{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T06:43:49Z","timestamp":1792219429770,"version":"4.4.0"},"posted":{"date-parts":[[2026]]},"group-title":"SSRN","reference-count":0,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.uspto.gov\/ip-policy\/copyright-policy\/copyright-basics"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>&lt;p&gt;Better forecasts do not necessarily produce a better global equity portfolio. The question is whether those signals improve the final portfolio once benchmark-relative risk, overlapping holdings, trading, and costs are taken into account. This paper examines a global equity ETF portfolio that combines machine-learning selection, benchmark-aware regional allocation, and a small allocation across themes, sectors, and styles. The components are evaluated together against the iShares MSCI ACWI ETF (ACWI).&lt;\/p&gt;\n&lt;p&gt;The common daily evaluation covers 31 January 2019 through 31 August 2026. In this reconstructed research history, the portfolio compounded at 18.9% annually versus 13.8% for ACWI, with 6.15% tracking error, a 0.819 information ratio, and a maximum relative drawdown of 8.10%. The advantage was uneven: the portfolio lagged ACWI in 2022, rolling active returns turned negative in some periods, and primary selection alone had a better recent trade-off between active return and tracking error than the complete portfolio.&lt;\/p&gt;\n&lt;p&gt;Longer component histories include additional market environments but are not a longer backtest of the integrated portfolio. Because the model combination was selected during research, the evidence does not establish an optimal mix. The relevant test is what each component contributes after the portfolios are combined.&lt;\/p&gt;<\/jats:p>","DOI":"10.2139\/ssrn.7622538","type":"posted-content","created":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T06:32:53Z","timestamp":1792218773000},"source":"Crossref","is-referenced-by-count":0,"title":["What Do Additional Models Add to a Global Equity Portfolio? Evidence from ETF Selection, Regional Allocation, and Selective Tilts"],"prefix":"10.2139","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5598-3346","authenticated-orcid":true,"given":"Sungkyu","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","container-title":[],"original-title":[],"deposited":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T06:32:53Z","timestamp":1792218773000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ssrn.com\/abstract=7622538"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":0,"URL":"https:\/\/doi.org\/10.2139\/ssrn.7622538","relation":{},"subject":[],"published":{"date-parts":[[2026]]},"subtype":"preprint"}}