Coût de transaction | EPFL Graph Search

Un coût de transaction est l'ensemble des différents coûts en argent, en temps, en énergie liés à un processus d'échange ou de négociation. Définition Un coût de transaction, un coût lié à un échange économique, plus précisément une transaction sur le marché. Ce coût n'est pas pris en compte dans le cadre de la concurrence pure et parfaite. Il peut être direct (commission de bourse) ou indirect (coût de prospection, temps et effort passés à la négociation et à la vérification de la transaction...). On peut le définir de manière générique comme l'ensemble des coûts engendrés par la coordination entre les agents ainsi que les coûts qui limitent la coordination entre les agents. Il peut s'agir, par exemple, des coûts liés à l'incertitude qui requiert que l'agent s'informe avant de réaliser une transaction économique. De plus, il peut s'agir également des coûts liés à la négociation et à la rédaction d'un contrat. Théories économiques L'idée d'un coût du système

Technology Strategy in Dynamic Environments: A Computational Analysis of the Automation of Routines, the Organization of AI, and the Evaluation of Technology Risk

In this dissertation, I develop theory and evidence to argue that new technologies are central to how firms organize to create and capture value. I use computational methods such as reinforcement learning and probabilistic topic modeling to investigate three topics: the automation of routines, the organization of artificial intelligence (AI), and the evaluation of technology risk. Overall, I argue that new technologies are not a panacea for the firm but require deliberate strategic planning to manage the potential downsides of myopic automation, AI interdependencies, and the disclosure of technology risks.In the first essay, I argue that while automation can increase productivity by reducing the costs of coordinating individuals, the automation of routines can also incur an indirect opportunity cost due to slow adaptation to environmental change. I develop a reinforcement learning simulation to model the impact of automation on the returns from the division of labor in dynamic environments and to show how automation incurs opportunity costs through lost learning and slow adaptation. Moreover, automation can be suboptimal when it brings about myopic behavior, i.e. high returns from the division of labor in the short term, but negative returns in the long term. Given the simulation results, I argue that firms need dynamic routines to simultaneously balance learning and automation. I open-source the simulation platform as OrgSim-RL on GitHub.In the second essay, I argue that a data-driven culture - what I define as a Data Clan - can help to coordinate complex interdependencies between AI components within a firm. I analyze in-depth semi-structured interview data with a hierarchical stochastic block model (hSBM) and hand-coding to find that managers focus primarily on building a strong culture and establishing high-quality data assets when allocating resources to AI initiatives. Given the results, I inductively develop implications for theory and argue that the emergence of a Data Clan can be a governance mechanism to reduce coordination frictions and build a competitive advantage in the age of AI.In the third essay, I argue that investors require a higher initial return to take on more technology risk disclosure during an IPO. I quantify the magnitude of disclosed risk and the risk disclosure topics based on a latent Dirichlet allocation (LDA) topic model of IPO prospectus text and find a return-for-risk association between text-based technology risk disclosure and underpricing. The study also finds evidence that owning granted patents is associated with a lower return-for-risk association, suggesting that intellectual property allows the disclosure of risk without losing the competitive advantage. I open-source the code for quantifying risk disclosure as RiskyData-LDA on GitHub.In summary, this dissertation develops theory and finds evidence across three essays to argue that leveraging new technologies requires deliberate strategic planning to manage potential downsides of new technologies, such as the opportunity costs of automation, coordination costs, and costs associated with raising capital. The results suggest three mitigating solutions: dynamic routines to balance learning and automation, a Data Clan to improve coordination, and disclosure through patents to reduce underpricing.

EPFL2022

Market Structure and Transaction Costs of Index CDSs

Pierre Collin Dufresne, Jan Benjamin Junge

Despite regulatory efforts to promote all-to-all trading, the post-Dodd-Frank index credit default swap market remains two-tiered. Transaction costs are higher for dealer-to-client than interdealer trades, but the difference is explained by the higher, largely permanent, price impact of client trades. Most interdealer trades are liquidity motivated and executed via low-cost, low-immediacy trading protocols. Dealer-to-client trades are nonanonymous; they almost always improve upon contemporaneous executable interdealer quotes, and dealers appear to price discriminate based on the perceived price impact of trades. Our results suggest that the market structure is a consequence of the characteristics of client trades: relatively infrequent, large, and differentially informed.

WILEY2020

Essays on the Market Structure and Pricing of Credit Derivatives

Jan Benjamin Junge

This thesis analyzes the interrelation between market structure and price formation in credit derivatives markets. Traditionally, credit derivatives are traded in relatively opaque over-the-counter markets in which trading is segmented and subject to many imperfections from which illiquidity may arise. Recent regulatory reforms have brought transparency to some credit derivatives markets without affecting their segmented structures. The first chapter, which is joint work with Anders B. Trolle, analyzes whether liquidity risk is priced in the cross section of returns on credit default swaps (CDSs). The analysis is based on a factor pricing model and a tradable liquidity factor that is constructed from returns on index arbitrage strategies. The underlying presumption is that violations of simple no-arbitrage relations between different CDS contracts reflect constraints on the risk-bearing capacity of CDS market intermediaries and, in broad terms, CDS market illiquidity. The analysis reveals priced liquidity risk in that credit protection sellers earn higher expected excess returns on CDS contracts with higher liquidity exposures. The liquidity risk premium is significant and accounts for 24% of CDS spreads, on average. CDS risk premia correlate negatively with proxies for the risk-bearing capacity of CDS market intermediaries, which is consistent with intermediary frictions affecting the pricing of CDSs. The second chapter, which is joint work with Pierre Collin-Dufresne and Anders B. Trolle, analyzes transaction costs in the dealer-to-customer (D2C) and dealer-to-dealer (D2D) segments of the post-Dodd-Frank index CDS market. Dodd-Frank regulations that made all-to-all trading possible had the potential to break up the market's segmented structure but failed to do so. This led to a controversy with some market participants arguing that the segmented structure is optimal and other market participants arguing that dealers maintain the segmented structure in order to limit competition by alternative liquidity providers. The analysis reveals that D2C trades indeed have larger transaction costs than D2D trades but that the differences in transaction costs reflect differences in price impacts rather than differences in profits from liquidity provision. D2C trades are even competitive relative to executable bids and offers in the D2D segment, suggesting that the market structure delivers favorable prices for customers who value immediacy. The third chapter documents a decline of transaction costs and profits from liquidity provision in the index CDS market over a two-and-a-half-year period during which Dodd-Frank regulations were implemented. Transaction costs and profits from liquidity provision declined around the introduction of so-called swap execution facilities (SEFs); i.e., regulated trading platforms that offer pre-trade transparent methods of trade execution. Trades that are executed on SEFs have lower transaction costs and are less profitable from a liquidity provider's perspective in comparison to bilaterally negotiated trades, which is consistent with better comparison shopping and stronger price competition on SEFs. Dodd-Frank regulations mandating on-SEF trade execution that were implemented after the introduction of SEFs did not affect transaction costs and profits from liquidity provision, suggesting that there is no incremental effect associated with mandatory pre-trade transparency.

EPFL2016

Technology Strategy in Dynamic Environments: A Computational Analysis of the Automation of Routines, the Organization of AI, and the Evaluation of Technology Risk

EPFL2022

Essays on the Market Structure and Pricing of Credit Derivatives

Jan Benjamin Junge

EPFL2016