Introducing Risk Appetite Spread

What is Risk Appetite Spread? Denote R(x) to be the fundamental rank of an ETF. Risk Appetite Spread is

((R(IWF) + R(IWO)) /2 + R(SLY)) / 2 - ((R(IWD) + R(IWN)) /2 + R(ELR)) / 2

Where (R(IWF) + R(IWO)) /2 represent the fundamental rank of growth stocks, R(SLY) represent that of small caps. The average of these two represent the fundamental rank of risky assets. (R(IWD) + R(IWN)) /2 represent the fundamental rank of value stocks, R(ELR) represent that of large caps. The average of these two represent the fundamental rank of safe assets. In theory a high RSA indicates that risk appetite is on and will add fuel to a bull market.

Read more on ETF Ranking and Fundamental Ranking.

Where is RAS? It is located to the lower right corner on my blog. Similar to Sector Rank Spread, RAS is calculated and updated every weekend.

Cloud Computing Portfolio Rose 2% in One Week, Four Times Market Return

Five trading days have passed since we published our cloud computing portfolio on SeekingAlpha: "Cloud Computing: Design a Portfolio for the Best, Normal and Worst". Because we were boasting that our ranking system is for short term, specifically one week return, it is a good time to have a check on the performance.

Comparing to Jun. 13th close price, the equal weighted portfolio rose 2%. In the same time, S&P 500 rose 0.5%. In our article we estimated that our cloud computing portfolio will beat the market by three times. It actually outperformed by four times. The extra return may be explained by the halo of cloud computing.

Inverse Relation between Market Cap and Performance Echoes My Research

Empirical Finance Blog has an interesting post today discussed an inverse relation between market cap and performance of Magic Formula. This echoes what I have observed in my research.

According to my research, there is an inverse relation between liquidity and performance. Annualized return drops if I require more liquidity. Because large market cap generally fetch better liquidity, what I observed in my research generally echoes what Empirical Finance Blog has observed. I think 30% annualized return by Magic Formula is doable if we loosen the requirement on liquidity. However, investors may or may not be able to get rich with that because they couldn't invest too much money on a thinly traded stock. At least it is the case with my ranking system as I do weekly rebalance. But it is still arguable that if the holding period is 6 months to 1 year, investors can spend weeks or even months to accumulate a position, so liquidity may not matter.

ETF Ranking: Growth vs. Value

In an early post I showed that we can use ETF rankings to gain insight on where we are in a business cycle by mapping it to the Sector Rotation road map. I found another road map of Growth vs. Value on Interactive Investor's blog, of which I can make similar use.

The research was carried out by Morgan Stanley. It states that value stocks will outperform during early stage recovery and mid-stage bull market, growth stocks will outperform during mid-stage bull market to peak of bull market, then in bear market, balance sheet will outperform till the bottom. Although I'm not aware any ETFs representing balance sheet, there are value ETFs and growth ETFs. Because a highly ranked ETF will outperform the market, we can use this predictive power to learn where we are in the business cycle.

I select iShares growth and value ETFs: IWD, IWF, IWN, IWO. Their ranks are listed below:

• IWO, Russell 2000 Growth Index ETF: 63.19
• IWF, Russell 1000 Growth Index ETF: 58.54
• IWD, Russell 1000 Value Index ETF: 44.77
• IWN, Russell 2000 Value Index ETF: 42.21

Clearly growth is more fundamentally attractive than value at this moment. According to the chart, probably we are in the late stage of a bull market. This matches with the conclusion in our ETF ranking and sector rotation post.

To understand when we will be in a bear market, it would be handy to have an ETF represent balance sheet. If its rank is higher than growth ETFs, then highly likely we are at the beginning of a bear market. If you know a good balance sheet ETF, please let me know.

My Comment to Interactive Investor's Latest Blog Post

Below is my comment to Interactive Investor's latest blog post: "Mines flood Nifty Thrifty screen". The author worries that "if next year is a bad year for resource companies, it will be a bad year for the Nifty Thrifty", though I think this is just the normal Sector Rotation phenomenon. I think my comments is informative so I just repost it here.

* * *

Thanks for the post. I have similar experiences.

I also have a ranking system similar to Magic Formula. Roughly speaking, Magic Formula has two components: valuation and return on capital. I added one more: financial condition. I tweaked a little bit the formulas within valuation and return on capital, though.

Although I traded my ranking system for only a couple of months, I see the same thing you discussed here. One month ago, all the top names are mining companies, including RIO. But not anymore. I think some bad fundamental numbers entered the earnings reports during this earnings season. As a result, the ranks of top ranked mining companies dropped abruptly.

Nonetheless, I believe it is normal that certain sectors / industries are favored at certain phases of a business cycle. This is actually the well studied Sector Rotation phenomenon. I don't have predictive power so I worry less what's going to happen one year down the road. But I believe I'm in good hands as long as my ranking system tracks the fundamentals closely. If commodity price crashed in the future, the fundamental numbers will crash, too. My ranking system will reflect the changes and I'll exit the positions. In fact this already happened with my ranking system. As mentioned before, now the mining companies are not top ranked anymore and I don't have any of them in my portfolio.

One key point I'd like to mention is: How often do you update the ranks. I insisted to update the ranks every week with my ranking system. Fundamental numbers change slowly, but the changes are usually abrupt. For example, the rank of RIO dropped about 20% one month ago. I think if I don't update the ranks frequently, I'll miss big profit or get caught by big loss.

Another thing is I'm not sure which EY are you using. Some value investor will use 10 year average. But I'll use ttm number. For one, ttm is more popular so it's going to have bigger impact on price, at least in short term. For two, ttm is more sensitive to changes. There will be fluctuations, but I think it pays to follow the fluctuations in the long run.

Sector ETF Rankings Match with S&P Upgrades and Downgrades

A recent Barron's blog said that S&P rated energy sector to overweight, downgraded materials to market weight and financials to underweight. I mention this because it matches with our Sector ETF Rankings for the week, which put XLE at top, followed by XLB, and XLF at bottom.

Gossip on Sector ETF Rankings - May 17, 2011

Although I have only a few data points, I think I have some proof to show that ETF ranking does drive short term return, barring any fundamental changes.

For the week starting on May 9th, my ETF ranking put XLB slightly above XLE. And for the first two day of that week, XLB rose 2.31%, slightly higher than the 2.23% rise of XLE. But then both reversed their courses in the rest of the week. By Friday, XLB was down 1.78% for the week, a bigger one than the 1.35% drop of XLE for the week. My guess is that fundamentals deteriorated in materials sector duing that three days so XLB was down more severely for the week. Actually, if you pay attention to this week's Sector ETF ranking article, you'll find that we did see fundamentals deterioration in materials sector in the week before.

The same happened for the first two days of this week. My ETF ranking put XLE slightly above XLB for this week. By today's close, XLE is down 0.76% for the week, relatively better than the 1.53% drop of XLB. As the earnings season is approaching to its end, hopefully we won't see any fundamental changes for the rest of the week. And hopefully XLE will continue to outperform XLB in near future.

ETF Ranking: My First SeekingAlpha Article And Valuable Comments

My first SeekingAlpha article is titled: "ETF Ranking: A New Fundamental Approach That Drives Short-Term Return". I'm not allowed to publish the same content on my blog. Just copy the first paragraph over.

ETF Ranking Favors XLE and Dislikes XLF for the Week

The ETF ranking is an extension of our newly designed stock ranking system that ranks every stock based on its valuation, financial condition, and return on capital. Although the ranking system is fundamental based, it actually drives short term return. We observed that stocks with higher ranks had a strong tendency to outperform those with lower ranks over a period of one week. The data show that moving up 10 rank points translates to an extra annualized return of 1.7% in the past 10 years, if ranks range from 0 to 100.

People posted valuable comments following my article and I feel it would be good to copy some over. The discussion is mainly centered on growth vs. value. My take away is that

• Chasing trailing twelve months (ttm) growth number is too late to the game
• Projected growth number may be a better choice, but it's inherently difficult to evaluate the quality and credibility of the projected number
• Hence the best way to evaluate growth is to do it on a case-by-case basis. There may not be a good formula to represent growth, and hence it's difficult to integrate it into my fundamental ranking system.

It's just my take away and it's always debatable.

Below are the comments:

By the way, don't be so sure growth and value are antagonistic. Books, etc, make it seem that way and that's understandable; it's a lot easier to sell a book if it can be easily classified per a particular style so devotees of that style can recognize it as something that would be of interest to them. Actually, though, growth and value are much more aligned than many realize.

[Y]ou may want to work with PEG (the PE to growth ratio). For PE, I suggest using price divided by estimated EPS. For growth, use the consensus estimated long-term EPS growth rate. Many say a PEG should be equal to or less than 1.00. Actually, though, that's folklore. Realistically, PEGs below 2 tend to be reasonable. Now, here's the hard part. Critically evaluate the growth projection. That's important. Value errors usually come from latching onto a not-so-credible growth forecast.

If you work that way, you'll develop a strong sense of stock market value (one that is not at all antagonistic to growth).

I'm not talking about quality of growth; I'm talking about the credibility -- believability -- of the projections. A P/E of 25 on shares of a company with a projected 30% growth rate sounds great . . . unless you look more closely and decide that the 30% expectation is nonsense. The hard part is that there is no easy way to assess this; if there were, then everybody would be spot on in terms of stock valuation and there would be no opportunity for a value investor!

My Pet Project

Hedge: Another Try

Previously I discussed the unexpected behavior of the bottom 20 stocks by fundamental ranking that resulted an unsuccessful hedge strategy. Later on I found that stocks only within the bottom 50 has such unexpected behavior. The finding encouraged me to device a new hedge strategy with stocks that ranked bottom 51 through 70. Hopefully with the bottom 50 (those ill-behaved stocks) removed, I could see some improvement.

The result is charted below. I do see some improvement, but there are things I don't like:

• The maximum drawdown is reduced, but not by much. Originally it's about 60%, after it's about 50%.
• The hedged portfolio shows higher volatility than the original one. Such as July, 2009 to September, 2009, and January, 2010 to May, 2010.

I may not choose this strategy, either.

ETF Ranking: Related Research

I was browsing on the web for related research on ETF ranking. My first impression is that this topic is overly crowded. Just look at how many Google ads associated to the keyword when you Google "ETF ranking".

Then I noticed that my blog post "ETF Ranking, Sector Rotation, And Business Cycle" is at the 6th place on Google. Not bad considering I've started this blog only a month ago.


OK, this is not the purpose of this post. The purpose is to give an overview on others' research on ETF ranking.

First, something about fundamental ranking.

Fluent investors may have noticed the similarity of my fundamental ranking system and Greenblatt’s magic formula. Although I didn’t know about magic formula when I started to work on my ranking system, I do agree with Greenblatt in many fronts. He suggests using magic formula on large groups of stocks, that’s why I settled with ETF ranking. He also commented that once applied to large groups of stocks, any differences between the various return on capital formulas will not have much effect on the performance. I'd like to use this as an excuse of not revealing my formulas.

Now on ETF ranking. It appears that many companies provide ETF ranking as a financial service to investors. Enumerating all service providers is not my purpose. I just try to enumerate all different approaches, and pick one representative website for each approach. And by no means the list is complete. I stopped at about page 4 on Google.

A little bit brag first: although there are many different approaches, none of them provides data to show the relation of their ranking system and short term return, at least I didn't see any public data. It appears that my ETF ranking is the only one that is designed for short term return and has data to show the strong statistical relation between ranks and short term return.

Enough for bragging.

1. XTF’s structural integrity. ETFs are ranked based on (a) Tracking error, (b) Efficiency: daily alpha before expenses, (c) Market Impact, (d) Concentration Risk, (e) Tax Efficiency: Capital Gains, (f) Expense Ratio, and (g) Bid-Ask Ratio. No doubt all of them are important for institutional investors. But by no means this is a fundamental based ranking and I'm certain there is less likely to be any relation to short term return.
2. NewConstruct.com. Admittedly their idea is similar with mine. They first rank stocks in the ETF's portfolio by their risk and reward, and then sum up to the rank of the ETF. Although I don't have additional information, my guess is the risk and reward should have at least some fundamental flavor. And the sum-of-parts approach is the same with mine. Nonetheless, there is no data show the relation to short term return.
3. Value line. An introduction is here. This is by far the closest one to mine. It is value based, which has to be fundamental. And it is sum-of-parts. However, their ranking system is designed for 6 months to 12 months holding, while mine is for 1 week rebalance. The introduction did say they have one year data to show the performance, though.
4. Sabrient’s SectorCast model. The rank consists of two parts: fundamental data and analyst's projection of company's future performance, such as forward P/E. The idea sounds brilliant, but I'd like to question the soundness of using analyst's projection. More often than not, analysts are just chasing public opinions. The performance data is not persuading. Actually the best ranked ETFs generated negative return while the market is rallying. Due to this, their model is evolving constantly. The risk is that they are just tweaking the model to fit the curve.
5. Ranks based on past 3 months or 6 months' performance. Such as ETFTable.com. The information is good for momentum or trend trader. Both are mysterious and certainly not based on fundamentals.
6. Ranks based on technicals such as 20 day moving average and 50 day moving average from masterdata.com. This is another way to express trend, and certainly not based on fundamentals.
7. Ranks based on number of new highs and new lows of constitutes from ETFinvestmentoutlook.com. Yet another way to describe the technical strength, and certainy not based on fundamentals.

Short Low Rank ETF Still Good Hedge

I spent a little bit more time to dig into the problem whether shorting lower ranked stocks or ETFs is a good hedging strategy. The data I got yesterday are on the bottom 20 stocks. If you still remember what I have discussed in my post on effectiveness of fundamental ranking, you will know that those stocks' ranks are below 1. They are not good hedge because they are garbages, and when the market turns from bear to bull, the rising tide lifted garbages a lot faster than those boats, i.e., stocks with higher ranks. Miserably you will see the market rallying while your portfolio falling if you short the garbages as a hedge.

But currently the sector ETF with the lowest rank is XLF. Its rank is around 20 as discussed in my post on ETF ranking. The question is whether rank 20 behaves like garbages or boats. If it is like boats, it may still server as good hedge.

In my post on effectiveness of fundamental ranking, I only calculated the estimated annualized return of the upper half of the spectrum: from rank 100 to rank 50. Using the same method, I calculated the return of the lower half. The result is shown in the chart below. Unlike the upper half where the best curve fit is 1/x^4, the lower half's best least square fit is a straight line.

On the chart each point represents a group of 100 stocks. The lowest 100 stocks, rank 0 to 3, still behave like boats. They are the worst performing stocks in the spectrum. So the garbage is only the lowest 20 stocks? To answer this question, I calculated the return of the lowest 50, 20, 10, and 5 stocks.

The bottom 50's return is lower than that of bottom 100. But bottom 20 is a lot higher, and it go up steeply all the way to bottom 5. So really the garbages are the bottom 20. No wonder I see such a bad performance when hedging with them.

Some key take away:

• The bottom 20 stocks behave like garbages, one should never touch them, no matter long or short.
• The market neutral strategy mentioned in ETF ranking would still be a good strategy if the shorted ETF's rank is above 15.
• One can short an ETF with low rank to hedge a portfolio with top k stocks, as long as the shorted ETF's rank is above 15. But that would be a little bit too complicated. In this case, short SPY may be a simpler hedge.

By the way, now you can subscribe to weekly ETF ranking update via email. You'll receive the highest ranked and lowest ranked offensive sector ETF every weekend. Go to the top right corner of my blog, enter your email address and click on "Subscribe". The "Subscribe" button will pop up a confirmation page hosted by tinyletter.com, a free service provider. You will also receive a confirmation email from TinyLetter (subscribe@tinyletter.com) titled as "Confirm your subscription to Weekly Update On ETF Ranking". You need to click on the link within to confirm your subscription.

Not A Good Hedge

Investors and traders that ever thought about a mechanical trading system may be familiar with Sharpe ratio and maximum drawdown. Both measure the risk associated to a system. While I have not decided yet whether to device a mechanical system on top of my fundamental ranking system, this is an interesting topic and I'd like to waste some of my spare time. Calculating Sharpe ratio takes a little bit more effort. But maximum drawdown I can simply eyeball on the chart, which is roughly more than 60%. Admittedly one has to have a super strong stomach to trade such a system.

Hedging is a technique adopted by professional investors to manage risk. Naturally I'm thinking to short the bottom ranked k stocks all the time as a hedge. Those stocks are on the other extreme and should constantly move lower. So by shorting the bottom ranked stock maybe I'll earn extra returns. I pulled out the data of the bottom 20 stocks. As shown in the chart below, they generated a negative return in the past 10 years. Looks good so far.

But when putting together with the top 20 stocks, the overall return is not pretty. I scrutinized the data and find out the problem. Below is the chart of the top 20, the bottom 20, and the hedged portfolio between November, 2007, around the top of last bull market, and present. Bottom 20 moved down in lock steps with top 20 during the 2008 bear market, providing a good hedge to the portfolio and reducing the drawdown from over 60% to about 20% (in the bear market). It is good, but it comes at a price. During the 2009 sizzling rally, bottom 20 moved up a lot faster. A rising tide lifts all boats, and it also lift all garbages, and garbages got lift sooner than boats. Only until recently the bottom 20 slowed down a little bit, and the hedged portfolio is catching up. But still, if you didn't notice, the hedged portfolio is in the negative territory since November, 2007, while both top 20 and bottom 20 are up more than 60% from previous top.


I shall admit that the chart didn't tell the whole story. Bottom 20 started to move down in mid 2006. So from mid 2006 till end of 2007, the hedged portfolio gained extra return. But is it a good hedge? It depends on which one you'd prefer between a 60%+ maximum draw down; and a 40%+ maximum drawdown and watching the entire market rally but your own portfolio plunge. I'd choose neither.

Graham in his early years adopted similar strategy: longing undervalued and shorting overvalued. But later on he focused on only the long part. I think I know the reason now.

And by the way, in my ETF ranking post, I suggested to long the top ranked sector ETF and short bottom ranked one to stay market neutral. Now it looks not a brilliant idea. Yes you are "neutral", but if your portfolio is not moving anywhere, neutral is not a good word.

Introducing Sector Rank Spread

What is Sector Rank Spread? It is the maximum spread over the ranks of the offensive SPDR sector ETFs. For example, as of last week, the highest rank in the offensive SPDR sector ETFs is 69.86 of XLE, the lowest rank is 22.18 of XLF. Thus the Sector Rank Spread is 69.86 - 22.18 = 47.68. This week the highest is still XLE and the lowest is still XLF, but the spread increased a little bit to 47.83. Read more on ETF Ranking and Fundamental Ranking.

Where is SRS? It is located to the lower right corner on my blog. Unlike the Big Money Index, SRS is calculated and updated every weekend.

Why SRS? Economists use the velocity of money to measure the activeness of the economy. My understanding is that standing still money doesn't create any economic value, money create economic value whenever it flows. GDP, the economic barometer, is the sum of the dollar amount of trade, i.e., money flow among parties. Similarly in the stock market, money flow drives price appreciation and depreciation. Money flowing into an asset class will help to mark up the price of the asset class, and vice versa. The SRS is designed to gauge the tendency of money flow among offensive sectors. Sectors, represented here by the SPDR sector ETFs, with higher fundamental ranks offer better economic value, and thus attract more money flow. Therefore, the larger the SRS, the better the chance that money will flow out of the one sector (supposedly with lower rank) and into another sector (with higher rank). If the spread is minimal, money flow would stall. So SRS is another way to measure the money flow, and the activeness of the stock market. Intuitively, high SRS indicates the market can still move up, while low SRS may prelude consolidation or correction. That said, I don't have historical data to show how high is high and how low is low. This is a new indicator and I only have two data points. Let's watch it overtime to see how it evolves.

Performance Review Of Fundamental Ranking

Last weekend I've picked some stocks from gurufocus.com and published their fundamental ranks. Now it's time to review their 1 week performance. As shown in the chart below, there is clear tendency that higher ranked stocks outperform lower ranked ones, though the deviation is much larger than those ETFs as shown here. I wouldn't say anythings more. The number speaks for itself.

The Underlying Ideas Of Fundamental Ranking

Below are what I've said during the discussion with a value investor. It is about the underlying ideas of fundamental raking. This may help one to understand where it comes from.

I think although every individual should be objective when valuing a company, [...] the valuation is collectively subjective. It is collectively subjective because the market is a voting machine (in short term, of course). With this in mind, should a value investor always try to use the most popular ratio, i.e., P/E, when he [tries] to value a company? At least to start with?

[Using P/E is] not following the majority, [it is] taking advantage of it. [Value investor's] logic is fact = right = profit, and non-fact = wrong = loss. I'm OK with the first part, [the] part that fact = right and non-fact = wrong. But I just want to point out that more often than not, right <> profit and wrong <> loss.

The [fundamental ranking] system is not based on fact, it is based on behavior, i.e., it tries to chase what the market would think the most important and make profit in short period. So I think I couldn't afford to reveal the formulas, because behavior, not like fact, does evolve over time, and it does respond to [feedback].

[The idea of fundamental ranking is] from the opposite side [of value investors]. [It is designed for] a trader [...]. I spend time to study the structure and the behavior of the market. But then I learned that one couldn't make big profit by only knowing structure and behavior. The market has something else in its mind, and lately I start to realize it is "value". It may not be the true value in the mind of a successful value investor, but that would be OK. As long as the market chases it, it might be a good idea to chase it in short term. That's how I come up with this ranking system.

Revealing the formulas has at least three effects. [One is to invite verification.] The other two are: to invite followers, and to invite competitors. In some sense followers are competitors, but they have different functions. In the world of trading, followers help you to mark up the price, while competitors squeeze out your profit margin. I'd like to have followers but not competitors. So I think the best way to do is, for a specific company, [to reveal] only the formulas that is most important to value that company. Most brand name research firms are doing this. [...] But to reveal the whole package? It's too dangerous to me.

ETF Ranking

There are two problems with the fundamental ranking system T-Rank 2.0 if one would like to use it as a mechanical trading system: liquidity and volatility. Liquidity is mainly discussed here, and volatility is touched, though lightly, here. I was struggling with them for a couple of days and didn't have any good solution that satisfies me. Then suddenly an idea popped up to me following the discussion with an value investor on something totally irrelevant (it was good discussion and it opened my eyes). I can rank and trade ETFs. The rank of an ETF is the weighted average of ranks of the stocks in its portfolio. ETF hit two birds with one stone: it is naturally diversified, thus less volatility; and it has much better liquidity.

My research tool doesn't support the calculation required by my ETF ranking method. So I couldn't do back test on this. I manually calculated the ranks of the 9 popular sector ETFs: XLB, XLE, XLF, XLI, XLK, XLP, XLU, XLV, XLY, using today's weights and last weekend's stock ranks. Then I relate it to their one week return. Tomorrow the market won't open so today's close is weekly close. The result is charted below.

At the first glance, there is tendency that higher ranked ETF has better return. But the points do not align nicely on a straight line. I looked at it for a couple of minutes and figured out that it is because the offensive sectors vs. defensive sectors. Among the 9 ETFs, 6 are deemed to be offensive, they are B, E, F, I, K, and Y. The rest 3, P, U, and V, are defensive. It appears that offensive ETFs aligns well to a straight line, as well as the defensive ones. The least square fits are plotted on the chart also.

Although I have only one week data, the deviation from the least square fit is minimal and I believe this shows that there is significant statistical link between the ranks of ETFs and their short term return. Two remarks:

• Offensive sectors and defensive sectors do behave differently.
• An market neutral strategy is to long the top ranked ETF and short the bottom ranked ETF.

Liquidity And Performance Of A Fundamental Ranking System

I was worrying that the performance of T-Rank 2.0 would be impacted if I require higher liquidity. I ran some back tests and the result shows that requiring more liquidity will have negative impact on overall return, but the impact is manageable. In fact, there is really no big difference on overall return among portfolio size $100K, $200K, and $500K.

• Worst case $40K: $3 per share and average 50K shares per day in the past 20 days. So the worst case daily dollar volume is $150K and one would like to not put more than $2K per stock. With 20 stocks in the portfolio, the worst case size is $40K. Annualized return is 26.70%.
• $100K: The liquidity requirements are combined into one such that the average dollar volume traded per day in the past 60 days is at least $375K, and no more than $5K per stock. Annualized return is 22.96%.
• $200K: average dollar volume is at least $750K, and no more than $10K per stock. Annualized return is 21.51%.
• $500K: average dollar volume is at least $1.875M, and no more than $25K per stock. Annualized return is 22.72%.

Below is the performance chart.

Usage Models Of A Fundamental Ranking System

A friend asked me whether I'm going to rebalance every week if I trade T-Rank 2.0. My answer was that I'll if I trade the top 20 stocks returned by it. I think that's the discipline a trader wants to follow if he ever wants to trade a mechanical system. However this raised another question: How to make good use of the fundamental ranking system. Some options are discussed below.

1. Use It As A Mechanical Trading System

I derived T-Rank 2.0's historical performance data from back testing. A trader may want to follow strictly the trading schemes used in the back testing to copy its performance. The schemes are straightforward and should be easy to follow:

• On every weekend compute the ranks of all stocks that satisfies the liquidity requirements documented here.
• Pick the top k stocks. In my back testing I used 20 because I think 20 is the maximum size that is manageable by an individual to reduce volatility. But any constant k would work.
• Allocate fund equally among the k stocks. If you have $200K and want to trade 20 stocks, then each stock gets $10K.
• Place order to buy at market price at market open on Monday.
• Hold the portfolio for n weeks. Any n between 1 and 6 would work and should generate similar returns.
• On the Monday of the (n+1)-th week, liquidate the portfolio at market price at market open.
• Repeat the first step. If a stock is reselected, you can save the steps to sell and buy it back.

In the back test, a 0.5% slippage was deducted from every transaction. As long as that 0.5% covers your commission and bid / ask spread, you should get the same performance.

But there is one concern and that is the liquidity. The liquidity requirements ask for $3 per share and 50K shares daily average volume. So the worst case scenario is that you only have $3 * 50K = $150K traded per day. I think you couldn't put in more than $2K to your market order without significantly moving the price. Multiply that with 20, the size of your portfolio, in the worst case scenario, is only $40K, and the annualized return is $40K * 26.70% = $10.68K. Not bad but you are not going to get rich with this.

I know this is the worst case scenario and you can always allocate more money on stocks with higher liquidity to circumvent this problem. But that may or may not hurt your overall return. I don't have data on this yet. Maybe I should run more back test on this.

Furthermore, the liquidity constraints prevent one from sharing his trading ideas with his friends, which may upset either the trader or his friends.

2. Use It As A Screener

In this way maybe I could publish the top 300 stocks, or stocks with rank above 90 each weekend, and my fellow friends could pick several, say up to 20, from the list and trade. This model works the best if each of my friend has his own way to pick stocks, be it fundamental based or technical based. One may use support and resistance to select the most promising opportunities, while another may check overbought and oversold conditions.

However there are still restrictions:

• Their methods should be different from one another.
• The ranks may not be published together with the stocks, to prevent everyone from simply picking the top ranked stocks.
• Everyone still needs to spend a lot of effort to comb through the 300 stocks, which is time consuming unless they have some automated way to pick stocks.
• It is up to the trader to determine entry and exit. The general suggestion is that one can hold a stock as long as it keeps showing up in the top 300 list.

3. Use It As A Ranking System

So each trader has his own watch list. They are supposed to derive such list based on their own knowledge, market sense, and interest. Then they consult the ranking system to decide which stocks (those with high ranks) they want to put on trade and for the rest they may want to wait a little bit.

This will remove the conflicts among traders that share the ranking system, but it also has higher requirements on each trader.

So each option has its pros and cons. There is really no such thing as free lunch.

Weekly Summary

It has been a week since my first post on fundamental ranking. Looking back, it appears I've made good progress this week. Below I summarize the key points of my little research.

• T-Rank 2.0 is a fundamental based ranking system. It ranks stocks purely by their fundamental indicators, including: valuation, financial condition, and return on capital. Growth was not included because it actually has negative impact on the short term return. Blog posts covering this topic are here, here, and here.
• T-Rank 2.0 is designed for short term trading. If an investor build a portfolio with the top 20 stocks returned by T-Rank 2.0 and rebalance the portfolio every week, his annualized return in the past 10 year is 26.70%. Considering that we have a giant bear market in the past 10 years, this result is fairly good. Related blog post is here.
• T-Rank 2.0 is effective. Effectiveness is defined as the relation between the rank of a stock and its 1 week return. T-Rank 2.0 returns a numerical rank from 0 to 100. If an investor moves up 10 rank point, his annualized weekly return should improve 1.7%. And the further he pushes up, the better it gets. If he moves from 90 to 100, his annualized weekly return should improve 3.6%. Related blog post is here.

Other interesting observations are listed below:

• Although T-Rank 2.0 uses weekly rebalance, it actually automatically finds the optimal holding period, which is 6 weeks in average per stock. Related blog posts are here, here, and here.