Multimodal risk disentanglement, where the model breaks down threats from images and text separately, improves MLLM safety at inference and during fine-tuning.
Regression and Forecasting of U.S. Stock Returns Based on LSTM
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abstract
This paper analyses the investment returns of three stock sectors, Manuf, Hitec, and Other, in the U.S. stock market, based on the Fama-French three-factor model, the Carhart four-factor model, and the Fama-French five-factor model, in order to test the validity of the Fama-French three-factor model, the Carhart four-factor model, and the Fama-French five-factor model for the three sectors of the market. French five-factor model for the three sectors of the market. Also, the LSTM model is used to explore the additional factors affecting stock returns. The empirical results show that the Fama-French five-factor model has better validity for the three segments of the market under study, and the LSTM model has the ability to capture the factors affecting the returns of certain industries, and can better regress and predict the stock returns of the relevant industries. Keywords- Fama-French model; Carhart model; Factor model; LSTM model.
fields
cs.CL 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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DREAM: Disentangling Risks to Enhance Safety Alignment in Multimodal Large Language Models
Multimodal risk disentanglement, where the model breaks down threats from images and text separately, improves MLLM safety at inference and during fine-tuning.