10

I think you are interpreting too much into the matter. The $-\frac12\sigma^2$ is just a correction term that comes from Jensen's inequality. You need this when switching from supposedly symmetric returns (normal distribution) to the skewed price process (log-normal distribution). I think there are no deeper truths to be found here.


8

Proof: Recall that $$\beta_{i} = \frac{\mathrm{Cov}(r_{i},r_{m})}{\mathrm{Var}(r_{m})}.$$ Now, the returns on unlevered and levered equity are given by $$r_{U} = \frac{\mathrm{EBIT}(1-\tau) - \mathrm{CAPEX} + \mathrm{Depreciation}}{E_{U}}$$ $$r_{L} = \frac{\mathrm{EBIT}(1-\tau) - \mathrm{CAPEX} + \mathrm{Depreciation} + \mathrm{Net\ Debt} - \mathrm{...


6

Morningstar Morningstar partnered with Quantopian, and the latter published the structure of Morningstar's equity fundamentals database: https://www.quantopian.com/help/fundamentals Quantopian users can use this data for free.


5

Quantopian provides both the fundamental data (from Morningstar), as well as the backtest platform to reproduce results from the books you mentioned. Here's the introduction to our fundamentals offering: https://www.quantopian.com/posts/fundamental-data-from-morningstar-now-available-for-backtesting (disclosure: I'm the ceo of quantopian)


4

This is a very subjective question. One thing you need to understand is that there are many types of quants and it is not always about predicting the future returns. Many quantitative analysts are involved in market-making, this is where you sell products at a slight cost to customers and try to stay more or less neutral to market moves. When you read about ...


4

Quandl has two premium fundamental datasets that may be of interest to you, Robur Global Select Stock Fundamentals and Mergent Global Fundamentals Data. Quandl also has fundamental datasets for specific countries including the US, China, and India. If you have an Interactive Brokers account and can program against their API, you can sign up for Reuters ...


4

Just figured it out with the help from someone else... The market cap is in Singapore dollar because it's traded on Singapore exchange, but their income statement is in Thai Baht... That's why :)


4

You can use refined methodologies but if you just need a rough estimation of liquidity, you can simply use an average of daily volume over N days. In practice, for equities, people tend to use N = 20 or 30. Once you have the average daily volume (say 100,000 shares), you compare it to your holding (say 50,000 shares) to determine the the size of your ...


4

Compustat supports unlimited data export keeps the history of disbanded entities provides restatements since 1950 + point-in-time data since 1986 coverage since 1950 list of variables (data guide) Compustat is a S&P subsidiary. It goes as a plugin for CapitalIQ (also S&P), WRDS, CRSP, and other platforms. Pricing starts from \$3k. A platform ...


4

Welcome. You're looking at 1995 data. Back then, Edgar was just coming online. They did not have documents in electronic form. If you want data that old, you may have to pay a vendor, such as S&P. If you look at the same Макдак for 2019 https://www.sec.gov/cgi-bin/browse-edgar?action=getcompany&CIK=0000063908 , everything can be downloaded.


3

The highly respected CXO Advisory Group has done some research on this topic on basis of a suggestion from me. The result is summarized as follows (cited with permission): In summary, evidence suggests that high-dividend stock ETFs mostly generate positive alpha with beta less than one relative to SPY, but other performance comparisons to the market ...


3

You can download the company list csvs from nasdaq.com, it has the mapping you need.


3

One thing to keep in mind here is that the world of risk-free/arbitrage-free models is not necessarily the real world. Specifically, this equation $$ \mu = r - \frac{1}{2}\sigma^2 $$ occurs not because this is the way stocks behave in reality (they don't! For S&P 500, long-run $\mu$ is closer to 6-9%, if I recall correctly), but because using any ...


3

If you have a friend studying at almost any university you can get access to WRDS. Inside WRDS just go to Compustat which has all the info you need for dates since 1950.


3

Let $\Omega$ be the outcome space at some future date and fix a specific outcome $\omega \in \Omega$. Now consider a portfolio that gives one unit of currency if $\omega$ happens and zero otherwise, i.e., with payoff $\mathbb{I}(\omega)$. Any other payoff function can be given as a linear combination of these portfolios. The price of this portfolio today is ...


2

Examples for cash-settled futures are: Interest Rate futures Futures on implied Volatility (e.g. on VIX) Futures on Commodity Indices: Indices such as the Dow Jones UBS consist of futures themselves. Furthermore in asset management you usually don't want physical delivery of the underlying (oil, gas, coal, pig, ... ;) Futures on Equity Indices The pricing ...


2

It's just cash settled, like a bet on a sports game. This was somewhat controversial when the financial index futures were first invented.


2

I see the question and answers are rather old here but I just ran across quandl which provides access to a variety of SEC data with a free API key.


2

There are two mainly (good) free sources available online: wolphramalpha.com Quandl They report the mainly market and fundamental data, so you will not find any particular fundamental accounting ratio. In the case you need particular ratio or data, you should get some better financial data provider, as, for instance, Bloomberg or Thompson Reuters.


2

It turns out that GBM with constant drift and constant volatility is not really used in real life. It is well known that volatility as well as drift may vary over time. Hence, if you want to use a model with time-varying parameters, you need to come up with a model to define $\mu_t$ and $\sigma_t$. There are classic models that use some mean-reverting ...


2

I think that it may be very simple but since I just started in the quant finance it would be great to have some feedback and recommendations about how to improve my backtesting. From my experience, most who begin testing a model straight from academia overlook several things that are quite different in the real world. Factoring them in will help to ...


2

I would consider Amihud (2002) as a good first approximation with that level of data.


2

The forex week starts on Monday 7am Wellington time when the Kiwi value date rolls. It ends on Friday 5pm New York. Within that each currency has its own “cut off” time when the value date rolls. The cut off depends on the time zone and liquidity in the currency managed by the dealers who make its market. The convention for an FX day “cut off” for a given ...


2

There are at least two ways of doing it: 1) Resampling them to their median frequency. 2) Build one ML model for each data type, then combine the 4 different forecasts into a single meta-ML model. (Courtesy: MARCOS LO´PEZ DE PRADO)


2

Let $c_t$ be the price of an European call with maturity $T$ and $D_{t,T}$ the discount factor from $T$ to $t$. We assume deterministic rates. Then note that for $s<t\leq T$: $$\begin{align} E^Q_s\left(c_t\right)&=E^Q_s\left(E^Q_t\left(D_{t,T}(S_T-K)^+\right)\right) \\[3pt] &=E^Q_s\left(D_{t,T}(S_T-K)^+\right) \\[3pt] &=E^Q_s\left(\frac{D_{s,t}...


1

This has been driving me nuts as well! Thanks for providing the spreadsheet. Looking at MSN Money there is a discrepancy of over 11B in market cap between viewing GOOG and GOOGL shares! GOOGL has a market cap associated with 537B and GOOG has a market cap of 526B. I don't understand how one site can list two different market caps based on the class of ...


1

This is a question of how to aggregate ratios. I see your two options, and raise you one more. Method 1: The mean (or median) value of Price-to-Book values for individual securities $$f(E[x]) = \frac{\sum_{i}^{n} \frac{P_i}{B_i}}{n} $$ Pros: Relatively simple to calculate and gives an idea of what the typical company's Price-to-Book is. Cons: ...


1

I think one of the main liquidity measures is the one from Pastor and Stambaugh (2003). You can use it for both individual stocks or indexes. Just run the following intra-month regression with daily data: $r^e_{i,d+1,t} = \theta_{i,t}+\phi_{i,t}r_{i,d,t}+\gamma_{i,t}sign(r^e_{i,d,t}) \times v_{i,d,t}+\epsilon_{i,d+1,t}$. Where $r^e_{i,d+1,t}$ is the ...


1

XBRL became mandatory for US filers on June 15th, 2011. The SEC requires XBRL data for: Quarterly and annual reports and transition reports Form 8-K revisions Limited Securities Act registration statements XBRL instances for quarterly and annual reports would typically contain the usual items found on Income statements, Balance Sheets, and Cash Flows.


1

You don't need to read/parse financial statements in XBRL format in the first place. There exists the method getFundamentals(ticker) provided by the package eodhistoricaldata-api (https://www.npmjs.com/package/eodhistoricaldata-api). The library returns quarterly (and yearly) financial statements (income statements, balance sheets, and cash flow ...


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