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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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You need to assign each of the target variables to their own column and then train a model for each of your forecast horizons library(quantmod) symbol= getSymbols("AAPL",from="2010-03-01", auto.assign=F) close<-Cl(symbol) open<-Op(symbol) lc1<-lag(close) lc2<-lag(close,2) lc3<-lag(close,3) lo1<-lag(open) lo2<-lag(open,2) lo3<-lag(...

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Whenever you are looking to estimate total return, you would use adjusted closing prices. If you are strictly looking for the future stock price, you would use unadjusted closing price. I assume, though, that you are looking to predict the value of holding a stock during a given period, so you would want to use adjusted prices. The only time I've used actual ...

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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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