Template-Type: ReDIF-Paper 1.0
Author-Name: Alessandro Spelta
Author-X-Name-First: Alessandro 
Author-X-Name-Last: Spelta
Author-Email: alessandro.spelta@unicatt.it
Author-Workplace-Name: Università Cattolica del Sacro Cuore
Author-Workplace-Name: Dipartimento di Economia e Finanza, Università Cattolica del Sacro Cuore


Title: Stock prices prediction via tensor decomposition and links forecast


Abstract: Many complex systems display fluctuations between alternative states in correspondence to tipping points.
These critical shifts are usually associated with generic empirical phenomena such as strengthening 
correlations between entities composing the system. In finance, for instance, market crashes are the consequence
of herding behaviors that make the units of the system strongly correlated, lowering their distances.
Consequently, determining future distances between stocks can be a valuable starting point for predicting
market down-turns. This is the scope of the work. It introduces a multi-way procedure for forecasting stock prices
by decomposing a distance tensor. This multidimensional method avoids aggregation processes that could lead to the
loss of crucial features of the system. The technique is applied to a basket of stocks composing the S&P500 
composite index and to the index itself so as to demonstrate its ability to predict the large market shifts that
arise in times of turbulence, such as the ongoing financial crisis.
Length: 29
Creation-Date: 2016-05
File-URL: http://dipartimenti.unicatt.it/economia-finanza-def041.pdf
File-Format: Application/pdf
File-Function: First version, 2016
Number: def041
Classification-JEL: C02, C63, C63. 
Keywords: Stock prices, Correlations, Tensor Decomposition, Forecast.
Handle: RePEc:ctc:serie1:def041