I came across an interesting paper that describes a simple but effective quantitative system that outperformed S&P 500 for several decades with lower draw downs then buy and hold. The system was originally published in 2006 and then updated in 2009.
The system is basically a long term mechanical system using monthly charts for timing the markets with tactical asset allocation. The annual returns average is 11% since 1973. The max draw down is below 10% and volatility is below 7%. End of the post has couple system equity curve charts from the paper.
This probably is more applicable to individual traders as fund's size and constraints a fund manager face are different. I like to hear a fund manager's perspective as well on this topic though.
Asset Diversification:
My take is most assets are correlated in recent years. This is probably due to growing popularity of the ETFs. So asset diversification is more useful only if an investment methodology uses dynamic correlations (instead of static correlations). But working off dynamic correlations require short term timing and shorter investment horizons. On other hand this is not the preference of most people or financial planners.
Asset Diversification:
My take is most assets are correlated in recent years. This is probably due to growing popularity of the ETFs. So asset diversification is more useful only if an investment methodology uses dynamic correlations (instead of static correlations). But working off dynamic correlations require short term timing and shorter investment horizons. On other hand this is not the preference of most people or financial planners.
Came across this interesting test report on $TICK. When you search on the net for "NYSE Tick Index", you will find many articles that claim that there are various levels and rules that can be used to time the market and generate profitable trades. For example, one theory is that the -1,000 and +1,000 levels are important action points on the Index.
In this report, the authors discuss the NYSE Tick Index, rules that are commonly associated with trading signals, and whether or not these rules can produce successful trading results along with results derived from their quantitative testing. I like reports that include quantitative results of the tests to back the authors assertions. That way the readers can make up their own mind. This report provides those stats.
Please let me know if you come across any similar reports & your thoughts on this report.
Link to the report - TICK_Report.pdf
Link to the report - TICK_Report.pdf
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