We consider the dynamic pricing problem of a monopolist seller who sells a set of mutually substitutable products over a finite time horizon. Customer demand is sensitive to the price of each individual product and the reference price which is formed from a comparison among the prices of all...

Due to the innovative nature of green-technology products (e.g., electric vehicles and solar panels), customers are usually a priori uncertain about their purchasing benefits and typically consult user experiences via online product reviews and word-of-mouth communications. Despite the critical...

This study seeks to construct a basic reinforcement learning-based AI-macroeconomic simulator. We use a deep RL (DRL) approach (DDPG) in an RBC macroeconomic model. We set up two learning scenarios, one of which is deterministic without the technological shock and the other is stochastic. The...

We examine how the return predictability of deep learning models varies with stocks’ vulnerability to investors’ behavioral biases. Using an extensive list of anomaly variables, we find that the long-short strategy based on deep learning signals generates greater returns for stocks more...

Introduced by the military regime of Myanmar (aka Burma) more than 50 years ago, distance learning aimed to alleviate university overcrowding and allow students from remote areas to earn a graduate degree for a reduced tuition. The Myanmar experience is not unique. Egypt, too, has for decades...

We propose a Reinforcement Learning (RL) algorithm for generating a trading strategy in a realistic setting, that includes transaction costs and factors driving the asset dynamics. We benchmark our algorithm against the analytical optimal solution, available when factors are linear and...

We give conditions under which the smoothness properties of the value and policy functions of a dynamic program at discount factor zero extend to small positive discount factors. We apply this to a model of Bayesian learning by a decision maker who does not know which of several parameters is...

Despite its high risk, cryptocurrency has gained popularity as a successful trading option. Cryptocurrencies are digital assets that fluctuate dramatically in a market that operates for 24 hours. Developing trading bots using machine learning-based artificial intelligence (AI) approaches has...

When pricing options, correctly modelling the dynamics of the underlying stock process, by accounting for all corporate events, is a challenge. The difficulties arise due to the resulting price gaps on the underlying process. Moreover, the resulting implied volatility surface is no-longer...

Option is an important financial derivative. Accurate option pricing is essential to the development of the financial market. For option pricing, existing time series models and neural networks are difficult to extract multi-scale temporal features from option data, which greatly limits their...