Predicting next day direction of stock price movement using machine learning methods with persistent homology: Evidence from Kuala Lumpur Stock Exchange
Predicting direction of stock price movement is notably important to provide a better
guidance to assist market participants in making their investment decisions. This study …
guidance to assist market participants in making their investment decisions. This study …
LSTM in algorithmic investment strategies on BTC and S&P500 index
We use LSTM networks to forecast the value of the BTC and S&P500 index, using data from
2013 to the end of 2020, with the following frequencies: daily, 1 h, and 15 min data. We …
2013 to the end of 2020, with the following frequencies: daily, 1 h, and 15 min data. We …
A correlation-embedded attention module to mitigate multicollinearity: An algorithmic trading application
Algorithmic trading is a common topic researched in the neural network due to the
abundance of data available. It is a phenomenon where an approximately linear relationship …
abundance of data available. It is a phenomenon where an approximately linear relationship …
[PDF][PDF] A Hybrid Model to Forecast Stock Trend Using Support Vector Machine and Neural Networks
JS Vaiz, M Ramaswami - International Journal of Engineering …, 2016 - academia.edu
Machine learning methods have trouble when dealing with large number of input features,
which is posing an interesting challenge for researchers. Pre-processing of the data is very …
which is posing an interesting challenge for researchers. Pre-processing of the data is very …
Market efficiency based on unconventional technical trading strategies in Malaysian stock market
This study examines the efficiency of Malaysian stock market based on the effectiveness of
unconventional technical trading strategies which combine buy recommendation of …
unconventional technical trading strategies which combine buy recommendation of …
A correlation-embedded attention approach to mitigate multicollinearity in foreign exchange data using LSTM
MHS Leow - 2023 - eprints.utar.edu.my
Technologies currently drive the collection of big data in various fields, including algorithmic
trading. This leads to a notable increase in the collection and storage of variables and data …
trading. This leads to a notable increase in the collection and storage of variables and data …
[PDF][PDF] A Study of Soft Computing Models for Stock Market Forecasting
JS VAIZ - 2019 - mkuniversity.ac.in
A Study of Soft Computing Models for Stock Market Forecasting Page 1 A Study of Soft Computing
Models for Stock Market Forecasting Synopsis of the Thesis submitted to Madurai Kamaraj …
Models for Stock Market Forecasting Synopsis of the Thesis submitted to Madurai Kamaraj …