A Deep Learning Model for Predicting Stock Prices in Tanzania

Joseph, Samuel; Mduma, Neema; Nyambo, Devotha G.

doi:10.48084/etasr.5710

Cited by 6 publications

(4 citation statements)

References 23 publications

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“…In contrast to the previous models, the LSTM model [23] demonstrated exceptional performance by perfectly replicating the test data, as shown in Figure 8. The predictions generated by the LSTM model are closely aligned with the actual values, indicating its ability to capture intricate temporal dependencies and complex patterns within the data.…”

Section: Resultsmentioning

confidence: 84%

Prediction of Agricultural Commodity Prices using Big Data Framework

Rana,

Farooq,

Kazi

et al. 2024

Eng. Technol. Appl. Sci. Res.

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The agriculture sector plays a crucial role in the economy of Pakistan, contributing significantly to the Gross Domestic Product (GDP) and the employment rate. However, this sector faces challenges such as climate change, water scarcity, and low productivity, which have a direct impact on agricultural commodity prices. Accurate forecasting of commodity prices is essential for farmers, traders, and policymakers to make informed decisions and improve economic outcomes. This paper explores the use of a big data framework for agricultural commodity price forecasting in Pakistan, using a historical dataset on commodity prices in various Pakistani cities from 2007 to 2022 and Apache Spark to preprocess and clean the data. Based on historical spinach prices in Vehari City, the machine learning models Auto-Regressive Moving Average (ARIMA), Random Forest, and Long-Short-Term Memory (LSTM) were applied to price trends, and their performance was compared using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and squared correlation coefficient (R2). LSTM outperformed ARIMA and Random Forest with a higher R2 value of 0.8 and the lowest MAE of 125.29. Such predictions can help farmers to effectively plan crop cultivation and traders to make well-informed decisions.

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Section: Resultsmentioning

confidence: 84%

Prediction of Agricultural Commodity Prices using Big Data Framework

Rana,

Farooq,

Kazi

et al. 2024

Eng. Technol. Appl. Sci. Res.

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“…The risks are the dissolution of the company (liquidation) and falling stock prices. As a result, someone is required to analyze the value of shares first before purchasing stocks to get maximum profits and minimize losses (Dewan Standar Akuntansi Keuangan IAI, 2018;Husnul et al, 2017;Jaggi et al, 2021;Joseph et al, 2023;Napitupulu, 2021).…”

Section: The Implementation Of Artificial Neural Network For Stock Pr...mentioning

confidence: 99%

The Implementation of Artificial Neural Networks for Stock Price Prediction

Akbar Maulana,

Enny Itje Sela

2023

jeei

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This research is based on a problem that is difficult to predict stock prices, especially for beginners. Stock prices are hard to predict because they are fluctuating. Users will be easier to predict stock prices through artificial neural networks using Multilayer Perceptron. This MLP is a variant of an artificial neural network and is a development of perceptron. The selection of the Multilayer Perceptron method is based on the ability to solve various problems both classification and regression. The research conducted by the author is a regression problem as the MLP is tasked to predict the close price or closing price of stock after seven days. The results of the model built are able to predict stock prices and produce good accuracy because the resulting RMSE value produced 0.042649862994352014, which is close to 0. Keywords: Machine Learning, Stock Price Prediction, Neural Network, Multilayer Perceptron, MLP.

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“…In [24], support vector machines, MLP, and logistic regression with technical indicators were used for NIFTY50 index prediction. In [25], a rigorous selection process was used to identify dynamic stocks on the Dar es Salaam Stock Exchange, using the LSTM and GRU deep learning models to forecast closing prices. The LSTM model outperformed GRU with a lower RMSE of 4.7524 and an MAE of 2.4377.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Stock Closing Prices using Transformer Learning

Mian

2023

Eng. Technol. Appl. Sci. Res.

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Predicting stock markets remains a critical and challenging task due to many factors, such as the enormous volume of generated price data, instant price data changes, and sensitivity to human sentiments, wars, and natural disasters. Since the previous three years of the COVID-19 pandemic, forecasting stock markets is more difficult, complex, and problematic for stock market analysts. However, technical analysts of the stock market and academic researchers are continuously trying to develop innovative and modern methods for forecasting stock market prices, using statistical techniques, machine learning, and deep learning-based algorithms. This study investigated a Transformer sequential-based approach to forecast the closing price for the next day. Ten sliding window timesteps were used to forecast next-day stock closing prices. This study aimed to investigate reliable techniques based on stock input features. The proposed Transformer-based method was compared with ARIMA, Long-Short Term Memory (LSTM), and Random Forest (RF) algorithms, showing its outstanding results on Yahoo Finance data, Facebook Intra data, and JPMorgan's Intra data. Each model was evaluated using Mean Absolute Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE).

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A Deep Learning Model for Predicting Stock Prices in Tanzania

Cited by 6 publications

References 23 publications

Prediction of Agricultural Commodity Prices using Big Data Framework

Prediction of Agricultural Commodity Prices using Big Data Framework

The Implementation of Artificial Neural Networks for Stock Price Prediction

Evaluation of Stock Closing Prices using Transformer Learning

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