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Le informazioni e le opinioni contenute in questa sezione del Sito cui sta accedendo sono destinate esclusivamente a Clienti Professionali come definiti dal Regolamento Consob n. 16190 del 29 ottobre 2007 (articolo 26 e Allegato 3) e dalla Direttiva CE n. 2004/39 (Allegato II), e sono concepite ad uso esclusivo di tali categorie di soggetti. Ne è vietata la divulgazione, anche solo parziale.
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In ogni caso, le informazioni e le opinioni ivi contenute non costituiscono un'offerta o una sollecitazione all'investimento e non costituiscono una raccomandazione o consiglio, anche di carattere fiscale, o un'offerta, finalizzate all'investimento, e non devono in alcun caso essere interpretate come tali.
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L’investimento in prodotti finanziari è soggetto a fluttuazioni, con conseguente variazione al rialzo o al ribasso dei prezzi, ed è possibile che non si riesca a recuperare l'importo originariamente investito.
Ronnie Sadka is a prominent academic voice on topics like liquidity in financial markets, high frequency trading and hedge funds. Having taught at a number of renowned universities, he currently chairs the finance department at the Carroll School of Management, Boston College, where he has been a professor since 2008.
“I belong to the post-LTCM generation of academics that recognizes the significant potential impact of systematic liquidity risk in financial markets. In the early 2000s, I was involved in the implementation of trading strategies exploiting market anomalies such as momentum at a large asset management firm. At the time, the difficulties in the implementation of relatively short-horizon strategies drew my attention to research on liquidity issues. The availability of intraday data for a relatively long period for a large cross-section of equities enabled a productive and impactful research agenda on liquidity risk.”
“Almost a decade ago, I also began to work on projects that center on the independent collection of data and the possible applications in asset management, a field now known as big data or alternative data. I published a few articles and developed some commercial products in this field These are based on various proprietary datasets, from thousands of distinct media sources to millions of mobile devices.”
People often confuse liquidity level and liquidity risk
“I think the key message is that liquidity risk should be better understood, both from a risk perspective and also as a potential alpha generator. In my view, people often confuse the average liquidity level of a security and its liquidity risk, which is what happens to the price of this asset during marketwide liquidity crunches. I also call it liquidity beta. Both liquidity level and beta have been shown to command a return premium, but they are clearly not the same.”
“No. For example, the common perspective on liquidity is that stocks with low levels of liquidity outperform and this is interpreted as a liquidity risk premium. However, this is not always true. As I show in my studies, illiquid securities, both equity and fixed income, outperform liquid assets during liquidity crises.’’
‘‘When there is a shock, investors tend to sell their most liquid assets first which turn out to be the hardest hit. Meanwhile, illiquid assets are by definition already illiquid. Therefore, the real systemic risk lies within the liquid assets, the large-cap firms whose stocks turn illiquid during crises.”
“I think the concept of monitoring liquidity is appealing to some people. However, most still fail to differentiate properly between liquidity level and liquidity risk. One major reason for this could be measurement issues. Gauging liquidity level is already hard enough, as there are many different kinds of measures. But computing liquidity betas adds another layer of measurement considerations.”
“Yes, definitely. I find similar results pertaining to liquidity level and beta applied to the universe of fixed income securities, hedge funds, mutual funds, and ETFs. For example, long-short equity funds are considered a relatively liquid asset class, and performed well prior to the financial crisis of 2008.’’
‘‘However, they also display a high liquidity beta, the premium of which can potentially explain their high returns. Indeed, this asset class suffered the most in the aftermath of the financial crisis, especially due to the mismatch between the liquidity offered to investors and the liquidity beta of the positions.”
“Yes, although I think it is important to recognize the role of investment horizon in this equation. One of my recent works examines the premiums of popular factors over different investment horizons. The results suggest that what looks like a beta premium to investors with a short investment horizon may look like alpha to investors with long investment horizons.’’
‘‘Long horizon investors are therefore the natural bearers of short-horizon risks. One example of such a short horizon risk factor is liquidity risk. Long-horizon investors might reap the returns of a liquidity beta factor that a short-run investor would otherwise consider risky.”
“To the best of my knowledge, most models may include some elements of an illiquidity level premium, in this case small-cap firms and high-volatility stocks are likely less liquid, and therefore such factors might include some element of liquidity, although the relationship is not completely straightforward. Nevertheless, I am not aware of any model that includes a liquidity risk/beta factor, and this is where there is some potential for added value.”
This article was initially published in our Quant Quarterly Magazine.