methodological techniques as well as empirical evidence in its initial use. It emphasizes the role additional datasets can play to increase signal granularity, reduce information latencies, and to generate a complimentary advantage over traditional factors. Simultaneously, some issues concerning the quality of data, regulatory limitations, ethical aspects, and the possibility of overfit are highlighted in the discussion. According to the analysis, alternative data when used in a responsible fashion may achieve a dual role of ensuring a source of innovation and stability as a routine throughout the purview of systematic investing. The article ends by stating strategic implications of this news on portfolio managers and the necessity to develop sturdy governance systems to establish sustainable integration.