# Tag Info

4

Start with http://www1.nyse.com/pdfs/closings.pdf which covers all closings through 2011 then use the following information from official exchange sources to get dates up to present day. 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/...

3

If you just want to run some simplistic technical analysis on quotes, then select the last quote for each unique timestamp. That will ensure that you don't have duplicate timestamps. If you must have it evenly spaced (i.e. no gaps from one second to another), then you can reuse the previous quote to fill-in the missing value.

3

To answer your first question: You need to make all sharpe ratios annual, or quartely, or monthly to be comparable. All of them must have the same periodicity. To answer your second question: From the year returns, you can compute the monthly returns by making $(1+R_{t+2})/(1+R_{t+1})$ and then compute the monthly sharpe ratio, or alternatively, just ...

3

Just use the what most finance research papers use, i.e. the risk-free rate from the Kenneth French data library. http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html The rates are annual. So if you want log returns just take the log of $1+r^f_t$ and divide by 365.

2

You can download the time series of e.g. S&P500 prices from NYSE, then their dates should well represent approximately the real NYSE trading days.

2

To help you understand why you need to follow recipes (like chrisaycock's) just have a look at your tick data. You will find ticks clustered at some points in time while they seem scarce at others. If you proceed with your recipe 2, you will lose those clusters of activity and stretch them out. In periods of low activity you will condense the market. ...

2

I would say it could be short for annual turnover (precent/portfolio) Higher portfolio turnover often means higher transaction costs. The definition is usually the lesser of all buys and sells in a year divided by the average monthly NAV of the strategy. (Morningstar) Be aware that turnover numbers come in all colors and flavors and can in or exclude ...

2

I think you need to ask your question differently to get better answers than mine. Your Black Scholes part has two problems. First positive infinity should be negative infinity. Second, you are assuming zero dividends in Black Scholes but you are assuming a possibly positive div yield q in the CEV part. If the div yield q is sufficiently positive in the ...

2

The Max Sharpe Ratio portfolio is determined ex-ante, using past data available at time t (say the previous 10 years returns and covariances). It is optimal given that data. At time t two people invest: A invests in the Max Sharpe Ratio portfolio and B invests in the Equal Weighted Portfolio. At time $T > t$ we compare the results: it is possible that (...

2

Choose John Deere. Some firms provide minimal disclosures, other firms are very good at making investors aware of every detail. Deere provides excellent and complete disclosures, well beyond what the law requires. It is a great firm to teach and learn with. As to what should you look for, that depends on the industry. Consider the electric utility ...

2

$$\log{\left(\frac{\sum_i{w_iP_{i,t+1}}}{\sum_i{w_iP_{i,t}}}\right)} = \log{\left({\sum_i{w_iP_{i,t+1}}}\right)} - \log{\left({\sum_i{w_iP_{i,t}}}\right)} \\ \neq \sum_i w_i \log(P_{i,t+1}) - \sum_i w_i \log(P_{i,t}) = \sum_i w_i \log\left(\frac{P_{i,t+1}}{P_{i,t}}\right)$$ Log-returns are not linear. So the log-return of the portfolio would have to be the ...

2

First I thought about voting to close this question as it deals with Matlab synthax a lot. I ignore the Matlab stuff. You have 5-minutes data. So an estmator of volatility over any sample of size $N$ (e.g. 100) will be an estimator of the vol of your 5-min returns. Usually volatility is quotes as "per annum" or "pa". This means that using the square root of ...

1

The problem with your question is phrase "continued success". Any formal test exploring linearity and upward direction would be based on the assumption that the future data are well-represented by the historical data. If this was the case, you could estimate model  \text{Portfolio} = \beta_0 + \beta_1 * \text{Day} + \beta_2 * \text{Day}^2 + \...

1

OK, so the 'alternative' mechanism (cumprod, weight, divide by row sum) is O(msec) and produces identical results. Old stupid method: 1.705sec for a 200×9 table. New sexy method: 0.004sec for the same table. And it's only 4 lines of code. T01 = cumprod(1+inTable/100,'reverse'); T02 = inWts' .* T01; R01 = sum(T02, 2); W01 = T02 ./ R01;

1

I will try to provide a complete answer, although valuation can get way too far. So let's stick to the essentials. Balance Sheet Cash & Cash Equivalents: Cash in bank and short-term securities, that can be used to settle short-term liabilities. Can be used to quantify liquidity risk (e.g Cash Ratio) Tangible and Intangible assets: What is their portion ...

1

There is no 100%-proof answer to this question, however there are few heuristics that minimize number of bad data points. 1) Cross check with time series from other sources: finance.yahoo.com, eodhistoricaldata.com, etc. While each of these sources is not ideal, the combination may bring a conclusive result. Yahoo finance tends to be more reliable than ...

1

Despite the rather unconventional terminology used I would say you are pretty much spot on with what you are doing and what you try to achieve. I would, however use log returns in order to get an identical percentage no matter whether you measure the distance from 100 -> 90 or 90 -> 100, for example. You can also standardize the value you capture by ...

1

Most simply look to the German Mark as a proxy. It should suffice, assuming you don't require detail greater than what OHLC daily periodicity data offers. A composite of Marks(75%), Francs(15%), Lira(5%) and Pesos(5%) would offer greater granularity if necessary.

1

From how you outlined your solution, you are computing the mean variance portfolio with minimum risk and with target return $\overline{r}$. I'd say that you are solving an optimization using Lagrange multiplier method given the values of matrix A. $\lambda$ and $\mu$ are the Lagrande multipliers: these parameters measure the sensitivity of the Lagrange ...

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