Quantpedia in March 2022

6.April 2022

Hello all,

What have we accomplished in the last month?

– A new Quantpedia Pro report – Fixed Income Phases Analysis
– A possibility to export data from Quantpedia Pro reports for further analysis
– A new faster integration with QuantConnect backtesting environment
– 10 new Quantpedia Premium strategies have been added to our database
– 12 new related research papers have been included in existing Premium strategies during the last month
– Additionally, we have produced 8 new backtests written in QuantConnect code
– And finally, 3+3 new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month

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Nuclear Threats and Factor Performance – Takeaway for Russia-Ukraine Conflict

31.March 2022

The Russian invasion of Ukraine and its repercussions continue to occupy front pages all around the world. While using nuclear forces in war is probably a red line for all of the mature world, there is still possible to use nuclear weapons for blackmailing. What will be the impact of such an event on financial markets? It’s not easy to determine, but we tried to identify multiple events in the past which were also slightly unexpected and carried an indication of nuclear threat and then analyzed their impact on financial markets.

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Is There Any Hidden Information in Annual Reports’ Images?

29.March 2022

Can the number or type of images in a firm’s annual report tell us anything about the firm? Or is it just a marketing strategy that doesn’t hold any further information? With the help of novel machine learning techniques, the authors Azi Ben-Rephael, Joshua Ronen, Tavy Ronen, and Mi Zhou study this problem in their paper “Do Images Provide Relevant Information to Investors? An Exploratory Study”. It seems that the proposed metrics help to forecast some of the firms’ fundamental ratios.

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What Can We Learn from Insider Trading in the 18th Century?

21.March 2022

Directors, board members, and large shareholders are just some of those who might have non-public material information about their firm. Even though this information could be easily used to profit by trading their own stocks, this insider trading behavior is strictly prohibited. But how profitable can it be? We can study insider trading in the time when it wasn’t regulated at all – in the early 1700s. The 300 hundred-year-old dataset consists of data recovered from original handwritten ledger books and transfer files of the three largest companies in the London stock market at the time. It gives us a glimpse into the evidence of how big the insider’s advantage is, and the result is quite surprising – the authors calculated their outperformance to just 7% per year.

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Trend-following and Mean-reversion in Bitcoin

15.March 2022

Indisputably, trend-following and mean-reversion are two key concepts in quantitative investing or technical analysis. What about the Bitcoin? Are there trend-following or mean-reversion patterns? Or are both effects present and co-exist? In this short research, we examine how Bitcoin’s price is affected by its maximal or minimal price over the previous 10 to 50 days. Our finding shows that when the BTC is at the local maxima, it tends to continue trending upwards. Furthermore, the local minima are also connected with abnormal price action.

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Full vs. Synthetic Replication and Tracking Errors in ETFs

11.March 2022

The growth of passive investing and ETFs is indisputable. Consequently, this boom also affects financial markets (e.g., market elasticity or by creating predictable buys and sells) and assets that ETFs track. Even though all passive ETFs aim to replicate some benchmark index, there are two distinct approaches to doing so. The first approach is directly replicating the benchmark (by buying underlying assets) either by full direct replication or sampling. The second approach consists of synthetic replication using derivatives – most commonly by total return swaps (or futures). How do replication methods influence tracking error?

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