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We investigate how the presence of physical bank branches moderates financial technology diffusion. Our identification strategy uses services suspensions caused by criminal groups that perform hit-and-run raids exploding branch facilities and rendering them inoperable for months. We show that the shock depletes the cash inventory of branches, but the stock of credit and deposits remain unaffected. We then document that customers increase their usage of noncash payments after the events. We investigate a new instant payment technology called Pix that was a remarkable success in terms of adoption. After robbery events, the number and value of Pix intra-municipality transactions increase, as well as the number of users. We also find Pix usage spillover effects that go beyond cash substitution. First, the number of Pix transactions and users also increases when either the payer or the payee is in an unaffected municipality. Second, we show that there are local spillovers to digital institutions, indicating that cash dependence can be an impediment to their expansion. Our results shed light on the determinants of technology adoption and the consequences of the recent transition in the banking industry from a physical branch-based model to an increasing reliance on digital services.

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We use Brazilian data to compute monthly idiosyncratic moments (expected skewness, realized skewness, and realized volatility) for equity returns and assess whether they are informative for the cross-section of future stock returns. Since there is evidence that lagged skewness alone does not adequately forecast skewness, we estimate a crosssectional model of expected skewness that uses additional predictive variables. Then, we sort stocks each month according to their idiosyncratic moments, forming quintile portfolios. We find a negative relationship between higher idiosyncratic moments and next-month stock returns. The trading strategy that sells stocks in the top quintile of expected skewness and buys stocks in the bottom quintile generates a significant monthly return of about 120 basis points. Our results are robust across sample periods, portfolio weightings, and to Fama and French (1993)'s risk adjustment factors. Finally, we identify a return reversal of stocks with high idiosyncratic skewness. Specifically, stocks with high idiosyncratic skewness have high contemporaneous returns. That tends to reverse, resulting in negative abnormal returns in the following month.

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