Return to Answer

Commonmark migration

Source Link

edited Jun 11, 2020 at 14:32

###Quick comments:

Stock prices almost certainly aren't stationary. If you try to do regression with a process containing a unit root, you probably won't be estimating what you think you're estimating. I personally don't understand what you're doing with stock prices and loess.
Generally when one works with stocks etc..., one works with:
- Returns: $ R_t = \frac{S_t + D_t}{S_{t-1}}$ where $D_t$ includes all distributions and $S_t$ is the stork price.
- Log returns: $r_t = \log R_t = \log\left(S_t + D_t \right) - \log S_{t-1}$
  
  You might also have some ratio like the dividends to price ratio, earnings to price ratio or some other ratio that is stationary.
Timing is incredibly important. You need to be meticulously precise about the timing of variables and match what you're doing to the question you're asking.
- For example, if you regress the stock return for January on a survey result for the end of January, a statistically significant result could be due to people reacting to what the stock return was! (eg. what if your end of January survey question was, "how much did the market go up?")
- If you're trying to predict returns at time $t$, it is cheating to have information on the right hand side of your regression that isn't available at time $t$.

###Quick comments:

Stock prices almost certainly aren't stationary. If you try to do regression with a process containing a unit root, you probably won't be estimating what you think you're estimating. I personally don't understand what you're doing with stock prices and loess.
Generally when one works with stocks etc..., one works with:
- Returns: $ R_t = \frac{S_t + D_t}{S_{t-1}}$ where $D_t$ includes all distributions and $S_t$ is the stork price.
- Log returns: $r_t = \log R_t = \log\left(S_t + D_t \right) - \log S_{t-1}$
  
  You might also have some ratio like the dividends to price ratio, earnings to price ratio or some other ratio that is stationary.
Timing is incredibly important. You need to be meticulously precise about the timing of variables and match what you're doing to the question you're asking.
- For example, if you regress the stock return for January on a survey result for the end of January, a statistically significant result could be due to people reacting to what the stock return was! (eg. what if your end of January survey question was, "how much did the market go up?")
- If you're trying to predict returns at time $t$, it is cheating to have information on the right hand side of your regression that isn't available at time $t$.

Stock prices almost certainly aren't stationary. If you try to do regression with a process containing a unit root, you probably won't be estimating what you think you're estimating. I personally don't understand what you're doing with stock prices and loess.
Generally when one works with stocks etc..., one works with:
- Returns: $ R_t = \frac{S_t + D_t}{S_{t-1}}$ where $D_t$ includes all distributions and $S_t$ is the stork price.
- Log returns: $r_t = \log R_t = \log\left(S_t + D_t \right) - \log S_{t-1}$
  
  You might also have some ratio like the dividends to price ratio, earnings to price ratio or some other ratio that is stationary.
Timing is incredibly important. You need to be meticulously precise about the timing of variables and match what you're doing to the question you're asking.
- For example, if you regress the stock return for January on a survey result for the end of January, a statistically significant result could be due to people reacting to what the stock return was! (eg. what if your end of January survey question was, "how much did the market go up?")
- If you're trying to predict returns at time $t$, it is cheating to have information on the right hand side of your regression that isn't available at time $t$.

Source Link

created Feb 28, 2017 at 20:55

###Quick comments:

Stock prices almost certainly aren't stationary. If you try to do regression with a process containing a unit root, you probably won't be estimating what you think you're estimating. I personally don't understand what you're doing with stock prices and loess.
Generally when one works with stocks etc..., one works with:
- Returns: $ R_t = \frac{S_t + D_t}{S_{t-1}}$ where $D_t$ includes all distributions and $S_t$ is the stork price.
- Log returns: $r_t = \log R_t = \log\left(S_t + D_t \right) - \log S_{t-1}$
  
  You might also have some ratio like the dividends to price ratio, earnings to price ratio or some other ratio that is stationary.
Timing is incredibly important. You need to be meticulously precise about the timing of variables and match what you're doing to the question you're asking.
- For example, if you regress the stock return for January on a survey result for the end of January, a statistically significant result could be due to people reacting to what the stock return was! (eg. what if your end of January survey question was, "how much did the market go up?")
- If you're trying to predict returns at time $t$, it is cheating to have information on the right hand side of your regression that isn't available at time $t$.