NFTs are taking the cryptocurrency trading world by storm. NFTs stand for the non-fungible tokens which have emerged as another possible usage of blockchain technology. NFT can be used to record/verify/track the ownership of a unique – hence the non-fungible asset. Commonly, NFTs are connected with art (visual art, music, etc.), but there are also several decentralized finance or gaming-related projects.
Same as for the other blockchain-related projects, the critics are easy to find, so a research paper with hard data concerning the NFTs can be of great importance. The research paper by Mazur (2021) studies the NFT startups traded in the crypto markets. Therefore, the paper does not analyze the individual NFTs (such as some piece of art), but rather the whole projects and their tokens traded on the Binance crypto exchange.

Our new article investigates “rebalancing premium” or “diversification return” in cryptocurrencies which can be achieved by periodically rebalancing portfolios. We analyze whether the daily/ monthly rebalanced portfolios outperform a simple buy-and-hold portfolio of cryptocurrencies and under which conditions. Additionally, we also look at the various combinations of volatile cryptocurrency portfolios with low-risk bonds.

It is indisputable that the data are changing financial markets – computing power has increased, allowing to rise the trends of ML/AI and big data (number of possible predictors or granularity) or HFT strategies. Indeed, not all the datasets are worth the time of academics, investors or traders, but we are always keen to analyze the novel and unique datasets. Of course, if we believe that the analysis is worthy of sharing, we are happy to do so. This post offers a shorter version of our newest research about Synthetic lending rates and subsequent market return. We hope that you find it enriching; enjoy the reading!

Hello all,

What’s new in November’s update of Quantpedia’s services?

– A new Benchmark Portfolio feature for Quantpedia Pro service
– A new Algo Trading Discounts table
– 10 new Quantpedia Premium strategies have been added to our database
– 10 new related research papers have been included in existing Premium strategies during the last month
– Additionally, we have produced 10 new backtests written in QuantConnect code
– And finally, 2+5 new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month

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.