The COVID-19 pandemic led to a great deal of financial uncertainty in the stock market. An
initial drop in March 2020 was followed by unexpected rapid growth over 2021. Therefore …

Recently, various corporate failure prediction models that use machine learning techniques
have received considerable attention. In particular, using a sequence of a company's …

Recent increases in stock price volatility have generated renewed interest in volatility-timing
strategies. Based on high-dimensional models including machine learning, we predict stock …

We use the recent development in deep learning technology to forecast stock prices.
Focusing on image-type big data, we predict future stock prices using a convolutional neural …

This study proposes a hedonic pricing model to efficiently capture the values of locations
without assuming a specific functional form or the factors affecting it. The K-means clustering …

This paper presents a promising approach using gated recurrent unit (GRU) network to
predict bankruptcy based on the whole sequence of financial statements of the companies …

For a very long time, bankruptcy models were considered ahistorical, as they were mostly
based on ratios measured over a single year. However, time is an essential variable that …

Can machine learning improve prediction for seasonal commodity prices? We explore the
effectiveness of a combined method that integrates seasonal trend decomposition using …

An Early Warning System (EWS) is a tool that enables the monitoring of the credit portfolio to
identify clients in financial distress. ARIA is the EWS used by ING to monitor their Wholesale …

After Implementing the machine learning (ML) algos for the prediction of potential defaulters
(someone who can't repay the loan properly and timely) has been connected with way better …