Tag Archives: factors

Real factors for Wealth creation with automated trading

Real factors for Wealth creation with automated trading

A newsletter subscriber response from my original email and video:

A comment on your video:


You highlight the 2008 loss below – the GFC correction, these happen usually every 7- 10 years. They don’t happen overnight, they build over period of time and can b e avoided. The canary in the coal mine  and a good indicator is the  yield curve. When you have a yield curve that is flat or inverted, it mean that we may be due for a correction. That is not the case for the US – see http://stockcharts.com/freecharts/yieldcurve.php  This still has a way to go i.e.  “risk on”

On the other hand, commodity economies such as Australia and  Canada are sensitive to demand for resources. Australia is highly exposed to China, so if China “sneezes”, Australia “catches a cold”. The yield curve for China is of concern. See below:


Other factors to be aware of are related to Geopolitics. The three arms of warfare are cyber, economic and kinetic. The last (kinetic) is the most potent, the stock market soars and in the past fortunes were made. Pay attention to the grand chessboard.  (https://www.stratfor.com/analysis/us-naval-update-map-april-6-2017 )


That’s enough from me,


I have 2 ONLINE events scheduled for lated in June:


These include with videos each:


Details here:


Your retirement your stock picks your automated trading


Monday Jun 19 at 7PM EDT

Retirement with investing/trading

→ other types of investments

→ real estate/franchise

Outlook for more serious and ‘profitable’ traders

Benefits automated/algo trading

→ auto pilot







Wealth creation with automated trading


Monday Jun 26 at 7PM EDT

Risk management/Portfolio management/Portfolio Optimization


long term positions

short term (day trading)







Don’t forget upcoming this Monday in Toronto!


(I need to get you confirmed by Sunday so use the Meetup.com links below)


J is for June and Jforex. Also J is for Genius!

Monday, June 12, 2017

7:00 PM


Jack Astors

5051 Yonge St, Toronto, ON (edit map)


June 12 will be a special evening for us. A visitor from Germany will be among us to help guide you through any automation/algo/systematic problems you may have. He specializes using Dukasopy with their propietary platform JForex. Won’t you be there for this highly rare treat?








NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!

Quandl Volatility and risk factors

Quandl Volatility and risk factors

Quandl is pretty impressive but IQFeed is more practical


EDI Stock Prices for China and Hong Kong

Exchange Data International has doubled their offerings on Quandl to include 3 new databases on China and Hong Kong stock prices. These databases include unadjusted and adjusted end of day stock prices from the Shanghai Stock Exchange, Shenzhen Stock Exchange, and the Hong Kong Stock Exchange. Stock price history goes back to 2007.

Start Free Trial

PortfolioEffect Volatility and Risk Factors

Snowfall Systems has released a new database on risk factors and price distributions for more than 8,000 financial instruments including stocks, indices and ETFs. These data include expected return values, volatility, skewness, and other portfolio performance metrics. Data history goes back to 2013.


Start Free Trial

If you have any questions, comments, or have built something interesting using Quandl data, please let me know.

Thanks for using Quandl!

Join my FREE newsletter to learn how I use solid data sources 

NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!

Why expected return factor models and risk factor models are different? Why expected return models and risk models use different factors?

Why expected return factor models and risk factor models are different? Why expected return models and risk models use different factors?

I was recently asked how to use factor models to create asset allocation input assumptions.

I always approached expected return and risk modeling as separate problems. Could you please point me to the literature that supports or contradicts this approach.

For example of the expected returns factor model, please see the Commonality In The Determinants Of Expected Stock Returns by R. Haugen, N. Baker (1996) (http://www.quantitativeinvestment.com/documents/common.pdf ) The up to date model performance is presented at Haugen Custom Financial Systems. (http://www.quantitativeinvestment.com/models.aspx )

For example of the risk (covariance matrix) factor model, please see MSCI Barra Equity Multi-Factor Models (http://www.msci.com/products/portfolio_management_analytics/equity_models/)


If your main concern is a joint framework for estimation and attribution of risks, then you may want to check out this paper:

Basically, the article argues it may be that the best model for jointly describing the securities you’re looking at is not the same as the model you use to attribute risk to different factors or industries. For instance, if I perform a PCA on all stocks in the U.S., the factors may not have an obvious risk intuition. So the question is how to link this with something that a PM might better understand, like the MSCI Barra factors. It’s rather obvious for stocks how to do this, but with non-linear payoffs for securities it is best to resort to the technique in that paper.


There is an issue with trade optimization that if the expected returns use factors that are not in the risk model, then the optimizer will like those factors too much. This is known — in some circles — as the factor alignment problem.

This is actually less mysterious than it might seem at first (true in my case at least). Basically you want there to be no directions (no portfolios) that the variance matrix thinks are virtually riskless. This can happen if the variance matrix used in the optimization is a factor model, and it is of concern when some of those directions are used in the expected returns.

I suspect a solution is to use something like a Ledoit-Wolf shrinkage estimate for the variance in the optimization. That seems to be a good choice on other grounds as well.


There are two main issues when using an expected returns factor model with a factor risk model, whose factors are different: portfolio construction and performance attribution.

As  mentioned above, when constructing a portfolio using an optimizer where there is misalignment (i.e. differences between factors), the optimizer tends to build portfolios that load up on the orthogonal component of alpha- i.e. the optimizer loads up on the expected return factors that are not in the factor risk model because the risk model doesn’t see any systematic risk associated with this expected return factor. Of course, there usually is systematic risk associated with this expected return factor and thus risk is often under-predicted and the portfolio may be ‘off the efficient frontier’ (i.e. non-optimal risk/return tradeoff). Below is a research paper that talks in much detail regarding the issue of factor misalignment: the causes, symptoms, and cure.


Factor misalignment is also problematic from a factor-based performance attribution perspective. A factor based performance attribution is meant to help understand where risk and return came from from different factors in your risk model. When your expected returns model has different factors than your risk model, you cannot get a true idea of how your actual expected return factors have performed: the performance attribution report quantifies performance through the lens of the factors in your risk model. It would be much better to have alignment between your expected returns factors and your risk model factors, so you can get a true understanding of how your expected return factors have behaved.


This issue is addressed in an MSCI Barra research paper that’s available online:http://www.msci.com/resources/research/articles/2008/RI_Do_Risk_Models_Eat_Alphas_April_08.pdf.
There is also a JPM paper authored by three people from Goldman Sachs that emphasizes the need for including alpha factors in a risk model:http://www.iijournals.com/doi/abs/10.3905/jpm.2007.674791.


Please also find the full sets of the research papers and case study on the topic of misalignment from MSCI,

[1] Refining Portfolio Construction by Penalizing Residual Alpha – Empirical Examples

[2] Refining Portfolio Construction When Alphas and Risk Factors Are Misaligned

[3] Do Risk Factors Eat Alphas?

Hope it helps.


Those references are interesting, thanks.
It seems like another approach would be to make an RMT-like adjustment to the non-dominant factors. Strictly speaking, this adjustment would be like setting the means and standard deviations equal (which may not be what the PM wants). Alternately, you could only set the means equal and allow the standard deviations to be different so that more volatile “alphas” would be penalized.


From a simple Asset Pricing theory point of view, adding a risk factor to an asset pricing model, increases the R2 of the regression but it does not change the intercept (i.e. the alpha).
Imagine running a typical regression of the GM returns over a number of “alpha factors” (say, beta, size, hml, momentum and so on). Now add as a variable a basket of automakers. The R2 of the regression increases dramatically but the intercept does not change. In other words, risk factors are “non-priced” but are still useful in explaining the volatility. In finance theory this means that they are “diversifiable”, i.e. they do not carry a risk premium.
A good source for a clarification of these concept is the excellent book by John Cochrane “Asset Pricing”



NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!

Thesis on “the factors of success and failure of hedge funds”

Thesis on “the factors of success and failure of hedge funds”
hello all, currently I am doing my thesis on “the factors of success and failure of hedge funds” and I wanted to know if any of you has an answer or an opinion on this subject. For those who are willing to take 15 minutes of their time I have a questionnaire to submit. In return I will send the results of my study.
(1) too much leverage and not understanding what happens to a levered posn in a black swan event which occurs much more frequently than most people realize
(2) arrogance and hubris and thinking you are infallible
(3) having huge exposure to highly illiquid positions with wide bid ask spreads so in a crisis when one of these positions actually trades due to a liquidation elsewhere at a low price this causes the PM to have to remark his/her book with huge paper losses causing a forced liquidation
(4) accounting fraud/insider trading
These are foundation building blocks. Adding the oversights in Paulson’s recent due diligence of Chinese timber would also provide very useful information. There is an important twist to this 540M loss, the complicit role of Governments in not auditing company information. As Fellows Co., Paulson and others have learned dealing in China without Hong Kong coverage requires moving the risk bar much higher.
I will try to help. Meanwhile, I have been invited to serve as Session Chairman for
Private Equity and Venture Capital International Business Summit in Asia 2011 in India
and I have served as Co-Chairman for Corporate Governance Summit in Asia 2010 in
Hong Kong where I was born in 1958 and is a Senior Fellow for Hong Kong Institute
of Directors (Corporate Governance Foundation Ambassador) and have just been
invited Guest, along with my daughter, Cassandra, for Asian Financial Forum 2011 in

NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!