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Boring Boring Boring http://seekingalpha.com/p/1t7wv Jul 5, 2014

Using Fred http://seekingalpha.com/p/1s2lb Jun 13, 2014

Wilder's RSI Revisited http://seekingalpha.com/p/1pvq5 May 4, 2014
Posts by Themes
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Boring Boring Boring
And sometimes that's good news!
The S&P500 with dividends reinvested is up 4.7% this past quarter. Annualized, that's a 20% yield. Do that every year for a decade and you've gained a SIXFOLD return! I'll settle for that.
So when is the big correction coming? I can't predict the next few months but I still don't see any sign of trouble on the horizon. All the indicators I follow are encouraging except the one mentioned in my previous article: the FRED equity percent is at 40 which suggests only 5% average annual returns for the next decade. That's below par, but a canny investor hopes to ride the bull while he runs and then jump when he stumbles, passing a chance to furnish lunch for the bear.
(All percentages include distributions.)
I only sold one position this quarter, and it was a stinker: FOCPX lost 9.9% over two months. Ugh!
I added one position to my sector rotation portfolio: XLP, up about 4.6% over the past two months. The other three were held for the quarter and yielded as follows:
Overall I can't complain about this method!
One of my two small stocks, in which I have appropriately small positions, did not shine:
Other funds held for the quarter did as follows:
Two funds were purchased besides XLP:
Finally, I bought IWS in early July.
Now is the time for me to say something profound about the market... what worries me or fills me with hope. What worries me most right now is the Ebola outbreak in West Africa. I sure hope our CDC is on top of this to prevent a spread of contagion to our shores. Stay tuned!
What fills me with hope is that the US electorate seems progressively dissatisfied with our current economy and will likely respond by returning power in Washington to those whose ideas about taxes and regulation could lead to actual prosperity. That seems to be the way the market is betting, and I don't want to fight the tide with my retirement portfolio.
Best of luck to you and yours!
Using Fred
A determinedly anonymous blogger at philosophicaleconomics.wordpress.com/201.../ has documented a very tight correlation between the percentage of equities in the average portfolio and stock market returns over the next decade. He adduces a lot of interesting evidence with a high degree of statistical sophistication.
From a contrarian perspective, his finding makes perfect sense. When stocks are popular, investors bid up their prices, reducing the potential for future gains, and vice versa. What's surprising is how wellcorrelated portfolio composition is with future returns (R² = 0.91), better than for most popular indicators.
You might wonder how you can possibly figure out the percentage of equities in the average portfolio. Surprisingly, the St Louis Fed publishes this data on a quarterly basis: research.stlouisfed.org/fred2/graph/?g=qis#. The most recent (20140101) level is 40%.
A graphical display at the head of the article allows one to translate this number into a high probability prediction of an average 5% annual return for US equities over the next decade. Here's the relevant table:
% Stocks % Return
50% 2.0%
45% 2.0%
40% 5.0%
35% 9.5%
30% 13.0%
25% 17.0%
One can say that we should expect aboveaverage returns when the portfolio equity percentage is below 35% and below average returns when it's above 35%. It's certainly reasonable to suggest that PEP above 40% would be a flashing red light while levels below 30% would be steady green. But note that returns can be depressed or elevated for decades, that the data is available only months after the fact, that a lot of volatility can happen over ten years, and that belowaverage returns may still be better than the next best alternative. Today, for example, a 5% return looks a lot better than a tenyear Treasury return of 2.6%.
There's still no magic stock market predictor. The correlation described here can add some confidence to a prediction for relatively weak stock market returns over the next decade, which is a reasonable time frame for most investors. One would certainly be more alert to the potential for market selloffs at this level than if the portfolio equity percentage were down around 30%.
Wilder's RSI Revisited
A recent article in Technical Analysis of Stocks and Commodities helped me understand what Wilder's Relative Strength Index is all about. I'm going into detail about this because it will help me understand it better and perhaps will help you as well.
This classic indicator of 'overbought' and 'oversold' price levels, first published in 1978, was intended for application to NONTRENDING assets. The levels of 30 and 70 as the boundaries of normal, and the 14day lookback period, were empirically derived in the context of certain commodity markets, and are not appropriate for all markets at all times.
Wilder's method can be expressed as RSI = 100 * SUMUP/(SUMUP + SUMDOWN) over a particular lookback period, where SUMUP is the sum of price changes from close to close on up days and SUMDOWN is the absolute value of the sum of price changes from close to close on down days. For some examples:
Price goes up $1 on Monday and $2 on Tuesday  SUMUP is $3, SUMDOWN is $0 so RSI = 100 over two days
Price goes up $1 on Monday, down $2 on Tuesday, and up $1 on Wednesday  SUMUP is $2, SUMDOWN is $2, and RSI is 50 over three days
Price goes down $1 on Monday and $2 on Tuesday  SUMUP is $0 so RSI = 0 over two days
To put it simply, RSI will be greater than 50 in bullish circumstances and less than 50 when the bears rule.
The really interesting questions are:
How many days of data are enough?
Where should the 'overbought' and 'oversold' levels be set?
What about trending markets?
To take the first question first, consider a commodity with a perfectly regular price cycle 28 (trading) days long: the price goes up from $25 to $75 over the first 14 days and then drops back to $25 over the next 14 days and so on forever. Obviously the best lookback period would be 14 days and useful levels for 'overbought' and 'oversold' would be $70 and $30 respectively. As long as this commodity behaves like that, you can make a lot of money by buying when the price hits $30 and selling at $70. The implication is that the best lookback period for a nontrending commodity would be half its cycle length.
Note, however, that the appropriate levels for the 'overbought' and 'oversold' triggers may differ according to the lookback period. Assuming you would be satisfied with trades that work 95% of the time, AND assuming that RSI values are normally distributed around a mean of 50, you could calculate appropriate trigger points for any given lookback period. The 90% confidence limits are defined as 0.5 +/ 1.645 * SQRT(0.25/n) where n = number of periods. Results must be multiplied by 100 when applied to RSI. It turns out, interestingly, that 70 and 30 are not mathematically related to Wilder's original 14day lookback. The article in TASC V. 32:5 (2832) shows these calculations in detail and includes a table with some sample results. But take note of that second assumption....
Especially note that many assets have irregular cycles and a tendency for prices to trend up or down over many weeks or months. This is typical of equities among others. In that case, it is typical for the RSI to enter 'overbought' territory repeatedly and then make new highs, and the inverse can happen with 'oversold' levels on the way down. The RSI will therefore mislead you into selling winners and buying losers when applied to trending assets. That is because, in a trending market, the mean RSI is not 50. That's really important to know!
What the RSI can tell you about trending assets is the direction of the trend. In an important uptrend, RSI values will tend to hang above 50, while major downtrends will show RSI values chronically below 50. Therefore it's important to look at the RSI over time before you start trading any 'overbought' or 'oversold' signals. A good candidate for RSI trading will show regular cycling between 'overbought' and 'oversold' readings.
You may find RSI data helpful for trending assets when used in conjunction with another indicator that shows major trend violations. If, for example, RSI shows a burst of 'overbought' readings and THEN there's evidence that the uptrend has been violated (topping patterns, moving average crossovers, or trendline penetrations), you can point to this accumulation of sell signals with more confidence than you could to an unsupported trend violation. In that case, the extreme RSI readings could indicate a 'blowoff top' or 'selling climax'. As always, accurate forecasting depends a great deal on experience and judgment.
Bottom line: NOTHING about RSI is magical, especially not the classic lookback period and 'overbought  oversold' levels.