|
| Ref |
Methodology |
Dataset |
Performance |
Limitations & Future Work |
|
|
| Ma et al. (2019) |
Hybrid model combining ANN and SVR with dynamic adjustment bias |
China Stock Exchange data (June 8, 2015 – May 26, 2016) |
79% accuracy rate |
Future work could involve more diverse market conditions and cross-market validation |
|
| Liu and Liu, (2018) |
GRU with movement trend-based preprocessing (two-step: trend extraction + discretization) |
Predicting the pattern of stock index movement |
Accuracy improved from 33% to 68% |
Needs validation across different indices and longer time frames |
|
| Tsang, Deng and Xie, (2018) |
Deep LSTM-based time-series prediction |
To predict the next day's closing price, six global market indicators |
Annual profitability up to 200% |
Risk-adjusted returns and robustness under volatile conditions not explored |
|
| Raimundo and Okamoto, (2018) |
SVR-Wavelet hybrid model using DWT for input enhancement |
FOREX market time series data |
Improved financial series prediction accuracy |
Scalability to other financial domains and high-frequency data not addressed |
|
| Althelaya, El-Alfy and Mohammed, (2018) |
Deep RNNs (LSTM and GRU, uni- & bi-directional, stacked) with multivariate input |
S&P500 historical index data |
Superior to shallow networks in short/long-term forecasting |
Needs improved interpretability and computational efficiency for real-time systems |
|
| Beyaz et al. 2018 |
Machine learning with clustering to account for market mood states |
Forecasting selected company stock prices with 126-day horizon |
Mood-based forecasting improved accuracy in 47% cases |
Further study needed on integrating sentiment analysis with fundamental and technical data |
|
| Bakhach, Tsang and Jalalian, (2017) |
Directional Change (DC) framework for trend prediction |
Forex market (EUR/CHF, GBP/CHF, USD/JPY) |
Accuracy often exceeded 80% |
Limited to one independent variable; expansion to multivariate models is needed |
|
