Applying machine learning algorithms to predict the stock price trend in the stock market – The case of Vietnam

Tran Phuoc · Pham Thi Kim Anh · Phan Huy Tam · Van Chien Nguyen

102 citations0/0 files storedHumanities and Social Sciences Communications

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Stock Market Forecasting Methods Financial Markets and Investment Strategies Forecasting Techniques and Applications

Abstract

Abstract The aims of this study are to predict the stock price trend in the stock market in an emerging economy. Using the Long Short Term Memory (LSTM) algorithm, and the corresponding technical analysis indicators for each stock code include: simple moving average (SMA), convergence divergence moving average (MACD), and relative strength index (RSI); and the secondary data from VN-Index and VN-30 stocks, the research results showed that the forecasting model has a high accuracy of 93% for most of the stock data used, demonstrating the appropriateness of the LSTM model and the test set data is used to evaluate the model’s performance. The research results showed that the forecasting model has a high accuracy of 93% for most of the stock data used, demonstrating the appropriateness of the LSTM model in analyzing and forecasting stock price movements on the machine learning platform.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 102 citations exist upstream.

References

1970Efficient Capital Markets: A Review of Theory and Empirical Work 1992Simple Technical Trading Rules and the Stochastic Properties of Stock Returns 2017Stock market's price movement prediction with LSTM neural networks 2016Deep Learning for Finance: Deep Portfolios 2015A LSTM-based method for stock returns prediction: A case study of China stock market 1988Economic prediction using neural networks: the case of IBM daily stock returns 1998Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks 2023Stock price prediction with optimized deep LSTM network with artificial rabbits optimization algorithm

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.