The spring is in the air (at least in the northern hemisphere), and we were not sitting idle in the last month. The most interesting development is two new reports for Quantpedia Pro (the Factor Cycle and Inter-Market Correlation reports) that I will describe soon.

But first, let’s recapitulate Quantpedia Premium development. Nine new Quantpedia Premium strategies have been added to our database, and fifteen 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 420 strategies with out-of-sample backtests/codes.

Additionally, four new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month …

The R&D investments represent a company’s unique expenditure, which is responsible for creating an information asymmetry about the firm’s growth potential and future prospects. In a case when market value reflects only the firm’s financial statements without taking the long-term benefits of R&D investments into consideration, the company’s stocks may be underpriced. On the other hand, the firm’s stock prices may also face overpricing. This might happen in a case when the investors judge the possible future outcomes of current R&D investment based on the past firm’s R&D success, which is not a guarantee by any means.

This week’s blog is unique compared to our previous posts. We have identified two papers that are connected, each with interesting findings and implications. One of today’s leading topics is the Robinhood trading platform, but not from the point of view of recent short squeezes and speculations. The Robinhood can be an interesting insight into retail investing and implications for the market. Research suggests that despite the very low share of retail investors, their power is significantly high. This seems to be caused by the inelastic market, which passive investing contributes to. Therefore, inelasticity is another crucial point.

Although the models cannot entirely capture the reality, they are essential in the analysis and problem solving, and the same could be said about asset pricing models. These models had a long journey from the CAPM model to the most recent Fama French five-factor model. However, the asset pricing models still rely on fundamentals, and as we see in the practice every day, the financial markets or investors are not always rational, and prices tend to deviate from their fundamental values. Past research has already suggested that the assets are driven by both the fundamentals and sentimen. The novel research of Koeppel (2021) continues in the exploration of the hypothesis mentioned above and connects the sentiment with the factors in Fama´s and French´s methodology. The most interesting result of the research is the construction of the sentiment risk factor based on the direct search-based sentiment indicators. The data are sourced by the MarketPsych that analyze information flowing on social media. For comparison, public news is not a source of such exploitable sentiment indicator.

The sentiment score extracted from social media can be exploited to augment the Fama French five factors model. Based on the results, this addition seems to be justified. Adding the sentiment to the pure fundamental model explains more variation and reduce the alphas (intercepts). Moreover, the factor is unrelated to the well-known and established risk factors utilized in the previous asset pricing models, including the momentum. Finally, the sentiment factor seems to be outperforming several other factors, even those established as the smart beta factors.

One of the most popular reports in the Portfolio Analysis section of our Quantpedia Pro tool is “Volatility Targeting”. In this article, we will explain some theory behind this portfolio management method. And then, we will go more in-depth, pick several examples and explain some common volatility targeting variants.

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: