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On average, the government detained a daily population of 19,416 noncitizens in fiscal year 2021. Of the 22,712 bond hearings immigration courts held during this same period, judges denied bond in sixty-nine percent of the cases. Despite the fact that many of these individuals could spend months or even years in detention, the decision of whether to detain belongs exclusively to the Executive Branch, and usually a single immigration judge, without opportunity for judicial review. Consequently, the lack of opportunity to contest the immigration judges' decisions in federal court has in part led to the prolonged detention of thousands of noncitizens by the United States government who are otherwise eligible for release. This Note demonstrates that the current detention scheme, including bond hearings, disadvantages noncitizens by presuming and favoring detention. Consequently, this Note argues that to ensure a fair bond hearing and reduce the number of detained noncitizens, Congress should permit federal review of immigration judges' discretionary custody decisions by repealing the jurisdiction stripping statute of 8 U.S.C. § 1226(e).

ABSTRACTEconometric methods used in foreign exchange rate forecasting have produced inferior out‐of‐sample results compared to a random walk model. Applications of neural networks have shown mixed findings. In this paper, we investigate the potentials of neural network models by employing two cross‐validation schemes. The effects of different in‐sample time periods and sample sizes are examined. Out‐of‐sample performance evaluated with four criteria across three forecasting horizons shows that neural networks are a more robust forecasting method than the random walk model. Moreover, neural network predictions are quite accurate even when the sample size is relatively small.