Empirical evidences on voluntary delisting phenomenon

The paper studies what drives firms to voluntary delist from capital markets and what differs in firms’ behavior and fundamentals between public-to-private transactions and M&A deals with listed corporations. Moreover, I study the relationship between ownership percentage in controlling shareholders’ hands and cumulative returns around the delisting public announcement. I perform my tests both for the Italian and the US markets and I compare the findings to better understand how the phenomenon works in these different institutional environments. Consistent with my expectations, I find that the likelihood of delisting is mainly related to size, underperformance and undervaluation, while shareholders are more rewarded when their companies are involved in PTP transactions than in M&As with public firms.

Predicting market movement with Artificial Neural Networks

This paper presents an application of an Artificial Neural Network (ANN) to the prediction of stock market direction in the US. Using a multilayer perceptron neural network and a backpropagation algorithm for the training process, the model aims at learning the hidden patterns in the daily movement of the S&P500 to correctly identify if the market will be in a Trend Following or Mean Reversion behavior. The ANN is able to produce a successful investment strategy which outperforms the buy and hold strategy, but presents instability in its overall results which compromises its practical application in real life investment decisions.