Publications

Choosing the appropriate assets to include in an investment portfolio is a complex andtedious task that traditionally uses outdated assumptions and mathematical methods; as aresult, many investors currently use the power of Artificial Intelligence to reinforce theirinvestment strategy. This project introduces an Intelligent Investment PortfolioGeneration, a platform that combines the power of Machine Learning with Finance forcreating optimised investment portfolios. The platform is designed to accept the investor'srisk tolerance, the maximum investment amount, and the investor's preferred StockMarket Sectors. The provided Stock Market Sectors in this project are composed of theSectors classified by the Global Industry Classification Standard in addition to theSustainable Sector, which includes only Sustainable companies based on theirEnvironmental, Social, and corporate Governance scores. In addition to creatingoptimised investment portfolios, a feature for portfolio rebalancing was also implementedin the platform, which can be either performed on demand by the investor or based on anotification provided by the platform. A fully functional web-based User Interface is alsodesigned to provide the investor with a simple and intuitive way to create and manage aPortfolio; the User Interface includes useful statistical metrics and graphs for assisting theinvestor's decisions.

This paper aims to further investigate the relationship of various environmental, mobility andsocioeconomic parameters with house prices in the area of Boston through spatial-econometric tools andtechniques. More specifically, the suggested model specification sheds light on the appropriate model andfunctional form of the parameters as to better determine the determinants of the Hedonic Price Function ofhouses with subsequent goal to enhance the existing literature. This research question was chosen asenhance the on-going research around modelling house prices and the implicit markets that determine thedemand and supply of houses and their various attributes with a spatial focus, as well as to address some ofthe inhibitors that implicate the accuracy of such models. In this paper it was found that alongsideconventional socioeconomic parameters, various environmental, geographical and mobility related factorswere found to contribute substantially on median house prices in the Metropolitan area of Boston. Finally,it was found that spatial dependencies are present within the specified model that implies potentialreconsideration of the model.

The fast-paced urbanization, and continuous advancement of the industrial sector in urban areas have led to the Environmental crisis becoming more prominent than ever as the COP26 conventions have highlighted earlier this year. One of the main sources of the climate change, that the global community is currently and widely acknowledging and aims to tackle, is the immense amount of CO2 emissions being produced and accumulated in our planet’s atmosphere leading to detrimental effects on the natural environment whose “well-being” is proven to be strongly correlated with both the planet and humanity’s prosperity.

The technological innovations of the 21st century in combination with impactful events at the capitalistic markets have altered the nature of the economy, services, and institutions rapidly. The above-mentioned set an unprecedented status quo, which needs to be legally regulated, in order to control the unpredictable development and application of technological advancements. The aim of this essay is to discuss the challenges, dangers, and opportunities involved in the engagement between technological innovations and legal regulation.