In this project we try to predict bad loans with Machine Learning models. We approach the problem as a Supervised Classification problem. We conduct a preprocessing step, and deal with imbalance. As a first step we apply various Machine Learning models and identify the best ones. With the...

We consider two major venues that exchange BTC-ETH on the blockchain, namely the Uniswap v3 WBTC-WETH liquidity pools with proportional transaction costs of 5 and 30 basis points respectively. A multivariate linear regression model with quadratic terms is introduced to predict the logarithm of...

We ask how machine learning can change a crucial step of the scientific process in economics: the advancement of theories through the discovery of "anomalies." Canonical examples of anomalies include the Allais Paradox and the Kahneman-Tversky choice experiments, which are concrete examples of...

Capital Asset Pricing Model (CAPM) and Arbitrage Pricing Theory are two basic models for understanding the relationship between stock return and risk in assessing shares traded in the capital market. This study aims to understand whether CAPM or APT is better and more accurate in predicting the...

“Meme Stocks'' are stocks that experience extreme price increases in short time periods as a result of attention/hype generated on social media and online forums (most notably Reddit and Twitter). Since the price increase of these stocks is artificial (relying on the fever of the Internet) and...

Through the lens of a nonlinear dynamic factor model, we study the role of exogenous shocks and internal propagation forces in driving the fluctuations of macroeconomic and financial data. The proposed model 1) allows for nonlinear dynamics in the state and measurement equations; 2) can generate...

We evaluate the performance of several linear and nonlinear machine learning (ML) models in forecasting the realized volatility (RV) of ten global stock market indices in the period from January 2000 to December 2021. We train models using a dataset that includes past values of the RV and...

Shelter (housing) costs constitute a large component of price indexes, including 42 percent of the widely followed core Consumer Price Index (CPI). The shelter prices measured in the CPI capture new and existing renters and tend to lag market rents. This lag explains how in recent months the...

Financial literacy (FL) represents a persons ability to turn assets into income, and understanding digital currencies has been added to the modern definition. FL can be predicted by exploiting unlabelled recorded data in financial networks via semi supervised learning (SSL). Measuring and...

Risk and uncertainty are vital for making economic decision. An individual's propensity to make riskier decisions depends on various circumstances. Most studies on general and financial risk preference focus on analyzing the correlation between the sociodemographic and economic factors, using...