One theme from the the first two weeks is that there are some important similarities between managerial and financial accounting. While managerial and financial accounting are designed to provide information to different types of users, the tools that are used are often the same. This is because, ultimately, external users would like to know as much about an organization as possible to better inform their decisions. So, some of the information that managers use to run the organization is passed on to external users for decision making purposes.

This raises an interesting point: Why do we bother with preparing different types of accounting information for internal and external users? External and internal users have different motives, so they require different types of information. However, assuming that external users of information are competent, it seems reasonable that they could make more informed decisions if given free access to an organization's internal accounting information (as opposed to the summary data contained in external financial reports). Thus, there must be some reason why companies choose to keep their accounting details hidden from the public.

We will only really scratch the surface of the principal-agent problem, but I do want to touch on a few issues. First, what is one reason that company managers (i.e. "agents") would want to avoid disclosing private accounting information of the company? Second, as an owner (i.e. "principal"), would you be concerned that there might be important information about the company that the managers are not disclosing in the financial statements. Finally, what might be a way that owners can ensure that managers are faithfully representing the financial position of the company without forcing them to disclose private company information?

Problem 8 [MATLAB exercise] The MA'I'LAB function 'randn' is designed to generate samples of a random variable, say X , which is governed by a unit-Gaussian p.d.f. (i.e., a Gaussian p.d.f. with px = 0 and a} = 1). In this exercise, we will investigate the ability of the routine to generate truly Gaussian samples. (a) Let X be a unit-Gaussian random variable. Using pencil and paper, compute P(0 S X S 0.5) and P(0.9 S X g 1). (b) Use the MATLAB function randn to generate 1000 samples of a unit-Gaussian random variable Let's call the vector X. (c) Use the MATLAB function 'histogram' to create a histogram of the values in X with a bin width of 0.05. ((1) Plot the histogram and comment on the approximation to a unit-Gaussian p.d.f. (9) Use the histogram data to estimate the two probabilities computed in part (a). For example, No. samples in the bin covering the range 0 S X