Juho Kanniainen
Juho Kanniainen
Professor of financial engineering, Tampere University
Verified email at tuni.fi - Homepage
Title
Cited by
Cited by
Year
Forecasting stock prices from the limit order book using convolutional neural networks
A Tsantekidis, N Passalis, A Tefas, J Kanniainen, M Gabbouj, A Iosifidis
2017 IEEE 19th Conference on Business Informatics (CBI) 1, 7-12, 2017
812017
Estimating and using GARCH models with VIX data for option valuation
J Kanniainen, B Lin, H Yang
Journal of Banking & Finance 43, 200-211, 2014
602014
Using deep learning to detect price change indications in financial markets
A Tsantekidis, N Passalis, A Tefas, J Kanniainen, M Gabbouj, A Iosifidis
2017 25th European Signal Processing Conference (EUSIPCO), 2511-2515, 2017
502017
Temporal attention-augmented bilinear network for financial time-series data analysis
DT Tran, A Iosifidis, J Kanniainen, M Gabbouj
IEEE transactions on neural networks and learning systems 30 (5), 1407-1418, 2018
422018
Investigating adoption of free beta applications in a platform‐based business ecosystem
SJ Mäkinen, J Kanniainen, I Peltola
Journal of Product Innovation Management 31 (3), 451-465, 2014
362014
A fast universal self-tuned sampler within Gibbs sampling
L Martino, H Yang, D Luengo, J Kanniainen, J Corander
Digital Signal Processing 47, 68-83, 2015
302015
Tensor representation in high-frequency financial data for price change prediction
DT Tran, M Magris, J Kanniainen, M Gabbouj, A Iosifidis
2017 IEEE Symposium Series on Computational Intelligence (SSCI), 1-7, 2017
292017
Time-series classification using neural bag-of-features
N Passalis, A Tsantekidis, A Tefas, J Kanniainen, M Gabbouj, A Iosifidis
2017 25th European Signal Processing Conference (EUSIPCO), 301-305, 2017
292017
Benchmark dataset for mid‐price forecasting of limit order book data with machine learning methods
A Ntakaris, M Magris, J Kanniainen, M Gabbouj, A Iosifidis
Journal of Forecasting 37 (8), 852-866, 2018
252018
Jump and volatility dynamics for the S&P 500: Evidence for infinite-activity jumps with non-affine volatility dynamics from stock and option markets
H Yang, J Kanniainen
Review of Finance 21 (2), 811-844, 2017
232017
Benchmark dataset for mid-price prediction of limit order book data
A Ntakaris, M Magris, J Kanniainen, M Gabbouj, A Iosifidis
arXiv preprint arXiv:1705.03233, 2017
222017
Limit order books and liquidity around scheduled and non-scheduled announcements: Empirical evidence from nasdaq nordic
M Siikanen, J Kanniainen, J Valli
Finance Research Letters 21, 264-271, 2017
202017
Temporal bag-of-features learning for predicting mid price movements using high frequency limit order book data
N Passalis, A Tefas, J Kanniainen, M Gabbouj, A Iosifidis
IEEE Transactions on Emerging Topics in Computational Intelligence, 2018
18*2018
Use of distributed computing in derivative pricing
J Kanniainen, R Piche, T Mikkonen
International Journal of Electronic Finance 3 (3), 270, 2009
172009
Forecasting jump arrivals in stock prices: new attention-based network architecture using limit order book data
Y Mäkinen, J Kanniainen, M Gabbouj, A Iosifidis
Quantitative Finance 19 (12), 2033-2050, 2019
16*2019
What drives the sensitivity of limit order books to company announcement arrivals?
M Siikanen, J Kanniainen, A Luoma
Economics Letters 159, 65-68, 2017
162017
Can properly discounted projects follow geometric Brownian motion?
J Kanniainen
Mathematical methods of operations research 70 (3), 435, 2009
162009
Multilayer aggregation with statistical validation: Application to investor networks
K Baltakys, J Kanniainen, F Emmert-Streib
Scientific reports 8 (1), 1-12, 2018
152018
Forecasting the diffusion of innovation: a stochastic Bass model with log-normal and mean-reverting error process
J Kanniainen, SJ Mäkinen, R Piché, A Chakrabarti
IEEE Transactions on Engineering Management 58 (2), 228-249, 2010
152010
Facebook drives behavior of passive households in stock markets
M Siikanen, K Baltakys, J Kanniainen, R Vatrapu, R Mukkamala, ...
Finance Research Letters 27, 208-213, 2018
142018
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Articles 1–20