*New* get it cheaper: . Complete & Concise summary of Research Skills, part of MSc Finance & Investments. The summary aims to cover 99% of theory as concise as possible. Perfect for use when practicing or revising for your exams or, in 2020, as source during the exam. It covers statistical theory f...
Writing was very unclear. This made the summary confusing and hard to follow.
By: thomaskonings • 3 year ago
Hi Bas, I'm sorry the writing was unclear, the aim of the summary is to be as concise as possible. Because of this, sometimes clarity is traded off for brevity. The summary assumes that you have actively taken part in the course and are familiar with the concepts, it is written to be a review of the course, not "first time" material.
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Complete & Concise: Research Skills
By: Thomas Konings
Table of Contents
Part 1: Introduction and Data ................................................................................................................. 2
Part 2: Event Studies ............................................................................................................................... 3
Workshop 1 ............................................................................................................................................. 4
Part 3: Basic Regression Analysis ............................................................................................................ 5
Part 4: Advanced Regression Analysis: Interaction Terms & Panel Data................................................ 7
Part 5: Advanced Regression Analysis: Fama-MacBeth & Binary Choice ............................................... 8
Workshop 2 ............................................................................................................................................. 8
Appendix – What data issues look like ................................................................................................... 9
Heteroscedasticity .............................................................................................................................. 9
Autocorrelation ................................................................................................................................... 9
Multicollinearity .................................................................................................................................. 9
, Part 1: Introduction and Data
Cross-sectional: comparing multiple units at a given point in time
Time series: track on unit over time Panel: track multiple units over multiple time periods
Checking data: use summary statistics & graphs to assess data quality (mean, median, std.dev., min,
max, percentiles etc. + histograms). Compare summary stats to other studies with same data.
Check data issues: things that are just illogical (trade data on non-trading days), jumps etc.
Outliers: [note: in general, do not remove actual data, even if it’s an extreme value]
Transformation: can do log to pull towards mean, Winsorizing: replace extreme values with cutoff
values Truncating: delete extreme observations. Then: recalculate descriptive statistics
Missing data: unbalanced does not mean unusable. Do not interpolate data (introduces bias), do not
replace values by zero (unless missing is a proxy for zero). Do you need the control variable if it
leaves you with only 10% of your data?
𝑝𝑡 −𝑝(𝑡−1)
Prices: denoted p, with suffix t for the time period. Returns: 𝑹𝒕 = 𝑝𝑡−1
Note: returns over two periods is obtained by multiplying, returns are not additive
Log returns: continuously compounded returns, 𝑟𝑡 = ln(𝑝𝑡 ) − ln(𝑝𝑡−1 ), these are additive across
periods. Difference is small for small returns (i.e. daily) → also works for adding portfolio weights
𝑝𝑡 +𝑑𝑡 −𝑝𝑡−1
Incorporating dividends: 𝑅𝑡 = (dt being dividend, paid before date t price)
𝑝𝑡−1
→ Dividend return: 𝑑𝑡 /𝑝𝑡−1 Excess returns: in excess of risk-free rate, or in excess of some other
portfolio (payoff of an arbitrage portfolio)
Returns are random, are not known in advance, they have a distribution, characterized by mean,
variance and higher moments (skewness/kurtosis), which are not observed only estimated.
Theories are about expected rather than average returns. In Finance, volatility = σ = sqrt(variance)
Portfolios can be equally-weighted (1/N), value-weighted (by market cap) or price-weighted (equal
number of stocks for each firm [Dow-Jones]). Scholars: portfolio sorts, then look at 10% (decile)
portfolios (e.g. 10% largest), can be equally-weighted or value-weighted.
Data organization: depends on type of data and analysis. Time series: variables in columns, dates in
rows. Cross-sectional: variables in columns, firms in rows. Panel: variables in columns, dates & firm
identifiers in rows. Note: keep firm identifiers and names, better for output (readability)
STATA Summary
Creates consistent time indicator: gen long month = month(date)
format month %tm
Optionally: xtset month (sets as time variable, allows lags)
Concert panel date long/wide: reshape
Note: type help [command] in Stata for docs
Merge: Single firm/month match merge 1:1 firm month using “dataset2.dta”
or Per month for all firms merge m:1 month using “dataset3.dta”
Append more data (rows): append using “additionalrows.dta”
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