Data science approach to stock prices forecasting in Indonesia during Covid-19 using Long Short-Term Memory (LSTM)

Budiharto, Widodo

doi:10.1186/s40537-021-00430-0

Cited by 80 publications

(38 citation statements)

References 6 publications

(3 reference statements)

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“…Moreover, in this study, we only used two well-known forecast error criteria as the performance evaluation metrics, namely the RMSE and MAPE. Other criteria, including the R2 score or coefficient of determination, could also be applied to get better and comprehensive analysis results (21).…”

Section: Discussionmentioning

confidence: 99%

Predicting LQ45 Financial Sector Indices using RNN-LSTM

Hansun

Young

2021

Preprint

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As one of the most popular financial market instruments, the stock has formed one of the most massive and complex financial markets in the world. It could handle millions of transactions within a short period and highly unpredictable. In this study, we aim to implement a famous Deep Learning method, namely the Long Short-Term Memory (LSTM) networks, for the stock price prediction. We limit the stocks to those that are included in the LQ45 financial sectors indices, i.e., BBCA, BBNI, BBRI, BBTN, BMRI, and BTPS. We propose to use a simple three layers LSTM network architecture in predicting the stocks’ closing prices and found that the prediction results fall in the reasonable forecasting category. It is worthy to note that two of the considered stocks, i.e., BBCA and BMRI, have the lowest MAPE values at 19.1020 and 18.6135 which fall in the good forecasting results. Hence, the proposed LSTM model is recommended to be used on those two stocks.

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Section: Discussionmentioning

confidence: 99%

Predicting LQ45 Financial Sector Indices using RNN-LSTM

Hansun

Young

2021

Preprint

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“…to get better and comprehensive analysis results [29]. Another possible future work is to compare the results of this study with other technical approaches, such as weighted exponential moving average [30] and double exponential smoothing [31] methods.…”

Section: Fundingmentioning

confidence: 99%

Predicting LQ45 financial sector indices using RNN-LSTM

Hansun

Young

2021

J Big Data

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As one of the most popular financial market instruments, the stock has formed one of the most massive and complex financial markets in the world. It could handle millions of transactions within a short period of time and highly unpredictable. In this study, we aim to implement a famous Deep Learning method, namely the long short-term memory (LSTM) networks, for the stock price prediction. We limit the stocks to those that are included in the LQ45 financial sectors indices, i.e., BBCA, BBNI, BBRI, BBTN, BMRI, and BTPS. Rather than using too deep network architecture, we propose using a simple three-layer LSTM network architecture to predict the stocks’ closing prices. We found that the prediction results fall in the reasonable forecasting category. Moreover, it is worth noting that two of the considered stocks, namely, BBCA and BMRI, have the lowest MAPE values at 19.1020 and 18.6135, which fall in the good forecasting results. Hence, the proposed LSTM model is most recommended to be used on those two stocks.

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“…The proposed model outperforms SVM and Multiple Kernel Learning (MKL) used as a benchmark. In [ 18 ] Budiharto propose an LSTM-based approach for stock price forecasting in Indonesia. Yadav et al [ 19 ] Propose an optimized LSTM for Indian stock market forecasts.…”

Section: Related Workmentioning

confidence: 99%

An LSTM and GRU based trading strategy adapted to the Moroccan market

Touzani

Douzi

2021

J Big Data

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Forecasting stock prices is an extremely challenging job considering the high volatility and the number of variables that influence it (political, economical, social, etc.). Predicting the closing price provides useful information and helps the investor make the right decision. The use of deep learning and more precisely of recurrent neural networks (RNNs) in stock market forecasting is an increasingly common practice in the literature. Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures are among the most widely used types of RNNs, given their suitability for sequential data. In this paper, we propose a trading strategy designed for the Moroccan stock market, based on two deep learning models: LSTM and GRU to predict the closing price in the short and medium term respectively. Decision rules for buying and selling stocks are implemented based on the forecasting given by the two models, then over four 3-year periods, we simulate transactions using these decision rules with different settings for each stock. The returns obtained will be used to estimate an expected return. We only hold stocks that outperform a benchmark index (expected return > threshold). The random search is then used to choose one of the available parameters and the performance of the portfolio built from the selected stocks will be tested over a further period. The repetition of this process with a variation of portfolio size makes it possible to select the best possible combination of stock each with the optimized parameter for the decision rules. The proposed strategy produces very promising results and outperforms the performance of indices used as benchmarks in the local market. Indeed, the annualized return of our strategy proposed during the test period is 27.13%, while it is 0.43% for Moroccan all share Indice (MASI), 15.24% for the distributor sector indices, and 19.94% for the pharmaceutical industry indices. Noted that brokerage fees are estimated and subtracted for each transaction. which makes the performance found even more realistic.

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Data science approach to stock prices forecasting in Indonesia during Covid-19 using Long Short-Term Memory (LSTM)

Cited by 80 publications

References 6 publications

Predicting LQ45 Financial Sector Indices using RNN-LSTM

Predicting LQ45 Financial Sector Indices using RNN-LSTM

Predicting LQ45 financial sector indices using RNN-LSTM

An LSTM and GRU based trading strategy adapted to the Moroccan market

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