Contributions published at Financial Engineering (Markus Leippold)

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In this paper, we develop the conic finance framework for optimal execution of a large portfolio in an illiquid market. We extend the classical optimal execution results by considering stochastic exogenous liquidity effects as well as temporary price impact functions. We depart from the traditionally assumed linear impact function and introduce both stochastic liquidity and volatility effects and nonlinear temporary market impact. Moreover, we allow for an additional stochastic exogenous liquidity effect, used to capture the base illiquidity of a market. We analyze various aspects of our model using a stylized example.

In light of the recent success of applying deep neural network technologies in natural language processing, we carried out an in-depth investigation into the use of dierent neural networks for the dicult task of nancial news understanding. In this work, we proposed several novel neural-network architectures and tested a multi-task learning framework aimed to predict the sentiment involved in the nancial news. Our results show that the deep networks we proposed outperform the existing solutions reported in SemEval 2017 Task 5. Meanwhile, we nd that deeper architectures could outperform shallow ones in this task. We present a well-designed architecture that takes companyspeci c information could help the model to understand the news better. Also, by training on the data set labeled by market response, we nd that there are synergy between sentiment prediction task and market response prediction task; these two tasks share a few abstract features at the very beginning. With these well-behaved model at hand, we design a sequential prediction model that summarizes daily news to predict stock returns. The cross-sectional test shows the scores our model predicts could lead to signicant returns.

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We present a prediction model to forecast corporate defaults. In a theoretical model, under incomplete information in a market with publicly traded equity, we show that our approach must outperform ratings, Altman’s Z-score, and Merton’s distance to default. We reconcile the statistical and structural approaches under a common framework, i.e., our approach nests Altman’s and Merton’s approaches as special cases. Empirically, we cannot reject the superiority of our approach.Furthermore, the numbers of observed defaults align well with the estimated probabilities. Finally, with rank transforms, we obtain cycle-adjusted forecasts that still outperform ratings.

We study the intra-horizon value at risk (iVaR) in a general jump diffusion setup and propose a new model of asset returns called displaced mixed-exponential model, which can arbitrarily closely approximate finite-activity jump-diffusions and completely monotone Levy processes. We derive analytical results for the iVaR and disentangle the risk contribution of jumps from diffusion. Estimating the iVaR for several popular jump models using on S&P 100 option data, we find that option-implied estimates are much more responsive to market changes relative to their historical counterparts. Moreover, disentangling jumps from diffusion, jump account for about 90 percent of iVaR on average.

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Pursuing risk-based allocation across a universe of commodity assets, we find diversified risk parity (DRP) strategies to provide convincing results. DRP strives for maximum diversification along uncorrelated risk sources. A straightforward way to derive uncorrelated risk sources relies on principal components analysis (PCA). While the ensuing statistical factors can be associated with commodity sector bets, the corresponding DRP strategy entails excessive turnover because of the instability of the PCA factors. We suggest an alternative design of the DRP strategy relative to common commodity risk factors that implicitly allows for a uniform exposure to commodity risk premia.

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