The $k$ period log return is defined as $$r_{t}(k)=log(S_{t}/S_{t-k}),$$ Where $S_{t}$ is the stock closing price at time $t$. For argument sake, assume that by time I mean a stock trading day and historical stock closing prices are known and denoted by $S_{1}, S_{2}, S_{3}, ...., S_{T-1}$. Here are my questions:

 1. I have seen people trying to predict the change in stock price direction e.g. one period log return i.e. $r_{T}(1)=log(S_{T}/S_{T-1})$ is positive or negative based on past historical data. The argument is that they buy if they expect $r_{T}(1)>0$ and they sell if  $r_{T}(1)<0$.

    - (a) Could anyone please tell me in this case that at which point of time during trading day $T$ exactly we should buy the stock and at which point of time we should sell?
    - (b) Should we buy in the morning of day $T$ and then wait until the end of day $T$ to sell it?
    - (c) Can we put a sell order exactly at closing time on day T?
    - (d) Given $k=1$, does this sort of trading strategy fall in the form of "day trading"?
 2. Suppose one predicts $r_{T}(5)=log(S_{T}/S_{T-5})>0$.
    - (a) Does this mean that investor should by the stock at time  $T-5$ i.e. 5 days earlier and hold on to it for the next five days and the sell it in trading day $T$?