Archive for March, 2008

XTR 100 - the broad Index

XTR 21 is market cap and fundamental weighted index. And to make it more replicable (tradable) we have studied the underlying free float for the market, to introduce the benchmarks FREE FLOAT version. Moreover the current statistics do suggest that XTR 21 could better its performance with a free float review.

However, since this will be the first attempt to create a free float benchmark in the country we wanted to be devote it some more time. And since we are in an emerging market, it is necessary to consider if float adjustment alters the structure, return characteristics, composition, liquidity and risk profile of the indices. Float adjustment reduces market capitalization of different stocks by different amounts, and might, therefore, alter index structure. To capture the change in relative weights in the full list of index constituents, we have plotted the sectoral free float rankings. This helps us understand the homogeneity and other characteristics of the universe free float.

From a sector rotation perspective, Financials are the top free floating sector at 69%. This might look coincidental, but considering Financials are the early economic cycle, a higher tradability and outperformance owing to high public appeal can only be balanced through a higher free float share. Industrials, Discretionary and Pharma are the other top free float share sectors. Materials, Energy and Utilities are low on the free float share. And if sector analogy is extended to these late economic sectors, market forces might effect an improvement here. Just like the market universe, Energy and Financials are the two top sectors in free float as a percentage of market capitalization. Another interesting aspect was the large capitalization free float at 79%. Mid capitalization and small capitalization free float share stood at 12% and 10% respectively. This statistics once again reinforces our large cap hypothesis (XTR.250208) and suggests that free float factors might reinforce XTR 21 positively. We also plotted the free float shares for Late Expansion, Early Expansion and Middle Expansion economic cycles. We have carried the annexure with the free float rankings (slide 14). Bank of Transylvania (BATR.BX) is top free float stock, followed closely by SSIF BROKER (BRKU.BX) and Transportation Major SOCEP (SOCC.BX) at 85%, 77% and 66% respectively. MECHEL (OTSP.BX) is the lowest free float stock ruling at 0.2.

We will delve on the real construction in the next issue of XTR. This week we are introducing the Free-float (FF) factor and how numeric ranking for BVB stocks can be based on Free flat factors. The FF methodology refers to an index construction methodology that takes into consideration only the free-float market capitalization of a company for the purpose of index calculation and assigning weight to stocks in Index. Free-float market capitalization is defined as that proportion of total shares issued by the company that are readily available for trading in the market. It generally excludes promoters holding, government holding, strategic holding and other locked-in shares that will not come to the market for trading in the normal course. In other words, the market capitalization of each company in a Free-float index is reduced to the extent of its readily available shares in the market. Under the ‘full-market capitalization’ methodology, the total market capitalization of a company, irrespective of who is holding the shares, is taken into consideration for computation of an index. However, if instead of taking the total market capitalization, only the Free-float market capitalization of a company is considered for index calculation, it is called the Free-float methodology.

There are many advantages of Free-float Methodology. It aids both active and passive investing styles. It aids active managers by enabling them to benchmark their fund returns vis-à-vis an investable index. And it enables passive managers to track the index with the least tracking error. Free-float Methodology improves index flexibility in terms of including any stock from the universe of listed stocks. This improves market coverage and sector coverage of the index. For example, under a Full-market capitalization methodology, companies with large market capitalization and low free-float cannot generally be included in the Index because they tend to distort the index by having an undue influence on the index movement. However, under the Free-float Methodology, since only the free-float market capitalization of each company is considered for index calculation, it becomes possible to include such closely held companies in the index while at the same time preventing their undue influence on the index movement.
Globally, the Free-float Methodology of index construction is considered to be an industry best practice and all major index providers like MSCI, FTSE, S&P and STOXX have adopted the same. MSCI, a leading global index provider, shifted all its indices to the Free-float Methodology in 2002. The MSCI India Standard Index, which is followed by Foreign Institutional Investors (FIIs) to track Indian equities, is also based on the Free-float Methodology. NASDAQ-100, the underlying index to the famous Exchange Traded Fund (ETF) - QQQ is based on the Free-float Methodology.

To construct the free float index, one needs to determine the Free-float factor for each Index Company. A few exchanges around the world have designed a detailed Free-float format to be filled and submitted by all index companies on a quarterly basis with the Exchange. The Exchange determines the Free-float factor for each company based on the detailed information submitted by the companies. Free-float factor is the multiple with which the total market capitalization of a company is adjusted to arrive at the Free-float market capitalization.

Apart from the free float factors, a few other considerations need to be explored. The first is breadth of coverage — how complete is the benchmark in covering the investment opportunity set? Does it take into account the sector rotation cycle? Does the index accurately reflect what the investor can actually buy? The second is transparency of construction — the portfolio construction rules should be clear and unambiguous. These rules ought to be predictable and consistently applied. We will try to address all these issues in the following issues of XTR. Owing to some special ownership rules, free float issues regarding SIFs are unclear and how to determine the free float factor for the respective stocks. The free float data taken for XTR was reported on 29 Feb 2008.

About the XTR21 performance week over week, the benchmark outperformed all the other indices.

To read the latest issue of XTR.310308 write to us for a free trial today or download the report from REUTERS KNOWLEDGE, YAHOO FINANCE, THOMPSON ONE or THOMPSON RESEARCH.

Enjoy the latest XTR.310308

XTR - Correlation

In probability theory and statistics, correlation indicates the strength and direction of a linear relationship between two random variables. This statistical parameter can not only help in stock selection but also create structured products based on market neutralizing.

Suggesting a hedging strategy, a week before market fell more than 8% was nothing short of timing. Whether the markets run up this week or not, March futures expiry should make your hedged portfolios richer. And the way we see thing at Orpheus, market neutral strategies should become more than a bread earner in the time ahead, especially if volatility continues to rise.

Building on where we left last time, the primary aim of creating correlation matrices is to formulate high integrity pairs. For this we have studied correlations over various time frames (historical, few years, quarterly, weekly and daily). The current XTR illustrates some of the historical correlation matrices.

The highest correlation pair stands at 0.985 between SIF 1 and SIF2. But SIF2 (SIF2.BX) and SIF5 (SIF5.BX) make a better active pair, owing to their tradability in futures. A long – short strategy can be a continuous market neutral strategy between SIF2 and SIF 5. And just like the SIF pairs, TLV (BATR.BX) and BRD (BRDX.BX) (Romanian banking majors) have the highest correlation at 0.95. This means irrespective of their separate fundamental drivers BRD and TLV make a good pair. And from a statistical view, exposure to one bank would have compensated for the exposure to the other or both the stocks.

To illustrate this case further, we can compare the price performance of the banking majors. Though on a short term basis say July 07 to Feb low BRD fell 13% more than TLV. From Jan 07 to July high the comparisons stood at 68%, 48% for BRD and TLV respectively. And if you look at overall performance starting Jan 2006, both the stocks returned similar price performances at around 45%. This is what a good correlation pair does. Annualized returns for good pairs are similar irrespective of the underlying events, which drive the respective stocks. It’s also to do with the large cap hypothesis we talked about. Two blue chips in a high correlation pair, which are also sector leaders can’t deliver divergent price performances. And a difference, if any can only be in the short term, over a few weeks to a few months like we have illustrated in our case above.

And as markets mature even these short term price inefficiencies (example BRD returning 13% less than TLV since July 07) can be taken care by arbitrageurs or strategists who play between such highly correlated pairs (Short BRD, Long TLV and vice versa). This is where market neutral comes in. We at Orpheus have highlighted these market neutral strategies between TLV vs. BRD, SIF2 vs. SIF5, SNP (SNPP.BX) vs. OIL (BRT-) on prior occasions. A few market neutral pairs (long one, short other) out of the many we illustrated in Orpheus market letters failed to give consistent results. One of them was the SNP-RRC (ROMP.BX) pair, which as we illustrated in the correlation matrices last time (XTR.170308) is a low correlation pair at 0.59, the very reason a continuous running long – short strategy is not very successful in the respective case. This is despite the fact the both stocks are from the same sector. Sector grouping can enhance a pair integrity, but it does not guarantee it.

And correlations are not just local, they are global in their scalability. We talk about them all the time. Like for example the correlation of OIL (Brent) with SNP. Many of our readers have expressed surprise at SNP an OIL relationship. How SNP fell despite the net rise in

OIL prices from $ 50 to $ 100? How BETFI fell despite DOW rise? And how is Euro Ron connected to BET or BETFI? Over the long term OIL and SNP has a 0.85 correlation (this makes it a better pair than the local RRC - SNP pair). However, owing to the long termism of the correlation indicator, price inefficiencies between stock and its underlying commodity (in this case, OIL and SNP) can extend for more than a few months. This is what happened, OIL rose and SNP fell (We will explain SNP-OIL pair more in our next issue on Intermarket). A similar relationship is assumed to be in DOW Jones Industrial and local market indices (BETFI, BET, BETC). However, the correlations are weaker when we consider the DOW and BETFI. And if you are really looking at CAC or DAX for trading BETFI, you are on a heading towards a losing streak. The DOW and BETFI correlation is higher when markets fall and extremely poor and sometimes negative. This means that we had many occasions when local indices were rising while the DOW was falling. The correlation has never reached 0.9 between DOW and BET.

Hungarian BUX (slide 14) on the other hand (has the highest correlation with BET) and can tell us more about the Romanian indices than DOW, which is more of a mass psychology play (herding). Panic is a bigger motivator than greed. No wonder when markets go down, the correlations increase all over, and not just with DOW. But with a host of global indices including Russia, Nikkei, Shanghai, India, Brazil etc. It’s the DOW link, which makes more news, the reason it get’s anchored in mass psychology.

Correlation cannot only help identify pairs, but also assist is stock selection. When we need to assume market risk (beta) we can chose highly correlated pair, but when markets are at euphoric levels, stock picking can be based on negative correlation stocks. Globally, negative correlation is a much desired strategy today. Hence the significance of understanding the correlation matrix cannot be undermined. In the previous issue of XTR (100308) we showcased the XTR 21 beta. This week we have shown a correlation matrix (slide 13) between component stocks. As you can see there are many negatively correlated pairs, no wonder XTR 21 falls lesser than BETFI every time we have a negative week. Since inception XTR 21 never fell more than BETFI (week over week). We need more history to validate this, but both portfolio beta and negatively correlated pair among XTR components confirm our view.

Negative correlation has better predictability than low correlation. DOW has a low correlation with local Romanian indices, but EURRON (slide 14)has a negative correlation with BET over the last four years. This also suggests the local currency is a better indicator than the global benchmark. And last but not least all these pair components lead and lag in performance against each other. This is why SNP underperformance against OIL is cyclical. And we might not be far away from the time when OIL corrects and SNP rises. Next week we extend this pair formulation strategy to sectors, to identify outperforming sectors from the ones that are set to underperform.

On the XTR 21 we had another week of drawdown, as the broad market corrected. The benchmark is down 10% from inception. But we are still positive for the coming months and continue to consider these low risk entry points. XTR 21 outperformed BETFI yet again. But since the fall was sizeable most market indices were down. From the late economic cycle sectors, it was the utilities, staples and materials which witnessed marginal losses.

To read the latest issue of XTR.240308 write to us for a free trial today or download the report from REUTERS KNOWLEDGE, YAHOO FINANCE, THOMPSON ONE or THOMPSON RESEARCH.

The Coffee Fractal

The Coffee God has woken up and he has decided to punish coffee lovers. The bean is set to rise for more than a few years to prices which not many of us can relate too.

Cycles and Fractals are a part of nature and date back before everything. Fractals of population were discovered 200 years back, and then came fractals of mass psychology discovered by Charles Dow in 1880s. And now scientists have joined the ranks with Fractal research, as few as 30 years ago. It may take another 15-20 years for fractals to become more popular, maybe more popular than what they are now. But stock markets or mass psychology fractals may never get popular despite all the amazing accuracy that they give. The force of the herd is strong, overestimation of personal skills normal, anchoring on to fixed ideas easy, believing that markets work on fair value science and every profit is linked to coming news are axioms the society considers the ultimate truth. To stand against all this beliefs and also trash them and say it’s all a fractal is gibberish.

But then, it works. What one needs is internet, a trading system, market and fractal watching experience, a few rules, ability to stand against the herd and a cup of coffee. Well most of these things can be acquired, even market experience. What is the toughest however is the cup of coffee and ability to sip it is alone and following the few rules.

Coffee has increased 60% (Since Dec 2007) on New York spot and 77% on MCX Robusta (Since Apr 2007). This was another high growth asset with another late news, which came more than a year late. It was here in Dec 2006 we mentioned about the emerging bean (The Coffee God). And then in Oct 2007 when we said that all the sideways action of coffee has come to an end and it’s time for the bean to go up more than 100% in about 12 months. While the prices have been growing the world’s largest coffee chain, Starbucks is coming under workers union pressure. This might look coincidental, but American customer is more sensitive now to coffee prices than a few years ago, when recession talks were unheard off and the state was not giving bailouts.

And mind it, this is just the beginning. We are in the first quarter of 2008 and coffee is already creating news. And the time ahead will indeed get trickier and tougher for coffee lovers and coffee chains. The consumers either have to become contrarians fast enough or stop drinking coffee. Both the things go together. You can’t get consistently wealthy if you are not an independent thinker and a contrarian. And if you are not rich you can’t afford the Starbucks cup every day, ok, twice a day. 100% rise from sub dollar 100 levels was our short term target. We see coffee becoming a luxury with long term targets three times higher than Oct 2007 lows.

This means we are indeed headed for a shakeout. It’s like oil, when it goes up a little, it’s good for the oil producing nations, for some companies and a little expensive for other business as cost goes up. But when oil goes up higher, it’s bad for all, as people start to innovate, start using alternative fuels, basically price elasticity. The higher it goes, the more consumer behavior it changes.

Now replace oil with coffee, the higher it goes, the more it will hurt. Maybe it’s still early to talk about the rupee 300 coffee mug. But “stop buying expensive coffee and save calculators” are already available on the web today. The coffee lovers are questioning the sanity of buying coffee from stores every single day instead of just contributing to the local “coffee pot fund”. Hugh U. Chou, of the local coffee pot fund fame also accepts that office coffee may not stack up to the store bought versions, but is it really worth the long term costs?

XTR - Market Neutral

It might look strange, but though the problems are more in America, it’s the Romanian markets that feel the heat. One of the reasons emerging markets like Romania are more volatile is because markets lack the knowledge of risk management. We are ideally speaking still a “LONG ONLY” market. We don’t have mechanisms to short sell, we don’t have many representative benchmarks, no index futures for hedging and just a few stock futures with some trading volume. No wonder emotional content is high owing to lack of hedging instruments. And the only executable strategy when markets fall is to ‘SELL’. This is the reason we want to dedicate some time to risk management. As a good risk management strategy can avoid unwarranted panic.

Market neutralizing of a portfolio can be done using many techniques. We can use market sensitivity indicator beta (XTR.100308), inter market and sectoral correlations and annualized interest rates in the futures market. Just like beta correlations can be used for stock selection. Stock pickers can avoid high beta in euphoric times and select the same stocks in a panic (as the same stocks fall the most and relatively become the most inexpensive) or use negative beta sectors and stocks to bring the overall portfolio beta (sensitivity) down (like we did for XTR 21). Or one can just select stocks with beta at 1 to create a portfolio that just simulates the market.

High and low correlations make stock selection a bit more easy. For example all of the BETFI components (SIFs) have near 1 betas, which makes stock selection easier for anyone wanting to take exposures to BETFI components (he can use any of the SIFs to capture the BETFI growth). The similar analogy can be extended to other sectors of the local equity universe. This is what we have tried to illustrate using sector based correlation matrices. We will be delving into correlation in more detail next issue when we will illustrate the meaning of high and low correlations and how market pairs can be identified using correlation matrices. What is the link of correlation with risk? And how can management of risk mean a profit when you execute a pair trading (market neutral) strategy? Understanding correlations can not only allow the fund manager to neutralize unwanted risk but also create relative alpha despite market negativity.

This issue builds on where we left last time with stock betas. In slide 4 we create two portfolios of different weights and beta and then we use SIF 2 and SIF 5 Futures to hedge the same portfolios. So we are using futures to hedge any portfolio made of various stocks. We can even use SIF Futures to hedge a portfolio without SIFs. Strange as it may sound, statistical hedging is mathematical and sound. Statistical hedging also highlights why thumb rule hedging may not work in markets and can be bad risk management. A simple example are SIF futures. Though conventionally believed that SIF futures are more volatile compared to SIF spot, the reality is opposite. Spot SIFs are more volatile than their respective futures and hence have a lower beta compared to spot SIF components. And this is why SHORTING 10 (10*1000 units) contracts of SIF 2 FUTURES against 10,000 units of SIF 2 spot is far from a perfect hedge and can lose money. We have illustrated the Hedge sheet on slide 4 and 5. How SIF 2 and SIF 5 futures used along and together can be used to hedge the underlying portfolio.

On the XTR 21 front, we had another week of XTR homogeneity compared to the market. It found its place yet again between the market indices. This makes it a good benchmark as it does not spike up or down compared to the overall market. XTR seems very selective about volatility. It captures the positive volatility (upsides) and shuns the negative volatilities (downsides). A model with a less drawdown is always preferred to one with a high drawdown, like BETFI this week, which fell the most out of BET, BETC and XTR 21. This high BETFI beta caused the first divergence in more than six weeks between XTR and BETFI (slide 7). Sectorally too this was another interesting week as energy outperformed all the other sectors. Energy is a late expansion sector and should continue to outperform along with materials followed by staples and utilities.

To read the latest issue of XTR.170308, write to us for a free trial today or download the report from REUTERS KNOWLEDGE, YAHOO FINANCE, THOMPSON ONE or THOMPSON RESEARCH.

Waking up to Gung - Ho -II

This is what we said on 17 JAN 2007 in our WAVES.FOREX report.

“Yen is moving sideways for the last 22 years and has touched near 120 levels for more than 10 times in the respective period. This means on average once every 2 years. This is a huge currency inaction compared to dollar. This we consider a multi year potentially rewarding opportunity. The USD/YEN ratio line broke the 22 year trend suggesting YEN is ready to move out of inaction and head to potentially below 80 (33% strengthening) for many years. And if we add the gung ho factor, dollar might just wake up against it.”

What happened to YEN after this is an evidence of Fractal science. Prices hit 100. The latest issue of WAVES.FOREX carries more of such long term updates on currencies around the world. And a closer look would tell you why one the world’s strongest currencies SWISS FRANC might still weaken against ROMANIA LEI. The report also carried the projections for Indian RUPEE still pointing lower to 38.

Enjoy the latest WAVES.FOREX.140308. To read the latest issue write to us for a free trial today or download the report from REUTERS KNOWLEDGE, YAHOO FINANCE, THOMPSON ONE or THOMPSON RESEARCH.

XTR - Stock Picking Using Beta

The Beta coefficient, in terms of finance and investing, is a measure of volatility of a stock or portfolio in relation to the rest of the financial market. An asset with a beta of 0 means that its price is not at all correlated with the market and that the asset is independent. A positive beta means that the asset generally follows the market. A negative beta on the other hand shows that the asset inversely follows the market and generally decreases in value if the market goes up. It might look like a coincidence but XTR 21 has two highest beta stocks viz. Petrom and Broker and two negative beta stocks viz. Turbomecanica and Alro. No wonder last week’s negativity saw XTR as the best performer. The negative beta stocks balancing the high beta stocks. Negative correlation helped reduce losses. We expect a similar performance in the coming future.

Beta correlations are not only evident between companies within the same sector, but also within the same asset class like equities. This is why a majority of the stock components fall or rise together. However, when you talk about emerging markets, this correlated risk, measured by beta can also assist in numeric rankings between sectors and stocks and assist in stock picking.

Emerging markets continue to be a driver for global investment and it’s presumptuous to assume that the degree of a bear market can become large enough to drive out liquidity from the Romanian capital market. Though markets are more integrated today than they were in 1980’s when Japan went into a depression, the very fact that global economy thrived for more than thirty years despite the Japanese slow down clearly highlights that the economic engine growth might come from a different asset class or a different region, but no global depression can stop it completely.

The intermarket factors will continue to play just like they do between large cap and small cap, commodities and equities, developed and emerging markets. High betas of emerging markets will continue to attract investments from both local and international investment pools. And even if things become chaotic, the degree of chaos in emerging markets cannot be compared to one in developed markets.

It’s keeping these comparisons in mind we present this issue of XTR dedicated to betas. Indices are a good measure of judging a fund manager’s performance, as they beat the index. Today we have funds in Romania, which have moved to risk-adjusted performance combining returns with volatility. This is alpha, a measure of a fund manager’s skill, defined as the ability to produce superior risk-adjusted returns. However, most stock market indices just like in Romania are dominated by larger companies.

This means that active manager’s chance of outperforming lies in buying the shares of smaller businesses or outperforming through the value approach i.e. buying the shares of companies that look cheap on valuation measures, such as low price earnings multiples etc.. Hence the fund managers edge of delivering real alpha continues to diminish as market sophistication increases and what fund managers might deliver may be more beta rather than alpha. According to recent research, the correlation between fund returns and the S&P 500 index is already high and getting higher.

And Bill Fung and Narayan Naik of the London Business School suggest that it seems possible that in time ahead the gap between alpha and beta will continue to reduce. And though there will be fund managers outperforming all the time, identifying them early will remain a challenge. Hence a conservative strategy might be to just concentrate on beta and invest in indices and ETFs that allow the relevant exposure. XTR 21 allows small capitalization and value exposure on a appropriate sample of the market universe , classic stock picking using beta.

To read the latest XTR issue write to us for a free trial today or download the report from REUTERS KNOWLEDGE, YAHOO FINANCE, THOMPSON ONE or THOMPSON RESEARCH.

XTR - Fund Managers Average

Also called as the “FUND MANAGER’S AVERAGE”, this is one of the most important indicators for measuring participation. As a general rule, investors, institutions and big players feel comfortable investing in a stock or a market when the asset is trading above it’s 200-day MA. When the stock or market falls below the 200-day MA, they are less likely to put new money to work in that particular stock or market or to defend their position if the stock or market drops.

The 200-day moving average is a long-term smoothing of price movement, and a stock’s price in relation to this moving average is a good indication of its long-term trend. For example, when the price index moves below the 200-day moving average, we can assume the long-term trend is down until the price index moves back above the 200-day moving average.

There are no automatic assumptions that can be made about this index. For example, just because 80% of stocks are above their 200-day moving average, there is no guarantee that a downside reversal can’t happen. In fact, once the index has moved to an extreme end of its range, it’s a good idea to be alert for a change in direction, because the market improves until it is as good as it can get, then it starts to deteriorate. Conversely, as soon as things are as bad as they can get, they start improving. So this is the behavioral pattern that creates an edge for the fund manager, stock pickers and traders.

This XTR issue analyses the BVB indices and stocks in relation to their 200 Day moving average. The results are not surprising. More than 80% of the market is below its respective average. Now this means that apart from the negativity that one can expect, it makes sense to be alert for a change in direction. Above this we have a few stocks that are above the 200 day moving average. We also back tested a long – short model for the three indices and have illustrated the results in the report.

The report also carries an update on the XTR 21 index, which for second week in a row has found its place among the other indices. There is a complete capitalization comparison, index comparisons, updated stock and sector allocations, intermarket sectoral performances and XTR 21 week over week observations.

To read the latest XTR issue write to us for a free trial today or download the report from REUTERS KNOWLEDGE, YAHOO FINANCE, THOMPSON ONE or THOMPSON RESEARCH.