This article uses the S&P 500 index as an example to analyze the impact of macroeconomic
factors on stock returns. By using the S&P 500 index data from 1968 to 2020 as the …

Due to the strong noise, nonlinearity and volatility of time series data for stock prices, in
order to provide better results in stock price forecasting, the paper brings up a CNN-Relstm …

Der Einfluss ausgewählter ökonomischer Variablen auf die Leistung von LSTM-Modellen zur
Aktienkursprognose Page 1 Der Einfluss ausgewählter ökonomischer Variablen auf die Leistung …

As I approach the completion of my Master of Science in Business Analytics at Vrije
Universiteit Amsterdam, I reflect on the incredible but tough journey that has brought me to …

Stock price prediction is an area of current research due to the intricate data structure and
many contributing elements. Many modern financial applications incorporate nonlinear and …

This research takes the constituent stocks of FTSE TWSE Taiwan 50 Index as the forecasting
targets and uses the random forest algorithm and regression tree to predict the return rate for …

Retail site selection is a critical stage for a new retailer since it helps them to decide which
locations have the best chance of delivering a good return on investment. Most of the new …

Purpose/Objective The main objective of this study was to predict inflation rates in Ghana
using standard econometric methods such as linear multivariate OLS regression, and the …

This chapter has been set out in the style of a tutorial and provides an overview of some
common features of Neo-Classical Quant (NCQ) models and newly emerging paradigms of …

Succeeding in accurately forecasting stock market indices is a sought-after capability for
investors, traders, and policymakers. However, many financial researchers regard financial …