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9

The volatility in the indices long ago was similar in magnitude to what it is today. The problem you are seeing in your plots is one of compounding and scaling. Think of it this way- back in the mid 70's the magnitude of NASDAQ pricing was around \$100. Today it is on the order of \$4000, a change of 40x. In linear terms, a 1% change in the index today ...


3

Your best bet might be to find lists of the companies on each exchange and cross-reference them with a list of the companies on the Russell 2000. It shouldn't be too hard to write a little script in python or something that does the comparison for you. It appears that nasdaq.com has a tool that allows you to download csv lists of the stocks listed on the ...


3

As @babelproofreader mentioned, I recently blogged about the Roll model (see the original paper), which provides a very simple method for inferring the bid/ask spread based on trade prices. In short, you can estimate the cost using using the covariance: $c = \sqrt{\gamma_1}$. Where $\gamma_1$ is the $Cov(r_t, r_{t-1})$. (The R code is provided in my post). ...


3

I was able to identify significant participants by order size on CME exchange. I think ITCH is even more informative that CME's data format. The trick is to learn very closely the incremental data and the order in which this data arrives. We can assume that exchange's Matching Engine and its market data distribution algorithm are programmed machines, ...


2

ITCH does not disseminate any identifiers for the buy-side. They have a match number (used to correct or break trades) and a reference number (for displayed liquidity) and that's it. No other identifying features are present.


2

To construct best bid/ask from ITCH you must build a book incrementally from the messages in the data. Every message, except for system oriented messages, and non-displayed Trades, represent an order or an action on an order. Process the data, build a book, and you will naturally be left with the best bid/ask at the top of each side.


2

Simply put, no, you won't find this. The most basic one-port ITCH feed with no redistribution rights runs \$750/mo. Historical ITCH data which is useful for backtesting is \$1,000/mo. with a 12 month initial minimum contract. Fees for distributors are much, much more expensive (all costs can be found on the NASDAQ OMX website), and the restrictions on ...


2

NASDAQ provides a list of traded stocks. It is available on their FTP server: ftp.nasdaqtrader.com. There you will find two files of interest: nasdaqlisted.txt and otherlisted.txt. nasdaqlisted.txt lists the NASDAQ stocks. otherlisted.txt contains a field that identifies the exchange, which includes NYSE. None of these will give you the CIK, but the ...


1

Always use a semi-logarithmic scale when looking at prices. It makes percentage moves of equal heights on your graphs.


1

What Tick data you have in mind? NASDAQ ITCH is tick data but you have to construct the limit order book yourself to keep track of the best bid and ask price for each stocks. Not a trivial task. If you get TAQ data, you will get the best bid and ask (NBBO) but TAQ data has some issues like no odd-lot trades and trades are not mark buyer or seller initiated.


1

The solution to my question can be found at the following webpage : http://rankandfiled.com/#/data/tickers For every stock you have on which stock exchange it is being traded, and the CIK (Central index key) which is exactly what I was searching for. I post it here since it will probably be very useful to many people.


1

SEC site showing Form 25 (delisting) filings from the last 4 years for all listing markets. As @user508 mentioned Nasdaq has then listed on their site here. NYSE lists them on their site here.


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Regarding the second part of your question, - if you have relatively precise timestamps, you can use those to distinguish the cases you're interested. E.g. if one party took all three levels, the timestamps will be very close, or identical.


1

The cross-validation procedure does not turn on the choice of algorithm. Yes - calculate the prediction error of the fitted models when predicting the V'th part of the data. Combine the V estimates of prediction average using a simple average. Subsets should be randomly sampled (roughly equally sized). 2a. Subsets should not overlap. No. As long as the ...



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