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Posts

Future Blog Post

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This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

CV

references

research

Anomaly return predictability using deep learning asset pricing

Working paper, 2019

This paper studies anomaly return predictability across deciles using a set of fifty anomaly variables built using individual stock characteristics. I construct deciles and study their predictability using their own past information, other macroeconomic variables, and limit-to-arbitrage variables. I find that some anomalies are persistent and that there are some predictors which help to forecast the decile portfolio returns. Deciles predictability is not uniform across anomaly variables and predictors. Namely, all deciles are not uniformly predictable but extreme deciles seem to be more often predictable. Stock variance, dividend yield, and dividend price ratio are strong predictors for decile portfolio returns. Most importantly, hedge portfolios are often predictable by the TED spread and Amihud illiquidity measure, which indicate that trading frictions may explain the persistence of these portfolio returns. Furthermore, I use the rich set of five hundred anomaly portfolios to investigate their prediction properties using Deep learning techniques.

Recommended citation: Stéphane, N Dri. (2019). "Anomaly return predictability using deep learning asset pricing ." Working paper.

Asset Pricing with Nonlinear Principal Components

Working paper, 2021

This paper shows how truly independent nonlinear factors improve the stochastic discount factor (SDF) estimation. The main purpose of the nonlinear factors is to serve as factors to predict future returns out-of-sample. We use the Fama-French 25 ME/BM- sorted portfolios and fifty anomaly portfolios adding interaction terms built using individual stock characteristics to test the effectiveness of the nonlinear factors. Then, we estimate the SDF using raw characteristic return, linear principal component, and a nonlinear principal component. We found that the SDF estimated using a mixture of nonlinear and linear factors outperform the one using solely linear factors or raw characteristic returns in terms of out-of-sample $R^2$ performance. More importantly, the hybrid model -using both nonlinear and linear principal components- requires less risk factors to achieve the highest out-of-sample performance compared to a model using only linear factors.

Recommended citation: Caio Almeida, René Garcia, Stéphane N Dri. (2021). "Asset Pricing with Nonlinear Principal Components." Working paper.

JMP - Long run carbon consumption risks and asset prices

Working paper, 2021

This paper analyzes how environmental policies that aim to reduce carbon emissions affect asset prices and household consumption. Using novel data, I propose a measure of carbon emissions from a consumer point of view and a carbon consumption growth risk measure. The measures are based on information on aggregate consumption and the carbon footprint for each good and service. To analyze the effects of environmental policies, a long-run risks model is developed where consumption growth is decomposed into two components: the growth rate of carbon consumption and the growth rate of the share of carbon consumption out of total consumption. This paper argues that the long-run risk in consumption growth comes mainly from the carbon consumption growth arising from policies and actions to curb emissions, such as the Paris Agreement and the U.N. Climate Change Conference (COP26). My model helps to detect long-run risk in consumption from climate policies while simultaneously solving the equity premium and volatility puzzles. The decomposition of consumption could lead to identifying the most polluting consumption items and to constructing an investment strategy that minimizes or maximizes a long-term environmental criterion.

Recommended citation: Stéphane, N Dri. (2021). "Long run carbon consumption risks and asset prices ." Working paper.

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