ETF Comparison: THNQ vs ROBT
ETF 說明
THNQ - ROBO Global Artificial Intelligence ETF
The ROBO Global Artificial Intelligence ETF is an exchange-traded fund that tracks the ROBO Global Artificial Intelligence Index, providing investors with exposure to companies involved in the development and application of artificial intelligence. The fund holds a diversified portfolio of 59 stocks, with a focus on global robotics and AI, and follows a fundamental weighting scheme.
ROBT - First Trust Nasdaq Artificial Intelligence & Robotics ETF
The First Trust Nasdaq Artificial Intelligence & Robotics ETF is an equity fund that tracks the Nasdaq CTA Artificial Intelligence and Robotics Index, providing investors with exposure to a diversified portfolio of 108 stocks in the robotics and artificial intelligence sector. The fund is designed to capture the growth potential of companies involved in AI and robotics, with a blend investment style and a multi-cap approach.
比較表
| THNQ | ROBT | |
|---|---|---|
| 基金名稱 | ROBO Global Artificial Intelligence ETF | First Trust Nasdaq Artificial Intelligence & Robotics ETF |
| Fund Provider | Exchange Traded Concepts | First Trust |
| Index | ROBO Global Artificial Intelligence Index | Nasdaq CTA Artificial Intelligence and Robotics Index |
| Asset Class | Equity | Equity |
| Listing | US-listed | US-listed |
| Expense Ratio | 0.68% | 0.65% |
| Inception Date | 2020-05-11 | 2018-02-21 |
| Number Of Holdings | 59 | 108 |
| Currency | USD | USD |
| Region | Developed Markets | Developed Markets |
| Investment Style | Growth | Blend |
| Market Cap | Blend | Blend |
| Sector | Technology | Technology |
| Sector Detail | Artificial Intelligence | Artificial Intelligence |
| Leveraged | Non-leveraged | Non-leveraged |
回測選項
摘要
關鍵指標
績效指標
風險指標
詳細報酬
績效分析
績效分析透過累積報酬、年底（EoY）報酬及 Sharpe 比率、Sortino 比率等風險調整指標，評估歷史資料以衡量投資策略報酬。這有助於投資人在不同市場條件下評估絕對和相對績效。
累積報酬
年底報酬表
年底報酬
風險分析
風險分析是指對可能導致資本損失的潛在負面事件進行評估。進行風險分析有助於決定是否應進行投資。這是透過回撤、波動率和 Beta 等風險指標來完成的，這些指標反映了利益相關者對投資策略一致性的信心。
回撤
回撤表
Monte Carlo 模擬
Monte Carlo 模擬是一種統計方法，透過從歷史資產價格資料中隨機抽樣產生大量潛在結果，用以預測投資組合報酬。它協助投資人評估各種市場條件下投資組合的潛在風險和報酬。模擬考慮初始投資，並可選擇性地模擬現金流情境，如固定投入、固定提款或比例提款。
重要提示：透過 Monte Carlo 模擬產生的預測純屬假設性質，不保證未來報酬。投資決策應考量多種因素，過去表現不代表未來結果。