Three Essays on Economics in Big Data Scenarios
Big Data. Companies’ Returns. Dictionary of ESG Terms. ESG News. Textual Analysis. Exchange Rates Forecasting. Machine Learning. Consumption Inequality. Electronic Payment Methods. Economic Complexity Index (ECI).
This work comprises three studies on economics in big data contexts. The rst analyzes the impact of ESG (Environmental, Social, and Governance) news on the stock returns of leading Brazilian companies, using an unprecedented Dictionary of ESG Terms specically developed for this study to select and classify news according to the standards of the Sustainability Accounting Standards Board (SASB). The research indicates that only news with content that is nancially material to investors inuences stock returns. In other words, investors do not react for reputational or non-pecuniary reasons. The second study explores the high-frequency predictability of the Brazilian exchange rate (at the 1, 5, and 15-minute frequencies), employing both machine learning techniques and traditional linear regression for forecasting. Two types of exercises are conducted: one with contemporary predictors and another using out-of-sample data. We show that it is possible to beat the benchmark, the Random Walk, over a horizon of up to four minutes at a frequency of 1 minute. We also show that the most important predictors are those that carry local information, as well as the exchange rates of the BRICS or countries with economies similar to Brazil’s. When the rates from B3’s foreign exchange futures contracts are considered as predictors, we can beat the Random Walk over a horizon of up to 6 minutes. The third study measures consumption inequality at the municipal level using data from electronic payment methods, specically data from credit card and Pix payments. Furthermore, as an application, we examine the relationship between inequality and economic complexity. We demonstrate that greater economic complexity is associated with lower consumption inequality, marking the rst assessment of this kind for Brazilian municipalities.