Graded forecasting using an array of bipolar predictions: application of probabilistic neural networks to a stock market index

1998 ◽  
Vol 14 (3) ◽  
pp. 323-337 ◽  
Author(s):  
Steven H Kim ◽  
Se Hak Chun
Keyword(s):  
Neural Networks ◽  
Stock Market ◽  
Probabilistic Neural Networks ◽  
Stock Market Index ◽  
Market Index

Stock market index prediction using neural networks

1994 ◽  
Author(s):  
Darmadi Komo ◽  
Chein-I Chang ◽  
Hanseok Ko
Keyword(s):  
Neural Networks ◽  
Stock Market ◽  
Stock Market Index ◽  
Market Index

NEURAL NETWORKS IN FINANCE AND ECONOMICS FORECASTING

2007 ◽  
Vol 06 (01) ◽  
pp. 113-140 ◽  
Author(s):  
WEI HUANG ◽  
KIN KEUNG LAI ◽  
YOSHITERU NAKAMORI ◽  
SHOUYANG WANG ◽  
LEAN YU
Keyword(s):  
Neural Network ◽  
Economic Growth ◽  
Neural Networks ◽  
Stock Market ◽  
Exchange Rates ◽  
Network Models ◽  
Stock Market Index ◽  
Neural Network Models ◽  
Market Index ◽  
Comparison Results

Artificial neural networks (ANNs) have been widely applied to finance and economic forecasting as a powerful modeling technique. By reviewing the related literature, we discuss the input variables, type of neural network models, performance comparisons for the prediction of foreign exchange rates, stock market index and economic growth. Economic fundamentals are important in driving exchange rates, stock market index price and economic growth. Most neural network inputs for exchange rate prediction are univariate, while those for stock market index prices and economic growth predictions are multivariate in most cases. There are mixed comparison results of forecasting performance between neural networks and other models. The reasons may be the difference of data, forecasting horizons, types of neural network models and so on. Prediction performance of neural networks can be improved by being integrated with other technologies. Nonlinear combining forecasting by neural networks also provides encouraging results.

On the sensibility of the Pairs Trading strategy: the case of the FTS stock market index

2020 ◽  
Vol 38 (3) ◽  
Author(s):  
Ainhoa Fernández-Pérez ◽  
María de las Nieves López-García ◽  
José Pedro Ramos Requena
Keyword(s):  
Standard Deviation ◽  
Stock Market ◽  
Trading Strategy ◽  
Stock Market Index ◽  
Pairs Trading ◽  
Empirical Application ◽  
Statistical Arbitrage ◽  
Market Index ◽  
Standard Deviations ◽  
Using Data

In this paper we present a non-conventional statistical arbitrage technique based in varying the number of standard deviations used to carry the trading strategy. We will show how values of 1 and 1,2 in the standard deviation provide better results that the classic strategy of Gatev et al (2006). An empirical application is performance using data of the FST100 index during the period 2010 to June 2019.

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