Idier Julien (2008), “Long term vs Short Term CoMovements in Stock Markets — The Use of Markov-Switching Multifractal Models”, Working Paper , Banque de France & Université de Paris 1.Idier, J. 2011. "Long-term vs. Short-term Comovements in Stock Markets: The Use of Markov-...
Idier Julien (2008), “Long term vs Short Term CoMovements in Stock Markets — The Use of Markov-Switching Multifractal Models”, Working Paper , Banque de France & Université de Paris 1.Idier, J. 2011. "Long-term vs. Short-term Comovements in Stock Markets: The Use of Markov-...
In this work, the development and implementation of a computational tool based on the Long Short-Term Memory method were showed. The code was written in python, and consist of a recurrent neural network used in the field of deep learning... E López,A Francisco - 《Ssrn Electronic Journal》...
preferred stocks, and bonds. However, the greater risk comes with a higher potential for rewards. Over the long term, stocks tend to outperform other investments but in the short term have more volatility.10
Your goals will influence your investment strategy. For instance, long-term goals like retirement might allow for more aggressive, growth-oriented investments, while short-term goals may require a moreconservativeapproach. Assess your risk tolerance: This refers to how much market volatility you can ...
The present study examines the short term dynamics and long term equilibrium relationship among the stock markets of 17 countries in Western Europe as well as the world market, using time series techniques. Weekly returns of market benchmark indices of t
the stock price may also shrink, but the situation of the uptrend will not last too long. Otherwise, the average position cost can not be raised, and the stock market is short of continuous upward momentum.Therefore, short term operation must choose stock with quantity, especially for stocks ...
This study introduces an augmented Long-Short Term Memory (LSTM) neural network architecture, integrating Symbolic Genetic Programming (SGP), with the objective of forecasting cross-sectional price returns across a comprehensive dataset comprising 4500 listed stocks in the Chinese market over the period ...
Different combinations of short and long horizons as well as definitions of excess returns, for example, concerning the traditional short-term interest rate but also the inflation, are easily accommodated in our model. Keywords: finance; investment analysis; stock returns; cross-validation; variation ...
The time series information, extracted using long short-term memory (LSTM), and relationship information, extracted with a graph convolutional network (GCN), are integrated to predict stock prices. The TSRM was tested in the Chinese Shanghai and Shenzhen stock markets, with results showing an ...