Cryptocurrencies

From the trading desk to the portfolio committee, investors face a familiar question: how should alternative stores of value fit into a diversified portfolio? This research explores that question through a systematic dual-momentum framework comparing Bitcoin and physical gold in a rules-based tactical allocation model. Rather than debating ideology, we focus on practical portfolio construction and risk-adjusted returns. The goal is to examine whether “digital gold” can complement its physical counterpart within a disciplined investment process, and whether the distinct behavior of these assets can be used to build a more effective systematic strategy.

In an era of increasingly fragmented market microstructure, the emergence of cross-asset connectedness between Crypto and public equity markets presents a critical challenge for modern portfolio construction. This blog post examines the recent working paper by Harin de Silva, “The Attention Factor: The Speculative Risk You May Already Own,” which identifies a previously underappreciated transmission channel: a speculative cohort of marginal investors whose sentiment shifts propagate correlated price movements across BTC, zero-day-to-expiration (0DTE) options, commission-free brokerages, and social-sentiment-driven equities. The author introduces the Attention factor—a capital-backed measure of collective conviction—as a systematic risk driver that persists after controlling for traditional macro factors, fundamentally reshaping how we model Equity Risk in multi-asset portfolios. For quantitative practitioners, this work underscores the need to augment conventional Risk Models with sentiment-aware factors to capture residual connectedness that standard factor frameworks may overlook.

Can crypto still help diversify an equity portfolio—or has that edge disappeared? That’s the practical question behind Crypto Contagion. The paper looks at how shocks move between crypto and U.S. equities, and more importantly, how that relationship changed after the launch of crypto ETFs. Instead of relying on simple correlations, the authors use a combination of jump detection (to isolate real stress events) and machine learning techniques to identify actual spillovers. By comparing periods before and after ETFs, they effectively show how the market structure—and with it, the behavior of crypto—has shifted .

The crypto-enthusiast’s mantra—“just add Bitcoin and watch the efficient frontier fly”—runs into a hard empirical wall when you extend the sample, tighten the econometrics, and force the asset to compete on identical risk-adjusted footing with equities. Alistair Milne’s new SSRN paper applies a textbook Markowitz mean–variance framework to a two-asset universe (S&P 500 vs. Bitcoin) and finds that the ex-ante optimal long-term weight on BTC is not merely small; it is outright negative. In other words, a rational, variance-averse allocator who believes expected returns equal historical equity premia plus a fair compensation for BTC’s non-diversifiable volatility should be short, not long, the flagship digital token.

One year is again behind us (in this case, it was 2025), and we are all a little older (and hopefully richer and/or wiser). Turn-of-the-year period is usually an excellent time for a short recap. Over the past 12 months, we have kept our pace and published nearly 70 short analyses of academic papers and our own research articles. So let’s summarize 10 of them, which were the most popular (based on the Google Analytics ranking). The top 10 is diverse, as usual; once again, we hope that you may find something you have not read yet …

Bitcoin is one of the most widely discussed financial assets of the modern era. Since its inception, it has evolved from a niche digital experiment into a globally recognized investment instrument with institutional adoption and billions in daily trading volume. Despite its inherent volatility, Bitcoin has demonstrated a strong long-term growth trajectory, making it an attractive candidate for trend-based and momentum-oriented trading strategies. In this study, we apply concepts from technical analysis to construct and refine a trend-following strategy for Bitcoin, progressing step by step from a simple MACD setup toward an improved multi-timeframe model.