# Tag Info

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Recently I attended a presentation by the first author of the following paper who gave us quite a creative and illuminating (kind of meta-)use of random forests in Quant Finance: All that Glitters Is Not Gold: Comparing Backtest and Out-of-Sample Performance on a Large Cohort of Trading Algorithms (March 2016) by Thomas Wiecki, Andrew Campbell, Justin Lent, ...

10

This is indeed an interesting question. According to this website, a paper by Goldman Sachs [Tierens and Anadu (2004)] proposes three alternative methods for estimating average stock correlations: Calculate a full correlation matrix, weighting its elements in line with the weight of the corresponding stocks in the portfolio/index, and excluding ...

10

What they gave you is Newton's formula. If you have a function $f(x)$ then you can find the value $x_0$ such that $f(x_0) = 0$ by this method. It uses the derivative $f'$ which in your case is the vega. Your function is: $$f(x) = BS(x) - M$$ where $BS$ is the theoretical price with volatility $x$ and $M$ is the marketprice. Then $f'(x)$ is the ...

8

1) Why would you trade the error on the residual instead of creating a long/short factor model and trade expected returns? I would posit that the biggest reason people do this is for orthogonality of return. There are about 2,000 incredibly mature firms trading value, momentum, vol, etc. You would be competing with the likes of AQR, LSV Asset Management, ...

8

In addition to getting the right transition model for the Kalman filter, the main obstacle to optimizing filter performance is to implement an optimal initialization. I use an iterative approach to initialize or "tune" the Kalman filter, known as adaptive tuning. I do this because I've found alternative methods of initializing the Kalman filter (such as the ...

6

I just want to add to vonjd's answer some info on the comparison of the 3 methods. This is too big for a comment so I'm posting as a separate answer but please upvote his answer, not mine. Do the differences in methodologies matter in practice? To gauge the practical importance of the biases in methods 2 and 3, we calculate the weighted stock correlation ...

5

As with many machine learning technologies, you can run a separate training and testing phase before deploying it live for prediction. All it does is build a collection of decision trees based on the parameters you give it - if the output field is a factor, you get classification (a finite enumerated set of values); if it's numeric, you get prediction. One ...

5

I have not used random forests myself but I know of a guy who applied this classification technique to machine learning algorithms applied to pattern recognition. Thus I think its advantages over classic regression approaches can be applied to discern patterns in financial data, though I get the impression that it vastly overfits the data and thus you end ...

5

Of course estimating expected returns is the very core of portfolio management. Finding a useful covariance matrix too. To find both fills a book. So I first thought about closing the question. But it is a chance to discuss today's approaches. A nice approach that is very up-to-date where mementum investing seems very fashionable is the following: Momentum ...

4

Keep in mind that Benford's law is not a universal or natural law. A violation of Benford's law is neither a necessary nor a sufficient condition to prove a flaw or a quality issue in the data. At the best, it can give you a hint, but it should not be trusted blindly. Moreover, note that for some types of data the law will not work at all, such as e.g Likert ...

4

2) Alternative to Fama-MacBeth is Fama-French approach. Explanation of difference see, for example, here: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1271935 Fama-French approach was used by Carhart (introduced momentum), Pastor-Stambaugh (introduced liquidity), Fama-French themselves (used it to build 5-factor model), and many other (elsevier or ...

4

PX_BID and PX_ASK are the static equivalents of BID and ASK, the latter two of which populate in "real time" (i.e. as they are dynamically updated). So the PX_BID and PX_ASK values are dependent upon when you pulled the data. Bloomberg's source depends on the asset in question and the exchange on which they are listed, but the data does come from the ...

4

It is very hard to answer this quiz as people might be good at different at tools. For example, if you are good at VBA, then you can achieve the same effect compared to R in most cases. The following parts are the reasons why I prefer to R based on my own situation. 'package'. This is the most obvious strength of R over Excel in terms of convenience. You ...

4

Theoretically speaking (as it's done in financial textbooks at b-school level), variance and covariance are calculated on historical performance of asset classes, forward looking returns are CAPM calculated returns. ARIMA. Practically speaking, ARIMA is useless for predicting long term returns (or portfolio management if you wish). Why? A short answer is ...

4

By definition, your loss cannot be positive, so you'd set the VaR to zero. But it really depends, on how you calculate your VaR. If you calculate your returns, sort them and look at the 5% quantile (which, as you say, may be positive), then you'd simply set your VaR to zero. But if you treat your returns as realizations of some (unknown) random variable, ...

4

The question above looks somewhat confused. Where's the error term? A recipe for a standard calculation It's customary to work with monthly returns. For each portfolio $i$, calculate monthly excess returns $R^x_{i,t} = R_{i,t} - R^f_t$ where $R^f_t$ denotes the 1-month risk free rate. Calculate or download the monthly excess return of the market $R^m_t - ... 4 It depends upon how you define risk. Assume a constant, positive equity risk premium and an equity index following geometric Brownian motion (GBM): $$d \log S_t = \mu \, dt + \sigma \, dZ_t = (\hat{\mu} - \frac{1}{2} \sigma^2) \, dt + \sigma \, d Z_t.$$ Let$T\$ denote the investment horizon. The standardized return is normally distributed as $$Z = \... 3 You can just take expectations on both sides of your SDE/corresponding integral equation and obtain an ODE on the expectation function m_t = \Bbb E[x_t]:$$ \dot m = \theta(f - m) $$which you can easily solve using ansatz m_t = c_t \mathrm e^{-\theta t} which brings you to$$ m_t = x_0\mathrm e^{-\theta t} + \theta\cdot\int_0^tf(s)\mathrm e^{\theta(...

3

The key assumption is that there is no time-series correlation between the error terms. Fama-MacBeth can deal with cross-sectional correlations. See Samuel Thompson's "Simple formulas for standard errors that cluster by both firm and time" in the Journal of Financial Economics (2011) for a treatment of different regression methods for testing equity ...

3

I would argue that indeed none of the so-called stylized facts you mentioned can be explained by classical economic theory. That there was a gross delta between the predictions of classical economic theory and empirical data was foremost found out by Benoit Mandelbrot as far back as 1963 in his seminal paper: The Variation of Certain Speculative Prices In ...

3

Some advantages of R over Excel: R is a scripting language, which allows to record a data manipulation script once and reuse it multiple times. R, as a [scripting] programming language is much more flexible than very limited Excel's GUI. In fact, R has become a de facto statistical programming environment, which delivers most recent statistical techniques. ...

3

There are lots of different sources out there where you can find various quantitative strategies. Usually, different blog aggregators like https://www.r-bloggers.com post on their websites interesting new approaches and techniques. You can also find great deal of information on Quantopian's dedicated page here: https://www.quantopian.com/posts/trading-...

3

Richard's answer is the correct answer to a slightly different question. I think what you’re asking for is the weighted average option implied volatility for a stock. The implied volatility of a stock is analogous to the CBOE’s VIX Index for the S&P 500 Index (other securities have IV indices as well). The VIX uses a known methodology for imputing the ...

3

Preliminary The main result of the Fama-MacBeth procedure is to calculate standard errors that correct for cross-sectional correlation in a panel. It is a commonly used method due to it's easily approach, and with regards to the time it was developed (1973), modern techniques like clustered robust standard errors were not yet invented. In this context, it ...

2

You should de-trend to whatever frequency scale you are testing. I.e. 1 min means de-trend 1 min data. Merely by moving to higher frequency data, you are eliminating much of the systematic bias present at higher scales -- as 1) you have many more samples to compare (minimizing standard error) 2) At smaller intervals, the drift component also shrinks ...

2

It could help with things like fraud detection, analysis of bankruptcy probability, default risk, unsupervised learning for qualitative/descriptive purposes, or for a purely backwards looking supervised analysis on returns again for descriptive/understanding purposes (variable important, etc, perhaps impulse response analysis). It may also be good at ...

2

Recently I found a book on earnings trading but did not have time to read thoroughly. Trading on Corporate Earnings News - John Shon I also had spent some time to see earnings surprise effects and it is a quite interesting but not easy to use topic. There is certainly a jump if the estimates and announced earnings have a large mismatch but the magnitude ...

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I think there is a slight misconception into the purpose of an economic theory. The market is a complex entity to be modeled and yes, it is neither efficient nor arbitrage free but it is trading and there is a price process that corresponds to the market one. You could say that classical economic theory has failed, but I would argue the idea of a theory is ...

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