If the late market cycle resembled a 4th down play vis-à-vis American football, then U.S. equities have been the default go-to option for investors even if the bull market is possibly entering into the final stages of its historic run. In the meantime, trade wars have been racking international and emerging market funds, but one in particular, the EquBot AI Powered International Equity ETF (NYSEArca: AIIQ), produced a strong third quarter performance using a proprietary, quantitative model driven by artificial intelligence.
Launched in June 2018, AIIQ is the first international-focused ETF that has successfully used AI to construct an international equity portfolio. While its developed market-focused fund peers experienced mixed results due to a choppy international market during Q3, AIIQ was able to buck the trend and deliver a return in excess of 3.4%, which bested its benchmark index–the MSCI ACWI ex-USA Index and the Vanguard FTSE Developed Markets ETF (NYSEArca: VEA).
“It is important to understand AIIQ is benchmarked against a developed international ex-US index and we have set the funds objectives to meet commensurate volatility measures and exceed performance with less than 250 names in the portfolio,” said EquBot COO Art Amador. “Given the volatility in international markets this past quarter we have been very pleased with the performance of the system as it has continually adjusted to find opportunities across sectors and geographic regions.
“When we look at some macro changes in the fund during Q3 we saw well time shifts into additional Canadian and Israeli exposures, as well as tailwinds from oil and pharmaceutical positions. From a single name stand point we were thrilled to share with investors one of our system’s best investment decisions. Gains over 300% were realized on Amarin positions in Q3 – a name we had been holding since the fund inception.”
EquBot’s AI-driven model ranks thousands of stocks based on the probability of each company benefiting from current economic conditions, trends and world events. By sifting through this bevy of data, the model is able to identify those which have the greatest potential for price appreciation over the next twelve months.