ETFs: What’s Better? Full Replication vs. Representative Sampling?

10.August 2022

ETFs employ two fundamentally distinct methods to replicate their underlying benchmark index. The more conventional method, physical replication, involves holding all constituent securities (full replication) or a representative sample (representative sampling) of the benchmark index. In contrast, the synthetic replication achieves the benchmark return by entering into a total return swap or another derivative contract with a counterparty, typically a large investment bank. As we have previously discussed, there is no significant difference in the tracking ability between the physical and synthetic ETFs in the long term. And while our article compares physical and synthetic ETFs, it does not address the differences between the full replication ETFs and sampling ETFs. Therefore, one may ask a question: “When selecting a physically replicated ETF, which replication method is better, full replication or representative sampling?”

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The Buffett Indicator

29.July 2022

Despite Warren Buffett’s claim that the MVE/GDP ratio is “probably the best single measure of where valuations stand at any given moment,” its predictive ability has been the subject of relatively little academic scrutiny. A novel paper by Swinkels and Umlauft (2022) fills this gap and examines whether the MVE/GDP ratio can forecast international equity returns, which complements the existing research limited to the United States. A simple trading strategy that invests in countries with the highest model-predicted returns yields statistically significant and economically meaningful alpha over a corresponding buy-and-hold benchmark while experiencing lower volatility and maximum drawdown.

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Cryptocurrency Stablecoins – A Review of Recent Research

15.February 2022

Since January 2020, the annualized volatility of Bitcoin stands around 70%, 6-times the volatility of commodities like Gold or Oil, more than twice the volatility of the S&P 500, and 10 times the volatility of the EURUSD exchange rate. Stablecoins represent a specific category of cryptocurrencies aiming to keep their value stable against a benchmark asset, usually a fiat currency like the US dollar. So how do stablecoins work, and do they really offer needed stability?

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Should Factor Investors Neutralize the Sector Exposure?

8.February 2022

Factor investors face numerous choices that do not end even after picking the set of factors. For instance, should they neutralize the factor exposure? If the investor pursues sector neutralization, does the decision depend on a particular factor? Or are the choices different for the long-only investor compared to the long-short investor? The research paper by Ehsani, Harvey, and Li (2021) answers these questions and provides investors with an interesting insight on this topic.

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Periodicity in Cryptocurrencies – Recurrent Patterns in Volatility and Volume

11.January 2022

The high-frequency data in cryptocurrency markets is available at any time of the day, which facilitates the studies of periodicity measures beyond what’s possible in other markets. The research paper by Hansen, Kim, and Kimbrough (2021) investigates the periodicity in volatility and liquidity in two major cryptocurrencies, Bitcoin and Ether, using data from three exchanges, Binance, Coinbase Pro, and Uniswap V2. In particular, the authors measure relative volatility and relative volume across days, hours, and minutes. Their results have confirmed the presence of recurrent patterns in volatility and volume in studied cryptocurrencies for the periods day-of-the-week, hour-of-the-day, and within the hour.

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The Quant Cycle – The Time Variation in Factor Returns

22.November 2021

Although the factors in asset pricing models offer a premium in the long run, they are undergoing bull and bear market cycles in the short term. One would expect that it is due to their connection to the business cycles as the factor premium represents a reward for bearing the macroeconomic risks. A novel study by Blitz (2021) finds that traditional business cycle indicators can’t explain much of the time variation of factor returns as the factors are a behavioral phenomenon driven by investor sentiment. To capture the large factor cyclical variation, the author proposes a quant cycle that is defined by the peaks and troughs in the factor returns corresponding to the bull and bear markets.

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