What Happens after 3 Consecutive 1% Up Days?

A follow up to yesterdays 2 strong days study i decided to see if such a phenomenum also persisted on the 3rd day. To may surprise it seems that the trade is even stronger.  Notably, once again the filter when the 200 SMA is above the 50 SMA works best for such a phenomenum.


Total Sample

T+1 T+2 T+3 T+4 T+5
Avg Returns (total) 0.70 2.88 2.47 2.66 2.66
No. of Losses 15 3 10 10 15
No. of Winners 27 39 32 32 27
Avg Losses -0.59 -1.61 -1.42 -1.53 -1.59
Avg Gains 1.42 3.23 3.69 3.97 5.02

200<50 SMA

T+1 T+2 T+3 T+4 T+5
Avg Returns (total) 0.53 2.62 2.48 2.36 2.55
No. of Losses 6 0 3 3 4
No. of Winners 9 15 12 12 11
Avg Losses -0.37 0.00 -1.44 -1.74 -1.96
Avg Gains 1.12 2.62 3.46 3.39 4.19

200>50 SMA

T+1 T+2 T+3 T+4 T+5
Avg Returns (total) 0.80 3.03 2.47 2.83 2.72
No. of Losses 9 3 7 7 11
No. of Winners 18 24 20 20 16
Avg Losses -0.74 -1.61 -1.41 -1.44 -1.45
Avg Gains 1.57 3.61 3.83 4.32 5.59
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What happens after 2 consecutive strong up days?

Finally decide to do some quant work and found some interesting results.

For the first of hopefully many quant studies,  I shall start by studying what happens after strong up days.  I quantify strong up days as those days that have experienced gains of 1% of more from current day to the next day’s close.  I shall start by studying the STI market.  We can see from below that the returns besides from T+1 are fairly good and are usually twice of average losses. In addition the occurrences of winners are 3:1 of losses. Below I will show results with a filter, for one  I wanted to test for the results for an uptrend which i quantified using the 50 SMA greater than the slower 200 SMA. Vice versa for a downtrend

Total Sample

T+1 T+2 T+3 T+4 T+5
Avg Returns (total) 0.24 2.53 2.57 2.51 2.47
No. of Losses 41 11 16 15 20
No. of Winners 53 82 78 79 74
Avg Losses -0.69 -1.18 -1.76 -1.89 -1.66
Avg Gains 1.35 3.37 3.65 3.59 3.87

When 200 SMA >50 SMA

Avg Returns (total) 0.24 2.77 2.74 2.61 2.65
No. of Losses 46 16 18 19 25
No. of Winners 48 78 76 75 69
Avg Losses -1.04 -1.64 -2.19 -2.06 -1.91
Avg Gains 1.53 3.52 3.85 3.79 4.32

When 200 SMA < 50 SMA

Avg Returns (total) 0.24 2.28 2.41 2.42 2.29
No. of Losses 37 4 11 12 15
No. of Winners 56 89 82 81 78
Avg Losses -0.59 -1.40 -1.21 -1.78 -2.19
Avg Gains 0.79 2.45 2.89 3.04 3.15

Conclusion

As we can see from the results above the returns are significantly better when the 200 SMA > 50 SMA meaning that the strength of 2 up days carries forward in a downtrend. In my sample I also noticed several double digit returns in after 2 up days when the 200 SMA > 50 SMA. Maybe this could be due to short selling covering their positions or just the drying up of sellers and buyers being the main pushers in the market but either way the win loss (returns) ratio, number of winners vs number of losses and general persistence of such a phenomenon makes this a good idea to follow.

My sample period is between 12/28/87 to 30/07/10

How has the Financial Markets Changed Over Time part 1

To say that the markets will always stay the same is naive.  In wall street the bankers and traders continue to evolve to try to take advantage of loopholes in regulation and improvements in technology to give themselves an edge to either make money through out right trades or create products and services to collect commissions.  This article shall give a general overview of how the game has changed and characteristics that is unlikely to come back and new anomalies that might be on the road ahead.  In addition the demographics on the world are also changing, this will also have an effect on the markets.

Firstly, the Possibly more prevalent in the western countries than the asian ones .  According to USA Today, next year there will be the US will have 79 million people hitting the retirement age of 65, whilst in Europe a similarly large number will also hit this number.  It was noted from PsyFiTec that:

The recent Barclays UK Equity-Gilt Study (which unfortunately isn’t available on-line although you can buy it at £100 a pop, if you’re especially interested) makes some interesting observations about the effect of demographics on markets. You might expect that when the surge of post-war Baby Boomers starts to retire and begins looking for income then you’d see a decline in the funds invested in stocks and a surge in those invested in bonds. As the study points out this, in fact, is exactly what was seen in Japan in the 1990’s and now appears to be what we’re seeing in the US: the fit of the curve of people approaching retirement, saving hugely for it and then slumping into a blissful Third-Age is very close to that of the changes in US market valuations.

He adds that:

So, maybe the US is set for a twenty year period of slow growth, but maybe enhanced immigration will change the population profile. Maybe the aging population will want to party their lives away on cruise ships, but maybe the falling value of their retirement income will force them to opt for a pedalo on the local duck pond instead. Maybe drug companies can look forward to a boom in sales to geriatrics but maybe cash-strapped governments will legislate to make them look more like utilities than Klondike gold-rushers. Maybe government bonds will be a bust, but maybe inflation will remain subdued due to overcapacity in the face of a declining market for consumption.

Meanwhile Asian societies continue their quest towards self-sustaining economies and as their wealth increases their appetite for Western assets continues to grow. Maybe this growth will be checked by internal problems or environmental constraints but maybe it won’t. Overall there are a lot of maybes which mean that the straightforward expectation that demographics will lead to a reduction in market returns isn’t quite so obvious as we might expect.

Prediction is never straightforward, but we can be fairly assured that the hoards of grey-pated elders streaming towards retirement are going to be looking for safe income streams. That’s probably where we should start to look for a demographic dividend.

In essence we should realize that with a greater demographic needing fixed returns, the bond markets and fixed income structured products are likely to balloon.  In addition with the recent market turmoil, its hard to believe that such fixed income products can promise good and steady returns.   It was noted that there are instances of underfunded pensions.

The reasons are:

  • The most obvious reason is investment performance, which has taken a hit.
  • The pension protection act of 2006 requires funds to be fully funded starting next year.
  • Misleading actuarial assumptions that assumed, on average, 8 percent returns.
  • The cost of benefits.
  • The aging workforce.
  • And the ratio of retired to active employees.

All of this means they will need to adjust various assumptions to make them more realistic.

Next, different asset classes are behaving unlike they were before for 2 reasons. First there has been dropping barriers to entry for different assets due to the introduction of ETFs, which replicate the exposure almost every asset class out there.  This means an invest-able benchmark  which creates an environment where fund managers and traders are judged by this instrument as most of the time they are not judged about their overall performance but rather how they outperform the market the particular ETF or index  as stated here. Abnormal Returns has done well in highlighting the effects of as they noted from Minyanville:

This point is illustrated nicely in a post today by Howard Simons at Minyanville.com.  In it he examines how the behavior of standard corporate indices changed after the introduction of an investment grade corporate bond ETFs:  the iShares iBoxx $ Invest Grade Corp Bond (LQD) .  Simons writes:

Indexation of any kind changes the behavior of investment managers who inevitably get their performance measured against an index. What’s worse is how even the most arbitrarily assembled index, one which might be important only to its creator and to anyone who has a vested interest in volume therefor, suddenly is elevated to exalted status by participants at all levels. Even the broadest and most senior indices change behavior; you’ll find individual investors fretting they’re underperforming the S&P 500 and not have an inkling as to why they should care.

An example of an asset class that has greatly changed is the commodities

Commo Finance

As can be seen from the chart between 1990-2003, the movement of commodities was inversely related the S&P 500. However after that, the movement of commodities mimicked the movements of the S&P closely. Such a phenomenon was also noted by the high correlation of 0.65 amongst the different asset classes recently this is seen below in a study done by barclays noted by zero hedge

correlation

However systematic relative strength did note that dispersion between different returns from different countries remains high and that this bodes well for momentum strategies which switch between various countries.

correlation

Who Makes More Money?

The final aim of investing, is simple, make money. So who really make the most money?

For Warren Buffet the most famous fundamental investor, a quote from his Annual Letter from 2009.

Over the last 44 years (that is, since present management took over) book value has grown from $19 to $70,530, a rate of 20.3% compounded annually.

Then lets move on to one of the most successful hedge funds in recent times. Renaissance Technologies  which has a knack of trading counter intuitive relationships .  They do not give a damn about whether you can understand financial theory, just whether you can exploit any kind of anomaly.

Renaissance employeed thinkers who had spent the bulk of their career in non-economic analytical fields, like mathematics, physics, and astronomy. Once at Renaissance, those thinkers would build data-processing models without any preconceptions about what should cause what, when. The firm’s advantage is in its willingness to trade what doesn’t necessarily make sense.

Notably

For the 11 years ending in December 1999, Renaissance’s Medallion Fund cumulative returns were 2,478.6 percent. Among all offshore funds over that same period, according to the database run by hedge fund observer Antoine Bernheim, the next-best performer was George SorosQuantum Fund, with a 1,710.1 percent return. A measurement of the risk (e.g., beta, volatility, or leverage figures) which accompanied its high annual returns is not publicly available. In 2009 the Medallion fund topped the list of the most profitable hedge funds with profits of over $1 billion.

Meanwhile a  popular  technical analysis blogger carl futia has the returns as follows below, You can follow his trades posted on his blog or follow his monthly trading records.

  • Year 2008 percentage gain: 86%
  • Year 2009 percentage gain: 89%
  • Year 2010 Q1 percentage gain: 26%

Now the ultimate question is how should you trade then? Well its your choice and you should make use of your own strengths. Do not be pigeonholed by any one form of trading or investing. As Falkenblog points out, you do not get higher returns from higher risk, we all know how the high notes which were backed by lehman turned out.  Take for instance a septic tank cleaner, takes more health risks than lets say a computer engineer. But a computer engineer has more salary, that because he has comparative advantage and a particular skill to be used in the job market. Similarly in investing you need to make use of any comparative advantage. Notably,  Abnormal Returns also recently pointed out the merits of being an retail individual investors and these are the edges we should be looking out for when trading or investing.

Individual investors have some distinct advantages over institutions.  Most institutions need to be acutely aware of the indices against which they benchmark.  Individuals, on the other hand, are beholden only to themselves.  The performance of the S&P 500, for example, should be but a data point to an individual.  Many institutions need days to enter (and exit) their equity positions so as not to move a stock’s price.  An individual can do this (usually) in seconds.  Maybe most importantly individuals don’t have clients breathing down their necks.  As an individual investor you are your own client.

China’s Lip Service

Headline news:  China wants to give more flexibility to Yuan. Seems great and all prissy doesn’t it, finally the Chinese are giving in to all the pressure that the methods of keeping their currency low for the sake of cheap exports is coming to an end.  The official report is here.  This statement comes amid pressure from the G20 to appreciate the Yuan. Elsehwere  an ANALYST from SocGen said that stocks should go higher based on the statements.

However if you took a closer look like Yves from naked capitalism did, you would probably have a better perspective and understand why this all just political lip service at play.  Below are the salient points he made

Read more of this post

Curation is Important

Business Insider’s, Steve Rosenbaum wrote about Curation being King due to the fact that there was a flood of news all around from facebook, tweeter, Mainstream Newfeeds, bloggers. Almost everyone and everybody is creating content. Hence managing and sieving the bset content is now a premium.  Although I would not go so far as to say as curation is king, I would say a healthy blend of both would be important.  I have also profiled the best Curator of the Web currently here.  For more details between the different types of contents you can refer here.

Notably I recently came across an interesting resource curation platform. Basically it was used during the Haiti earthquake to aggregate all forms of information ranging from SMS, tweets, Facebooks, blogs, wired news.  For each source of news there are various veracity scores (trustworthiness of scores). The higher the score the more likely the news generated would be used.  Ways of tracking veracity included, type of source, whether content from this source matches the other sources, the history of posts from this source and others. For more details refer to the video, here is the link to his curation platform.

Maybe this could be very useful in tracking the markets when there is widespread panic and possibly picking out turning points.  In addition, there are already quantitative algos hunting out key words in online and news  text and exploiting them to trade.

Notably, on the flipside of this, NewsCorp owned by Rupert Murdoch (aka big daddy of WSJ) is now making attempts to create more paywalls for his news content as the viability of the advertisement business through online content is going no where.  However, I think that this might be difficult to achieve especially with the onset of free content on the net.  My sentiments are also reflected in the video below:

The Two points of discretion for a quant trader

Came across a couple of interesting articles about quantitative traders recently. One of the most intriuging ones was one about an editor diving into the depths of the High Frequency Trading realm.  The article does well in shedding some light over the entire industry of HFT.  A salient point that i picked out below was this:

Narang lifted his profile on May 6 when he revealed to the Wall Street Journal that his firm turned off its high frequency trading computers during the flash crash. Tradeworx wasn’t the only one to do so. Kansas City–based Tradebot, started by BATS founder David Cummings, also stopped trading. Tradebot is one of the world’s two largest high frequency firms, reportedly trading as many as 1 billion shares a day in U.S. equities. Only Chicago-based Getco is thought to be bigger. Although Getco won’t comment on its daily trading volume, a spokeswoman for the firm did tell me that it continued to provide a two-sided market on all the electronic exchanges during the flash crash.

For all the modeling in the world, there are events whereby you know that your system will not perform resilient in. The choice is then to choose whether to turn it off or not. Other times an old model does not work anymore, as evidently shown by the equity curve of the system. The choice then is to whether to turn it off forever or make changes.

The next point of discretion for a systems trader would be the underlying logic behind it:

At Renaissance, models had to meet four principles, says Mr Frey. These were (and maybe still are): simplicity – “don’t make it more complicated than it needs to be”; commonality – “make it as broad as possible”; stability – “models you have to readjust constantly probably aren’t as good as ones that stand the test of time”; and rationality – “it can’t just be statistically valid”. You have to employ reason to identify a statistically significant but spurious pattern.