Nowadays, we see an increasing number of machine learning based strategies and other related financial analyses. But can the machines replace us? Undoubtedly, AI algorithms have greater capacities to “digest” big data, but as always in the markets, everything is not rational. Cao et al. (2021) dives deeper into this topic and examines the stock analysts. Target prices and earnings forecasts are crucial parts of the investing practice and are frequently used by traders and investors (and even ML-based strategies). The novel research examines and compares the abilities of human analysts versus the AI algorithm in forecasting the target price. As a whole, AI-based analysts, on average, outperforms human analysts, but it is not that straightforward. While AI can learn from large datasets, humans do not seem to be replaced soon. There are certain fields where human uniqueness is valuable. For example, in illiquid and smaller firms or firms with asset-light business models. Moreover, it seems that rather than competing with each other, AI and human analysts are complementary. The novel technology can be used with great success to help us in areas where we lag, and the combined knowledge and forecasts of AI and humans outperform the AI analyst in each year.

As we have promised, we present a short article as an introduction into the methodology of the Quantpedia Pro CPPI reports. Quantpedia Pro clients can use the model portfolio built in the Portfolio Manager as a risky asset to test various variants – Basic CPPI, Drawdown Based CPPI and Dynamic Multiplier CPPI.

Hello all,

What have we accomplished in the last month? New Quantpedia Pro reports, new Position Sizing course and as usual – a lot of new Quantpedia Premium strategies, papers and backtests.

– 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 11 new backtests written in QuantConnect code
– We have prepared a series of three new CPPI reports (Constant Proportion Portfolio Insurance) for Quantpedia Pro
– In cooperation with QuantInsti, we have launched a new practically oriented course called Position Sizing in Trading, which aims to explain the money management techniques to intermediate level traders
– And finally, four new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month

Five new strategies have been added.

Five new related research papers have been included into existing strategy reviews and two short free blog posts have been published during last few weeks. Plus, six trading strategies have been backtested in QuantConnect in the previous two weeks.

Quantitative based community is represented by the Quantconnect – Algorithmic Trading Platform, where quants can research, backtest and trade their systematic strategies. Additionally, similar to Seeking Alpha, there is a possibility to follow other quants/analysts through the open free market – Alpha Market.
To our best knowledge, the literature on community/social media alpha is scarce, and this paper aims to fill this gap. In the first part, we evaluate the benchmark strategy that consists of all strategies in the alpha market that are equally weighted. Moreover, through multidimensional scaling and clustering analysis, we examine how well can significantly lower amount of strategies track the aforementioned benchmark. This could solve the problem of costly and inconvenient following of every strategy in the market. Overall, this approach can lead to a strategy that follows the benchmark with drastically reduced costs, and these strategies can be even more profitable and less volatile.

Stay tuned for the 2nd, 3rd and 4th part of this series, where we will step on the gas and explore factor meta-strategies built on top of the QuantConnect’s Alpha Market.

There are many known relationships among macroeconomic variables in economics, while some of them are even presented as “laws”—for example, money supply and inflation or benchmark interest rates and inflation. However, the well-known economic models usually utilize only a small amount of variables. Nowadays, with the advances in machine learning and big data fields, these established models might be improved. A possible solution is presented in the research paper of Yang et al. (2020). The authors construct knowledge graphs where they connect widely recognized variables such as GDP, inflation, etc., with other more or less known variables based on the massive textual data from financial journals and research reports published by leading think tanks, consulting firms or asset management companies. With the help of advanced natural language processing, it is possible to basically “read “all the relevant published research and find the relationships among the macroeconomic variables. While this task could take years for human readers, the machine learning method can go through these texts in a much shorter time.