AIEQ: Mobilizing AI Resources For Disappointing Results

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Reviews with updated data are posted when necessary...</p>\n\n<a href=\"https://seekingalpha.com/article/4702788-aieq-mobilizing-ai-resources-for-disappointing-results\">Source Link</a>\n\n</div>\n","source":"seekingalpha_fund","html":"<!DOCTYPE html>\n<html>\n<head>\n<meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\" />\n<meta name=\"viewport\" content=\"width=device-width,initial-scale=1.0,minimum-scale=1.0,maximum-scale=1.0,user-scalable=no\"/>\n<meta name=\"format-detection\" content=\"telephone=no,email=no,address=no\" />\n<title>AIEQ: Mobilizing AI Resources For Disappointing Results</title>\n<style type=\"text/css\">\na,abbr,acronym,address,applet,article,aside,audio,b,big,blockquote,body,canvas,caption,center,cite,code,dd,del,details,dfn,div,dl,dt,\nem,embed,fieldset,figcaption,figure,footer,form,h1,h2,h3,h4,h5,h6,header,hgroup,html,i,iframe,img,ins,kbd,label,legend,li,mark,menu,nav,\nobject,ol,output,p,pre,q,ruby,s,samp,section,small,span,strike,strong,sub,summary,sup,table,tbody,td,tfoot,th,thead,time,tr,tt,u,ul,var,video{ font:inherit;margin:0;padding:0;vertical-align:baseline;border:0 }\nbody{ font-size:16px; line-height:1.5; color:#999; background:transparent; }\n.wrapper{ overflow:hidden;word-break:break-all;padding:10px; }\nh1,h2{ font-weight:normal; line-height:1.35; margin-bottom:.6em; }\nh3,h4,h5,h6{ line-height:1.35; margin-bottom:1em; }\nh1{ font-size:24px; }\nh2{ font-size:20px; }\nh3{ font-size:18px; }\nh4{ font-size:16px; }\nh5{ font-size:14px; }\nh6{ font-size:12px; }\np,ul,ol,blockquote,dl,table{ margin:1.2em 0; }\nul,ol{ margin-left:2em; }\nul{ list-style:disc; }\nol{ list-style:decimal; }\nli,li p{ margin:10px 0;}\nimg{ max-width:100%;display:block;margin:0 auto 1em; }\nblockquote{ color:#B5B2B1; border-left:3px solid #aaa; padding:1em; }\nstrong,b{font-weight:bold;}\nem,i{font-style:italic;}\ntable{ width:100%;border-collapse:collapse;border-spacing:1px;margin:1em 0;font-size:.9em; }\nth,td{ padding:5px;text-align:left;border:1px solid #aaa; }\nth{ font-weight:bold;background:#5d5d5d; }\n.symbol-link{font-weight:bold;}\n/* header{ border-bottom:1px solid #494756; } */\n.title{ margin:0 0 8px;line-height:1.3;color:#ddd; }\n.meta {color:#5e5c6d;font-size:13px;margin:0 0 .5em; }\na{text-decoration:none; color:#2a4b87;}\n.meta .head { display: inline-block; overflow: hidden}\n.head .h-thumb { width: 30px; height: 30px; margin: 0; padding: 0; border-radius: 50%; float: left;}\n.head .h-content { margin: 0; padding: 0 0 0 9px; float: left;}\n.head .h-name {font-size: 13px; color: #eee; margin: 0;}\n.head .h-time {font-size: 11px; color: #7E829C; margin: 0;line-height: 11px;}\n.small {font-size: 12.5px; display: inline-block; transform: scale(0.9); -webkit-transform: scale(0.9); transform-origin: left; -webkit-transform-origin: left;}\n.smaller {font-size: 12.5px; display: inline-block; transform: scale(0.8); -webkit-transform: scale(0.8); transform-origin: left; -webkit-transform-origin: left;}\n.bt-text {font-size: 12px;margin: 1.5em 0 0 0}\n.bt-text p {margin: 0}\n</style>\n</head>\n<body>\n<div class=\"wrapper\">\n<header>\n<h2 class=\"title\">\nAIEQ: Mobilizing AI Resources For Disappointing Results\n</h2>\n\n<h4 class=\"meta\">\n\n\n2024-07-07 23:26 GMT+8&nbsp;&nbsp;&nbsp;<a href=https://seekingalpha.com/article/4702788-aieq-mobilizing-ai-resources-for-disappointing-results><strong>seekingalpha</strong></a>\n\n\n</h4>\n\n</header>\n<article>\n<div>\n<p>sankai/iStock via Getty Images This article series aims at evaluating ETFs (exchange-traded funds) regarding past performance and portfolio metrics. Reviews with updated data are posted when necessary...</p>\n\n<a href=\"https://seekingalpha.com/article/4702788-aieq-mobilizing-ai-resources-for-disappointing-results\">Source Link</a>\n\n</div>\n\n\n</article>\n</div>\n</body>\n</html>\n","isBrief":false,"type":0,"news_type":1,"symbol":"LU0080751232.USD","symbol_name":"富达环球多元动力基金A","start_time":0,"source_url":"https://seekingalpha.com/article/4702788-aieq-mobilizing-ai-resources-for-disappointing-results","article_id":"2449276989","we_media_id":null,"thumbnails":[],"rights":null,"url":"https://stock-news.laohu8.com/highlight/detail?id=2449276989","pubTimestamp":1720365968,"sourceInfo":{"source_id":"seekingalpha_fund","name":"Seeking Alpha"},"weMediaInfo":null,"summary":"). The fund has been reorganized in 2023, keeping the same ticker, fee and objective. Portfolio The fund currently has 115 holdings, exclusively US companies and mostly large and mega caps . This article will use as a benchmark the S&P 500 index, represented by SPDR S&P 500 ETF Trust . Consumer discretionary is the heaviest sector in the portfolio, with 24% of asset value. Financials, technology and healthcare are between 15% and 17%, then come energy at 11%. Other sectors weigh no more than 7%. Compared to the large cap benchmark, AIEQ massively underweights technology, consumer staples and communication. It overweights mostly consumer discretionary, energy and financials. However, with such a high turnover, the sector breakdown may change significantly in a few months. AIEQ sector breakdown  The portfolio is quite concentrated: the current top 10 holdings, listed in the next table, represent 48% of asset value and the largest position  weighs over 9%.   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Reviews with updated data are posted when necessary. AIEQ strategy Amplify AI Powered Equity ETF (NYSEARCA:AIEQ) started investing operations on 10/17/2017 and tracks the AI Powered Equity Index based on deep learning. Deep learning is a branch of artificial intelligence using software components inspired from the human brain: neural networks. They are used to identify, extract and combine features from historical data so as to make predictions with new data. The learning process involves multiple iterations of training and testing, which result in an operational predictive model. A model is typically defined by a graph of nodes and connections associated with parameters and organized in multiple layers. Metaphorically, they can be considered as neurons and synapses of an artificial brain. The number of hidden layers and nodes in each layer may vary (Geeksforgeeks) The prediction process consists in feeding the model with new input data to classify elements in different categories with associated probabilities or calculate expected values (the output data). The model used by AIEQ runs on IBM's Watson AI platform. It takes into account the different aspects of the market: fundamental, technical, sentiment and economy to predict the expected price return of a stock over a 1-month horizon. Then, it selects and assign weights to a subset of stocks within the index universe to build the portfolio with the highest forecasted return under some allocation constraints. In particular, weights are capped at 10% for constituents, and 25% for industries (including SPACs and REITs). The index is rebalanced monthly. The expense ratio of AIEQ is quite high (0.75%) and the portfolio turnover rate points to intensive trading (2719% in the most recent fiscal year!). The fund has been reorganized in 2023, keeping the same ticker, fee and objective. Portfolio The fund currently has 115 holdings, exclusively US companies and mostly large and mega caps (65% of asset value). This article will use as a benchmark the S&P 500 index, represented by SPDR S&P 500 ETF Trust (SPY). Consumer discretionary is the heaviest sector in the portfolio, with 24% of asset value. Financials, technology and healthcare are between 15% and 17%, then come energy at 11%. Other sectors weigh no more than 7%. Compared to the large cap benchmark, AIEQ massively underweights technology, consumer staples and communication. It overweights mostly consumer discretionary, energy and financials. However, with such a high turnover, the sector breakdown may change significantly in a few months. AIEQ sector breakdown (Chart: author; data: Amplify ETFs, SSGA) The portfolio is quite concentrated: the current top 10 holdings, listed in the next table, represent 48% of asset value and the largest position (Tesla) weighs over 9%.   Ticker Name Weight EPS growth% P/E ttm P/E fwd Yield%   TSLA Tesla, Inc. 9.13% 15.14 64.27 104.54 0   AMD Advanced Micro Devices, Inc. 6.36% 183.83 251.10 48.97 0   NVDA NVIDIA Corp. 5.90% 788.51 73.61 46.57 0.03   COST Costco Wholesale Corp. 4.59% 19.53 54.90 54.78 0.52   PG Procter & Gamble Co. 4.13% 6.53 27.01 25.22 2.44   AAPL Apple, Inc. 4.06% 9.26 35.20 34.28 0.44   GOOGL Alphabet, Inc. 3.87% 45.00 29.23 25.25 0.42   EQIX Equinix, Inc. 3.77% 12.87 75.71 71.61 2.26   SNOW Snowflake, Inc. 3.32% -5.19 N/A 227.71 0   PEP PepsiCo, Inc. 2.98% 39.96 24.74 20.14 3.30   Click to enlarge Ratios: Portfolio123 Fundamentals AIEQ is slightly cheaper than the S&P 500 regarding valuation ratios, and it has significantly lower earnings and sales flow growth rates. Cash flow growth is superior, though.     AIEQ SPY   P/E TTM 25.43 26.18   Price/Book 3.58 4.63   Price/Sales 2.69 3.01   Price/Cash Flow 15.3 17.83   Earnings growth 14.47% 23.27%   Sales growth 5.18% 9.62%   Cash Flow growth 19.65% 13.66%   Click to enlarge Data: Fidelity In my ETF reviews, risky stocks are companies with at least 2 red flags among bad Piotroski score, negative ROA, unsustainable payout ratio, and bad or dubious Altman Z-score, excluding financials and real estate where these metrics are unreliable. With this assumption, risky stocks weigh 26% of asset value, which is a very bad point. However, based on my calculation of aggregate quality metrics, reported in the next table, the aggregate Return on Assets is superior to the benchmark.     AIEQ SPY   Atman Z-score 11.19 3.75   Piotroski F-score 5.85 5.99   ROA % TTM 10.9 7.27   Click to enlarge Performance Since 10/24/2017, AIEQ has underperformed SPY by 7.2% in annualized return. Moreover, the maximum drawdown and volatility (measured as the standard deviation of monthly returns) point to a significantly higher risk. As a consequence, the risk-adjusted performance (measured as Sharpe ratio) is underwhelming.     Total Return Annual Return Drawdown Sharpe ratio Volatility   AIEQ 56.22% 6.88% -38.97% 0.3 23.55%   SPY 141.79% 14.07% -33.72% 0.7 17.49%   Click to enlarge Data calculated with Portfolio123 Over the last 12 months, AIEQ is 8.5% behind the benchmark. AIEQ vs SPY, 12-month return (Seeking Alpha) Competitors The next table compares characteristics of AIEQ and five other AI-powered ETFs:  WisdomTree U.S. AI Enhanced Value Fund (AIVL) StockSnips AI-Powered Sentiment US All Cap ETF (NEWZ) QRAFT AI Enhanced U.S. Large Cap ETF (QRFT) QRAFT AI-Enhanced U.S. Large Cap Momentum ETF (AMOM) LG QRAFT AI-Powered U.S. Large Cap Core ETF (LQAI)      AIEQ AIVL NEWZ QRFT AMOM LQAI   Inception 10/17/2017 6/16/2006 4/10/2024 5/20/2019 5/20/2019 11/7/2023   Expense Ratio 0.75% 0.38% 0.65% 0.75% 0.75% 0.75%   AUM $108.25M $367.04M $12.21M $13.11M $22.35M $4.74M   Avg Daily Volume $326.41K $736.46K $249.07K $132.09K $228.48K $24.33K   Click to enlarge AIEQ is in second position behind AIVL based on size (in assets under management) and liquidity (in dollar volume). In fact, the three other funds are very small and thinly traded. The next chart plots total returns since 6/1/2019, excluding NEWZ and LQAI, which have less than one year of track record. AIEQ is the second-worst performer, ahead of AIVL. The two funds by QRAFT AI ETFs are almost on par and lead the pack. AIEQ vs competitors since 6/1/2019 (Seeking Alpha) AIEQ has also been lagging in 2024 to date: AIEQ vs AMOM, year-to-date (Seeking Alpha) Takeaway  Amplify AI Powered Equity ETF implements a strategy using deep learning to select stocks that are likely to outperform in a 1-month time frame and refreshes the portfolio every month. AIEQ has a high fee and an extremely high turnover rate. It has lagged the S&P 500 since inception and shows a higher volatility. Moreover, it has lagged two other AI-powered funds by a wide margin over the last 5 years. In conclusion, AIEQ track record is underwhelming, especially in regard to AI resources mobilized to design and execute the strategy.","kind":null,"is_publish_news":true,"is_publish_highlight":false,"is_publish_live":false,"is_publish_wemedia":null,"editions":null,"column":"","sentiment":"0","news_tag":"","news_rank":0,"symbols":[],"gpt_button":1},"commentList":[],"isCommentEnd":true,"newsSizeData":{"likeSize":0,"commentSize":0,"repostSize":0,"favoriteSize":0,"likeStatus":false,"favoriteStatus":false},"APP":{"userAgent":"Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)","isDev":false,"isTTM":true,"deviceId":"web-server-community-laohu8-v3","version":"4.29.2","shortVersion":"4.29.2","platform":"web","vendor":"web","appName":"ttm","isIOS":false,"isAndroid":false,"isTiger":false,"isTHS":false,"isWeiXin":false,"isWeiXinMini":false,"isWeiBo":false,"isQQ":false,"isBaiduSwan":false,"isBaiduBox":false,"isDingTalk":false,"isToutiao":false,"isOnePlus":false,"isHuaWei":false,"isXiaomi":false,"isXiaomiWebView":false,"isOppo":false,"isVivo":false,"isSamsung":false,"isMobile":false},"href":"/m/news/2449276989"}