Quantpedia in February 2021

4.March 2021

The most significant event on our page in February was the introduction of our new Quantpedia Pro platform. We have received positive feedback so far to it; therefore, feel free to revisit our short article describing the main features of this new service and its design and reporting capabilities.

But naturally, we have not forgotten to do our homework for our other services. So, let us recapitulate last month of Quantpedia’s research. Ten new Quantpedia Premium strategies have been added to our database, and ten new related research papers have been included in existing Premium strategies during the last month.

Additionally, we have produced 12 new backtests written in QuantConnect code. Our database currently contains over 410 strategies with out-of-sample backtests/codes.

Also, three new blog posts, that you may find interesting, have been published on our Quantpedia blog:

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A Robust Approach to Multi-Factor Regression Analysis

24.February 2021

Practitioners widely use asset pricing models such as CAPM or Fama French models to identify relationships between their portfolios and common factors. Moreover, each asset class has some widely-recognized asset pricing model, from equities through commodities to even cryptocurrencies. 

However, which model can we use if our portfolio is complex and consists of many asset classes? Which factors should we include and which should we omit? (Especially if we have a database that consists of several hundreds of potential factors). Additionally, we know that equities influence bonds, commodities influence equities and vice versa. Hence the question, what about the cross-asset relationships? 

These are the problems and questions we faced when looking for a methodology for our Multi-Factor Analysis report in the Quantpedia Pro platform. This blog post aims to introduce the model, its logic and the method we have decided to use. 

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Accelerate Design of Multi-Factor Multi-Asset Models with Quantpedia Pro

23.February 2021

We hinted in the past few blogs that we were preparing a small surprise. And now it’s time to unveil what we have been cooking during the previous several months.

Let us introduce Quantpedia Pro.

Quantpedia Pro is a new analytical platform built on top of our out-of-sample backtests of selected Quantpedia Premium strategies. It allows users to significantly speed up the process of building custom model multi-factor and multi-strategy portfolios. Instead of re-creating all ideas for systematic strategies in-house, users can explore ideas and do preliminary portfolio testing on Quantpedia Pro platform.

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Fake Trading on Crypto Exchanges

11.February 2021

At Quantpedia, we acknowledge that cryptocurrencies offer numerous trading opportunities and include them in the Screener. Yet, each participant should be cautious. Cryptocurrencies are not black or white; they have their pros but also cons. Perhaps now, with all the positive sentiment around cryptos, it is the right time to advert also the cons. It is not that long time ago when we published a blog about the Bitcoin´s price manipulation, where the anecdotal evidence was supported by the Benford´s law which is related to the distribution of leading digits. 

The novel research of Amiram et al. (2020) expands the previous work about the manipulation of the BTC. The authors include a tremendous amount of currencies, study various exchanges, and most importantly, they use more methods to examine the manipulations. To be more precise, the authors utilize the Benford´s law, deviations from the log-normal distribution and the novel machine-learning algorithm E-Divisive with medians that identifies structural breaks in time series. Moreover, they aggregate the measures by computing their principal components. While the results are as always best shown by the included figures, there are numerous practical suggestions. The fake trading benefits exchanges in the short term; however, it is harmful in the long term. Lastly, exchanges with the highest popularity, some regulations and the oldest ones tend to have the lowest fake trading levels. 

 

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Basic Properties of Various Real Asset Portfolios

5.February 2021

Do not put all your eggs in one basket is a common phrase that resonates among investors worldwide. The errand of such a famous saying is simple, diversify! However, how to diversify, if in the crisis, everything seems to be highly correlated? Last week, we wrote a blog about the Macro Factor Risk Parity, but it certainly is not the only option. Real assets such as REITs, various commodities, and the ever-popular gold are commonly added into portfolios as diversifiers. However, Parikh and Zhan (2019) research examine a much bigger set of real assets than the aforementioned evergreens. Real assets like Timberland, Farmland, Infrastructure, Natural Resources and many others are presented in the paper. All those assets have different sensitivities to inflation, GDP growth, equities or bonds. Therefore, real assets could have a value in the portfolios to protect an investor from inflation, stagnation, or simply distributing the eggs mentioned above in many baskets. All these strategies are presented in the paper and compared to equities, bonds and traditional 60/40. 

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