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Sorry, but despite being used as a popular example in machine learning, no one has ever achieved a stock market prediction. It does not work for several reasons (check random walk by Fama and quite a bit of others, rational decision making fallacy, wrong assumptions ...), but the most compelling one is that if it would work, someone would be able to become ...


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I cannot seem to find that article for free, so here is a more generalized answer. 1.what are the hidden states and what are the observation states. The hidden states are said to be that of an unobserved parameter process following the Markov property. The observation states are generated by the hidden parameter process. The parameter process changes ...


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I would recommend you the following econometrics textbook Basics Econometrics, with a particular focus on multinomial logit / probit models. I guess the challenging part in your case will consist of specifying the exogenous variables, collecting data, before doing the computations. The latter being quick to perform. As far as I am concerned it's better to ...


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Model them individualy and as a group. When you model them as a group you are essentially building a stock index that you can compare the performance of individual stocks to and can then calculate a subgroup beta for each stock. You can also calculate a beta coefficient for the group as a whole to the wider market. Since I assume that you are modeling them ...



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