Retail investors and their trading behaviour attract many research interests. One strand of literature uses proprietary datasets to identify retail investors. The other uses algorithms. A recent JF paper Boehmer et al. (2021) proposes a simple one based only on the trade price, which also signs the trade direction effectively. Even more interestingly, I just read a follow-up work forthcoming on JF by Barber et al. (2023). The authors placed 85,000 retail trades themselves to validate the Boehmer et al. (2021) algorithm.
This post focuses on the translog cost function.
I discuss the linear homogeneity constraint, the technique to impose the constraint, and its estimation via
In this post, I'll carefully explain the derivation of cost function from a CES
production function, as well as the derivation of translog (transcendental logarithmic) production and cost functions.
flowchart TB
subgraph Production
A[Production Function] -. approximation .-> D(Translog Production Function)
end
subgraph Cost
B[Cost Function] -. approximation .-> C(Translog Cost Function)
end
A == Conversion via Duality ==> B
Before I start, the graph above illustrate the relations. Specifically, we can derive the
cost function from a CES production function via the duality theorem. Translog production
and translog cost functions are approximations to the production and corresponding cost function, respectively, via Taylor expansion.
Empirical researchers have been using difference-in-differences (DiD) estimation to identify an event's Average Treatment effect on the Treated entities (ATT). This post is my understanding and a non-technical note of the DiD approach as it evolves over the past years, especially on the problems and solutions when multiple treatment events are staggered.
Since Stata 15, we can search, browse and import almost a million U.S. and international economic and financial time series made available by the St. Louis Federal Reserve's Federal Research Economic Data. This post briefly explains this great feature.
Can we estimate the coefficient of gender while controlling for individual fixed effects? This sounds impossible as an individual's gender typically does not vary and hence would be absorbed by individual fixed effects. However, Correlated Random Effects (CRE) may actually help.
At last year's FMA Annual Meeting, I learned this CRE estimation technique when discussing a paper titled "Gender Gap in Returns to Publications" by Piotr Spiewanowski, Ivan Stetsyuk and Oleksandr Talavera. Let me recollect my memory and summarize the technique in this post.
Merton (1974) Distance to Default (DD) model is useful in forecasting defaults. This post documents a few ways to empirically estimate Merton DD (and default probability) as in Bharath and Shumway (2008 RFS).
In a traditional principal-agent model, firm output is a function of the agent's effort and the principal observes only the output not agent's effort. The principal carefully designs the agent's compensation package, especially the sensitivity of the agent's pay to firm output, to maximize the firm value. Now, what if we add another factor to the relationship between firm output and agent's effort? How would the optimal pay sensitivity change?
As in Eisfeldt and Papanikolaou (2013), we obtain firm-year accounting data from the Compustat and compute the stock of organization capital for firms using the perpetual inventory method that recursively calculates the stock of OC by accumulating the deflated value of SG&A expenses.
Thomson One Banker SDC Platinum database provides comprehensive M&A transaction data from early 1980s, and is perhaps the most widely used M&A database in the world.
This post documents the steps of downloading M&A deals from the SDC Platinum database. Specifically, I show how to download the complete M&A data where:
both the acquiror and the target are US firms,
the acquiror is a public firm or a private firm,
the target is a public firm, a private firm, or a subsidiary,
the deal value is at least $1m, and
the form of the deal is a acquisition, a merger or an acquisition of majority interest.
More often than not, empirical researchers need to argue that their chosen model specification reigns. If not, they need to run a battery of tests on alternative specifications and report them. The problem is, researchers can fit a few tables each with a few models in the paper at best, and it's extremely hard for readers to know whether the reported results are being cherry-picked.
So, why not run all possible model specifications and find a concise way to report them all?
In the Compustat database, a firm's headquarter state (and other identification) is in fact the current record stored in comp.company. This means once a firm relocates (or updates its incorporate state, address, etc.), all historical observations will be updated and not recording historical state information anymore.
To resolve this issue, an effective way is to use the firm's historical SEC filings. You can follow my previous post Textual Analysis on SEC filings to extract the header information, which includes a wide range of meta data. Alternatively, the University of Notre Dame's Software Repository for Accounting and Finance provides an augmented 10-X header dataset.
In certain scenarios, we want to estimate a model's parameters on the sample for
each observation with itself excluded. This can be achieved by estimating the
model repeatedly on the leave-one-out samples but is very inefficient. If we
estimate the model on the full sample, however, the coefficient estimates will
certainly be biased. Thankfully, we have the Jackknife method to correct for the
bias, which produces the Jackknifed coefficient estimates for each
observation.
Nowadays top journals favour more granular studies. Sometimes it's useful to dig into the raw SEC filings and perform textual analysis. This note documents how I download all historical SEC filings via EDGAR and conduct some textual analyses.
Using the CRSP/Compustat Merged Database (CCM) to extract data is one of the
fundamental steps in most finance studies. Here I document several SAS programs
for annual, quarterly and monthly data, inspired by and adapted from several
examples from the WRDS.1
Herfindahl–Hirschman (HHI) Index is a well-known market concentration measure
determined by two factors:
the size distribution (variance) of firms, and
the number of firms.
Intuitively, having a hundred similar-sized gas stations in town means a far
less concentrated environment than just one or two available, and when the
number of firms is constant, their size distribution or variance determines the
magnitude of market concentration.
Since these two properties jointly determine the HHI measure of concentration,
naturally we want a decomposition of HHI that can reflects these two dimensions
respectively. This is particularly useful when two distinct markets have the
same level of HHI measure, but the concentration may result from different
sources. Note that here these two markets do not necessarily have to be industry
A versus industry B, but can be the same industry niche in two geographical
areas, for example.
Thus, we can think of HHI as the sum of the actual market state's deviation from
1) all firms having the same size, and the deviation from 2) a fully
competitive environment with infinite number of firms in the market. Some simple
math can solve our problem.
Computing the weekly returns from the CRSP daily stock data is a common task but
may be tricky sometimes. Let's discuss a few different ways to get it done
incorrectly and correctly.
Many research papers on Chinese firms include a control variable that indicates if the firm is a state-owned enterprise (SOE). This is important as SOEs and non-SOEs differ in many aspects and may have structural differences. This post documents the way to construct this indicator variable from the CSMAR databases.
The Wharton Research Data Services (WRDS) provides quite a handful of SAS macros that can be used directly. This article explains how to use those handy macros on WRDS when you use remote submission to run your code on the WRDS cloud. Lastly, it explains how to load and use third-party SAS macros from a URL.
Taking up the position of CEO means more than pressure from the board and investors. You’ll also face heavy scrutiny from academia. Whether or not a firm’s hiring and compensation committees use them as a reference, here are some of the findings that you may want to be aware of.
