# Tag Info

6

The primary quant skill needed to make the market is optimal control (a typical paper is Guéant, O., L, and J. Fernandez-Tapia (2013, September). Dealing with the inventory risk: a solution to the market making problem. Mathematics and Financial Economics 4 (7), 477-507), because you need to control your inventory and adjust your quotes accordingly: be ...

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Short answer It's complicated. A satisfactory solution is not known. Long answer A satisfactory solution is not known and research is ongoing. That doesn't mean there is nothing interesting to say about it. The phrasing in the question is not entirely correct: First off all, there's is no risk free arbitrage between bonds and stocks. Both are risky and ...

4

In the chart below, I'm showing the rolling correlations between stock returns and bond returns. (The relationship would be flipped if you are studying stock returns vs interest rates). As you can see, for the bulk of the history since 1960s, bond returns and stock returns were indeed positively correlated; i.e., when stocks went up, bonds went up too (and ...

4

Both free and paid access to data sets conatianing company financial statement items is available from Quandl. The free data sets are sourced from the SEC based on compnay electronic filings and go back about five years. For example, you could obtain five years of MSFT's quarterly net income using the R call Quandl("RAYMOND/MSFT_NET_INCOME_Q") Lists of ...

3

Are you sure the return for two years is 0.7214? It should be 0.3422 per year if you are using 31/12/2011, and 0.3416 if you are using 01/01/2012 as the end date. Assuming the last number (because it makes for two full years, therefore easier to calculate), yes, there is a formula to derive it from the return of the individual years. It's the geometric ...

3

Successful strategies in both areas can have the same math requirement. It just depends on the algorithm. PhD level mathematics is not a requirement in either area, despite the impression you may get from academic papers (note that a lot of these papers use math to build a sim market, which is completely dislocated from what a researcher needs to do). I feel ...

3

Unfortunately, the ability and tools to develop a low latency trading system are extremely commoditized and will be insufficient for you to make a living in this field. An overwhelming majority of electronic market makers are staffed 100% by PhDs because trading experience and research compose their primary differentiators, e.g.: SIG EMM - 100% PhD. DRW ...

3

SEC tends to keep CUSIPS handy: http://www.sec.gov/divisions/investment/13flists.htm

3

Short answer: It offers some degree -- and in many cases, a greater degree -- of comparability between two types of data (different assets, returns, etc.) Long answer: You may already know this, but keep in mind that "normalization" can mean different things (see this question). There are various methods and purposes for normalizing data (financial or ...

3

VG belongs in the family of variance-mean mixture models. Given a horizon $T$ the distribution of log-returns $f$ is a mixture of Gaussians $f_G$ with randomised mean and variance. The randomisation density is $g$ and its mean and variance increase with $T$. For the VG process this randomised factor is Gamma-distributed. More concretely, denote with ...

3

This mean that the reason why apple stock price went from 3 to 100 in 10years is the overnight variation in price. This is quite unexpected, if there was no overnight variation the stock price would have died a long time ago... Why is that ? Have we been lying to us ? This is because many business and financial news are reported at market close, either ...

3

As you've mentioned, it depends on the trading venue and the exact market data product that you're subscribed to. Unless otherwise stated, the data is usually updated at every occurrence of an event (explains the irregualr intervals), and often, the data is not disseminated immediately and multiple events may be batched in a single message informing you of ...

3

If you're looking for all transactions against any or a given set of securities on whatever exchange, you can get that from a data provider like IQFeed or eSignal. Most of them will have tick level data going back for at least several weeks. Some people have been collecting tick and market data for quite sometime against a variety of securities, and as ...

2

In the chapter that deals with NMF of the book "Programming collective intelligence" , the author did NMF on several stock trading volumes and found some comovement. I googled a little. This did NMF on 40 chinese stock close prices. This developed A variant of nonnegative matrix factorization for Stock Trend Extraction. Another google found this also did ...

2

I do not believe that the exchange is capable of tracking down the person or legal entity who has been part of any recorded transaction and in particular linking the activity of market intermediaries to the ultimate interests of beneficial owners. However, under certain conditions, the person or legal entity has to report to the SEC his identity and his ...

2

On exchanges, there is such orderbook with sufficient amount of limitorders, so when you place an order (market or limit), the "best" limitorders for you will be hitted and change the price last traded price. The price you see is actually just the midpoint between the currently best available bid and ask prices in the orderbook. Therefore, this price might ...

2

Actually, the historical returns, going back to the 1920s, took place in two different ways over two distinct time periods; 1980-present, and 1925-80. This is a more important premise than the fact that stocks have an average total return of 10 percent over the past 80-odd years, and bonds have an average total return of only 5 percent a year over that time. ...

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One possible route would be to get the historical price data from Yahoo and use disparities between the Close returns and the Adjusted Close returns, given a certain threshold value, and where the disparity passes that threshold is where the splits have occurred. Find the Close returns where that condition was met, maybe round the number to the nearest ...

2

If you are investing an amount $M$, split over deals indexed by $i$ and with a weight $w_i$, then your dollar position in each share will be $w_i M$. The exposure to the index will be $\sum \beta_i w_i M$ You should realize that this will not hedge idiosyncratic risks. In general, the more deals you have, the better this type of hedge should work (assuming ...

2

Start with: http://www1.nyse.com/pdfs/closings.pdf which covers all closings through 2011 then use the following... 2012/2013: http://www1.nyse.com/press/1294398514465.html Weather related closures happened on Monday, Oct. 29, 2012 and Tuesday, Oct. 30, 2012. (http://markets.nyx.com/nyse/trader-updates/view/11507) 2014/2015: ...

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As background, Floating point precision is a way of storing numbers such that the precision is relative to the largest digit. For instance, the number $0.00123$ stored in fixed precision needs 6 digits of precision (3 zeros and the 3 non-zero numbers). However, this same number stored as floating point precision $1.23 \cdot 10^{-3}$ needs only 3 ...

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I faced the same issue some years ago and I solved by implementing the R script reported here; now, with new Yahoo disclaimer rules, it seems to be broken, but, anyway you should be able to replicate the data mining process using that script together with this. If you're pretty confident with R, you should be able to do that. Alternatively, you can visit ...

2

$\sigma S$ is in units of dollars per square root of a unit of time. $\sigma$ is usually quoted as an annual or daily percentage. $dX ^2$ is in units of time, as $E[(dX)^2] = dt$. Here is an online tutorial which you may find helpful. EDIT by kotozna: $\sigma$ has dimensions 1/(square root of time) and $dX$ has dimensions square root of time. ...

2

I'll assume the rest of the world doesn't have access to a similar oracle. Indeed if it did future returns would converge to the risk free rate instantly. In this case, I would prefer holding the AAA bond instead of the stock because the rest of the world would consider it to be much less risky. As a financial institution, reducing the risk of your ...

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The January effect is easy to demonstrate. Always the same dates for multiple shares.

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The easy answer would be to look for exchanges that only have pit trading, ie people in a room that match up buyers and sellers. As far as I know no such exchange exists any more. In my opinion the best you are going to be able to do is to compare the NYSE now with the NYSE in 1998, which is to say you wont be able to do much of a comparison at all as ...

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The TA_lib Technical Analysis library here has open source code for numerous indicators.

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Both R and Python can do this very nicely. For Python you would need the pandas package and its dependencies. pandas has a lot of basic statistics, but for more advanced statistics like it looks like you want to do, you can use the statsmodels package, which can work directly with pandas data types. It can also download the csv files directly off the ...

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That is what XIRR does or can you read this answer. Basically it tries to find an interest rate that works out to the same numbers. I think Excel and Google Docs use the Newton approximation http://www.mftransparency.org/calculating-interest-rates-using-newtons-method/ There's also a java implementation available on github called jxirr.

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some links which might help you http://quant.caltech.edu/historical-stock-data.html In the quantmod R package ,the split information is in the "Dividend Only" CSV: http://ichart.finance.yahoo.com/x?s=IBM&a=00&b=2&c=1962&d=04&e=25&f=2011&g=v&y=0&z=30000

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