Rayliant Wilshire NxtGen Emerging Markets Equity ETF (RWEM)
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Rayliant Wilshire NxtGen Emerging Markets Equity ETF Investors
| Name | Quantity | Cost | Value | Profit ($) | Gain (%) |
|---|---|---|---|---|---|
| MK Matthew Kolesky ARBOR CAPITAL MANAGEMENT Inc. /ADV | 9,718 | $257,525.52 | $356,942.14 | $99,416.62 | 38.6% |
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Rayliant Wilshire NxtGen Emerging Markets Equity ETF Profile
| ARCA Exchange | US Country |
Overview
RWEM is an investment fund that specializes in tracking a carefully selected range of large-cap stocks located in emerging markets. The selection process is driven by a combination of various analytical factors and enhanced through advanced machine learning models. By utilizing over 100 distinct signals, encompassing market-level, fundamental, and technical assessments from 12 key factor categories, RWEM aims to optimize its stock selection effectively. In addition, the index construction process incorporates constraints designed to curb stock concentration and manage industry exposure, ensuring alignment with the FT Wilshire Emerging Large Cap Index. The overall methodology is comprised of three critical steps: estimating risk-adjusted returns, calculating the covariance matrix, and applying mean tracking error optimization. To maintain relevance and accuracy, the index is reconstituted quarterly based on the most recent month-end data from the prior month. Notably, before December 19, 2025, RWEM operated as an actively managed fund previously known as Rayliant Quantamental Emerging Market ex-China Equity ETF (RAYE).
Products and Services
- Large-Cap Emerging Markets Stock Tracking
The RWEM fund focuses on large-cap stocks from emerging markets, utilizing a sophisticated selection process that integrates multiple data points and machine learning techniques to optimize performance.
- Dynamic Index Construction
RWEM employs a dynamic index construction approach that consists of three main components: risk-adjusted return estimation, covariance matrix estimation, and mean tracking error optimization, ensuring optimal representation and performance.
- Quarterly Reconstitution
To ensure that the fund reflects current market conditions, RWEM undergoes a quarterly reconstitution process. This involves updating its underlying data based on the month-end figures from the preceding month, which aids in maintaining responsiveness to market changes.
- Machine Learning Optimization
Utilizing advanced machine learning algorithms, RWEM enhances its stock selection process, analyzing extensive signals from various categories to identify opportunities and mitigate risks effectively.
- Constraints Management
RWEM applies specific constraints to limit stock concentration and industry exposure, which reduces the risk of overexposure to particular sectors and aligns the investment strategy with the established benchmarks.
- Historical Performance Review
The fund's methodology, including its transition from an actively managed fund (RAYE) to the current indexed approach, is backed by historical performance analysis, providing insights into its effectiveness in various market conditions.
