Title

Predicting Financial Recession using Machine Learning

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science and Info Sys

Date of Award

Spring 2021

Abstract

Financial decision plays an important role in the society and therefore, a crash in the market or a major economic recession can lead to social anxiety. Many statistical models have been developed to forecast the financial recession but there is always human input required which leads to human judgement and can eventually lead to wrong results. More recently, machine learning algorithms are built to predict the crisis of asset classes like currency and stocks. In this study, we take these models to one step further and try to predict the financial recession using machine learning classification algorithms for next six-months period. To ensure financial stability in the economy, policy makers using this model can intervene and adjust the monetary policy. Moreover, asset managers / investors can adjust their portfolio by increasing allocation in less risky assets (like cash and government bonds) and decreasing allocation in more risky assets (like stocks) if the crisis is imminent.

Advisor

Omar El Ariss

Subject Categories

Computer Sciences | Physical Sciences and Mathematics

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