Over the last decades, dynamic pricing has become increasingly popular. To solve pricing problems, however, is particularly challenging if the customers' and competitors' behavior are both strategic and unknown. Reinforcement Learning (RL) methods are promising for solving such dynamic problems...

This paper studies pricing and observational learning in a reward-based crowdfunding campaign context. The creator sets a funding target and uses different pricing strategies in order to make the project successful: menu price and other static or dynamic price strategies, such as low price, high...

A modern effective business model involves the use of an appropriate pricing strategy. However, what matters is not only short-term profitability but also the long-term loyalty of clients. The main purpose of this paper is to present a specific transactional pricing strategy for a second-hand...

The aim of the research is to assess the degree of the pricing-to-market (PTM) strategy used in selected European countries, focusing on selected groups of consumer goods. The study uses a literature review in the field of macroeconomics and international finance, as well as statistical and...

Metaverses have been evolving following the popularity of blockchain technology. They build their own cryptocurrencies for transactions inside their platforms. These new cryptocurrencies are, however, still highly speculative, volatile, and risky, motivating us to manage their risk. In this...

We consider a general framework of optimal contract design under the heterogeneity and short-termism of agents. Our research shows that the optimal contract must weigh the agent's information rent, incentive cost, and benefit to overcome the contract's adverse selection and moral hazards. Agents...

In this paper, we document the importance of memory in machine learning (ML)-based models relying on firm characteristics for asset pricing. We find that predictive algorithms perform best when they are trained on long samples, with long-term returns as dependent variables. In addition, we...

Real-time Trajectory Optimization (RTOP) is a machine learning-based flight path optimization framework that automatically performs in-flight re-planning continuously with the latest weather information. The framework leverages three core algorithms: (1) a supervised learning algorithm to...

We show that Bayesian posteriors concentrate on the outcome distributions that approximately minimize the Kullback–Leibler divergence from the empirical distribution, uniformly over sample paths, even when the prior does not have full support. This generalizes Diaconis and Freedman's (1990)...

We explore the impact of fake news on asset price dynamics within the asset-pricing model of Brock and Hommes (Brock, W. A., and C. H. Hommes. 1998. "Heterogeneous Beliefs and Routes to Chaos in a Simple Asset Pricing Model." Journal of Economic Dynamics and Control 22 (8): 1235-74). By...