Does it make sense for a financial predictive models to use each day in a time series as a model input?

I know ML models can accept any number of inputs but does too many cause issues? I’m considering taking a time series of a key metric and using every daily value as an input instead of 24 hour change, 7 day change, etc.

So rather than 10 features I will likely have 1000’s. could this cause issues other than a longer runtime? Would the data become overfit?

datascience financial predictive sense time time series