Network design problems, such as flexibility design, are ubiquitous in modern marketplaces where firms constantly innovate new ways to match supply and demand. We develop a primal-dual based approach to analyze the flexibility design problem, i.e., allocating subsets of limited resources to each...

An efficient photovoltaic (PV) water pumping system requires two most important things: A suitable motor to have an efficient run, and to provide a good control to the photovoltaic system, it needs a proper maximum power point tracking (MPPT) system. Power conversion system has two levels,...

Credit card default payment prediction studies are very important for any financial institution dealing with credit cards. The purpose of this work is to evaluate the performance of machine learning methods on credit card default payment prediction using logistic regression, C4.5 decision tree,...

Recent turmoil in financial markets endorses the need for rigorous handling and integration of asset liquidity risk into Value-at-Risk (VaR) models. In this work we develop and test measures of certain kinds of asset liquidity risk that is useful for completing the definition of market risk and...

Algorithmic impact assessments (AIA) are increasingly being proposed as a mechanism for algorithmic accountability. These assessments are seen as potentially useful for anticipating, avoiding, and mitigating the negative consequences of algorithmic decision-making systems (ADS). At the same...

We investigate collusive pricing in laboratory markets when human players interact with an algorithm. We compare the degree of (tacit) collusion when exclusively humans interact to the case of one firm in the market delegating its decisions to an algorithm. We further vary whether participants...

The Pricing strategy is one of the major determinants of the success or failure of any business endeavor. Such determinants are made through algorithms virtually now-a-days, which causes vertical agreement, which is not per se anti-competitive in nature. The advancement of 'pricing algorithms'...

We propose an automatic machine-learning system to forecast realized volatility for S&P 100 stocks using 118 features and five machine learning algorithms. A simple average ensemble model combining all learning algorithms delivers extraordinary performance across forecast horizons, and the...

Subject and purpose of work: Fake news and disinformation are polluting information environment. Hence, this paper proposes a methodology for fake news detection through the combined weighted accuracies of seven machine learning algorithms. Materials and methods: This paper uses natural language...

In this paper we use the tools and frameworks from Oxford University’s postgraduate diploma in financial strategy to study the performance and benefits of algorithmic trading strategies (algos), and specifically those that use artificial intelligence (AI) and machine learning (ML).We discover...