249 citations · 586 across the 43 of their papers we have counts for
4 papers · 1 filter
Option market (in)efficiency and implied volatility dynamics after return jumps
Juho Kanniainen, Martin Magris
In informationally efficient financial markets, option prices and this implied volatility should immediately be adjusted to new information that arrives along with a jump in underl…
Forecasting of Jump Arrivals in Stock Prices: New Attention-based Network Architecture using Limit Order Book Data
Ymir Mäkinen, Juho Kanniainen, Moncef Gabbouj +1
The existing literature provides evidence that limit order book data can be used to predict short-term price movements in stock markets. This paper proposes a new neural network ar…
Using Deep Learning for price prediction by exploiting stationary limit order book features
Avraam Tsantekidis, Nikolaos Passalis, Anastasios Tefas +3
The recent surge in Deep Learning (DL) research of the past decade has successfully provided solutions to many difficult problems. The field of quantitative analysis has been slowl…
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…