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Cette thèse est composée de quatre chapitres qui contribuent à une meilleure connaissance des comportements d’échange des investisseurs individuels et de leur performance. Dans le premier chapitre, nous réalisons la première étude consacrée aux performances de portefeuille des investisseurs individuels français. A partir d’une base de données de plus de 8 millions de transactions réalisées par 56 723 investisseurs, nous montrons que les investisseurs français affichent des rentabilités ajustées au risque négatives sur leurs portefeuilles et font des choix d’investissement pénalisants. De plus, nous mettons en évidence que les investisseurs les plus sophistiqués ne sont pas plus performants que leurs pairs.Dans le second chapitre, nous montrons que l’aspiration individuelle constitue un déterminant clé pour expliquer l’hétérogénéité des performances de portefeuille. Nous définissons les aspirations selon la Théorie Comportementale du Portefeuille. Les investisseurs qui ont de fortes aspirations détiennent des portefeuilles plus risqués, échangent plus fréquemment et diversifient moins que les investisseurs ayant de faibles aspirations. En contrôlant de la fréquence des échanges, de la diversification et des facteurs de risque habituels, nous montrons que les investisseurs ayant de fortes aspirations sous-performent les investisseurs ayant de faibles aspirations.Dans le troisième chapitre nous analysons les performances des investisseurs individuels via des mesures adaptées à leurs préférences. Lorsque leurs performances sont évaluées avec ces mesures plutôt qu’avec le ratio de Sharpe, une plus grande part des investisseurs bat l’indice de marché. Cette observation jette un regard nouveau sur les capacités de gestion des investisseurs individuels. Cependant, nous montrons que l’amélioration des performances est liée à la skewness des portefeuilles plutôt qu’à une sélection de titres pertinente.Dans le dernier chapitre, nous explorons les comportements de rachat des investisseurs individuels. Nous montrons que les investisseurs préfèrent racheter (1) les titres pour lesquels ils ont réalisé une plus-value lors de la vente (2) les titres dont le prix a diminué depuis la vente. Nos tests excluent les explications rationnelles et confirment que l’évitement du regret est à l’origine de tels comportements. Sur la base d’une analyse de survie, nous montrons que les investisseurs sophistiqués sont moins sujets à ces préférences. / This dissertation is composed of four chapters that make a substantial contribution to existing knowledge of the trading behavior and performance of individual investors. The first chapter provides the most extensive study of the trading performance of French individual investors to date. Based on a large database of nearly 8 million trades realized by56,723 investors, we show that French investors exhibit negative risk-adjusted returns on their portfolios, and make penalizing choices in their trades. We find that more sophisticated investors do not perform better than their peers, and we conclude that investors would gain more from applying a passive strategy. In the second chapter, we evidence that individual aspiration is a key determinant of existing heterogeneity in portfolio performance. We define aspirations according to the Behavioral Portfolio Theory. Investors who have high aspirations hold riskier portfolios, trade more frequently and diversify less than investors who have low aspirations. After controlling for turnover, diversification and usual risk factors, we find that investors with high aspirations underperform investors with low aspirations.In the third chapter we highlight alternative measures of performance that efficiently convey the real preferences of investors. When they are evaluated with these alternative measures rather than with the Sharpe ratio, a higher proportion of investors beat the market index. This observation challenges the global evidence that individual investors are poor portfolio managers. However, our evidence suggests that the improvement of an investor’s performance is linked to portfolio skewness rather than relevant stock selection.In the last chapter, we explore the repurchase behavior of individual investors. We find that French investors prefer to repurchase (1) stocks that have been sold for a gain and (2) stocks that have lost value since their sale. Our tests exclude rational explanations for these preferences and confirm our hypothesis that such patterns can be traced to the avoidance of regret in trades. We use survival analysis to demonstrate that sophisticated investors suffer less from there purchase preferences.

<p>Algorithmic trading is an automated process of order execution on electronic stock markets. It can be applied to a broad range of financial instruments, and it is&nbsp; characterized by a signicant investors&#39; control over the execution of his/her orders, with the principal goal of finding the right balance between costs and risk of not (fully) executing an order. As the measurement of execution performance gives information whether best execution is achieved, a signicant number of diffeerent benchmarks is&nbsp; used in practice. The most frequently used are price benchmarks, where some of them are determined before trading (Pre-trade benchmarks), some during the trading&nbsp; day (In-traday benchmarks), and some are determined after the trade (Post-trade benchmarks). The two most dominant are VWAP and Arrival Price, which is along with other pre-trade price benchmarks known as the Implementation Shortfall (IS).</p><p>We introduce Negative Selection as a posteriori measure of the execution algorithm performance. It is based on the concept of Optimal Placement, which represents the ideal order that could be executed in a given time win-dow, where the notion of ideal means that it is an order with the best execution price considering&nbsp; market &nbsp;conditions&nbsp; during the time window. Negative Selection is dened as a difference between vectors of optimal and executed orders, with vectors dened as a quantity of shares at specied price positionsin the order book. It is equal to zero when the order is optimally executed; negative if the order is not (completely) filled, and positive if the order is executed but at an unfavorable price.</p><p>Negative Selection is based on the idea to offer a new, alternative performance measure, which will enable us to find the&nbsp; optimal trajectories and construct optimal execution of an order.</p><p>The first chapter of the thesis includes a list of notation and an overview of denitions and theorems that will be used further in the thesis. Chapters 2 and 3 follow with a&nbsp; theoretical overview of concepts related to market microstructure, basic information regarding benchmarks, and theoretical background of algorithmic trading. Original results are presented in chapters 4 and 5. Chapter 4 includes a construction of optimal placement, definition and properties of Negative Selection. The results regarding the properties of a Negative Selection are given in [35]. Chapter 5 contains the theoretical background for stochastic optimization, a model of the optimal execution formulated as a stochastic optimization problem with regard to Negative Selection, as well as original work on nonmonotone line search method [31], while numerical results are in the last, 6th chapter.</p> / <p>Algoritamsko trgovanje je automatizovani proces izvr&scaron;avanja naloga na elektronskim berzama. Može se primeniti na &scaron;irok spektar nansijskih instrumenata kojima se trguje na berzi i karakteri&scaron;e ga značajna kontrola investitora nad izvr&scaron;avanjem njegovih naloga, pri čemu se teži nalaženju pravog balansa izmedu tro&scaron;ka i rizika u vezi sa izvr&scaron;enjem naloga. S ozirom da se merenjem performasi izvr&scaron;enja naloga određuje da li je postignuto najbolje izvr&scaron;enje, u praksi postoji značajan broj različitih pokazatelja. Najče&scaron;će su to pokazatelji cena, neki od njih se određuju pre trgovanja (eng. Pre-trade), neki u toku trgovanja (eng. Intraday), a neki nakon trgovanja (eng. Post-trade). Dva najdominantnija pokazatelja cena su VWAP i Arrival Price koji je zajedno sa ostalim &quot;pre-trade&quot; pokazateljima cena poznat kao Implementation shortfall (IS).</p><p>Pojam negative selekcije se uvodi kao &quot;post-trade&quot; mera performansi algoritama izvr&scaron;enja, polazeći od pojma optimalnog naloga, koji predstavlja idealni nalog koji se&nbsp; mogao izvrsiti u datom vremenskom intervalu, pri ćemu se pod pojmom &quot;idealni&quot; podrazumeva nalog kojim se postiže najbolja cena u trži&scaron;nim uslovima koji su vladali&nbsp; u toku tog vremenskog intervala. Negativna selekcija se defini&scaron;e kao razlika vektora optimalnog i izvr&scaron;enog naloga, pri čemu su vektori naloga defisani kao količine akcija na odgovarajućim pozicijama cena knjige naloga. Ona je jednaka nuli kada je nalog optimalno izvr&scaron;en; negativna, ako nalog nije (u potpunosti) izvr&scaron;en, a pozitivna ako je nalog izvr&scaron;en, ali po nepovoljnoj ceni.</p><p>Uvođenje mere negativne selekcije zasnovano je na ideji da se ponudi nova, alternativna, mera performansi i da se u odnosu na nju nađe optimalna trajektorija i konstrui&scaron;e optimalno izvr&scaron;enje naloga.</p><p>U prvom poglavlju teze dati su lista notacija kao i pregled definicija i teorema&nbsp; neophodnih za izlaganje materije. Poglavlja 2 i 3 bave se teorijskim pregledom pojmova i literature u vezi sa mikrostrukturom trži&scaron;ta, pokazateljima trgovanja i algoritamskim trgovanjem. Originalni rezultati su predstavljeni u 4. i 5. poglavlju. Poglavlje 4 sadrži konstrukciju optimalnog naloga, definiciju i osobine negativne selekcije. Teorijski i praktični rezultati u vezi sa osobinama negativna selekcije dati su u [35]. Poglavlje 5 sadrži teorijske osnove stohastičke optimizacije, definiciju modela za optimalno izvr&scaron;enje, kao i originalni rad u vezi sa metodom nemonotonog linijskog pretraživanja [31], dok 6. poglavlje sadrži empirijske rezultate.</p>