Modélisation financière

Résumé

La modélisation financière consiste à représenter une situation financière grâce à un modèle mathématique, en fonction de différents paramètres. La modélisation financière facilite ainsi la prise de décision, en permettant de simuler divers scénarios et d’aboutir à des recommandations. La modélisation s’applique principalement à deux grands domaines de la finance, la finance d’entreprise et la finance de marché. Finance d’entreprise La modélisation financière a pris une place importante dans le domaine de la finance d’entreprise, de par le besoin des dirigeants de prévisions précises sur la situation financière future de leur entreprise, en fonction de différents paramètres, afin de pouvoir prendre les décisions adéquates. Les applications comprennent :

Évaluation d'entreprise (différentes méthodes d’évaluation sont en général modélisées)
Construction d’un business plan
Calcul d'indicateurs économiques (ex : coût des capitaux propres) Les modèles sont principalement

Source officielle

https://fr.wikipedia.org/wiki/Mod%C3%A9lisation_financi%C3%A8re

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Essays in Empirical Asset Pricing

Alexis Arilès Marchal

This thesis consists of three applications of machine learning techniques to empirical asset pricing.In the first part, which is co-authored work with Oksana Bashchenko, we develop a new method that detects jumps nonparametrically in financial time series and significantly outperforms the current benchmark on simulated data. We use a long short-term memory (LSTM) neural network that is trained on labelled data generated by a process that experiences both jumps and volatility bursts. As a result, the network learns how to disentangle the two. Then it is applied to out-of-sample simulated data and delivers results that considerably differ from the benchmark: we obtain fewer spurious detection and identify a larger number of true jumps. When applied to real data, our approach for jump screening allows to extract a more precise signal about future volatility.In the second part, which is co-authored work with Oksana Bashchenko, we develop a methodology for detecting asset bubbles using a neural network. We rely on the theory of local martingales in continuous-time and use a deep network to estimate the diffusion coefficient of the price process more accurately than the current estimator, obtaining an improved detection of bubbles. We show the outperformance of our algorithm over the existing statistical method in a laboratory created with simulated data. We then apply the network classification to real data and build a zero net exposure trading strategy that exploits the risky arbitrage emanating from the presence of bubbles in the US equity market from 2006 to 2008. The profitability of the strategy provides an estimation of the economical magnitude of bubbles as well as support for the theoretical assumptions relied on.In the third part, I propose a new tool to characterize the resolution of uncertainty around FOMC press conferences. It relies on the construction of a measure capturing the level of discussion complexity between the Fed Chair and reporters during the Q&A sessions. I show that complex discussions are associated with higher equity returns and a drop in realized volatility. The method creates an attention score by quantifying how much the Chair needs to rely on reading internal documents to be able to answer a question. This is accomplished by building a novel dataset of video images of the press conferences and leveraging recent deep learning algorithms from computer vision. This alternative data provides new information on nonverbal communication that cannot be extracted from the widely analyzed FOMC transcripts. This chapter can be seen as a proof of concept that certain videos contain valuable information for the study of financial markets.

EPFL2022

Chargement

Valuation is a key topic in the financing and development of high growth companies. The goal of this study is to bridge the existing gap between the assessment of a company and the financial valuation. Many models exist to capture soft factors, and many more studies have been conducted to understand financial valuation models. In this study, the first step is to develop an assessment framework to capture the value drivers for high growth companies. Based on literature research, an empirical study with 52 valuations, and an iterative process this assessment framework has been built. It contains the following three main factors: Management Market environment Product, Sciences & Technology Each of these main criteria contain a number of sub-criteria shown in Figure 1. For each criterion, a rating between 1 and 6 is given with a specific scale that can be implemented in practice for high growth companies. Even though every company is different, the provided framework allows a systematic and transparent assessment of a high growth company. The proposed valuation methods are specifically targeted for high growth companies and include operational based methods such as discounted cash flow and option pricing, as well as relative methods such as the market comparable and comparable deal methods. Some others, such as the venture capital method, are also a combination of an operational and relative approach. The methodical assessment framework enables a link to be made with the financial valuation models to obtain a consistent and comprehensive assessment and valuation approach for high growth companies. The link is made by splitting up the discount rate into different premiums and by introducing a calculation schema. The assessment framework and its criteria have been tested in an empirical study to find correlations between the success of a company and the provided rating/framework. With the available sample of 25 valuations, this correlation could be shown for the market environment and the product, science & technology. For the management factor, it was not possible to show a statistical significance, even though practical experience had shown that management is a key factor. A main reason why no statistical significance was shown could be that the management team can be changed very quickly. Assessment and valuation of high growth companies 2 Thus, the assessment and valuation is a done at a specific moment in time but did not capture the development of the management team. However, in conclusion, the assessment and valuation is still an art. The proposed framework provides a roadmap to becoming an artist in this field and should serve as a practical guideline.

EPFL2005

Chargement

I started my PhD studies in August 2014 with a strong desire to push my own limits without knowing precisely the areas I wanted to cover in detail. To me, it was clear that I was interested by many different fields, however, I was particularly concerned with behavioural finance and with the fact that simple actions could be followed by strong market reactions. It is in this context that my supervisor, Prof. Semyon Malamud, advised me to derive/measure the consequences of the large acceptance of the RiskMetrics variance model on the price of financial assets. Indeed, this method has the advantage of providing a simple formula to estimate the volatility of any financial asset, but above all, has been used significantly by practitioners in the financial industry. The question then arises, ``Is there a link between this method and the price of financial assets?'' In order to answer this question, I have designed a simple portfolio optimization model in which agents update volatility estimates with the RiskMetrics formula. Thanks to this simple idea my first project was born and I quickly realized that I could design an elegant model. With this framework I have been able to establish the existence of a risk factor of which the economic literature was unaware. Moreover, the empirical strategy allows me to estimate the relative risk aversion coefficient independently from established procedures. Importantly, my estimates are in line with the ones obtained with these (standard) approaches. Meanwhile, I was also interested in a topic that covers a large part of all trades and is known as ``over-the-counter markets''. These markets are characterized by their high level of decentralization. Indeed, every transaction is settled directly between a buyer and a seller. In these markets, the only way to secure a trade is to find another agent that is willing to take the counter-party. I became very interested in a series of books and articles that were modeling financial assets traded under these conditions. Hence, I have started to work in this field by solving different models. I was particularly interested in understanding how the price of assets traded with this constraint would react under stressful situations, that is, when agents had to liquidate their investments. After a trial and error process, I found that my model generated puzzling results. Indeed, this model made predictions that were against traditional wisdom. Above all, that model predicted that a large level of capital mobility could impair welfare. Hence, I had eventually found the link between all the fields I wanted to cover in my thesis, where I debate the optimal allocation of capital under different types of frictions. While my first article treats the case of a centralized market with an agent who forecasts volatility using a particular method, the second article concerns how capital flows across markets when agents are subject to searching frictions. My third article is based on the second and discusses the interaction between innovation and the competition between firms supplying the same products. This article focuses on how capital is used by firms to innovate and how firms grow.

EPFL2019

Chargement

Essays in Empirical Asset Pricing

Alexis Arilès Marchal

EPFL2022

EPFL2005

EPFL2019