| 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 | 108 | 2017 |
| 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 | 64 | 2014 |
| 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 | 58 | 2018 |
| 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 | 55 | 2017 |
| 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 | 39 | 2015 |
| 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 | 39 | 2014 |
| 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 | 34 | 2018 |
| 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 | 30 | 2017 |
| 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 | 30 | 2017 |
| 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 | 24* | 2018 |
| 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 | 23 | 2017 |
| 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 | 23 | 2017 |
| 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 | 21 | 2017 |
| 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 | 19* | 2019 |
| Multilayer aggregation with statistical validation: Application to investor networks K Baltakys, J Kanniainen, F Emmert-Streib Scientific reports 8 (1), 1-12, 2018 | 19 | 2018 |
| Using deep learning for price prediction by exploiting stationary limit order book features A Tsantekidis, N Passalis, A Tefas, J Kanniainen, M Gabbouj, A Iosifidis Applied Soft Computing, 106401, 2020 | 18 | 2020 |
| Use of distributed computing in derivative pricing J Kanniainen, R Piche, T Mikkonen International Journal of Electronic Finance 3 (3), 270-283, 2009 | 17 | 2009 |
| 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 | 16 | 2018 |
| What drives the sensitivity of limit order books to company announcement arrivals? M Siikanen, J Kanniainen, A Luoma Economics Letters 159, 65-68, 2017 | 16 | 2017 |
| Can properly discounted projects follow geometric Brownian motion? J Kanniainen Mathematical methods of operations research 70 (3), 435, 2009 | 16 | 2009 |
