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26

It's an interesting question. I particularly agree with the $\mathbb{Q}-\mathbb{P}$ dichotomy mentioned by many. I would add to the other answers that, come to think of it, the Black-Scholes postulated Geometric Brownian Motion could be interpreted as an AR(1) process on the logarithm of the stock price as you discretise the SDE from which it is a solution,...


25

By "cryptography" you mean information theory. Information theory is useful for portfolio optimization and for optimally allocating capital between trading strategies (a problem which is not well addressed by other theoretical frameworks.) See: --- J. L. Kelly, Jr., "A New Interpretation of Information Rate," Bell System Technical Journal, Vol. ...


25

The risk-neutral measure $\mathbb{Q}$ is a mathematical construct which stems from the law of one price, also known as the principle of no riskless arbitrage and which you may already have heard of in the following terms: "there is no free lunch in financial markets". This law is at the heart of securities' relative valuation, see this very nice paper by ...


20

I would say in the context of trading in general (for HFT see my comment above) further developments of recurrent neural networks (RNN), e.g. so called historical consistent neural networks (HCNN) together with forecasting ensembles, are state of the art. I published an article on that which will be published this month by Springer Verlag (Zimmermann, ...


17

There's a lot of people here talking about how GAs are empirical, don't have theoretical foundations, are black-boxes, and the like. I beg to differ! There's a whole branch of economics devoted to looking at markets in terms of evolutionary metaphors: Evolutionary Economics! I highly recommend the Dopfer book, The Evolutionary Foundations of Economics, ...


14

You can forecast stock prices thru time-series models, cross-sectional, or panel models. There is considerable variation within these categories. In time-series models you would use an auto-regressive model such as an AR(1) where the independent variable is the dependent variable lagged by one period. Naturally, an AR(2) would consist of 2 lags and so on. ...


14

I think you need to differentiate between Q-quants vs P-quants. The former might not use Econometrics, but P-quants use them a lot.


13

In a very, very general sense, what Renaissance Technologies does well [and others try to do, many do less well] is understand where the "true" signal is (i.e. where prices should be) and what is noise (i.e. over-/under-reactions by others in the market) in the total signal of market prices. Generally, trading profits are made by taking the opposing ...


12

I honestly think that most people who could be able to answer to this question simply won't either because they actually work for Renaissance, or because they work in a top quant hedge fund and they'll keep it a secret. I discussed this topic once during an interview and the guy said "we'll discuss this further if you get the job" lol. About papers, I'm ...


11

Traditional econometric (time series) models are of little or no value in forecasting market prices for purposes of "making money", i.e, generating excess return over a benchmark in an asset management setting. They have some limited value in strategic and tactical asset allocation. The ineffectiveness of time-series modeling in asset management stems ...


10

A few thoughts. Yes, your return series are autocorrelated (i.e., stocks don't exactly follow a random walk), so you should use Newey-West standard errors. If you do this as a univariate regression $$R_{i,t} = \alpha_i + \beta_i R_{j,t-1} + \epsilon_{i,t}$$ then there's almost certainly an omitted variable inside $\epsilon$ that is moving both $R_i$ and $...


10

Upon close reading, this appears to be 3 (interesting) questions, not one. I'm not sure if the mods have the tools needed to split it up, so I'm just going to write down the three questions as I see them and then deal with them one by one. Note, it is simpler for me to talk about variance instead of volatility. This has no material impact on the answer. ...


9

You may want to consider splitting two important, yet very different concepts: Pricing a derivative security with contingent payoff and forecasting an asset. Pricing a derivative can be achieved through setting up a hedge portfolio and track its evolution and "value" at any point in time before the derivative security pays off. Risk-neutral pricing is a ...


8

I have been learning more about speech recognition motivated by its application to financial forecasting. I have identified a couple connect points. Turns out each of these tools can and are regularly used in financial modeling as well. Use of Markov Models Use of Fourier transforms (sine/cosine decompositions) Use of component analysis


8

Deutsche Bank's Quantitative Strategy (US) team put together the following piece on this topic (note: their research is available for clients, but I found that somebody uploaded the piece to a sketchy web site). In case the link dies, some of the academic papers they site are: Akbras, F., E. Kocatulum, and S. Sorescu, 2008, “Mispricing following public ...


8

Having thought about this I think the following reason is also important and wasn't mentioned so far: When you look at the inner working of this whole class of econometric models it all boils down to the following: It is possible (under some reasonable assumptions) to express any $MA(q)$ model as an $AR(\infty)$ model (and vice-versa for expressing $AR(p)$ ...


7

A cautionary tale on all these approaches it told by Tim Loughran and Bill MacDonald in the Journal of Finance, 2011 (When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks, here). In their analysis they show that the commonly used Harvard Psychosociological Dictionary is inadequate for sentiment classification in a financial ...


7

I just made a Genetic Algorithms calculator you can try at http://www.gregthatcher.com/Stocks/GeneticAlgorithmCalculator.aspx I'm not a "quant expert" like all of you (I'm just a programmer), but here is what I've found. 1.) If you set the constraints up correctly, the results are amazing. e.g. you can get portfolios that have very high return and low risk....


7

CXO-Advisory investigate the claim of this paper and conclude that evidence indicates that changes in open interest in futures markets are strong predictors of returns for associated asset classes, even after controlling for a number of conventional predictors. They state that investors may be able to exploit these predictive powers via tactical asset class ...


7

I don't think that it is a real applicable trading system but it is more general work concerning the connection between chaos and financial markets. A good starting point is this (relatively recent) article: http://deepeco.ucsd.edu/~george/publications/08_ecology_bankers.pdf You can find his publications here: http://sio.ucsd.edu/Profile/gsugihara#pubs


7

As far as I know the short answer is negative: there isn't a well developed theory of how to forecast cross-sectional realized volatility. From the perspective of statistics/econometrics, most of the recent research is still trying to find its way around estimation of cross-sectional realized volatility, and so far even in these area the progress is slow. ...


7

The only "indicators" that I believe add value in academic research are time series smoothing functions. ( I don't call them indicators because they are all lagging thus do not indicate anything into the future). There is clear empirical evidence and a number of academic papers have been published that show that none of the common indicators (common defined ...


6

Relevant paper: Efficient Estimation of Volatility using High Frequency Data (Zumbach, Corsi, and Trapletti 2002) http://www.olsen.ch/fileadmin/Publications/Working_Papers/020221-efficientVolEstimator.pdf


6

Personally, I've dealt with volatility estimation using wavelets with HF data. Estimations seem reasonable and also it's fairly quick computation wise compared to other methods. There's quite a bit of literature on the subject, I would recommended starting off with An Introduction to Wavelets and Other Filtering Methods in Finance and Economics


6

Maybe a better question title is "Can futures market open interest predict commodity, treasury, and equity returns"? I saw this paper in an earlier form and it still baffles me. Superficially, it makes sense that price*quantity holds more information than just price when quantity can change quickly (i.e., outstanding futures contracts changes more quickly ...


6

Have you considered fitting ARIMA with exogenous regressors model? Linear regression with autocorrelated errors might be appropriate. R can do this with the arima() function via specifying the xreg argument.


6

Benoit Mandelbrot, pioneer of fractals theory, has a set of papers on the Multifractal Model of Asset Returns. I believe when most people in finance are saying they use "chaos theory", they are really referring to fractals, and particularly multifractals. I am skeptical as to whether it can be useful to predict stock prices or even to predict volatility, ...


6

They are not mutually exclusive. For example, the class you refer to as "econometric" are simply linear regression models that include as factors prior returns or residuals of the return series sometimes with weightings on the observations. You could easily design a neural network with no hidden layers and the same inputs. So each of the econometric models ...


6

Pre-payment rates are difficult to forecast because of path dependency. The historical interest rate path - not just current market conditions and borrower characteristics - matters because borrowers may have exercised their right to call the mortgage bond and re-finance if rates had previously been at lower levels than the current rate. None on the ...


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