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69

There seems to be a basic fallacy that someone can come along and learn some machine learning or AI algorithms, set them up as a black box, hit go, and sit back while they retire. My advice to you: Learn statistics and machine learning first, then worry about how to apply them to a given problem. There is no free lunch here. Data analysis is hard work. ...


28

My Advice to You: There are several Machine Learning/Artificial Intelligence (ML/AI) branches out there: http://www-formal.stanford.edu/jmc/whatisai/node2.html I have only tried genetic programming and some neural networks, and I personally think that the "learning from experience" branch seems to have the most potential. GP/GA and neural nets seem to be ...


19

I know that I have seen things like this in the past. Wasn't there something recently that used Twitter? Here are a few recent papers as examples, although I will be brutally honest that I don't know if they speak to your decent quality requirement: "Trading Strategies to Exploit Blog and News Sentiment" (Zhang, Skiena 2010) "The Predictive Power of ...


17

First, let's speak about perceptrons in general: their input $X_0$ is a $K$-dimensional vector. So if you want to use $(P_{bid}(t),P_{ask}(t), Q_{bid}(t),Q_{ask}(t))$, it would mean that without any effort (but later we will see that is would be better to do some efforts, as usual): $$X_0(t)=(P_{bid}(t),P_{ask}(t), Q_{bid}(t),Q_{ask}(t))'\in\mathbb{R}^4$$ ...


11

Two aspects of statistical learning are useful for trading 1. First the ones mentioned earlier: some statistical methods focused on working on live datasets. It means that you know you are observing only a sample of data and you want to extrapolate. You thus have to deal with in sample and out of sample issues, overfitting and so on... From this viewpoint, ...


11

The short and brutal answer is: you don't. First, because ML and Statistics is not something you can command well in one or two years. My recommended time horizon to learn anything non-trivial is 10 years. ML not a recipe to make money, but just another ens to observe reality. Second, because any good statistician knows that understanding the data and the ...


11

Just FYI the Reuters product is called NewsScope. The selling point is that they provide a sentiment reading per news item so the user doesn't have to do any NLP. If you have a Reuters sales rep or contact them then they can get you several research/white papers that are interesting. Here are the ones I have been able to find online (my sales rep has ...


10

Most contemporary NN systems are just made to use the raw price time series for input (maybe with some kind of simple normalization), but for my thesis I wrote a system which traded equities with an ANN with technical indicator inputs (MAs, MACD, even pattern matching for stuff like Head-Shoulders, support levels, etc.). So at least conceptually it's ...


9

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 ...


8

The predictor variables would consist of the input layer to the neural network. The output layer would consist of your target. You need to specify the hidden layer, number of nodes per layer, the learning algorithm, and the learning algorithm stopping criteria. Typically inputs are normalized (first-differenced, z-scored, etc.) before inputting into the ...


8

An equity represents ownership of a company and may be thought of as a derivative on the cash flow. For this reason, equities are valued through discounted cash-flow (DCF) analysis. An option is a right, though not an obligation, to buy or sell an asset at a fixed price at some point in the future. As per Black-Scholes, the value of an at-the-money option ...


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

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 ...


7

You can look at equity as a call option on the firm. In theory this illustrates the differences between holding equity or debt. The quick and dirty is that equity holders own the firm, but only after the debt holders are repaid. If you have a simple levered firm with one outstanding debt issue, it as though the equity holders have a call option on the firm ...


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.


5

One basic application is predicting financial distress. Get a bunch of data with some companies that have defaulted, and others that haven't, with a variety of financial information and ratios. Use a machine learning method such as SVM to see if you can predict which companies will default and which will not. Use that SVM in the future to short ...


5

I will break up your question in to some parts to make answering easier. "people use various economic indicators with their networks (moving average, MACD, etc.) However, how do these come into play in a NN context?"--the 'indicators' MA, MACD etc. come from the data. They are measures of the data capturing some aspect. You could try to capture/replicate ...


4

“Make things as simple as possible, but not simpler.” The problem you want to avoid is (near) multicollinearity. The tip-off will be that adding/removing a regressor will significantly change the coefficients on the other regressors. In practice (well, in the research that I read) I rarely see this explicitly tested. If you think that you have ...


4

I can offer three suggestions: (a) Since any model, however sophisticated, will miss tail cases (such as Oct 2008) I would increase the number of high-frequency factors (eg weekly jobless claims - I don't know if that is a relevant example in your case - but just to give you an idea) in the model. Not only does that make the model more responsive to current ...


4

Hopefully these ideas open up some solution strategies. A. Calibration approach: In the case of a volatility model such as Axioma's above, you could perform an instantaneous volatility adjustment. Procedure: You build your usual T+H volatility model. You measure the realized volatility and implied volatility of the training set. You measure the ...


3

Although not directly related to financial modeling, I've found the following quotation to be very instructive: "I remember my friend Johnny von Neumann used to say, 'with four parameters I can fit an elephant and with five I can make him wiggle his trunk.'" -- E. Fermi You may also read this: http://mahalanobis.twoday.net/stories/264091/


3

Well vix is a measure of volatitity which would make it an estimate of a second moment for S&P 500 so you might try an arch/garch in the mean type model on S&P. A good starting place for a project like this is to just do Vector Autoregressions on industry groups that you think might be related and see what comes up. N+30 is a long way in the ...


3

Van Belle describes a basic correction for autocorrelation in a t-test, although it may be hard to wedge it into the regression t-test. For the 1-sample t-test of the mean, the correction is to multiply the t-statistic by $\sqrt{\frac{1 - \rho}{1 + \rho}}$, where $\rho$ is the 1-period autocorrelation (or estimate thereof).


3

People seem to think that using ML is going to circumvent the process of actually learning to trade, it doesn't. ML can be used to refine trading ideas, but it doesn't generate them, you need to use your brain for that.


3

There are two excellent choices for implementing prediction markets: (1) Use book orders that stand until filled, just as intrade.com does. (2) Use an automated market maker (like Robin Hanson's) that stands ready to make trades. The book orders model is very simple to implement, but can suffer from very wide Bid/Ask spreads. And, it can be tough to bet ...


3

I think this one has a clear answer (I am solely talking about equities here): The change magnitude is much more predictable than the direction. The reason being that equity volatility is much more predictable than equity risk premiums. Volatility is nothing else but change magnitude and due to the stylized facts of volatility clustering together with mean ...


2

I'm currently working on this task, to apply machine learning to stock trading. However, the concerns raised in other answers are major obstacles. So, I'm taking a different tact. My strategy is more akin to teaching a car to drive - the machine learning is not based on the underlying data, but rather on the driver's reaction to the data. So based on what ...


2

I echo much of what @Shane wrote. In addition to reading ESL, I would suggest an even more fundamental study of statistics first. Beyond that, the problems I outlined in in another question on this exchange are highly relevant. In particular, the problem of datamining bias is a serious roadblock to any machine-learning based strategy.


2

This question has been answered many times over already, though hopefully this will provide a bit more insight. If I understand your question correctly, you're basically asking if you can use BSM as a trading indicator. So let's think about what it really means to be trading an option. Every single variable (i.e. price of underlying asset, strike price, ...


2

There's no rule to answer this question for you. You need some combination of: Judgment: Are the parameters you're including reasonable? Sniff test: Is there theory to justify your parameter choices, or are you just hunting for chance associations? Hold-outs: You correctly mention that the problem is "in sample performance." The solution is therefore to ...



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