Quantitative Analysis = "Highly" Technical Analysis (?)

Branding Quantitative Analysis as "Technical Analysis" will probably bring in some violent reactions from quants. But I just want to point out the similarities that they share. In fact, it can be seen that Quantitative Analysis is a higher form of Technical Analysis.

Technical Analysis is commonly described as Charting. It is the study of charts (graphical representation of past price movements) and finding patterns in them. Investment decisions are then based on these patterns. People say this is superstition as price moves randomly and just forms these patterns by chance. Technical analysis also utilize quantitative techniques via Technical Indicators. Technical Indicators aren't just numbers, they are results of some statistical modelling. Indicators like MACD and Bollinger Bands are actually similar to statistical measures used by quants today (mean and standard deviation respectively). These measures are used for momentum and mean reversion strategies. Technical analysis also looks into other quantifiable variables found in the market like traded volume, open interest, bid ask spreads, etc. Technical analysis gives rise to automatic trading rules which is also done with quantitative analysis.

In the Jan/Feb 2007 Issue of CFA Magazine, there is an article ("Perpetual Motion by Susan Trammell, CFA") about a recent study on trends in quantitative investing. Below are some findings:

Phenomena Being Modeled:

• Fund Capacity: 20%
• Impact of Trades: 24%
• Textual Data: 2%
• Higher Moments: 2%
• Regime Shifts: 10%
• Volatility: 20%
• Extreme Events: 10%
• Momentum / Reversal: 31%
• Trends: 28%

Modeling Methodologies Used:

• Shrinkage / Averaging: 9%
• Regime Shifting: 4%
• Nonlinear: 7%
• Contegration: 7%
• Cash Flow: 17%
• Behavioral: 16%
• Momentum / Reversal: 28%
• Regression: 36%

As seen in the survey results, trends, momentum, and reversal models are quite popular in quantitative analysis. These are also the same phenomena being modeled by technical analysis but at a less "scientific" degree.

The relationship of Technical and Quantitative analysis can be likened to the relationship between Astrology and Astronomy. One is seen as superstition while the other as a science. Astrology came about due to the lack of sophisticated tools and theories. The same with Technical Analysis -- people relied on charts because it was easier to analyze than numbers. But in the advent of faster and more powerful computers, large amounts of numbers can be analyzed with ease.

To see the survey results, please refer to www.theintertekgroup.com.

Tags: financial engineering investments quant technical analysis

Losing Money When There is No Volatilty

It is common knowledge that there is more risk when there is more volatility. But it is also possible to lose (a lot of) money in the absence of volatility as well. This case was illustrated in a recent article published by Financial Engineering News. It was reported that Credit Suisse recently lost $120 million in Korean Derivatives -- particularly reverse convertible bonds.

A conventional convertible bond offers lower interest rates but gives the investors an option to call a company's stock. The bondholder is effectively the owner of the option and the issuer is the option writer. A reverse convertible bond gives investors higher interest rates but gives the issuer the right to put shares to the investor. In this case, the bondholder is the seller of the option and the issuer is the option buyer. When volatility increases, option prices increase as well. This added value stems from a higher possibility of going in-the-money. Conversely, a decrease in volatility will lower the option value. So if Credit Suisse was the one who "bought" the stock options via the reverse convertible structure, a decrease in volatility will decrease option value and will result into a mark-to-market loss on their end.

Now as market makers (structurers), shouldn't Credit Suisse be hedging their exposure? The problem with this particular structure is that the option is not based on one stock. It issued reverse convertibles on a number of shares. Hedging proved to be quite difficult and luck was not on their side, as stated in the article:

The problem however came in the hedging. Credit Suisse no longer had a single put option, nor did it have a portfolio of put options, since it could exercise its put into only one share. Instead it had an option on an option, a put option under which it could choose the share on which the option would be exercised. This instrument could be reasonably hedged by an appropriate portfolio of the shares provided volatility remained approximately constant, but it was effectively unhedgeable against a sharp change in volatility. If volatility in Korean shares had increased, there would be no problem; Credit Suisse’s multiple put option would be more valuable. There was, however, no effective way to hedge against a decline in volatility, which is what happened.

The lessons that we can learn here are the following:

1) You can lose when there is less volatility -- particularly in options since volatility is explicitly included in valuation.
2) When building a structure, one should know how to hedge it properly.

Tags: derivatives financial engineering investments valuation volatility structured products options

Model Validation - Not Just for Quants

In an article recently published in the ERisk Monthly Newsletter, it is stated that model validation is not a purely quantitative endeavor. Below is a quote from the article.

Model validation is often thought of as a rather technical and mathematical exercise. However, bank losses from model risk are often caused by poor governance of the wider modeling process, or by a poor understanding of the assumptions and limitations surrounding the model results, rather than by errors in equations.

The growing importance of models in helping executives answer some of banking’s most critical questions – from compliance and capital adequacy to business performance and risk-adjusted compensation – suggests that model validation is too important to be narrowly defined or left to the “quants”.

For both best practice and regulatory compliance reasons, senior bank executives must begin to take a more commanding role in ensuring that model validation is aligned with the overall interests of the bank – and that the bank’s investment in sound risk modeling can be easily communicated and proved to third parties.

Tags: financial engineering derivatives model validation governance audit

Quant Cartoons

This cartoon was sent to me by Financial Engineering News. Enjoy!

FENtoon

Tags: financial engineering derivatives algorithms