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FORECASTING STOCK MARKET DYNAMICS USING BIDIRECTIONAL LONG SHORT-TERM MEMORY

D. Park & Doojin Ryu

Romanian Journal of Economic Forecasting2021article
ABDC C
Weight
0.46

What the paper says

This study forecasts stock market dynamics using machine learning techniques. Specifically, we use long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM) networks to predict the spot index return and implied volatility series in the Korean market. The Bi-LSTM model exhibits better out-of-sample forecasting performance than the LSTM and classic autoregressive models do, reflecting the fact that the Bi-LSTM model learns data patterns more accurately through a bidirectional process. The Bi-LSTM model with the longest time lag (i.e., 22 days) exhibits the best performance in predicting returns and volatility over the entire sample period. In contrast, during the global financial crisis and COVID-19 pandemic periods, when the stock market dynamics are unstable, Bi-LSTM models with shorter time lags (i.e., five or ten days) predict volatility more accurately.

7 citations

Cite this paper

@article{d.2021,
  title        = {{FORECASTING STOCK MARKET DYNAMICS USING BIDIRECTIONAL LONG SHORT-TERM MEMORY}},
  author       = {D. Park & Doojin Ryu},
  journal      = {Romanian Journal of Economic Forecasting},
  year         = {2021},
}

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Evidence weight

0.46

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.30 × 0.4 = 0.12
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.