Statistics Test for bivariate frequency table First time user here! I have some troubles in finding some statistic method that would fit my purpose for testing bivariate frequency table. Below is a sample frequency table I have created with ordinal data.  

The rows and columns are labeled with ranks from 1 to 4. As you can see, most of my data is concentrated at the bottom-left corner for the sample. My goal is to quantitatively define this characteristic and perhaps perform a statistical test for estimating the dispersion in the population. 
I have tried to the Chi-squared contingency table and Fisher's exact, but my table contains a few zeros cells. 
Please advice any other appropriate test that I should attempt.  Thank you. 
ADDed: The experiment is designed as follow, 
Four stocks are picked and their expected return and standard deviation are measured for 40 observations/periods. Within each observation, rankings are assigned to each stock for both y (expected return) and x (standard deviation) as 1 being the lowest and 4 being the highest. The frequency table above focuses on one of the four stocks. 
if most of the observations fall into region of (y >= 3 & x <=2), I would characterize this stock as "low risk, high reward" option. 
 A: Because both the $\chi^{2}$ test and Fisher's exact test ignore the ordering of the categories in your variables, these are not appropriate tests. However, Spearman's r (sometimes Spearman's $\rho$) is a nonparametric statistic measuring monotonic association between two variables which can capture such ordering, such as $x$ and $y$. It is more or less Pearson's correlation coefficient applied to ranks. You can easily construct a $t$ test statistic to pose and answer the question "is the monotonic association statistically different than zero?" With a little more work, you can also pose and that question for a null hypothesis with a non-zero value.)
Constructing the correlation coefficient, $r_{\text{s}}$, is not terribly difficult:
$$r_{\text{s}}=1 - \frac{6\sum_{i=1}^{n}{d_{i}^{2}}}{n\left(n^{2}-1\right)}$$
Where $d_{i}$ is the difference in (independently) ranked values of $x$ and $y$.
Constructing a t test statistic with $n-1$ degrees of freedom for H$_{0}\text{: }\rho_{\text{s}} = 0$ is likewise not too difficult:
$$t = r_{\text{s}}\sqrt{\frac{n-2}{1-r_{\text{s}}^{2}}}$$
