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Asset Allocation Part 2: Technical and Fundamental Trading Rules

Please note, as ART Consulting/Research is a fee based service, in the following the results have been "sanitised" to disguise the specific markets, trading factors, strategy parameters and many other essentials.  Of course all of the analyses is based on real market conditions and real world trading considerations.  For access to the "un-sanitised" results, and for analysis tailored to your needs please submit an email via  Request More Information.

Asset allocation between equity and interest rate markets represents a very large component of the investing communities resources.  Though equities can provide higher returns, they can also be riskier, and so the notion of a (relative) safe haven for capital during "nasty" periods in the equity markets has driven considerable interest [1].

So, are there any trading rules that help improve (holding period) risk-adjusted returns when considering asset allocation decisions?  

For example, in another ARBLab analysis (P&L Performance of MVO Asset Allocation, and  all of TG2RM1st - Chapter 12 is dedicated to the introduction of PaR analysis), it was shown that "brute force" Mean-Variance Optimal (MVO) methods (though intuitive and common) are not quite as effective in the real world as theory might suggest.  However, it was also shown that careful analysis may be able to indicate market conditions which are more "MVO-friendly", than a naive application, and so assist in the decision whether or not to base asset allocation decisions on MVO.

Similarly, other traditional methods may be used to assist asset allocation decisions, such as Technical Analysis, and Fundamental Analysis.

In another ARBLab analysis, Asset Allocation Part 3: P&L-Optimal Calibrated (POC) Trading Rules, these methods are combined to see how TA and FA methods can help "calibrate" MVO based asset allocation as illustration of compound holding period risk-adjusted P&L analysis of trading strategies.

An approach to trading rule verification

For the moment, though, consider analysing the P&L of many (asset allocation) trades over a long period using backward and forward testing of trading rules.  For example, TA trading rules may include pattern matching (bull flags, triangles, etc), momentum/oscillators, Moving Averages (MA) and trends.   One TA rule may be: "allocate funds into equities (and out of bonds) when the 100-day MA crosses above the 30-day MA.  FA methods include the assessment of economic parameters such as CPI, GDP, Un-Employment, etc.  and then rebalancing the portfolio accordingly.

There are, of course, a very large number of possible trading rules, and within any one rule there may be a large number or parameterisations (e.g. 30-day MA vs. 100-day MA etc).  As such this type of analysis is necessarily time consuming.

TRule Test 17 a.jpg (139012 bytes)Even worse, the results of such trading rules may have subtle "high dimensional" interactions that are not immediately obvious when doing any single trading analysis in isolation.  For example, the figure to the right (click to enlarge)  illustrates a trading strategy based on a combination of TA and FA rules.  Each point in the chart is a "net holding period P&L" for a complete trade cycle.  The "Factors" are "sanitised" market/trading rule indicators (e.g. MA cross-overs, GDP, Bond yield, etc etc).  The vertical axis is a restated/normalised  P&L.  The colouring of the P&L points represents yet another market/trading factor, and so the pattern in the colours is also a beneficial indicator.

If only the 2-dimensional isolated analysis were performed (as indicated by the smaller 2-D plots at the right of image), then one may concluded that there is not very much benefit to using the trading rules applied here.  However, using the 4-dimensional combination of the 3-D plot + the colouring, a pattern emerges.  For example, if Factor X is in a specific range, then an increasing Factor Y implies higher returns (i.e. if you were holding a portfolio, as Y was increasing you would move increasing funds from bonds to equities).

TRule Test 17 b.jpg (140346 bytes)Importantly it  is also necessary to test the combined impact of other Factors (trading rules and market conditions).  The figure to the right (click to enlarge) shows that making a small change to the trading strategy above can, for the same market conditions, increase the relative profitability (i.e. the Factor characteristics are the same, just now we are making more money due to a more efficient rebalance process).

TRule Test 17 c.jpg (168170 bytes)Moreover, introducing additional Factors further helps to narrow the market/trading conditions that lead to profitability as shown on the right.  The trick here is to compare the Figure above (in terms of Factor X & Y) with this one (in terms of Factor X & Z).  This "super imposing" of views shows that certain combinations of Factors X, Y, and Z are indicators of increased profitability.

As usual, caution is required.  The analysis here, though including thousands of trades, and incorporating many real world factors cannot be taken as any perfect predictor of the future.  Moreover, there are all manner of wrinkles, for example, Fundamental Analysis relies on economic factors which generally take a long time to form patterns (e.g. economic cycles can be 4-11 years), and also economic indicators can be notoriously unreliable (e.g. just compare the "volatility" in economic release data vs. their revisions).  The implication being that deep assessment of all reality impact components is required.  Having said so, high dimensional holding period P&L analysis appears helpful in the assessment of asset  allocation considerations and trading rules.


If you are interested in obtaining research results on this issue please Request More Information and please feel free to indicate a few specifics of interest to you.



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[1]  It is noteworthy that during some market periods the returns and risk of bond markets can rival that of equity markets, even though it is normal to consider equities to be higher risk/return.



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Last modified: July 25, 2011