118 citations · 152 across the 6 of their papers we have counts for
7 papers
Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading
Adamantios Ntakaris, Gbenga Ibikunle
High-frequency trading (HFT) has transformed modern financial markets, making reliable short-term price forecasting models essential. In this study, we present a novel approach to…
Online High-Frequency Trading Stock Forecasting with Automated Feature Clustering and Radial Basis Function Neural Networks
Adamantios Ntakaris, Gbenga Ibikunle
This study presents an autonomous experimental machine learning protocol for high-frequency trading (HFT) stock price forecasting that involves a dual competitive feature importanc…
Optimum Output Long Short-Term Memory Cell for High-Frequency Trading Forecasting
Adamantios Ntakaris, Moncef Gabbouj, Juho Kanniainen
High-frequency trading requires fast data processing without information lags for precise stock price forecasting. This high-paced stock price forecasting is usually based on vecto…
Mid-price Prediction Based on Machine Learning Methods with Technical and Quantitative Indicators
Adamantios Ntakaris, Juho Kanniainen, Moncef Gabbouj +1
Stock price prediction is a challenging task, but machine learning methods have recently been used successfully for this purpose. In this paper, we extract over 270 hand-crafted fe…
Feature Engineering for Mid-Price Prediction with Deep Learning
Adamantios Ntakaris, Giorgio Mirone, Juho Kanniainen +2
Mid-price movement prediction based on limit order book (LOB) data is a challenging task due to the complexity and dynamics of the LOB. So far, there have been very limited attempt…
Machine Learning for Forecasting Mid Price Movement using Limit Order Book Data
Paraskevi Nousi, Avraam Tsantekidis, Nikolaos Passalis +5
Forecasting the movements of stock prices is one the most challenging problems in financial markets analysis. In this paper, we use Machine Learning (ML) algorithms for the predict…
Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methods
Adamantios Ntakaris, Martin Magris, Juho Kanniainen +2
Managing the prediction of metrics in high-frequency financial markets is a challenging task. An efficient way is by monitoring the dynamics of a limit order book to identify the i…