Factor allocation

Quantitative strategy research is rarely about discovering a single “perfect” trading rule. In practice, robust portfolios emerge from a structured research process that filters ideas, evaluates evidence, and combines complementary strategies.

To make the process concrete, we use value-based equity strategies as our working example. However, the goal of the article is not to identify the ultimate value strategy, but to illustrate how a systematic research workflow can be used to build a diversified portfolio of strategies around any investment hypothesis.

An interesting transaction-level study we take a closer look at today asks who takes the other side of trades when the most pro-cyclical players in markets — primarily asset managers — buy in booms and sell in busts. The paper uses comprehensive transaction data across major European equity and interest-rate cash and derivatives markets to classify counterparties by sector and to measure, at horizons from 15 minutes to one month, which sectors absorb net flows from pro-cyclical investors. Dealer banks emerge as the dominant liquidity providers across asset classes. At intraday and daily horizons, dealer banks absorb the vast majority of the net flow coming from asset managers. Other active liquidity sources, such as principal trading firms and hedge funds, play only minor roles.

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 …

Cryptocurrencies have matured from experimental curiosities into a viable investable asset class whose return-generation and risk characteristics merit treatment within empirical asset pricing. A recent paper by Nicola Borri, Yukun Liu, Aleh Tsyvinski, Xi Wu summarizes ten facts from the literature that show cryptocurrencies share important similarities with traditional markets—comparable risk-adjusted performance and a small set of cross-sectional factors—while retaining distinctive features such as frequent large jumps and price signals embedded in blockchain data. Key themes include portfolio diversification, factor structure, market microstructure, and the evolving role of regulation and derivatives in shaping market discovery and stability.

The clustering of excess returns on the final trading days of the month constitutes a robust empirical regularity with significant implications for portfolio construction. We document a month-end premium that is both statistically and economically significant, distinct from the canonical turn-of-the-month (ToM) effect. Our strategy highlights systematic style rotations—particularly shifts in value versus growth exposures, as proxied by the IVE–IVW spread—and documents parallel contemporaneous dislocations between real-estate and broad-equity benchmarks, as measured by the IYR–SPY spread.

The U.S. equity market has periodically been dominated by a few technology-driven stocks, most recently the so-called “Magnificent Seven.” Historically, similar dominance occurred during the Nifty Fifty era in the 1960s–1970s and the dot-com boom in the 1990s. These periods of concentrated leadership often led to temporary outperformance, but systematically capturing such gains has proven challenging. Our study investigates the potential to exploit technology sector dominance using momentum-based strategies across Fama–French 12 industry portfolios, analyzing whether long-only, long-short, and rolling-basis approaches can generate persistent alpha, and assessing the limitations of simple timing methods.