# Tag Info

42

Consider the standard error, and in particular the distance between the upper and lower limits: $$\Delta = (\bar{x} + SE \cdot \alpha) - (\bar{x} - SE \cdot \alpha) = 2 \cdot SE \cdot \alpha$$ Using the formula for standard error, we can solve for sample size: n = \left(\frac{2 \cdot s \cdot \alpha}{\Delta}\...

25

I can help you beat random walk 'in the way you want', i.e. the expected value $E[\$]$will always be positive even assuming no drift. However, I have to warn people that$E[\$] > 0$ is NOT really an adequate condition for 'beating' in reality (at least to myself). Let's define some mathematical notations for derivation, and rephrase (simplify) vonjd's ...

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A quick google search retrieves the syllabus for the Stanford STATS 242 class. You can find it here. Just in case it's taken down at some point I'll copy-paste the source material. Keep in mind that I have no idea if this material is good or bad -- I didn't make this list. Also keep in mind that it contains treatments of what does and does not work. With ...

18

I did some digging and found the following papers - most of them offering quite a distinct perspective compared to classical option pricing theory! Stock Options as Lotteries by Brian H. Boyer et al. (2011) The Efficiency of the Buy-Write Strategy: Evidence from Australia by Tafadzwa Mugwagwa et al. (2010) The following is my favorite: You could do some ...

18

To respond to your questions in order: The formula looks deceptively simple. Does it actually work? That depends on what you mean by "work". Chan spends the rest of the chapter discussing the pitfalls of investing at "full Kelly". Do professionals use it at all? Professionals may maximize geometric growth, but I don't know anyone who does so with such a ...

17

Yes. First, it is much easier to proceed if you standardize the output of your forecast so they are in the same units (returns, for example, or probabilities of an event/condition occurring). After you have done this, there are 3 general approaches: Signal weighting: Then you need to define a weighting scheme for your factors. Richard Grinold has an one ...

16

I unfortunately can't point you to a great book on the exact subject that you're describing. The closest thing for beginners is "Quantitative Trading". It's a reasonable introduction, but I really wouldn't recommend it as a primary source. The author is at best incomplete (if not misleading) on a number of issues. My favorite book at the moment is ...

15

An interesting starting point is The Cost of Latency by Moallemi and Saglam. After setting up a simple order execution problem --- in which a trader must chose between a market order and a limit order and guarantee execution over a fixed interval $[0,T]$, they proceed to derive a (complex) close form solution for the optimal strategy and evaluate the impact ...

14

First of all a very warm welcome to Quantitative Finance Stack Exchange :-) Concerning your question there are some basic points that seem to be unclear. In general "Quantitative Trading" by Ernie Chan is a good starting point for learning about quantitative trading strategies. The problem is of course that in this small book there are many concepts whose ...

14

There are few things to consider. Trading moves the price, to minimize market impact and maximize return it is generally optimal to split an order in several child orders. See the Kyle model. Splitting optimally dependents on specific assumptions that you make. The simplest (and first) approach is that of Berstsimas and Lo (Optimal Control of Execution ...

14

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, ...

13

Accounting is a vital skill if you end up in a managerial position, and unless your career goal is to always be a cog in someone else's clockwork, then you will eventually find yourself in a managerial/senior partnership position even through quant research. I still play a critical role in my firm's quant strategies team, but here's a few things I've had to ...

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There are two key concerns (which in practice, may be difficult to distinguish): Previous research overestimated an effect. The effect shrinks over time. 1. Problems with reproducibility and replicability Previous research may have found an effect, but was the effect really there? There may be problems with: Reproducing results using the same data. ...

12

HFT seems to be the big money making mystery machine these days. That's not correct. By its very nature, HFT can only produce a limited amount of revenue. The big money makers are still the large hedge funds that charge 2-and-20 on their \\$10B worth of assets. There are not too many players there at the moment so markets are not completely efficient? ...

12

I'll not say how most people do it, but rather how I think most people should do it. You should compare the actual strategy with a number of goes of randomly trading through the time period using the same constraints as the strategy. Basically this is a way of not mixing species of fruit and seeing what the distribution of luck is for the particular fruit ...

12

Windham Capital Management is using hidden markov models for their Risk Regime Strategies. Mark Kritzman, who is also CEO, has published an article about the general outline of the strategy (with source code so you can replicate the results!): Regime Shifts: Implications for Dynamic Strategies (corrected August 2012) by M. Kritzman, S. Page, D. Turkington]...

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If you do this, you would destroy the value of the statistical tests that you performed on the backtest. You had a hypothesis that the strategy would make money, but the hypothesis was rejected. You cannot say "I will accept the hypothesis that the opposite strategy is successful"; no statistician would agree with this conclusion. In that case, you might as ...

11

Interestingly enough there is no scientific theory that suggests what fraction of the data should be assigned to training and testing and results can be very sensitive to these choices. From Quantitative Trading by Ernest Chan (p. 53-54): Out-of-Sample Testing Divide your historical data into two parts. Save the second (more recent) part of the data ...

11

A good place to start learning about option market making using quantitative techniques is Euan Sinclair's Option Trading (chapter 10 is devoted to market making techniques). He also gives a decent introduction to a more sophisticated quantitative market making technique which he calls information-based market making. Specifically, he explains how to apply ...

11

This answer summarizes some of my comments. HFT is certainly a very hot topic these days, but it's hard to point to any one reason. A large part of it is the mystery and the profits, but also part of it is the relative novelty. Note that there is no lack of papers about medium and low frequency strategies, it's just that they are not labeled as such. Medium ...

11

The only way to find out is to try it! It shouldn't take very long to write some simple code to simulate the computations you plan to do, and run it in a loop. With current versions of Visual Basic (VB.net), performance should be comparable to Java in most cases because the basic technology (compiling to intermediate code and then running a just-in-time ...

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A public order book gives traders information not only on the current price of a security, but also the volume and structure of the entire supply and demand schedule. Such information can be used for arbitrage and market manipulation strategies in various ways: Spoofing: Inserting a large limit order as an apparent buy or sell signal which is canceled any ...

10

All .NET languages are perfectly able to compete with the speed of C and even FORTRAN. It all depends on if they are used the correct way. 1) Both Java and .NET have considerable longer startup times than most native app. Therefore, you will have to have the application running and not starting it over and over on request. 2) Memory management is crucial ...

10

I found this solid overview of different trading algorithms by Deutsche Bank Research: Trade execution algorithms Designed to minimise the price impact of executing trades of large volumes by ‘shredding’ orders into smaller parcels and slowly releasing these into the market. Strategy implementation algorithms Designed to read real-time market data and ...

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I am not sure Dark Pools (DP) have been created to avoid "market manipulation". They have been created by firms because they found an advantage to create them (see Market Microstructure in Practice, L and Laruelle Eds.). The main reasons have been: spare market fees, for DP created by brokers (like UBS MTF); spare market impact, for block pools (like ITG/...

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Since I, too, have been very interested in this question, I will share some of my findings in the dual hope of encouraging comments on the papers and eliciting more activity on this question. Ammann, Skovmand, and Verhofen (2008): Implied and Realized Volatility in the Cross-Section of Equity Options Ang, Bali, and Cakici (2010): The Joint Cross Section of ...

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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

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This is an evergreen. I've been discussing this with many people - without any clear-cut conclusion. The answer and the preferred solution depend on your trading style (e.g. frequency), your skills, the size of the team, and many other factors. For simplicity, I call "Research" the Matlab/R/etc. environments, whereas "Live" refers to the re-programmed C++/...

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Sell Side Macquarie Quant - Venkat Eleswarapu Bernstein Research - Vadim Zlotnikov Nomura - Joe Mezrich JPMorgan Investment Strategies series Societe Generale - Alain Bokobza Independent CXO Advisory Empirical Finance Blog Russell Indexes: Research and Insights MSCI Research Papers Axioma Research Papers

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At higher frequencies the coastline is longer. Thus you can be more selective in your entries, or trade more. And by trading more you can get a higher statistical relevance for you system. When it will stop having an edge, you will be able to stop trading it before it eats into your previous profits. ie: if each day you make 0.5%, in 80 days you will have ...

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