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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Quantpedia in January 2021

2.February 2021

Ten new Quantpedia Premium strategies have been added into our database, and ten new related research papers have been included in existing Premium strategies during last month.

Additionally, we have produced 12 new backtests written in QuantConnect code. Our database currently contains exactly 400 strategies with out-of-sample backtests/codes.

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

Plus we continue to re-run some of our codes on a monthly basis systematically, over 230 codes are at the moment part of this activity.

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Macro Factor Risk Parity

29.January 2021

Risk and diversification are critical interests of every investor, especially when things go south since the correlations across assets tend to rise during stressful times. Therefore, in the asset allocation, the risk parity allocation is one of the key topics. Factors are commonly known as underlying sources of both risk and returns, and it is assumed that they can be utilized to achieve superior risk-adjusted returns and diversification. However, there seems to be a lack of research that would be related to the macro factors. This gap is quite striking since there is a general consent that macro factors (for example, inflation) largely influence the broad set of assets. Amato and Lohre (2020) research paper fills the gap and studies the usage of macro factors as diversifiers in asset allocation. 

The authors divide the macro factors to two groups, where the first consists of TERM, MARKET, USD, OIL and DEF (default risk), and the second group consists of CLI (a measure of output by OECD), G7.INFLATION, G7.Short.Rate and VIXThe research shows, that when the diversification matters the most, only the second group improves both the risk and returns, acting as a successful diversification during various economic regimes and particularly, during high economic uncertainty. Overall, the paper offers exciting insights into diversification and macro factors, accompanied by more complex mathematical models definitely worth looking into. 

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