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The research use LSTM algorithm and technical Evaluation indicators to forecast price trends within the Vietnamese stock market. To perform the above research aim, the authors made use of research data, like price record of VN-Index and stocks in group VN-30, collected from secondary knowledge sources at Web site vietstock.vn. The particular list of stocks to be gathered is as follows:
The K-means is a commonly applied unsupervised learning algorithm for cluster analysis. Its principal purpose would be to divide a set of n observations right into a set of k clusters, with Every single observation getting assigned on the cluster whose mean is closest to it and performing to be a prototype for your cluster [11]. The imply on the observations in that individual cluster serves given that the cluster’s center [eleven].
Prices are higher from the months which happen to be quarter conclude as compared to that in the non-quarter end months.
These quarterly results influence the stock prices greatly Which is the reason We have now additional this element because this can be a helpful aspect with the learning model.
One method to ensure the viability of ML from a company perspective should be to formulate exact needs and objectives, examine the organization’s present assets, and only then initiate a project.
techniques and knowledge for stock market forecasting: A literature review,” Professional Programs with Purposes
The RMSE value is greater when compared to the previous approach, displaying that linear regression has carried out inadequately. Enable’s think about the plot and realize why linear regression hasn't finished well:
Multifactor prediction model for stock market Investigation based on deep learning techniques Kangyi Wang
Furthermore, the random wander speculation states that a stock’s price improvements independently of its background, in other words, tomorrow’s price will rely only on tomorrow’s information in spite of these days’s price (Burton, 2018). Both of these hypotheses build that there is no suggests of accurately predicting stock prices.
When speaking about the stock market, with its inherent and complexity, the predictability of stock returns has constantly been a topic of debate that attracts Significantly research. Fama (1970) postulates the efficient market speculation that establishes that The present price of the asset often reflects all prior information and facts available to it straight away.
Aishwarya Singh Final Current : fourteen Apr, 2025 12 min browse Predicting stock market prices is complicated resulting from a great number of aspects, from economic information to human actions, generating prices risky and difficult to forecast. Deep learning will help read more by analyzing styles in historic info, enterprise bulletins, and economic reports to uncover insights regular techniques miss out on.
Jia (2016) discussed the performance of LSTM in stock price prediction research and confirmed that LSTM is an efficient strategy to predict stock returns. The actual-time wavelet completely transform was combined with the LSTM network to predict the East Asian stock index, which corrected check here some logic defects in former studies. As opposed Together with the model using only LSTM, the combined model can greatly Enhance the prediction diploma as well as regression mistake is tiny. Also, Gülmez (2023) thought that the LSTM model is ideal for time sequence facts on financial markets inside the context of stock prices recognized on source and demand from customers interactions.
More studies can use the conclusions from this research to reference and Examine, Appraise the effects, and supplement the robustness from the academic conclusions. In addition, the research and application of actual and up to date stock market data has higher sensible applicability. The conclusions from this research could be extensively applied by investors, financial establishments, and governing administration market regulators.