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High-frequency Trading AI-based high-frequency trading (HFT) emerges as the undisputed champion for accurately predicting stock prices. The AI algorithms execute trades within milliseconds, allowing investors and financial institutions to capitalize on minuscule price discrepancies.
The result has shown that Attention-LSTM beats all other models in terms of prediction error and shows much higher return in our trading strategy over other models. Furthermore, we discovered that the stacked-LSTM model does not improve the predictive power over LSTM, even though it has more complex model structure.
Linear Regression This method examines historical stock price data and various relevant factors to create a simple linear equation that predicts future prices based on past trends. Its useful for short-term predictions when theres a linear relationship between factors.
Across all forecasts, accuracy was worse than the flip of a coinon average, just under 47%. The distribution of forecasting accuracy by the gurus looked very much like the bell curvewhat you would expect from random outcomes. The highest accuracy score was 68% and the lowest was 22%.
AltIndex We found that AltIndex is the most accurate stock predictor for 2024. Unlike other providers in this space, AltIndex relies on alternative data points, such as social media sentiment and website analytics. It also uses artificial intelligence to convert its findings into risk-averse stock picks.
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AltIndex We found that AltIndex is the most accurate stock predictor for 2024. Unlike other providers in this space, AltIndex relies on alternative data points, such as social media sentiment and website analytics. It also uses artificial intelligence to convert its findings into risk-averse stock picks.
Besides, it has shown that in addition to Recurrent Neural Network, the convolutional neural network can also solve the problem like stock movement prediction or other predictions in financial areas thanks to its ability of capturing micro-change of data at different time.
This module predicts the average trend of the next three days from day t and achieves 66.32% accuracy. Although they have proved the effectiveness of sentiment analysis by improving prediction performance, they have not utilized the strength of the LSTM model by passing input data of succeeding days.

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