Extra Project
The goal of the project: We examine how macroeconomic factors affect stock returns.
Empirically, we can test the following model;
Rt= ?0 + ?1*Market Indext-1+ ?2*Inflationt-1+ ?3*GDP Growtht-1+ ?4*TERMt-1+ ?5*RISKt-1+?
1. Dependent variable: firms� stock returns
I posted firms� stock returns in three industries (air, auto, and computer) to Blackboard. You can analyze any firm as you wish. You can pick multiple firms from three different industries, or a single firm from a specific industry.
Variable Explanations
DATE: the end of trading date at each month
COMNAM: Company name
EXCHCD: Exchange code (1: NYSE, 2: AMEX, and 3:NASDAQ)
HSICCD: Industry classification (e.g., The SIC 4512 represents an airline industry.
PRC: Stock price at the end of each month�s trading date
RET: Stock returns at the end of each month�s trading date
SHROUT: shares outstanding
VWRETD: Market index
2. Independent variables: macroeconomic variables
* The Source of data
Download data as long as we believe that variables may affect stock returns.
There are some candidates for independent variables.
Market Index=VWRETD, and Firm Size= PRC*SHROUT
Inflation= log (CPIt / CPIt-1), and GDP Growth=log (GDPt / GDPt-1)
TERM= 10-year T/B � 3-month T/B, and RISK= BAAt � 10-year T/B?
Questions
1) Report summary statistics (n, mean, median, standard deviation, min, max) of your picked variables.
2)Why do you include such independent variables? Give me a brief explanation.
3) Run a regression and report coefficients and t-statistics for the explanatory variables.
4) Interpret coefficients of each variable. Compare it with your prediction
5) What is your investment strategy based on your findings?
* To obtain full credit (20 points), you need to submit it by July 8th, 2014.Grade below 10 points will be counted as zero.
* The minimum requirement is 5 different firms and 5 independent variables.
* TERM and RISK should be included as independent variables.
* If you need a reference, please look at the paper written by Nai-Fu Chen, Richard Roll, and Stephen A. Ross. The title is �Economic Forces and the Stock Market (Journal of Business, 1986)�.
Date
Examination of how Macroeconomic factors Affect Stock returns.
Research Findings
The goal of the project is to examine macroeconomic factors affecting stock return using the following model Rt= ?0 + ?1*Market Index-1+ ?2*Inflation-1+ ?3*GDP Growth-1+ 4*Terms-1+?5*RISK-1+?, The study uses stocks of from stock returns in three industries (air, auto, and computer), the dependent variable is the firms stock return, out of the several proposed independent variables the model used market index, inflation, GDP Growth , terms and risk assessment, the variables included in this study were chosen on the similar variables of existing literature from previous study on the relationship between stock return and macroeconomic variables (Chen et al, 1986).
| market index | inflation | production | terms | risk | |
| Mean | 1.99 | 32.58 | 4.35 | 1.79 | 0.02 |
| Min | 1.86 | 14 | 3.68 | 1.119 | -0.49 |
| Max | 2.12 | 67.90 | 4.86 | 2.14 | 0.33 |
| SD | 0.05 | 10.89 | 0.37 | 0.29 | 38.37 |
| Skewness | 0.11 | 0.19 | -0.17 | -0.83 | -1.31 |
| Kurtosis | 2.67 | 2.33 | 1.73 | 2.58 | 14.63 |
The basic descriptive statistics from the raw stock data is as following indicating mean, minimum, standard deviation, maximum, skewness and kurtosis, the mean of all the exploratory variables indicating a volatile market, the standard deviation is very high which is an indicator of a very volatile market, positive and negative minimum and maximum are signs of that a market that sometimes is profit and other times brings huge losses, similarly the table indicates negative skewing with a very extreme kurtosis which also indicates that the returns are not normally distributed (Chen et al, 1986).
Regression Results
- by examining all the proposed macroeconomic variables and how they are affecting stock return using the following model From the model, the significance of the predictors variables. R is a measure of the correlation between the observed value and the predicted value of the criterion variable. R Square (R2) is the square of this measure of correlation and indicates the proportion of the variance in the criterion variable which is accounted for by our model. In essence, this is a measure of how good a prediction of the criterion variable we can make by knowing the predictor variables. However, R square tends to somewhat over-estimate the success of the model when applied to the real world, so an Adjusted R Square value is calculated which takes into account the number of variables in the model and the number of observations (participants) our model predictors based on which is Market Index=VWRETD, and Firm Size= PRC*SHROUT
Inflation= log (CPIt / CPIt-1), and GDP Growth=log (GDPt / GDPt-1)
TERM= 10-year T/B – 3-month T/B, and RISK= BAAt – 10-year T/B?
We now have an adjusted R Square value of 0.667 we can say that our model now accounted for 66.7 % of the variance in the criterion variable. Therefore it can be deduced that the five predictors which includes inflation, interest rate,, market index, GDP Growth account for stock returns well.
Table 14
| Model Summary | ||||
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate |
| 1 | .667a | .752 | .621 | 4.804 |
| ANOVAb | ||||||
| Model | Sum of Squares | df | Mean Square | F | Sig. | |
| 1 | Regression | 178794.461 | 6 | 29799.077 | 6.991 | .000a |
| Residual | 477429.379 | 112 | 4262.762 | |||
| Total | 656223.840 | 118 | ||||
| a. predictors: (Constant), Money Index, inflation, risk, consumption, treasury bill rate, production | ||||||
| b. Dependent Variable: stockreturn |
| coeffecient | ||||||
| Model | Unstandardized Coefficient | Standardized Coefficient | t | Sig. | ||
| B | Std. Error | Beta | ||||
| 1 | (Constant) | -192.676 | 156.067 | -1.235 | .220 | |
| inflation | 1.986 | 1.971 | .308 | 1.008 | .016 | |
| production | -2.742 | 1.852 | -.520 | -1.481 | .141 | |
| consumption | 25.930 | 13.747 | .367 | 1.886 | .062 | |
| treasury bill rate | -.397 | .311 | -.313 | -1.275 | .005 | |
| risk | 4.286 | 1.066 | .460 | 4.022 | .000 | |
| Money Index | -2.526 | 2.876 | -.120 | -.878 | .382 | |
| a. Dependent Variable: stockreturn |
Beta (standardized Beta coefficients is a measure of the contribution of each variable to the model. A large value indicates that a unit change in this predictor variable has a large effect on the criterion variable. The t and sig(p) values gives a rough indication of the impact of each predictor variable, a big absolute t value and a small p value suggests that a predictor variable is having a large impact on the criterion variable. Our regression output evaluated indicate that all the independent variables have impact on stock return except interest rate and explaining the independent variables as used by the model, it is proposed that there will be an inverse relationship between stock price and interest rate thereby an increase in interest rate leads to an increase of the return of interest. There is a relationship between money supply or inflation and stock return where the high inflation has a negative effect on stock prices and then the exchange rate exchange rate and inflation outcomes affect cash flow which in turn affects stock return, industrial production index is aggregation of overall economic performance, and therefore when economic performance improves it will affect stock return directly risk in this case covers the effect on returns of anticipated changes on money market (Chen et al, 1986).
References
Chen, N. F., Roll, R., & Ross, S. A. (1986). Economic forces and the stock market. Journal of business, 383-403.
