# Tag Info

26

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 ...

19

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, ...

14

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 ...

14

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. ...

14

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 ...

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]...

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 ...

11

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 ...

10

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/...

10

Here are some general directions: Alternative Risk Premia The ARP, or "smart beta," space has gained a lot of tractions over the past few years. These are rule-based strategies that provide systematic exposures to risk factors that have historically generated positive excess returns. Some of the best-known factors are, of course, trend, value, carry, etc. ...

9

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++/...

9

High VIX arguably leads to less predictability of the market factor (i.e. market timing), but high volatility does lead to greater predictability of the cross-section of returns. Indeed, linear risk factor models have higher explanatory power during bear markets. However, your goal is to build a better market timing model where the forecasts (and perhaps ...

9

A Sharpe ratio of at least 1 in backtesting is a promising start, but that is just one of many statistics of interest. The Sharpe ratio measures return per unit volatility, i.e., return per unit risk. Some other important Sharpe-like measures with different definitions of risk include: Return per unit turnover (aka yield): A high yielding strategy is more ...

9

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, ...

9

I would say that most ML methods risk overfitting and it depends very much on the asset class. The only area where more sophisticated ML methods such as deep learning appear to make a major difference is in cash equities, where the feature space is very rich (NLP, news and announcements, corporate earnings, other financials) and the data is relatively good, ...

9

If your strategy truly has no directional bias, then the benchmark should be cash (ie whatever you would earn using the capital in your trading account and taking no risk).

8

This depends a little bit on your definition of volatility arbitrage but in general what is meant is a strategy that takes advantage of the difference between implied volatility and realized volatility. Normally you receive implied variance and pay realized variance. This strategy is the classical example of picking up nickles in front of a steamroller ...

8

I believe the concept you are looking for without really knowing it is the information coefficient (IC). IC is the correlation between your forecast and actual subsequent returns. If your IC is 1 (perfect correlation, also known in this context as perfect foresight), then your maximum return is the compounded sum of the greatest daily return of any stock ...

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

The short answer (which represents one way of surely many ways to do it) is to watch the t-stat of a performance metric such as information coefficient vanish over time. IC is the correlation of predicted expected returns from your alpha strategy to the underlying benchmark. Look at the expected returns your alpha strategy predicted over the past N time ...

Only top voted, non community-wiki answers of a minimum length are eligible