Continuous Cash-Overlay Filters for a Static Growth--Defensive Risk Sleeve: Slow-Tail Compensation, V-Shape Crash Brakes, Walk-Forward Validation, and Max-Cash Combination

Abstract: This paper studies a cash-overlay allocation problem between a static growth-defensive risky sleeve and interest-bearing cash. The risky sleeve is fixed as a 50/50 combination of equal-weight growth and defensive ETF baskets, so the cash overlay is evaluated independently of any dynamic growth-defensive style-timing policy. The target is future risky-sleeve return over cash, with the cash leg measured using the contemporaneous cash rate. I develop two continuous filters. The slow-tail compensation filter targets persistent deterioration in risky-sleeve compensation, especially regimes in which cash yield rises and risky assets remain unstable. The V-shape crash-brake filter targets fast drawdown episodes and subsequent re-entry. The two filters are combined using a fixed max-cash rule, under which the portfolio uses the larger of the two cash weights each day. On the common 2017-2026 window, the selected-weight max-cash combination earns a 20.45 percent CAGR versus 16.62 percent for the static risky sleeve, and improves maximum drawdown from -33.59 percent to -16.77 percent. A stricter version combines each component's own walk-forward out-of-sample weights. In the main OOS window, the expanding max-cash combination earns 18.05 percent versus 16.09 percent for the static risky sleeve, with maximum drawdown of -22.05 percent versus -33.59 percent. The evidence supports modular continuous cash overlays as drawdown-control tools, while leaving multiple-testing-adjusted inference and real-time variable re-screening for future work.