| October 29, 2013
In this paper we investigate whether stocks' current prices reflect "mispricing" or a reflection of their intrinsic value?
This is important as identifying between these competing hypotheses can aid investors in making rational decisions on whether to exploit the market's misunderstanding of the stocks' future potential or to avoid these companies as they are "cheap" or "expensive" for a genuine reason.
We classify stock returns unexplained by the valuation model as „mispriced‟ and evaluate the efficacy of this signal. We find „mispriced‟ stocks deliver an IC of 3.8% or return of 5.1% pa, which is better than that for value factors. They also have low correlations to style factors like value and analyst sentiment. This makes it an attractive signal for systematic managers. Moreover, we note that the signal works well across global regions, albeit better in larger markets.
We attempt to rationalise whether a stock is „cheap‟ or „expensive‟ for a reason, or „mispriced‟. We highlight the limitations of valuation models in that stock price can be driven by sentiment, which is difficult to capture, or due to errors in forecasting earnings or discount rates which limits the usefulness of valuation models. We show that identifying additional drivers of returns like sentiment, management quality, earnings visibility and leverage helps to discriminate between stocks that are „mispriced‟ and „cheap/expensive‟ for a reason.
We leverage RavenPack‟s news-flow database to identify corporate actions like Share Buybacks, M&A, Executive Employment, Clinical Trials, etc. that act as catalysts in either driving mean reversion or explain the persistence of stock „mispricing‟. We show that complementing the „mispricing‟ signal with corporate action news-flow helps to gain a better understanding of stock price behaviour and improves the performances of these trading strategies.
Conceptually this approach is not different from an alpha model; however, its advantage is that we start with a valuation framework, which is how fundamental analysts evaluate investment opportunities. Additionally a valuation approach broadens the appeal to investors who view investment decisions outside the dimensions of styles. To us, this approach appeals to both fundamental and quantitative managers, i.e. a „quantamental‟ approach to stock selection.
Please use your business email. If you don't have one, please email us at email@example.com.
We will process your personal data with the purpose of managing your personal account on
RavenPack and offering our services. You can exercise your rights of access, rectification,
erasure, restriction of processing, data portability and objection by emailing us at firstname.lastname@example.org. For more information, you can
Your request has been recorded and a team member will be in touch soon.
High inflation has returned in developed markets after decades of lying low. In our latest paper, we show how to build an inflation-based asset allocation strategy using sentiment data and we illustrate that sentiment-based strategies outperform models that depend merely on past observed inflation values.
This year's RavenPack Research Symposium brought two intense days of knowledge sharing in London and New York, from 25 top experts in natural language processing, quantitative investing and machine learning. Together, we explored how firms can leverage new language models to generate alpha, better manage risk and respond to calls for more sustainable investment practices.
Human capital is at the heart of value creation. Our latest research demonstrates how unprecedented workforce insights, sourced from over 200 million job postings, can generate more alpha.