<![CDATA[Multi-asset allocations combining Bitcoin, gold, and stocks have become increasingly common, yet they raise heightened challenges for financial reporting reliability, downside-risk communication, and governance assurance because these assets differ in volatility, custody mechanisms, and valuation evidence chains. Objective: This study aims to develop and apply a multi-asset investment audit perspective for Bitcoin, gold, and stocks over 2022–2024 by integrating market-risk evidence with financial reporting and governance implications to support more risk-informed assurance and disclosure practices. Methodology: The research uses a quantitative design based on secondary daily closing price data for Bitcoin (BTCUSD), gold (XAUUSD), and stocks proxied by the S&P 500 index (^SPX) for 2022–2024. Data were collected from a public market database and analyzed using comparative return–risk measures, cross-asset correlation, drawdown analysis, and tail-risk indicators (VaR and CVaR/Expected Shortfall), complemented by a structured audit evidence-chain mapping to translate risk signatures into audit and control priorities. Findings: Bitcoin produced the highest annualized mean return (38.90%) but also the highest annualized volatility (55.47%), deepest maximum drawdown (-67.02%), and most severe tail risk (daily CVaR/ES 95% = -7.59%). Gold exhibited the most stable profile (14.51% volatility; -20.84% drawdown), while stocks were moderate (17.50% volatility; -25.38% drawdown). An equal-weight portfolio reduced overall volatility (22.89%) and tail risk (daily CVaR/ES 95% = -3.12%) relative to Bitcoin alone, yet still experienced a meaningful maximum drawdown (-35.65%). Bitcoin also showed stronger co-movement with stocks (0.441) than with gold (0.116), indicating equity-like risk sensitivity during this period. Implications: The results support a risk-weighted approach to auditing multi-asset investments, emphasizing valuation governance, disclosure specificity, and custody/existence assurance for digital assets, while aligning portfolio narratives with observed tail-risk concentration and dependence patterns. Originality/value: The study contributes an integrated framework that links downside-focused risk analytics (drawdowns and CVaR) with audit evidence-chain and governance mapping in a single multi-asset setting.]]>
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