We aim to demystify digitalization in accounting (DIA) based on the case study of Ash Cloud, a digital factory in Shenzhen, China. From the perspective of dynamic capabilities, we develop the "momentum" theory of DIA to illustrate that firm and executive characteristics drive digital...

In this paper, we introduce a clustering method to approximate the solution to a general Backward Stochastic Differential Equation with Jumps (BSDEJ). We show the convergence of the sequence of approximate solutions to the true one. The method is implemented for an application in finance....

In this paper, we provide insights on the prediction of asset returns via novel machine learning methodologies. Machine learning clustering-enhanced classification and regression techniques to predict future asset return movements are proposed and compared. Numerical experiments show good...

In this paper, we propose a general framework of optimal investment and a collection of trading ideas, which combine probability and statistical theory with, potentially, machine learning techniques. The trading ideas are easy to implement and their validity is justified by full mathematical...

Stock market index enhancement is a popular strategy among hedge funds. The algorithm tries to adjust the weights of individual stocks of a benchmark index to boost performance of the target portfolio with respect to the original benchmark. Therefore, the key to success of this strategy is the...

In this paper, we document a novel machine learning based bottom-up approach for static and dynamic portfolio optimization on, potentially, a large number of assets. The methodology overcomes many major difficulties arising in current optimization schemes. For example, we no longer need to...