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A wider use of technology has contributed to the rapid growth of trading in stock markets in the last decades, resulting in an increase in the number of participants and a sharp decline in the price of information. High-frequency trading could be seen as a manifestation of this development. A review of the main findings in the academic literature leads to the identification of four main systemic vulnerabilities related to high-frequency trading: (i) adverse selection in orders, with the potential of crowding-out non-HFT market makers in times of stress; (ii) correlation of positions and herd behaviour; (iii) market power that, via technological costs, may impose barriers to entry; and (iv) negative contribution, in some circumstances, to price discovery. The first vulnerability could create systemic risk and several scholars have discussed the introduction of a limit in the speed of trading to address it. This could also contribute to reduce market power of high-frequency traders and over-investment in information technologies. Despite intense research efforts, further data and research is still needed to better understand these vulnerabilities and the adequacy of policies to address them.

This paper contributes to the sparse debate on the effect of capital adequacy requirements on banks’ economic efficiency measures. Precisely, we evaluate the out-of-sample predictability of capital adequacy requirements on banks’ economic efficiency measures using Support Vector Regression (SVR) model with Linear, Polynomial and Radial Basis Function kernels and ordinary least squares (OLS) model. This analysis is important because a prediction of economic efficiency measures allows for an untangle view of bank’s progress that is useful for management as it gains a high degree of transparency in the evaluation of future events. Our framework adapts optimization of h-block cross-validation to account for serial correlation of economic variables to produce robust sets of tuning parameters for SVR model. Using a total of 10,380 December quarterly observations of U.S. Commercial and Domestic banks spanning from 2008 through 2019, empirical results show that SVR model provides better benchmarking insights in the evaluation of economic efficiency measures compared to the OLS model. Furthermore, in contrast to previous approaches identifying a single “best” model among competing models, the results of Model Confidence Test suggests that the out-of-sample forecasting confidently identifies superior predictive accuracy of SVR model-based forecasts over OLS model.

To combat COVID-19 the entire world has resorted to global lockdown implying restriction on international labor migration and trade. This paper aims to check the effect of such restrictions on the unemployment of unskilled labor in the source country. In competitive general equilibrium framework with three goods and four factors, restriction on migration raises unemployment for given factor intensity. The results remain same even in a slightly different structure of the economy. In case of trade restriction, however, the rise or fall in unemployment depends on both the structure of the economy and the factor intensity assumption.