SEMINAR:Developing Data-Driven Methods Using Machine Learning...

Speaker: Davood Pirayesh Neghab

Title:  Developing Data-Driven Methods Using Machine Learning in Operations and Finance

Date/Time: 27 October 2021 / 13:40 - 14:30 PM

Zoom: Meeting ID:https://sabanciuniv.zoom.us/j/3923709046 

Abstract: With the recent advances in data collection and analysis technologies, data-driven approaches to operations management and financial problems have gained traction. In particular, machine learning methods are increasingly being integrated into optimization problems. This integration facilitates translating the historical data into prescriptive solutions in comprehensive frameworks. This study aims at building data-driven methods to link the data with the actions and improve the goals in operational and financial decision systems. We consider an integrated learning and optimization approach using machine learning techniques for optimizing strategy for decision-makers facing a complex problem with additional information about the state of the system. We give algorithms based on integrating optimization, neural networks, and adapting time-series properties, and develop models that are capable of estimating nonlinear relations between data. In this presentation, we focus on two problems from the fields of inventory and financial investment:

1- In the context of inventory, we consider an integrated learning and optimization problem for optimizing a newsvendor’s strategy facing a complex correlated demand with additional information about the unobservable state of the system. We, therefore, combine estimation, inference, and optimization using a multi-layered neural network. To assess the performance of this integrated approach, we compare the results from our approach against data-driven methods that ignore the hidden factor information or that employ separate inference and optimization steps. Numerical examples on both a synthetic data set and real data which might have an unobservable state demonstrate that our approach compares favorably against the other benchmarks.

2- In the context of financial investment, we propose a new approach to asset allocation based on neural networks; it analyzes historical market states and asset returns and identifies the optimal portfolio choice in a new period when new observations become available. In this approach, we directly relate state variables to portfolio weights, rather than first modeling the return distribution and subsequently estimating the portfolio choice. The method captures nonlinearity among the state (predicting) variables and portfolio weights without assuming any particular distribution of returns and other data, and without fitting a model with a fixed number of predicting variables to data. The empirical results for a portfolio of stock and bond indices show the proposed approach generates a more efficient outcome compared to traditional methods and is robust in using different objective functions across different sample periods.

Bio: Davood Pirayesh Neghab has a PhD in Industrial Engineering and Operations Research from Koc University, Turkey. He holds a master’s degree in Financial Engineering from the University of Tehran and a bachelor’s degree in Industrial Engineering from Iran University of Science and Technology. He has been involved in research projects with a focus on machine learning applications in Operations and Finance. His research interests include portfolio optimization, risk management, data science, and inventory control systems.