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\n\uba87 \uc77c\uc804 \uc77c\ubcf8 Line\uc774 Line Conference 2019\ub97c \uac1c\ucd5c\ud558\uc600\uc2b5\ub2c8\ub2e4. Life on Line\uc774\ub77c\ub294 \ube44\uc804\uc744 \uc81c\uc2dc\ud558\uc600\uc2b5\ub2c8\ub2e4. \ud2b9\ubcc4\ud788 \uad00\uc2ec\uc744 \uac00\uc9c8 \uc77c\uc740 \uc544\ub2c8\uc5c8\uc2b5\ub2c8\ub2e4.<\/p>\n
\n<\/p>\n
\uadf8\ub7f0\ub370 \uc81c\uac00 \uad6c\ub3c5\ud558\ub294 \u65e5\u7d4c\uc5d0\uc11c \ubcf4\ub0b8 \uba54\uc77c\uc18d\uc5d0\uc11c \uc7ac\ubbf8\uc788\ub294 \uc81c\ubaa9\uc744 \ubcf4\uc558\uc2b5\ub2c8\ub2e4. Line\uc774 IBM\uc758 Watson\uae30\uc220\ucc98\ub7fc \uc790\uccb4\ub85c \uac1c\ubc1c\ud55c AI\uae30\uc220\uc744 \uae30\uc5c5\uc744 \ub300\uc0c1\uc73c\ub85c \ud310\ub9e4\ud55c\ub2e4\ub294 \ub0b4\uc6a9\uc774\uc5c8\uc2b5\ub2c8\ub2e4.<\/p>\n
IBM\u30ef\u30c8\u30bd\u30f3\u306b\u52dd\u3066\u308b\u304b\u3001AI\u6280\u8853\u5916\u8ca9\u306b\u6311\u3080LINE\u306e\u601d\u60d1<\/a><\/p>\n Line\uc774 \uac1c\ubc1c\ud55c AI\ub97c Line Brain\uc774\ub77c\uace0 \ud569\ub2c8\ub2e4. Chatbot, OCR \ubc0f Speech To Text\uac00 \uc81c\ud488\ud654\ud558\uc5ec \uc11c\ube44\uc2a4\ub97c \uc81c\uacf5\ud558\ub294\ub370 \u81ea\u7136\u8a00\u8a9e\u89e3\u6790, \u6587\u5b57\u8a8d\u8b58\u6280\u8853, \u97f3\u58f0\u89e3\u6790 \ubc0f \u753b\u50cf\u89e3\u6790\uacfc \uad00\ub828\ud55c AI\uae30\uc220\uc744 \uc11c\ube44\uc2a4\ud654\ud55c \uac83\uc785\ub2c8\ub2e4.<\/p>\n Line\uc740 \uac01 \uc11c\ube44\uc2a4\ub97c \uc5b4\ub5a4 \ube44\uc804\uc73c\ub85c \uc811\uadfc\ud558\ub294\uc9c0 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uba3c\uc800 Chatbot\uc785\ub2c8\ub2e4.<\/p>\n \uad6d\ub0b4 \uc778\ud130\ub137\uc740\ud589\ub4e4\uc774 \uc0ac\uc6a9\ud558\ub294 \uc8fc\ubbfc\ub4f1\ub85d\uc99d\uc774\ub098 \uc6b4\uc804\uba74\ud5c8\uc99d\uc744 \uc774\uc6a9\ud55c \uac04\ud3b8\uc778\uc99d\ucc98\ub7fc OCR\uae30\uc220\uc740 eKYC\uc5d0 \ud65c\uc6a9\ud55c\ub2e4\uace0 \ud569\ub2c8\ub2e4. \uc815\ud615 \ubb38\uc11c\ubfd0 \uc544\ub2c8\ub77c \ube44\uc815\ud615 \ubb38\uc11c\ub97c \uc774\uc6a9\ud55c OCR\uae30\uc220\ub85c \ub2e4\uc591\ud55c \uc0c1\uac70\ub798\uc5d0 \uc801\uc6a9\ud560 \uacc4\ud68d\uc774\ub77c\uace0 \ud569\ub2c8\ub2e4.<\/p>\n OCR\u6280\u8853\u306e\u60f3\u5b9a\u7528\u9014\u306e1\u3064\u304c\u3001\u30aa\u30f3\u30e9\u30a4\u30f3\u3067\u672c\u4eba\u78ba\u8a8d\u3059\u308b\u4ed5\u7d44\u307f\u300ceKYC\uff08Know Your Customer\uff09\u300d\u3002\u5229\u7528\u8005\u306f\u30b9\u30de\u30db\u306e\u30ab\u30e1\u30e9\u3067\u904b\u8ee2\u514d\u8a31\u8a3c\u3068\u81ea\u5206\u306e\u5199\u771f\u3092\u64ae\u5f71\u3059\u308b\u306a\u3069\u6bd4\u8f03\u7684\u7c21\u7d20\u306a\u4f5c\u696d\u3067\u3001\u672c\u4eba\u78ba\u8a8d\u624b\u7d9a\u304d\u3092\u6e08\u307e\u305b\u3089\u308c\u308b\u3002\u91d1\u878d\u3084EC\uff08\u96fb\u5b50\u5546\u53d6\u5f15\uff09\u3001C to C\uff08\u6d88\u8cbb\u8005\u9593\u53d6\u5f15\uff09\u3068\u3044\u3063\u305f\u30b5\u30fc\u30d3\u30b9\u3092\u63d0\u4f9b\u3059\u308b\u3046\u3048\u3067\u91cd\u8981\u306a\u6280\u8853\u3060\u3002\u3000LINE\u306f\u81ea\u3089\u3082\u30b9\u30de\u30db\u6c7a\u6e08\u30b5\u30fc\u30d3\u30b9\u300cLINE Pay\u300d\u306beKYC\u3092\u5c0e\u5165\u6e08\u307f\u3002OCR\u6280\u8853\u3092\u5916\u8ca9\u3059\u308b\u3053\u3068\u3067\u3001\u540c\u69d8\u306a\u9700\u8981\u306e\u9ad8\u307e\u308a\u3092\u53d6\u308a\u8fbc\u3080\u3002\u4ed6\u306b\u3082\u5bb6\u8a08\u7c3f\u30a2\u30d7\u30ea\u5411\u3051\u306e\u30ec\u30b7\u30fc\u30c8\u64ae\u5f71\u6a5f\u80fd\u3084\u3001\u4f01\u696d\u306b\u304a\u3051\u308b\u9818\u53ce\u66f8\u3084\u8acb\u6c42\u66f8\u306e\u96fb\u5b50\u5316\u3068\u3044\u3063\u305f\u7528\u9014\u3092\u898b\u8fbc\u3080\u3002<\/p><\/blockquote>\n \uc601\uc0c1\ubd84\uc11d\uae30\uc220\ub3c4 \uc5ed\uc2dc \uc0c1\uac70\ub798\uc640 \uacb0\ud569\uc2dc\ud0ac \uc608\uc815\uc774\ub77c\uace0 \ud569\ub2c8\ub2e4. “\u97f3\u58f0\u8a8d\u8b58\u3084\u52d5\u753b\u89e3\u6790\u3092\u30d5\u30eb\u6d3b\u7528\u3057\u305f\u300c\u6b21\u4e16\u4ee3\u578b\u30c6\u30ec\u30d3\u300d\u306e\u69cb\u60f3”\ub77c\ub294 \uc81c\ubaa9\ucc98\ub7fc \uc74c\uc131\uc778\uc2dd\uae30\uc220, \ub3d9\uc601\uc0c1\ubd84\uc11d\uae30\uc220\uc744 \uacb0\ud569\ud55c \ucc28\uc138\ub300 TV\ub97c \uad6c\uc0c1\uc911\uc774\ub77c\uace0 \ud569\ub2c8\ub2e4. \uc544\uc6b8\ub7ec AI\uae30\uc220\uc744 Line\uc5d0 \uc81c\uacf5\ud558\ub294 \uc11c\ube44\uc2a4\uc5d0 \uae30\ubc18\uae30\uc220\ub85c \ud65c\uc6a9\ud558\ub294 \uc804\ub7b5\ub3c4 \ucde8\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n \uc194\ub958\uc158\uc73c\ub85c \ud310\ub9e4\ud558\ub294 Line Brain\uc744 \uc774\uc6a9\ud558\uc5ec \uc790\uc0b0\uad00\ub9ac\uc2dc\uc7a5\uc5d0 \ub3c4\uc804\ud574\ubcfc \uc218 \uc788\uc744\uae4c\uc694?<\/p>\n 2. 2018\ub144 \ubd04\uc5d0 \uc4f0\uc5ec\uc9c4 Trust the machines? Funds run by artificial intelligence<\/a>\uc744 \ubcf4\uba74 AI\ub97c \uae30\ubc18\uc73c\ub85c ETF\uac00 \ub458 \ub4f1\uc7a5\ud569\ub2c8\ub2e4. \uccab\uc9f8\ub294 BIKR (Rogers AI Global Macro ETF)\uc774\uace0 \ub458\uc9f8\ub294 AIEQ( AI Powered Equity ETF)\uc785\ub2c8\ub2e4.<\/p>\n ETF Managers Group and Ocean Capital Advisors launched an AI-powered fund last month dubbed the Rogers AI Global Macro ETF (BIKR) that invests primarily in single-country ETFs. The fund\u2019s AI sifts through millions of data points from countries around the globe and uses what it learns to determine how best to allocate the fund\u2019s holdings. (Humans carry out the trades, however.)<\/strong> “Humans carry out the trades, however”\ub77c\ub294 \ubb38\uc7a5\uc73c\ub85c \ub193\uace0 \ubcfc \ub54c \ub450 ETF\ub97c \ube44\uad50\ud574\ubcf4\uba74 BIKR\uc740 \uc804\ub2f4 \ud3ec\ud2b8\ud3f4\ub9ac\uc624 \ub9e4\ub2c8\uc800\uac00 \uc788\uc9c0\ub9cc AIEQ\ub294 \uc5c6\ub294 \ub4ef \ud569\ub2c8\ub2e4. AIEQ\ub098 BIKR\uac00 \uc5b4\ub5a4 \uc54c\uace0\ub9ac\uc998\uc5d0 \uae30\ubc18\ud55c \uc804\ub7b5\uc744 \uc6b4\uc6a9\ud558\ub294\uc9c0 \ud655\uc778\ud560 \uc790\ub8cc\uac00 \uac70\uc758 \uc5c6\uc2b5\ub2c8\ub2e4. \uadf8\ub098\ub9c8 \ucc38\uace0\ud560 \ub9cc\ud55c \uac83\uc774 SEC\uc5d0 \ubcf4\uace0\ud55c \uacf5\uc2dc\uc790\ub8cc\uc785\ub2c8\ub2e4. \uba3c\uc800 BIKR\uc785\ub2c8\ub2e4.<\/p>\n Principal Investment Strategies<\/p>\n The Fund uses a \u201cpassive\u201d or indexing approach to try to achieve its investment objective. Unlike many investment companies, the Fund does not try to \u201cbeat\u201d the Index and does not seek temporary defensive positions when markets decline or appear overvalued other than those indicated in the Index. The Fund will use a replication strategy. A replication strategy is an indexing strategy that involves investing in the securities of the Index in approximately the same proportions as in the Index.<\/strong> However, the Fund may utilize a representative sampling strategy with respect to the Index when a replication strategy might be detrimental to shareholders, such as when there are practical difficulties or substantial costs involved in compiling a portfolio of equity securities to follow the Index, in instances in which a security in the Index becomes temporarily illiquid, unavailable or less liquid, or as a result of legal restrictions or limitations (such as tax diversification requirements) that apply to the Fund but not the Index.<\/p>\n The Rogers AI Global Macro Index<\/p>\n The Index was developed in 2018 by Ocean Capital Advisors\u00ae, LLC, the Fund\u2019s index provider (\u201cOcean\u201d). The Index tracks the performance of single-country (including emerging markets) exchange-traded funds (\u201cETFs\u201d) that each track a broad-based index composed of equity securities primarily listed on an exchange in the applicable country (collectively, the \u201cUnderlying ETFs\u201d) or an ETF tracking the 1-3 year U.S. Treasury Bond market (a \u201cTreasury ETF\u201d). The underlying index of each single-country Underlying ETF included in the Index may include equity securities of small-, mid-, and large-capitalization companies, and such equity securities are expected to have exposure to a wide range of industries reflective of the economy of the applicable country. The Index includes a single Underlying ETF per country. Each eligible Underlying ETF is specified The allocation of the Index\u2019s weight among the Underlying ETFs or Treasury ETF is based on a proprietary artificial intelligence-driven algorithm (the \u201cModel\u201d) that analyzes macroeconomic data (e.g., volatility, interest rates, productivity, gross national product) monthly to identify likely changes in market directions in individual countries and within the global economy. The Model uses objective data to calculate the magnitude and probability of such market movements over an approximately 18-month period, while seeking to identify any \u201cmicro-cycles\u201d that might develop in shorter time periods, to determine the optimal investment allocations based on the relative expected performance of the market in each country in the Index universe. The Model\u2019s bias toward longer-term market movements is generally expected to minimize turnover at the time of each rebalance<\/strong>. When the Model determines to reduce or eliminate exposure to a country, the reduction to such allocation (whether all or a portion of the country\u2019s allocation in the Index) is replaced with the Treasury ETF. From time to time the Index may be significantly allocated to the Treasury ETF. The Index is rebalanced as of the first business day of each month using data as of the last business day of the previous month. The weight of each country is capped at 10% at the time of each rebalance of the Index, with any excess weight reallocated equally to each other country included in the Index. At the time of each rebalance, the Index will have a positive allocation to at least twenty individual countries. The Index was developed by Ocean for purposes of creating the Fund. The Index is calculated and maintained by Solactive AG, an independent third-party calculation agent. As of January 14, 2019, the Index consisted of single-country ETFs representing 43 countries. The four largest country allocations in the Index were Brazil (6.89%), China (4.59%), Malaysia (3.65%), and Nigeria (2.79%), and 48.37% was allocated to the Treasury ETF. <\/p><\/blockquote>\n \ub2e4\uc74c\uc740 AIEQ\uc785\ub2c8\ub2e4 AIEQ\ub294 Equbot\uc774 \uac1c\ubc1c\ud558\uc600\uace0 IBM\uc758 Whatson\uae30\uc220\uc744 \ucc44\ud0dd\ud558\uc5ec \ub9cc\ub4e0 ETF\uc785\ub2c8\ub2e4. <\/p>\n \nPrincipal Investment Strategies<\/p>\n The Fund is actively<\/strong> managed and seeks to achieve its investment objective by investing primarily in equity securities (or depositary receipts) of companies in developed markets outside the United States based on the results of a proprietary, quantitative model (the \u201cEquBot Model\u201d) developed by EquBot LLC (\u201cEquBot\u201d or the \u201cAdviser\u201d) that runs on the Watson\u2122 platform. EquBot, the Fund\u2019s investment adviser, is a technology based company focused on applying artificial intelligence (\u201cAI\u201d) based solutions to investment analyses. As an IBM Global Entrepreneur company, EquBot leverages IBM\u2019s Watson AI to conduct an objective, fundamental analysis of companies in non-U.S. developed markets based on up to ten years of historical data and apply that analysis to recent economic and news data. The EquBot Model recommends a weight for each company based on its potential for appreciation and correlation to the other companies in the Fund\u2019s portfolio. The EquBot Model limits the weight of any individual company to 10%. IBM\u2019s Watson AI is a computing platform capable of answering natural language questions by connecting large amounts of data, both structured (e.g., spreadsheets) and unstructured (e.g., news articles), and learning from each analysis it conducts (e.g., by recognizing patterns) to produce a more accurate answer with each subsequent question. The Adviser utilizes the recommendations of the EquBot Model to decide which securities to purchase and sell, while complying with the Investment Company Act of 1940 (the \u201c1940 Act\u201d) and its rules and regulations. The Adviser anticipates primarily making purchase \uc774\uc678\uc5d0 \ud55c\uad6d Qraft\uc640 EXCHANGE LISTED Funds\uac00 \uc81c\ud734\ud558\uc5ec QRFT\ub97c \uc0c1\uc7a5\ud558\uc600\uc2b5\ub2c8\ub2e4. SEC\uc5d0 \ubcf4\uace0\ud55c \ubb38\uc11c\uac00 \ub2f4\uace0 \uc788\ub294 \uc804\ub7b5\uc785\ub2c8\ub2e4.<\/p>\n Principal Investment Strategies<\/p>\n The Fund is an actively-managed exchange-traded fund (\u201cETF\u201d) that seeks to achieve its investment objective by investing at least 80% of its net assets, plus the amounts of any borrowings for investment purposes, in securities of U.S.-listed large capitalization companies. The Fund defines large capitalization companies as companies having a market capitalization in excess of $4 billion at the time of purchase. The Fund will invest in equity securities of such companies, including common stock, American Depositary Receipts (\u201cADRs\u201d) and Global Depositary Receipts (\u201cGDRs\u201d). The Fund\u2019s adviser, Exchange Traded Concepts, LLC (the \u201cAdviser\u201d) uses an investment process based on a proprietary artificial intelligence security selection process that extracts patterns from analyzing data, as discussed below, developed by QRAFT Technologies, Inc. (\u201cQraft\u201d). Qraft is a South Korea-based provider of artificial intelligence investment systems and currently offers services to various financial institutions in Korea. The Adviser has licensed Qraft\u2019s proprietary artificial intelligence security selection process for the management of the Fund.<\/p>\n In pursuing the Fund\u2019s investment objective, the Adviser consults a database generated by Qraft\u2019s AI Quantitative Investment System (\u201cAQUA\u201d), which automatically selects and weights portfolios of companies to provide a balanced exposure to five main factors affecting the U.S. market (the \u201cFive Factors\u201d): quality (generally, a company\u2019s profitability), size (market capitalization), value (comparison of a company\u2019s market value versus its book value), momentum (a security\u2019s recent price returns compared to the overall market over time), and volatility (a security\u2019s systematic risk as compared to the market as a whole) (the \u201cU.S. Large Cap Database\u201d). In creating the U.S. Large Cap Database, AQUA utilizes automated data feed and data processing using deep learning technologies (i.e., exposure to and processing of large amounts of data). <\/strong>First, on a monthly basis, AQUA automatically sends queries to and collects from various data vendors company fundamental data (such as historical stock prices and other financial information) of all companies listed on the New York Stock Exchange and NASDAQ as well as macroeconomic data. AQUA then processes and stores newly received data with stored historical data. AQUA processes such data first by creating five indices each representing one of the Five Factors of companies in the top 20% of U.S.-listed companies based on market capitalization. Then, AQUA compresses such data over the last 60 month period and, using deep learning technologies, evaluates how each individual factor would change and\/or affect a company over time and identifies 300 to 350 companies that have the greatest potential to outperform their U.S. large cap peers over the next three-month period. AQUA estimates a weighting for each such company based on its potential for maximum return as compared to other companies, and the final portfolios are then delivered to the U.S. Large Cap Database for use by the Adviser. AQUA repeats such processes on the first business day of every month and the Adviser makes or changes investments in the Fund based on the newly generated information.<\/p>\n The Fund expects to hold 300 to 350 companies in its portfolio. While it is anticipated that the Adviser will purchase and sell securities based on recommendations by the U.S. Large Cap Database, the Adviser has full discretion over investment decisions for the Fund. Therefore, the Adviser has full decision-making power not only if it identifies a potential technical issue or error with the U.S. Large Cap Database, but also if it believes that the recommended portfolio does not further the Fund\u2019s investment objective or fails to take into account company events such as corporate actions, mergers and spin-offs. Additionally, the Adviser has discretion over the amount of cash maintained in the Fund\u2019s portfolio and the reinvestment of dividends in the Fund\u2019s portfolio, subject to the Fund\u2019s distribution requirements as a regulated investment company for federal income tax purposes. See \u201cFederal Income Taxes\u201d in the Fund\u2019s Statement of Additional Information (\u201cSAI\u201d) for a more complete discussion. Notwithstanding the foregoing, the Fund limits the weighting of a single company to 10% and no more than 40% of the Fund\u2019s assets may be invested in securities with a more than 5% weighting in the Fund\u2019s portfolio. Because the U.S. Large Cap Database is adjusted on a monthly basis, the Adviser expects that the Fund will frequently purchase and sell shares of securities. <\/p><\/blockquote>\n \uae30\ubcf8\uc801\uc73c\ub85c Active\uc640 Passive\uc758 \ucc28\uc774\uac00 \uc874\uc7ac\ud569\ub2c8\ub2e4.<\/p>\n \uc561\ud2f0\ube0c \ud380\ub4dc(Active Fund) : \uc2dc\uc7a5 \uc218\uc775\ub960\uc744 \ucd08\uacfc\ud558\ub294 \uc218\uc775\uc744 \uc62c\ub9ac\uae30 \uc704\ud574\uc11c \ud380\ub4dc\ub9e4\ub2c8\uc800\uac00 \uc801\uadf9\uc801\uc73c\ub85c \uc2dc\uc7a5\uc774\ub098 \uc885\ubaa9\uc758 \uac00\uaca9\uc744 \uc608\uce21\ud558\uc5ec \uc88b\uc740 \uc885\ubaa9\uc744 \uc801\uc808\ud788 \ub9e4\uc218, \ub9e4\ub3c4\ud558\ub294 \ud380\ub4dc. \ud328\uc2dc\ube0c \ud380\ub4dc\ubcf4\ub2e4 \uc6b4\uc6a9\ube44\uc6a9\uc774 \ub354 \ub4e4\uace0, \uae30\ub300\uc218\uc775\uc774 \ub192\uc740 \ub9cc\ud07c \ubcc0\ub3d9\uc131\uacfc \ub9ac\uc2a4\ud06c\ub3c4 \uc0c1\ub300\uc801\uc73c\ub85c \ub192\uc74c. \ud328\uc2dc\ube0c \ud380\ub4dc(Passive Fund) : \ub300\ud45c\uc8fc\uac00\uc9c0\uc218\ub97c \uad6c\uc131\ud558\ub294 \uc885\ubaa9\ub4e4\ub85c \ud380\ub4dc\ub97c \uad6c\uc131\ud574 \uadf8 \uc9c0\uc218\uac00 \uc624\ub974\ub294 \ub9cc\ud07c\uc758 \uc218\uc775\uc744 \ucd94\uad6c\ud558\ub294 \ud380\ub4dc. \uc2dc\uc7a5\uc774 \ud6a8\uc728\uc801\uc774\ub77c\uace0 \ubbff\uae30 \ub54c\ubb38\uc5d0 \ud380\ub4dc\ub9e4\ub2c8\uc800\uc758 \uc8fc\uad00\uc801\uc778 \uc6b4\uc6a9\uc774\ub098 \uac1c\uc785\uc744 \ud544\uc694\ub85c \ud558\uc9c0 \uc54a\uc73c\uba70 \ubcf4\uc218\uac00 \uc800\ub834\ud568.<\/p><\/blockquote>\n \uadf8\uc678 Qraft\ub294 \ud329\ud130\ubaa8\ub378\uc5d0 Deep Learning\uc744 \uc801\uc6a9\ud55c \ubc29\uc2dd\uc785\ub2c8\ub2e4. AI ETF\uc758 \uc81c\uc791 \ubc0f NYSE \uc0c1\uc7a5, \ud55c\ub2ec\ub3d9\uc548\uc758 \uc131\uacfc<\/a>\uc774 \uc790\uc138\ud788(?) \uc18c\uac1c\ud558\uace0 \uc788\uc9c0\ub9cc \uc704 \uc778\uc6a9\ubb38\uc911 Bold\ub85c \ucc98\ub9ac\ud55c \ubd80\ubd84\uc785\ub2c8\ub2e4. \ubc18\uba74 AIEQ\uc740 \uac70\uc758 \ubaa8\ub4e0 \uc0c1\uc7a5\uc885\ubaa9\uc744 \ubd84\uc11d\ud558\uc5ec \uc885\ubaa9\uc744 \uc120\ud0dd\ud558\ub294 \ubc29\uc2dd\uc778 \ub4ef \ud569\ub2c8\ub2e4. EquBot Taps AI To Perform The Work Of 3,000 Research Analysts<\/a>\uc774\ub098 SEC \uc778\uc6a9\ubb38\uc5d0\uc11c Bold\ub85c \ucc98\ub9ac\ud55c \ubd80\ubd84\uc785\ub2c8\ub2e4. <\/p>\n The EquBot model, with the help of powerful computing platforms like IBM Watson, compiles and computes over a million data points daily, from quarterly reports, news articles, social media posts, financial statements and more. The model then builds predictive financial models for thousands of domestic and global companies to identify timing and positioning for optimal stock selection and potential capital appreciation. We’ve set the fund’s objectives to meet commensurate volatility measures and have so far delivered benchmark-beating returns in both ETFs.<\/p><\/blockquote>\n AIEQ\uc640 QRFT\uc758 \ucc28\uc774\uc810\uc740 \ub370\uc774\ud0c0\ucc98\ub9ac\uc8fc\uae30\uc785\ub2c8\ub2e4. QRFT\ub294 \uc6d4\ub2e8\uc704\ub85c \uc774\ub8e8\uc5b4\uc9c0\ub294 \ubc18\uba74 AIEQ\ub294 \uc77c\ub2e8\uc704\ub85c \uc774\ub8e8\uc5b4\uc9d1\ub2c8\ub2e4. \uadf8\ub807\uae30 \ub54c\ubb38\uc5d0 \ucc98\ub9ac\ud558\ub294 \ub370\uc774\ud0c0\ub3c4 \ucc28\uc774\uac00 \ub9ce\uc774 \ub0a9\ub2c8\ub2e4. Rebalancing\ub3c4 \uc6d4\ub2e8\uc704, \uc77c\ub2e8\uc704\ub85c \uc774\ub8e8\uc5b4\uc9d1\ub2c8\ub2e4. <\/p>\n 3. This section raises a number of questions, such as:<\/p>\n What data points compose the \u201cone million pieces of information\u201d being analyzed daily? \uadf8\ub7ec\uba74 New Constructs\uc774 \uc81c\uc2dc\ud558\ub294 \ubc29\ubc95\ub860\uc740 \ubb34\uc5c7\uc77c\uac00\uc694? Origin of the Robo Analyst<\/a>\uc5d0\uc11c \uc790\uc138\ud788 \uc18c\uac1c\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n![\"\"](\"https:\/\/i0.wp.com\/www.smallake.kr\/wp-content\/uploads\/2019\/08\/01_o.jpg?w=1130&ssl=1\")
\n![\"\"](\"https:\/\/i0.wp.com\/www.smallake.kr\/wp-content\/uploads\/2019\/08\/009.jpg?w=1130&ssl=1\")
\n![\"\"](\"https:\/\/i0.wp.com\/www.smallake.kr\/wp-content\/uploads\/2019\/08\/002-w960.jpg?w=1130&ssl=1\")
<\/p>\n![\"\"](\"https:\/\/i0.wp.com\/www.smallake.kr\/wp-content\/uploads\/2019\/08\/l08_o.jpg?resize=800%2C450&ssl=1\")
<\/p>\n![\"\"](\"https:\/\/i0.wp.com\/www.smallake.kr\/wp-content\/uploads\/2019\/08\/10_o.jpg?resize=800%2C533&ssl=1\")
<\/p>\n![\"\"](\"https:\/\/i0.wp.com\/www.smallake.kr\/wp-content\/uploads\/2019\/08\/11_o.jpg?resize=800%2C533&ssl=1\")
<\/p>\n![\"\"](\"https:\/\/i0.wp.com\/www.smallake.kr\/wp-content\/uploads\/2019\/08\/l11_o.jpg?resize=800%2C450&ssl=1\")
\n![\"\"](\"https:\/\/i0.wp.com\/www.smallake.kr\/wp-content\/uploads\/2019\/08\/l10_o.jpg?resize=800%2C450&ssl=1\")
<\/p>\n
\n \uc774\uc0c1\uacfc \uac19\uc740 AI\uae30\uc220\uc744 \uc790\ubcf8\uc2dc\uc7a5\uc5d0 \ub3c4\uc785\ud55c\ub2e4\uace0 \uc0dd\uac01\ud560 \ub54c \uac00\uc7a5 \uba3c\uc800 \ub5a0\uc624\ub974\ub294 \uac83\uc774 \ub85c\ubcf4\uc5b4\ub4dc\ubc14\uc774\uc800\uc785\ub2c8\ub2e4. \uc774\uc81c\ub294 \uc0c8\ub86d\uc9c0 \uc54a\uc740 \ubd84\uc57c\uc778 \ub85c\ubcf4\uc5b4\ub4dc\ubc14\uc774\uc800\uac00 \uc0dd\uac01\ubcf4\ub2e4 \uc601\ud5a5\ub825\uc744 \ud0a4\uc6cc\ub098\uac00\uc9c0 \ubabb\ud55c \ub290\ub08c\uc785\ub2c8\ub2e4. \ub85c\ubcf4\uc5b4\ub4dc\ubc14\uc774\uc800\uac00 \uac01\uad11\uc744 \ubc1b\uc744 \ub54c \uad00\uc2ec\uc740 \ub85c\ubd07\uc774\uc5c8\uc2b5\ub2c8\ub2e4. \uc54c\ud30c\uace0\uc758 \uc601\ud5a5\ub54c\ubb38\uc778\uc9c0 \ubab0\ub77c\ub3c4 \ub85c\ubd07\uc774 \ud3b8\ub4dc\ub97c \uad00\ub9ac\ud558\uba74 \ub354 \ub192\uc740 \uc218\uc775\uc744 \ub0bc \uc218 \uc788\uc744 \uac83\uc774\ub77c\ub294 \ub9c9\uc5f0\ud55c \ubbff\uc74c\uc774\uc5c8\uc2b5\ub2c8\ub2e4. \uc774 \ubbff\uc74c\uc774 \uc0ac\ub77c\uc9c0\ub294\ub370 \ud544\uc694\ud55c \uc2dc\uac04\uc740 \uc9e7\uc558\uc2b5\ub2c8\ub2e4. \ud22c\uc790\uc790\ub4e4\ub294 \uc218\uc775\uc728\uc744 \uae30\ub300\ud588\uc9c0\ub9cc \uae30\ub300\ubcf4\ub2e4 \ub0ae\uc740 \uc218\uc775\ub54c\ubb38\uc785\ub2c8\ub2e4. \ud575\uc2ec\uc740 \uc218\uc775\ub960\uc774\uace0 \uc778\uacf5\uc9c0\ub2a5\uc774\ub098 \uae30\uacc4\ud559\uc2b5\uc744 \uae30\ubc18\ud55c \ud22c\uc790\uac00 \uac01\uad11\uc744 \ubc1b\uc73c\ub824\uba74 \uacb0\uad6d \uc2dc\uc7a5\uc5d0\uc11c \ub6f0\uc5b4\ub09c \uc218\uc775\uc728\uc744 \ubcf4\uc5ec\uc57c \ud569\ub2c8\ub2e4. \ud55c\uad6d\ubcf4\ub2e4 \uc55e\uc120 \ud574\uc678\uc758 \uc0ac\ub840\ub294 \uc5b4\ub5a4\uc9c0 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\nDoes this approach work? Another AI fund launched last November by ETF Managers Group and Equbot shows some promise.The fund, which is called the AI Powered Equity ETF (AEIQ), invests in a variety of U.S.-based companies and seeks to beat the returns of the S&P 500. So far, it\u2019s getting it done. The ETF is up 8.1 percent this year, while the S&P 500 has gained about 1.5 percent.<\/p><\/blockquote>\n
\nin the Index\u2019s rules, and the Underlying ETFs generally reflect the ETF with the broadest exposure to equity securities in the applicable country and meeting certain minimum investibility criteria.<\/p>\n
\nEach day, the EquBot Model ranks each company based on the probability of the company benefiting from current economic conditions, trends, and world events and identifies approximately 80 to 250 companies with the greatest potential over the next twelve months for appreciation and their corresponding weights, while maintaining volatility (i.e., the range in which the portfolio\u2019s returns vary) comparable to the broader non-U.S. developed market. The Fund may invest in the securities of companies of any market capitalization.<\/strong><\/p>\n
\nand sale decisions based on information from the EquBot Model. The Fund may frequently and actively purchase and sell securities. <\/p><\/blockquote>\n
\n\uc0ac\uc2e4 AI ETF\uc5d0 \ub300\ud55c \ube44\ud310\ub3c4 \ub9ce\uc2b5\ub2c8\ub2e4. \uadf8 \uc911 \uae30\uacc4\ud559\uc2b5\uc744 \ub9ac\uc11c\uce58\uc5d0 \ub3c4\uc785\ud55c New Constructs\uac00 \uc81c\uc2dc\ud55c \ube44\ud310\uc785\ub2c8\ub2e4. Robo Analyst\ub77c\uace0 \ud558\uba70 HBR\uc740 New Constructs: Disrupting Fundamental Analysis with Robo-Analysts<\/a>\ub77c\ub294 \uc0ac\ub840\ubcf4\uace0\uc11c\ub3c4 \ub0b4\ub193\uc740 \ud68c\uc0ac\uc785\ub2c8\ub2e4. Don\u2019t Believe the Hype About this \u201cAI Powered\u201d ETF<\/a>\uc911 \uc77c\ubd80\uc785\ub2c8\ub2e4.<\/p>\n
\n Where does AIEQ get the data from, and what processes are in place to ensure data quality?
\n What is the methodology behind its \u201cpredictive models\u201d?
\n How does the AI process the data to decide which companies have \u201chigh opportunities of capital appreciation\u201d?<\/p><\/blockquote>\n