Quantitative Analysis and Sentiment Analysis for Stock Price Forecast: The Case of Colombo Stock Exchange

Pavithya, M. B. D.; Perera, G. S. D.; Munasinghe, S. L.; Karunarathna, Suneth Namal

doi:10.1109/iciafs52090.2021.9606034

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…Technical analysis uses historical quantitative values, such as volume and market value, to forecast the exchange. We tend to think that historical stock trend patterns will eventually repeat themselves when we investigate this example [6].…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of the Stock Market Based on Machine Learning and Sentiment Analysis

Jishtu¹,

Prajapati²,

Fiaidhi³

2022

Preprint

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<p>Nothing is rock-steady in the stock market, which isa very volatile market. Nevertheless, there are a variety of ways and approaches one may utilise to learn about this dynamic movement and be prepared for it as technology develops. The focus of this essay is on different methods for quickly identifying market trends. The suggested strategy is comprehensive because it includes pre-processing the stock market dataset, a range of feature engineering techniques, and the integration of a customised deep learning-based system for forecasting stock market price patterns. The best and most suggested method for prediction is the model with the least amount of error. In order to conduct this study, we used three distinct models and ran sentiment analysis on news articles mentioning the firm or the stock. The results of this classification have given investors additional information to help them make decisions about where to stake their money as well as clear and incisive insight into the market’s irregular ups and downs.</p>

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Section: Literature Reviewmentioning

confidence: 99%

Prediction of the Stock Market Based on Machine Learning and Sentiment Analysis

Jishtu¹,

Prajapati²,

Fiaidhi³

2022

Preprint

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“…Machine Learning Algorithms: These include Decision Trees, Random Forests, Neural Networks, etc. These algorithms can process large amounts of data, identify patterns and generate predictions; Statistical Arbitrage Strategies: use statistical methods to find price differences or correlations to construct arbitrage trading strategies [3] The results and conclusions of the models vary depending on the model type, input data and parameter settings. It should be emphasized that stock market forecasting is a very complex and uncertain task, and no model can predict stock prices with 100% accuracy.…”

Section: Introductionmentioning

confidence: 99%

Stock Price Analysis and Prediction Method Based on Machine Learning: Taking Apple Inc as an Example

Jin

2023

HBEM

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Stock forecasts are analyses of Apple's future performance based on financial data, market dynamics and macroeconomic factors. However, there are conflicting arguments that the wider the time horizon of the data, the more accurate the forecast. These forecasts are crucial for investment decisions, risk management and corporate governance. Therefore, in this paper, we will use vector autoregressive modelling to compare nine training sets with different time horizons and evaluate these nine sets of predictions by calculating the weights of the corresponding variables in the predictions. Knowledge of machine learning and graphical visualization is used to evaluate the share of five factors affecting stock prices as well as the training time horizon. This paper demonstrates that in the field of stock prediction the closer the time horizon is to the prediction the closer it is to the actual value. At the same time investors should consider multiple factors to diversify the risk.

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