Quantpedia

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?”

https://quantpedia.com/etfs-whats-better-full-replication-vs-representative-sampling/

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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.

https://quantpedia.com/the-buffett-indicator/

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Choosing the correct portfolio-construction techniques is very important. The new paper that is written by Amar Soebhag, Bart van Vliet, and Patrick Verwijmeren explores the various ways in which different design choices in portfolio construction can, either intentionally or unintentionally, influence and distort the statistical results of a market factor’s research. Their takeaway is that seemingly small differences in design can significantly impact the resultant portfolio’s performance.

https://quantpedia.com/the-importance-of-factor-construction-choices/

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The size effect assumes a negative relationship between average stock returns and firm size. In other words, it states that low capitalization stocks outperform stocks with large capitalization. Although generally accepted, the size effect keeps being challenged. Researchers have been asking how important the firm size characteristic actually is, and whether it is possible to replace the traditional size factor of Fama and French asset pricing model (1993) with more accurate factor. Recently, one potential challenger has emerged — so-called takeover factor, employed by Easterwood et al. (2022). In their study, they work on the assumption that small firms are often targets of takeovers, which gives us a different perspective on merger and acquisition news in regards to size effect. Their results show that M&A component of average returns explains the size premium entirely.

https://quantpedia.com/takover-factor-explains-the-size-effect/

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We dedicated several articles to how we created 100-year history for bonds, stocks, and commodities. Now we analyze the 50 worst one-day shocks and the following days in each of the abovementioned asset classes. In addition to that, we also look at how the Multi-Asset Trend-Following strategy performed during the same periods. Further, the second part of this article focuses on critical geopolitical events (the starts of major wars, international crises, and deterioration of US presidents’ health) and their effect on bonds, stocks, commodities, and the multi-asset trend-following strategy.

https://quantpedia.com/the-worst-one-day-shocks-and-the-biggest-geopolitical-events-of-the-past-century/

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Every year, Quantpedia’s team investigates thousands of academic research papers to bring you the most promising ideas from the academic world. We read papers, identify ideas and backtest them to build our unique database. As a result, we have already identified hundreds of factors and built tools to help you orient better in the broad universe of trading strategies and systematic investment factors.

And now, we are opening the possibility to all external researchers, quants, and portfolio managers to contribute to Quantpedia.

https://quantpedia.com/quantpedia-introduces-3rd-party-factors/

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The summer is slowly approaching; therefore, our new article will be on a little lighter tone. We will examine a research paper on a periodic event with sentiment implications. The authors (Abudy, Mugerman, Shust) focused on a specific song competition — the Eurovision Song Contest, an international song competition organized annually. They examined a positive swing in investor mood in the winning country the day after the Eurovision Song Contest and documented an average abnormal return of 0.381%. On the contrary, they did not find any negative sentiment in other participating countries.

https://quantpedia.com/investor-sentiment-and-the-eurovision-song-contest/

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A recent spring 2022 crisis in the cryptocurrency market emphasized the importance of market-neutral crypto trading strategies. It’s not enough just to HODL crypto market and hope for the everlasting bull market. Therefore, we continue our series of research articles about the cryptocurrency market and offer an analysis of the skewness anomaly. So after our description of the skewness effect in commodities, an article about the multi-asset skewness strategy, and observation of the skewness/lottery effect in ETFs, we have one more asset class, where we can find lottery/skewness anomaly — in cryptocurrencies.

https://quantpedia.com/skewness-lottery-trading-strategy-in-cryptocurrencies/

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Quantpedia

Quantpedia

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