This paper provides empirical evidence that the low volatility anomaly in stock prices, characterized by high alpha and low idiosyncratic volatility, is an externality that results from mutual funds' performance evaluation against benchmark indexes. Using a unique dataset of mutual funds benchmarked exclusively to the S&P 500 index, I show that mutual funds that tend to keep up with performances of other same-benchmark peers have the tendency to deviate the least possible from past peer trades. This study examines mutual fund holdings of non-benchmark stocks. Mutual fund managers invest heavily in certain stocks and create a clustering effect, causing these stocks to experience higher trade volumes compared to other stocks. High trade volumes rapidly reveals all private information about a stock to the market, causing the stock price to quickly converge to its full information equilibrium, and reduces stock volatility. Stocks with high trade volumes also rapidly adjust to marketwide information which increases stock alpha. Capital flows analyses show that the low volatility anomaly is an outcome due to the behaviour of same-benchmark mutual funds. It lasts for three quarters during which the anomaly remains unexploited.

Existing studies, focused primarily on the U.S., show the improved value factor performance when adjusting book values for intangible assets. However, there is little evidence whether this is a U.S. specific, or wider international phenomenon. My findings expand the existing evidence to multiple international regions and suggest that the intangible-adjusted book-to-market ratio better measures the value factor globally. Especially in more recent decades, where the size of intangible assets increased dramatically, the relative outperformance of the intangible-adjusted value factor over the traditional value factor has become stronger. Economically, the adjusted value factor bears additional risk related to liquidity, financial distress and funding constraints.

Whilst the predictability of fund flows based on prior-period returns is empirically well-established in the literature, we extend its insights to tradeable macro ecosystems, where there is anecdotal evidence of distribution divergences between flows and prior-period returns. We study the relative entropy of flows to capture an exogenous price signal, which we hypothesise to represent the aggregate directional positioning of informed investors. We construct and assess factor portfolios and a spectra of unconstrained systematic long-only portfolios to test our hypothesis, in pursuit of demonstrating the informativeness and additionality of the factor in augmenting traditional weighting schemes to produce superior systematic portfolios, and in adding to the discretionary investor’s toolbox a method for identifying the positioning of supposed “smart money” in the aggregate. Our findings indicate that the relative entropy signal demonstrates relevance in predicting future returns and identifying relative value within our designated tradeable macro ecosystem and that, within certain portfolio configurations, it can contribute positively to portfolio construction outcomes based on historical evidence.