![]() However, numerous studies conducted over the past few decades have shown that it is possible to predict future market prices through techniques such as fundamental analysis, technical analysis, and sentiment analysis. If we assume that the EMH is true, then the random walk hypothesis ( Fama, 1965) states that future news is random and investors cannot use their prior knowledge to generate excess profits. Additionally, the proposed ensemble support vector machine improves the accuracy of stock price movement predictions when compared to the original support vector machine in a series of experiments.Īccording to the efficient market hypothesis (EMH) ( Fama, 1970), the stock market is efficient because it is assumed that all available information has been reflected in stock prices. Through comparing various techniques for generating sentiment, our results show that using the FinBERT model for sentiment analysis yields the best results, with an F1-score that is 4–5% higher than other techniques. Then, it predicts the future movement of SPDR S&P 500 Index Exchange Traded Funds using the rolling window approach to prevent look-ahead bias. This study proposes an ensemble support vector machine for improving the accuracy of stock price movement predictions. To overcome these challenges, this study proposes an alternative approach using FinBERT, a pre-trained language model specifically designed to analyze the sentiment of financial text. However, using investor sentiment on Stocktwits to predict stock price movements may be challenging due to a lack of user-initiated sentiment data and the limitations of existing sentiment analyzers, which may inaccurately classify neutral comments. This study investigates the use of investor sentiment from social media, with a focus on Stocktwits, a social media platform for investors. Investor sentiment plays a crucial role in the stock market, and in recent years, numerous studies have aimed to predict future stock prices by analyzing market sentiment obtained from social media or news. ![]()
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