Predictive AI is increasingly used to guide decisions on agents. I show that even a bias-neutral predictive AI can potentially amplify exogenous (human) bias in settings where the predictive AI represents a cost-adjusted precision gain to unbiased predictions, and the final judgments are made by...

Forecasting macroeconomic variables is key to developing a view on a country's economic outlook.Most traditional forecasting models rely on fitting data to a pre-specified relationship between inputand output variables, thereby assuming a specific functional and stochastic process underlying...

The Prediction of a dynamic, volatile and unpredictable stock market has been a challenging issue for the researchers over the past few years. This paper discusses stock market related technical indicators, computing mathematical models , most preferred algorithms used in data science industries...

Background: Statutory health insurers in Germany offer a variety of disease management, prevention and health promotion programs to their insurees. Identifying patients with a high probability of leaving these programs prematurely helps insurers to offer better support to those at the highest...

This paper evaluates multiple machine learning algorithms for forecasting the 30 second forward spread of ETH/USD. The experiments include using boosted decision trees (LightGBM, XGBoost), higher forms of linear regression and Ensembling. The Ethereum orderbook was constructed through the...

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...