Explaining the bid-ask spread

This file uses the same data on bid-ask spreads (tradecost) and candidate determinants used in lab2 HW2.
We are interested in the relation between the average bid-ask spreads on stocks and the characteristics of the
corresponding companies.
The data file lab2.xls contains information for the 100 stocks in the S$&$P 100 index.
Our variable of interest (the $Y$ variable) is the bid-ask spread (constructed as an average over the day) – or
tradecost – of the
S&P100 stocks. The explanatory, or $X$, variables are:

1. log volatility – The log of the daily return standard deviation
2. log size – The log of the size of the stock. Size is total outstanding number of shares multiplied by share
   price. Size is measured in thousands of dollars
3. log trades – This is the log of the average number of trades per day
4. log turn – This is the log of the ratio of the average number of shares traded per day (in dollars) over the
   number of shares outstanding (in dollars
5. NumberAnalysts – This is the number of analysts following the stock
   .

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. Import the data set and set up variable names
% import dataset lab2.xls (using Import Dataset)
% rename variables
tradecost = lab2{ : ,1};
logvolatility= lab2{ : ,2};
logsize = lab2{ : ,3};
logtrades = lab2{ : ,4};
logturn = lab2{ : ,5};
numberanalysts = lab2{ : ,6};
logtradecost = log(tradecost);

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Run a regression of the log of the bid-ask spread on the 5 explanatory variables.
Q1. (2 points) Are all independent variables significant at a 3% significance level? Explain.
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1
Answer:

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Q2. (2 points)Test the assumption that the coefficient associated with logvolatility is equal to 1. If this is the
case, how would you interpret the relation between daily volatility and bid-ask srepads?
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Answer:

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Q3. (2 points) Test the assumption that the coefficients associated with logsize and logtrades are equal to each
other. Interpret the result.
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Answer:

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Q4. (2 points) Would you exclude any variable from the regression? Explain.
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2
Answer:

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Q5. (2 points) Let us use this model to predict what the spread will look like tomorrow.
For prediction, use a regression which does not include the number of analysts to predict.
Consider a stock which has a log size of 10.5. Suppose that for this stock we expect that for the following day
the log turnover will be
-1.1, the log of the number of trades will be 7.6, and the log of the standard deviation will be -3.5.
Predict what the spread will be for this stock tomorrow. (Note that since the regression is run with log spreads
you
will have to make a transformation to convert your prediction for the log spread into a prediction for the actual
spread).
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Answer:
3