Yang, IrisCheung, Matthew J.Yu, DavidGarcia, LukeAli, Hamza2026-10-022006-05-06Iris Yang, Mathew J. Cheung, David Yu, Luke Garcia, and Hamza Ali. 2026. Bitcoin Forecast Implementation Under an Institutional Valuation Clock. Masters Captstone, Harvard University Division of Continuing Education.https://p2p8-sa-zuvru-a9vusux.re-cotta.com/handle/1/42758586Under a fixed 4:00 p.m. Eastern Time (ET) timing rule aligned with U.S. spot Bitcoin exchange-traded fund (ETF) valuation practice, we test whether broader public information improves next-day Bitcoin forecasts and costed implementation outcomes relative to Bitcoin-only benchmarks. A Full Logit-Lasso direction model does not beat Bitcoin (BTC)-only Logit on unconditional probability loss (Brier, log loss), but conditional on the model’s activation threshold (τ = 0.55) it identifies a 26% subset of dates with mean next-day BTC return 72 basis points above non-activated dates (heteroskedasticity- and autocorrelation-consistent (HAC) p = 0.0001). Volatility model rankings are loss-dependent (Ridge on point errors; heterogeneous autoregressive ordinary least squares (HAR-OLS) on quasi-likelihood loss (QLIKE)). Carrying the direction and volatility forecasts into a dual decision rule that combines direction-based gating with volatility-targeted sizing produces a point-estimate net-Sharpe cluster of approximately 1.55 to 1.61 and compresses maximum drawdown from –0.67 for buy-and-hold to approximately –0.10 across the three Full Logit-Lasso dual variants on the 1,450-date realized-return panel. Family-wise inference on these two performance dimensions, conducted as parallel stationary-block-bootstrap stepdown maxT procedures over the paper’s fixed nine contrast reduced family, yields materially different conclusions. Sharpedifference inference does not separate any active rule from buy-and-hold or from direction gating; the closest contrasts sit at padj ≈ 0.063 to 0.066, and the incremental Sharpe contribution of adding volatility sizing on top of direction gating is approximately zero (padj ≈ 0.92). Maximum-drawdown-difference inference, by contrast, separates all nine reduced-family contrasts at padj ≤ 0.003: direction gating, volatility sizing alone, sizing over gating, and the full dual policy all reduce drawdown under the same multiplicity discipline. A conservative T−1 observability audit attenuates richer-model magnitudes but does not overturn the benchmark-first forecast conclusions or the qualitative pattern of family-wise drawdown separation. The paper’s contribution is therefore methodological as well as substantive: once timing, implementation costs, and multiplicity are treated as binding constraints, the economic value of the dual policy in this sample is concentrated in path-risk compression rather than in family-wise Sharpe separation.en-USBitcoinspot Bitcoin ETFsreturn directionrealized volatilityforecast evaluationdrawdown controlThesis