UBCB — UBC Algorithmic Fundamentals ETF

UBCB Fund Overview

UBCB — UBC Algorithmic Fundamentals ETF is a US ETF managed by ETF Opportunities Trust, categorised as United States Blend / Core Equity. ABC INVEST provides holdings, performance, costs, cashflows, risk data, prospectus documents and SEC filings, sourced from SEC filings.

• Type: US ETF
• Manager: ETF Opportunities Trust
• Category: United States Blend / Core Equity
• Assets under management: $2.29M
• 1-year return: -20.0%
• Ticker: UBCB
• SEC CIK: 0001771146
• SEC series ID: S000074411
• Share class ID: C000232320

UBCB Investment Objective and Strategy

UBC Algorithmic Fundamentals ETF describes its objective and strategy as follows, from its latest prospectus filed with the SEC by ETF Opportunities Trust.

Investment objective

The UBC Algorithmic Fundamentals ETF (the Fund) seeks long-term capital appreciation.

Principal investment strategy

The Fund is actively managed by proprietary artificial intelligence (AI) algorithms. Under normal circumstances, the Fund invests at least 80% of its net assets (including borrowing for investment purposes) in large capitalization (large cap) companies listed on U.S. stock exchanges and markets, including common stocks, American Depositary Receipts (ADRs) and exchange-traded funds (ETFs) that provide exposure to large cap companies. The Adviser defines large cap equity securities as companies with market capitalizations of $10 billion or more, measured at the time of purchase. In normal market conditions, the Adviser anticipates the Fund will hold 40-100 different positions across a broad spectrum of industries as dictated by its proprietary investment models. The Fund may take larger positions in certain companies and/or industries as dictated by its proprietary investment models.

The Fund operates as a non-diversified fund which means it can invest in fewer securities at any one time than a diversified fund. The Funds systematic investment process is based on rigorous back testing of proprietary and evolving data-driven strategies and is designed to allow the Fund to achieve attractive risk-adjusted returns (i.e., returns made relative to the amount of risk taken). The Funds use of a systematic investment process does not guarantee that such risk-adjusted returns will be achieved. In making investment decisions, the Advisers algorithms are trained to invest in profitable companies with predicted expanding fundamentals (i.e., companies demonstrating such things as improved cash flows and earnings per share, reasonable price-to-earnings ratio, and improving price-to-earnings growth and dividend yield).

In predicting fundamentals, the Adviser uses its proprietary machine-learnings forecasting algorithms. The proprietary forecasting algorithms generate a wide range of short- to mid-term fundamental predictions for economic metrics, industries, and companies fundamentals (including financials and operating metrics) across multiple sectors. These fundamental predictions are underpinned by a highly scalable proprietary backend system which processes factual and market datasets available in the public domain. These datasets include historical financial statements, historical price action for companies, investor sentiment, and leading economic indicators, such as gross domestic product, purchasing managers index and consumer purchasing index. The Funds portfolio will be actively managed and may have exposure to growth and/or value companies.

The Adviser believes that by running multiple independent models it serves as a cross-check for the predictions generated by the models. The Advisers AI-driven algorithms actively identify opportunities and automatically invest, divest, and rebalance the Funds portfolio allocation, as well as provide real-time performance monitoring and systematic risk management through both rule-based and machine-learning algorithms to optimize portfolio performance. The risk/performance metrics the Adviser regularly calculates, and uses include Sharpe ratio, Sortino ratio, maximum drawdown, beta, alpha, standard deviation, portfolio turnover, and trading costs. In addition, the Adviser utilizes portfolio optimization techniques to determine trading activity, taking into account anticipated transaction costs associated with trading a particular security.

The model parameters the Adviser may optimize include security selection criteria, weighting, diversification, rebalancing frequency, and cash allocation. The Adviser has full discretion to override the machine-learning algorithms at any time, but it is unlikely the Adviser will do so on a regular basis. This would generally occur when portfolio security weightings and/or the portfolio turnover exceed expected thresholds. The Adviser may employ various overlay strategies for the Fund that are designed to increase return and/or hedge against market risks and/or generate income. One strategy that may be employed by the Adviser involves writing covered calls on the broad market using ETF(s) based options and/or on stock holdings in the Fund. The Funds use of covered calls will provide the Fund with income, but it will limit the Funds opportunity to profit from an increase in the market value of the underlying security to the exercise price (plus the premium received).

The Adviser may write call options on securities indices when the Adviser believes the underlying index is going to be flat or down. Both of these overlay strategies are designed to generate income for the Fund during periods when market conditions are expected to be flat to neutral. Another overlay strategy the Adviser may employ involves the use of short selling. Short selling involves investing in such a way that the Fund will benefit from a decline in value of an asset. The Advisers use of short selling will be primarily used to hedge/protect against a perceived risk such as a major market downturn. It is anticipated that the Funds use of short selling will typically involve shorting ETF(s) and/or stock holdings in the Fund. The Adviser may purchase put options on securities indices and/or stock holdings that provide downside protection as it relates to the Funds exposure to large cap stocks and/or to a market sector that the Adviser has identified as a risk for the Fund.

UBCB Performance

Total returns for UBCB (as of 2026-10-01), from SEC filings.

UBCB Risk Information

Risk metrics for UBCB, derived from monthly returns in SEC filings.

• 1-year volatility (annualised): 29.8%

UBCB Costs and Fees

UBCB costs about $75 per $10,000 invested per year in fund expenses.

• Net expense ratio: 0.75%
• Gross expense ratio: 0.75%
• Portfolio turnover: 253%
• Brokerage commissions: 20.75 bps of average net assets (SEC N-CEN)

UBCB Cashflows

Over the 12 months to 2023-01, UBC Algorithmic Fundamentals ETF had net inflows of $2.61M, from monthly SEC N-PORT filings.

UBCB Debt Constituents

No individual debt constituents are reported in UBC Algorithmic Fundamentals ETF's latest SEC N-PORT filing.

UBCB Prospectus and SEC Filings

Official UBC Algorithmic Fundamentals ETF filings on SEC EDGAR — prospectus, portfolio holdings and annual reports.

• Prospectus (485BPOS) — filed 2022-11-28
• Prospectus supplement (497) — filed 2021-12-17
• Portfolio holdings (N-PORT) — filed 2023-03-30
• Portfolio holdings (N-PORT) — filed 2022-12-27
• Portfolio holdings (N-PORT) — filed 2022-09-28
• Annual census (N-CEN) — filed 2022-10-13

Related Funds

Other United States Blend / Core Equity funds tracked on ABC INVEST:

• International Small Company Portfolio (DFISX)
• iShares Preferred and Income Securities ETF (PFF)
• Guggenheim Macro Opportunities Fund (GIOAX)
• Emerging Markets Portfolio (DFEMX)
• Eaton Vance Global Macro Absolute Return Advantage Fund (EGRAX)

Data Sources

ABC INVEST compiles this page from public filings made to the U.S. Securities and Exchange Commission (SEC) through EDGAR: portfolio holdings and monthly cashflows from Form N-PORT, expenses and returns from fund prospectuses (Form 485BPOS) and the SEC DERA Risk/Return Summary data sets, annual data from Form N-CEN, and shareholder reports from Form N-CSR.