Do Informational Cascades Happen with Non-myopic Agents?

We consider an environment where players need to decide whether to buy a certain product (or adopt a technology) or not. The product is either good or bad but its true value is not known to the players. Instead, each player has her own private information on its quality. Each player can observe the previous actions of other players and estimate the quality of the product. A classic result in the literature shows that in similar settings information cascades occur where learning stops for the whole network and players repeat the actions of their predecessors. In contrast to the existing literature on informational cascades, in this work, players get more than one opportunity to act. In each turn, a player is chosen uniformly at random and can decide to buy the product and leave the market or to wait. We provide a characterization of structured perfect Bayesian equilibria (sPBE) with forward-looking strategies through a fixed-point equation of dimensionality that grows only quadratically with the number of players. In particular, a sufficient state for players strategies at each time instance is a pair of two integers, the first corresponding to the estimated quality of the good and the second indicating the number of players that cannot offer additional information about the good to the rest of the players. Based on this characterization we study informational cascades in two regimes. First, we show that for a discount factor strictly smaller than one, informational cascades happen with high probability as the number of players increases. Furthermore, only a small portion of the total information in the system is revealed before a cascade occurs. Secondly, and more surprisingly, we show that for a fixed number of players, as the discount factor approaches one, bad informational cascades are benign when the product is bad, and are completely eliminated when the discount factor equals one.

Determining Fundamental Supply and Demand Curves in a Wholesale Electricity Market

In this paper we develop a novel method of wholesale electricity market modeling. Our optimization-based model decomposes wholesale supply and demand curves into buy and sell orders of individual market participants. In doing so, the model detects and removes arbitrage orders. As a result, we construct an innovative fundamental model of a wholesale electricity market. First, our fundamental demand curve has a unique composition. The demand curve lies in between the wholesale demand curve and a perfectly inelastic demand curve. Second, our fundamental supply and demand curves contain only actual (i.e. non-arbitrage) transactions with physical assets on buy and sell sides. Third, these transactions are designated to one of the three groups of wholesale electricity market participants: retailers, suppliers, or utility companies. To evaluate the performance of our model, we use the German wholesale market data. Our fundamental model yields a more precise approximation of the actual load values than a model with perfectly inelastic demand. Moreover, we conduct a study of wholesale demand elasticities. The obtained conclusions regarding wholesale demand elasticity are consistent with the existing academic literature.

Reintegration des refuses en Credit Scoring

The granting process of all credit institutions rejects applicants who seem risky regarding the repayment of their debt. A credit score is calculated and associated with a cut-off value beneath which an applicant is rejected. Developing a new score implies having a learning dataset in which the response variable good/bad borrower is known, so that rejects are de facto excluded from the learning process. We first introduce the context and some useful notations. Then we formalize if this particular sampling has consequences on the scores relevance. Finally, we elaborate on methods that use not-financed clients characteristics and conclude that none of these methods are satisfactory in practice using data from Credit Agricole Consumer Finance. ----- Un syst`eme doctroi de credit peut refuser des demandes de pr^et jugees trop risquees. Au sein de ce syst`eme, le score de credit fournit une valeur mesurant un risque de defaut, valeur qui est comparee `a un seuil dacceptabilite. Ce score est construit exclusivement sur des donnees de clients finances, contenant en particulier linformation `bon ou mauvais payeur, alors quil est par la suite applique `a lensemble des demandes. Un tel score est-il statistiquement pertinent ? Dans cette note, nous precisons et formalisons cette question et etudions leffet de labsence des non-finances sur les scores elabores. Nous presentons ensuite des methodes pour reintegrer les non-finances et concluons sur leur inefficacite en pratique, `a partir de donnees issues de Credit Agricole Consumer Finance.

The Impact of Renewable Energy Forecasts on Intraday Electricity Prices

In this paper we study the impact of errors in wind and solar power forecasts on intraday electricity prices. We develop a novel econometric model which is based on day-ahead wholesale auction curves data and errors in wind and solar power forecasts. The model shifts day-ahead supply curves to calculate intraday prices. We apply our model to the German EPEX SPOT SE data. Our model outperforms both linear and non-linear benchmarks. Our study allows us to conclude that errors in renewable energy forecasts exert a non-linear impact on intraday prices. We demonstrate that additional wind and solar power capacities induce non-linear changes in the intraday price volatility. Finally, we comment on economical and policy implications of our findings.

Influencing factors that determine the usage of the crowd-shipping services

The objective of this study is to understand how senders choose shipping services for different products, given the availability of both emerging crowd-shipping (CS) and traditional carriers in a logistics market. Using data collected from a US survey, Random Utility Maximization (RUM) and Random Regret Minimization (RRM) models have been employed to reveal factors that influence the diversity of decisions made by senders. Shipping costs, along with additional real-time services such as courier reputations, tracking info, e-notifications, and customized delivery time and location, have been found to have remarkable impacts on senders choices. Interestingly, potential senders were willing to pay more to ship grocery items such as food, beverages, and medicines by CS services. Moreover, the real-time services have low elasticities, meaning that only a slight change in those services will lead to a change in sender-behavior. Finally, data-science techniques were used to assess the performance of the RUM and RRM models and found to have similar accuracies. The findings from this research will help logistics firms address potential market segments, prepare service configurations to fulfill senders expectations, and develop effective business operations strategies.