In the realm of finance and economics, the search for reliable stock predictions remains a focal point for researchers [1]. Contrary to common belief, stock market prices are not merely random guesses, but rather powe...In the realm of finance and economics, the search for reliable stock predictions remains a focal point for researchers [1]. Contrary to common belief, stock market prices are not merely random guesses, but rather powerful tools that allow people to decide where to put their money, proving to be a significant aspect of finance. In this paper, by applying machine learning techniques, we propose to predict stock prices based on trends from previous years’ stock data using learning models, such as Linear Regression, MLP Regressor, Decision Tree Regressor and Random Forest Regressor. To enhance the model’s decision-making capabilities, the model was programmed to decide whether to sell or buy stocks using the predictions from the linear model. If the model anticipates an increase in stock prices, it suggests buying more stocks. On the contrary, if the model predicts a downturn, it suggests selling stocks in order to benefit the investor and enhance profitability. If the investor began with no stocks and $20,000, through the use of our model, the investor was able to make 161.3% profit. In another scenario where the investor holds 200 stocks and $10,000, the investor was able to make a 546.3% profit. Ultimately, the model results in profitable outcomes.展开更多
文摘In the realm of finance and economics, the search for reliable stock predictions remains a focal point for researchers [1]. Contrary to common belief, stock market prices are not merely random guesses, but rather powerful tools that allow people to decide where to put their money, proving to be a significant aspect of finance. In this paper, by applying machine learning techniques, we propose to predict stock prices based on trends from previous years’ stock data using learning models, such as Linear Regression, MLP Regressor, Decision Tree Regressor and Random Forest Regressor. To enhance the model’s decision-making capabilities, the model was programmed to decide whether to sell or buy stocks using the predictions from the linear model. If the model anticipates an increase in stock prices, it suggests buying more stocks. On the contrary, if the model predicts a downturn, it suggests selling stocks in order to benefit the investor and enhance profitability. If the investor began with no stocks and $20,000, through the use of our model, the investor was able to make 161.3% profit. In another scenario where the investor holds 200 stocks and $10,000, the investor was able to make a 546.3% profit. Ultimately, the model results in profitable outcomes.
