Continue to site >
Trending ETFs

AI Powered Equity ETF

Active ETF
AIEQ
Payout Change
Pending
Price as of:
$32.33 +0.64 +2.02%
primary theme
U.S. Large-Cap Growth Equity
AIEQ (ETF)

AI Powered Equity ETF

Payout Change
Pending
Price as of:
$32.33 +0.64 +2.02%
primary theme
U.S. Large-Cap Growth Equity
AIEQ (ETF)

AI Powered Equity ETF

Payout Change
Pending
Price as of:
$32.33 +0.64 +2.02%
primary theme
U.S. Large-Cap Growth Equity

Name

Price

Aum/Mkt Cap

YIELD

Exp Ratio

Watchlist

AI Powered Equity ETF

AIEQ | Active ETF

$32.33

$124 M

0.40%

$0.13

0.75%

Vitals

YTD Return

13.5%

1 yr return

-8.9%

3 Yr Avg Return

4.3%

5 Yr Avg Return

5.6%

Net Assets

$124 M

Holdings in Top 10

38.4%

52 WEEK LOW AND HIGH

$31.7
$27.77
$38.26

Expenses

OPERATING FEES

Expense Ratio 0.75%

SALES FEES

Front Load N/A

Deferred Load N/A

TRADING FEES

Turnover N/A

Redemption Fee N/A


Min Investment

Standard (Taxable)

N/A

IRA

N/A


Fund Classification

Fund Type

Exchange Traded Fund


Name

Price

Aum/Mkt Cap

YIELD

Exp Ratio

Watchlist

AI Powered Equity ETF

AIEQ | Active ETF

$32.33

$124 M

0.40%

$0.13

0.75%

AIEQ - Profile

Distributions

  • YTD Total Return 13.5%
  • 3 Yr Annualized Total Return 4.3%
  • 5 Yr Annualized Total Return 5.6%
  • Capital Gain Distribution Frequency Annually
  • Net Income Ratio -0.09%
DIVIDENDS
  • Dividend Yield 0.4%
  • Dividend Distribution Frequency Quarterly

Fund Details

  • Legal Name
    AI Powered Equity ETF
  • Fund Family Name
    ETFMG
  • Inception Date
    Oct 17, 2017
  • Shares Outstanding
    3625000
  • Share Class
    N/A
  • Currency
    USD
  • Domiciled Country
    United States
  • Manager
    Samuel Masucci

Fund Description

The Fund is actively managed and invests primarily in equity securities listed on a U.S. exchange based on the results of a proprietary, quantitative model (the “EquBot Model”) developed by EquBot Inc. (“EquBot”) that runs on the IBM Watson™ platform. EquBot, the Fund’s sub-adviser, is a technology based company focused on applying artificial intelligence (“AI”) based solutions to investment analyses. As an IBM Global Entrepreneur company, EquBot leverages IBM’s Watson AI to conduct an objective, fundamental analysis of U.S. domiciled common stocks, including Special Purpose Acquisitions Corporations (“SPAC”), and real estate investment trusts (“REITs”) based on up to ten years of historical data and apply that analysis to recent economic and news data. A SPAC is a “blank check” company with no commercial operations that is designed to raise capital via an initial public offering for the purpose of engaging in a merger, acquisition, reorganization, or similar business combination (a “Combination”) with one or more operating companies (each a SPAC-derived company).
Each 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 30 to 200 companies with the greatest potential over the next twelve months for appreciation and their corresponding weights, targeting a maximum risk adjusted return versus the broader U.S. equity market. The Fund may invest in the securities of companies of any market capitalization. The EquBot model recommends a weight for each company based on its potential for appreciation and correlation to the other companies in the Fund’s portfolio. If a SPAC that is selected for investment by the Fund announces a Combination with an operating company, the pre-Combination SPAC and, subsequently, the SPAC-derived company will be screened for investment and may continue to be held by the Fund so long as it continues to meet the requirements of the EquBot Model. If the SPAC announces a Combination with a business which does not meet the criteria of the EquBot Model, the SPAC will be removed from the Fund as promptly as practicable following the determination being made. The EquBot model limits the weight of any individual company to 10%. At times, a significant portion of the Fund’s assets may consist of cash and cash equivalents.
IBM’s 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 Fund’s investment 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 “1940 Act”) and its rules and regulations. The Fund’s sub-adviser anticipates primarily making purchase and sale decisions based on information from the EquBot Model. Additionally, the model will systematically take into consideration the tax treatment of a particular transaction or series of transactions and liquidity or other constraints relating to trading a security selected pursuant to the EquBot Model. The Fund may frequently and actively purchase and sell securities.
The Fund may lend its portfolio securities to brokers, dealers, and other financial organizations. These loans, if and when made, may not exceed 33 1/3% of the total asset value of the Fund (including the loan collateral). By lending its securities, the Fund may increase its income by receiving payments from the borrower.
Read More

AIEQ - Performance

Return Ranking - Trailing

Period AIEQ Return Category Return Low Category Return High Rank in Category (%)
YTD 13.5% -44.2% 26.6% 6.21%
1 Yr -8.9% -98.5% 124.6% 8.87%
3 Yr 4.3%* -76.8% 26.3% 15.88%
5 Yr 5.6%* -60.8% 23.2% 16.47%
10 Yr N/A* -35.0% 18.9% N/A

* Annualized

Return Ranking - Calendar

Period AIEQ Return Category Return Low Category Return High Rank in Category (%)
2022 -31.9% -98.8% 81.6% 40.08%
2021 9.6% -39.5% 48.7% 18.17%
2020 7.8% -13.0% 34.8% 53.19%
2019 7.0% -27.1% 10.6% 14.74%
2018 -1.6% -15.9% 33.2% 36.09%

Total Return Ranking - Trailing

Period AIEQ Return Category Return Low Category Return High Rank in Category (%)
YTD 13.5% -44.2% 26.6% 6.21%
1 Yr -8.9% -98.5% 124.6% 9.94%
3 Yr 4.3%* -76.8% 32.9% 16.96%
5 Yr 5.6%* -60.8% 22.9% 19.60%
10 Yr N/A* -35.0% 19.0% N/A

* Annualized

Total Return Ranking - Calendar

Period AIEQ Return Category Return Low Category Return High Rank in Category (%)
2022 -31.9% -98.8% 81.6% 40.24%
2021 9.6% -39.5% 48.7% 18.08%
2020 7.8% -13.0% 34.8% 53.19%
2019 7.0% -16.8% 10.6% 14.74%
2018 -1.6% -15.9% 35.6% 58.05%

AIEQ - Holdings

Concentration Analysis

AIEQ Category Low Category High AIEQ % Rank
Net Assets 124 M 189 K 222 B 80.74%
Number of Holdings 133 1 3509 15.32%
Net Assets in Top 10 47.8 M -1.37 M 104 B 81.48%
Weighting of Top 10 38.40% 9.4% 100.0% 81.70%

Top 10 Holdings

  1. Microsoft Corp 7.37%
  2. ONEOK Inc 5.90%
  3. Autodesk Inc 5.20%
  4. The Travelers Companies Inc 5.13%
  5. Palo Alto Networks Inc 4.58%
  6. Denbury Inc Ordinary Shares - New 4.53%
  7. Eaton Corp PLC 4.46%
  8. McDonald's Corp 4.44%
  9. Cheniere Energy Inc 4.29%
  10. Applied Materials Inc 4.26%

Asset Allocation

Weighting Return Low Return High AIEQ % Rank
Stocks
99.18% 0.00% 107.71% 37.80%
Cash
0.82% -10.83% 100.00% 58.54%
Preferred Stocks
0.00% 0.00% 4.41% 97.64%
Other
0.00% -2.66% 17.15% 96.27%
Convertible Bonds
0.00% 0.00% 1.94% 96.88%
Bonds
0.00% -1.84% 98.58% 96.49%

Stock Sector Breakdown

Weighting Return Low Return High AIEQ % Rank
Technology
21.33% 0.00% 69.82% 92.21%
Energy
16.79% 0.00% 41.09% 1.30%
Financial Services
11.00% 0.00% 43.06% 33.82%
Consumer Cyclical
8.91% 0.00% 62.57% 88.63%
Healthcare
8.83% 0.00% 39.76% 84.27%
Consumer Defense
7.80% 0.00% 25.50% 10.31%
Communication Services
7.38% 0.00% 66.40% 76.41%
Industrials
7.30% 0.00% 30.65% 37.10%
Utilities
5.35% 0.00% 16.07% 2.14%
Basic Materials
3.66% 0.00% 22.00% 12.60%
Real Estate
1.64% 0.00% 29.57% 38.47%

Stock Geographic Breakdown

Weighting Return Low Return High AIEQ % Rank
US
95.79% 0.00% 105.43% 43.22%
Non US
3.39% 0.00% 54.22% 45.88%

AIEQ - Expenses

Operational Fees

AIEQ Fees (% of AUM) Category Return Low Category Return High Rank in Category (%)
Expense Ratio 0.75% 0.01% 7.09% 64.01%
Management Fee 0.75% 0.00% 1.50% 86.57%
12b-1 Fee N/A 0.00% 1.00% 19.24%
Administrative Fee N/A 0.00% 1.02% N/A

Sales Fees

AIEQ Fees (% of AUM) Category Return Low Category Return High Rank in Category (%)
Front Load N/A 0.00% 8.50% N/A
Deferred Load N/A 1.00% 5.50% N/A

Trading Fees

AIEQ Fees (% of AUM) Category Return Low Category Return High Rank in Category (%)
Max Redemption Fee N/A 1.00% 5.00% N/A

Related Fees

Turnover provides investors a proxy for the trading fees incurred by mutual fund managers who frequently adjust position allocations. Higher turnover means higher trading fees.

AIEQ Fees (% of AUM) Category Return Low Category Return High Rank in Category (%)
Turnover N/A 0.00% 316.74% 98.37%

AIEQ - Distributions

Dividend Yield Analysis

AIEQ Category Low Category High AIEQ % Rank
Dividend Yield 0.40% 0.00% 6.81% 14.22%

Dividend Distribution Analysis

AIEQ Category Low Category High Category Mod
Dividend Distribution Frequency Quarterly Annually Monthly Annually

Net Income Ratio Analysis

AIEQ Category Low Category High AIEQ % Rank
Net Income Ratio -0.09% -6.13% 2.90% 33.63%

Capital Gain Distribution Analysis

AIEQ Category Low Category High Capital Mode
Capital Gain Distribution Frequency Annually Annually Semi-Annually Annually

Distributions History

View More +

AIEQ - Fund Manager Analysis

Managers

Samuel Masucci


Start Date

Tenure

Tenure Rank

Jan 31, 2018

4.33

4.3%

Samuel Masucci, III has more than 25 years’ experience in investment banking, structured product development, sales and trading. In the last 5 years, he founded ETF Managers Group (ETFMG) hich has led to the launch of 15 funds and $3 billion in assets. . Prior to ETFMG, Mr. Samuel Masucci, III has held senior positions at Bear Stearns, UBS, SBC Warburg, and Merrill Lynch and has experience in creating, building and managing businesses for the issuance, sales and trading of: ETFs, index products, commodity products, hedge funds, ABS, and OTC structured products in the U.S. and Europe.

Devin Ryder


Start Date

Tenure

Tenure Rank

May 07, 2018

4.07

4.1%

Devin Ryder began her career with ETF Managers Group LLC during the summer of 2017 and re‑joined ETF Managers Group LLC on a permanent basis in 2018 to be a part of the portfolio management team. Prior to joining ETF Managers Group LLC, Ms. Ryder was pursuing studies in the quantitative aspects of risk management and finance, for which she received a B.S. in Mathematics of Finance and Risk Management from the University of Michigan in 2017.

Frank Vallario


Start Date

Tenure

Tenure Rank

Sep 30, 2019

2.67

2.7%

Frank Vallario serves in the role of Chief Investment Officer for the ETF Managers Group, LLC. Mr. Vallario is responsible for the portfolio construction, trading, risk management and portfolio analysis processes associated with ETF strategies. Prior to his current role, Mr. Vallario has had a variety of senior roles over his 25-year career in financial services. He joined Oppenheimer Funds in 2017 where he was Head of Equity Portfolio Management for Smart Beta ETFs. Prior to that he was Senior Portfolio Manager at Columbia Threadneedle from September 2015 to June 2017 where he was responsible for the day to day management of the firm’s ETF business, which was acquired from his previous firm, Emerging Global Advisors (EGA). From September 2010 to September 2015, he was relationship manager at MSCI responsible for providing investment solutions to complex problems using MSCI Barra’s fundamental models and portfolio construction tools. Previously, he was a partner in a start-up asset management firm where he served as the director of portfolio management. Mr. Vallario began his career at UBS Global Asset Management where he spent over a decade in various quantitative portfolio management equity roles including equity market neutral, tactical asset allocation, structured active equities, enhanced index, passive management and factor research. Mr. Vallario serves on the Investment Committee for the Girl Scouts of Connecticut and is a University Affiliate at the University of Utah - David Eccles School of Business. He received a B.S. in Finance from Lehigh University and a M.B.A. with a concentration in Finance from Rutgers University.

Tenure Analysis

Category Low Category High Category Average Category Mode
0.04 54.45 8.08 2.92