Cross-correlation analysis is a powerful tool for understanding the mutual dynamics of time series.This study introduces a new method for predicting the future state of synchronization of the dynamics of two financial time series. To this end, we use the cross-recurrence plot analysis as a...

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 underlying's return, whereas gradual changes in implied volatility would indicate market inefficiency. Using...

We develop a novel, option-based approach for detecting intraday jumps in stock prices. One of the components involved in intraday jump detection is instantaneous volatility, by which intraday returns are scaled. The existing intraday jump detection approaches assume that volatility does not...

This paper proposes a new method for predicting jump arrivals in stock markets with high-frequency limit order book data. We introduce a new model architecture, based on Convolutional Long Short-Term Memory with attention, to apply time series representation learning with memory and to focus the...

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 features (factors) inspired by technical and quantitative analysis and tested their validity on short-term mid-price...

We find that investors' future trading decisions are driven by the patterns of their social neighborhood and the trading activity therein. Moreover, we provide evidence that investors weigh their social connections differently in terms of information transfer. Methodologically, we tackle the...