Free UK Technical Analysis 03/03/2015

DISCLAIMER: I am not FCA authorised or authorised in any sense to give financial advice. Do not regard any of the following links, or information as investment or trading advice.


Today's Analyses:

https://drive.google.com/open?id=0B0wd9XTIWftmfjBPQWtvSTZubHd4bjFzQ2E4NTc5eng1d2t6REUtYm9SY2hiNXV6eG8wMXM&authuser=0

How to use these reports:

• This system is currently based around Simple Moving Averages (SMAs) that are based around the principals of Fibonacci numbers. These are used alongside a Slow Stochastic Oscillator (SSO) to create buy and sell signals, which are given numbers based upon their strengths.

• Over time, I adjust the stochastic coefficients and the weightings of the moving averages to create more reliable results during my back testing (for my use), but to also make the system more reliable generally.

• Attached in the linked folder will be a file called "buysignallers.txt" and "sell signallers.txt", which can both be used to show the position of a stock against the others that are analysed. This is where you will find the total signal strengths for stocks.

• It is worth noting that not all stocks and shares may load or process due to where the program gets its data from and some stocks with share prices below 0.10p will currently not analyse properly.

• These reports are all made up of a list of signals and comments and the data for these are taken daily. E.G. Today's signals use yesterday's closing stock prices - given in pence, not pounds! 

• The "signals" show the fibonacci SMAs that crossover and or any slow stochastic notifications.

• The "comments" show the value of the said SMA with the value of the previous day shown in brackets.

e.g. 

# Signals

Sell - 24.802226076843513707865168540 - 34 crossed 144

# Comments

SMA 3 - 25.50 (25.50)

SMA 5 - 25.50 (25.50)

• More information regarding this project can be found here:

http://themaskedstocktrader.blogspot.co.uk/2014/11/my-experience-with-quantitative-finance.html

Self-fulfilling Betas

Good evening (again),


This actually follows on from something I wrote a while ago:

http://themaskedaimtrader.blogspot.co.uk/2014/09/understanding-and-rethinking-financial.html


A recent trend amongst many analysts has been to add something called a "small cap premium" to their beta calculations.

This takes the usual beta calculation of running a load of regressions, taking the gradient of these and then making a beta, but in this case, there is the bizarre additional step of adding a premium because the company is small.


Now, I'm not necessarily against this, but when it's done with a lack of explanation it can be highly annoying and has a tendency to wind me up, because it then requires that your thinking is parallel with that of the analyst's work.

In some cases, it's actually a pretty good idea, because large or small free floats for example, have the potential to effect your practical beta in the event of market worry or crisis, but this does have to at least be stated if you're going to trust the beta calculation you're given.


The bit that's very intriguing though is the idea that a financial beta could actually be self-fulfilling, especially on AIM where the private investor dominates the field.


This requires you making the general assumption that most people only care about betas in times of financial worry - no one is going to care about their portfolio's beta risk when the market is up forty percent.

If we assume that this is true, then a perceived increase in a company's reported beta (because it's given a "small cap premium") then results in a greater sell off when market turmoil increases, as a result of people actively trying to reduce the beta risk in their portfolios, leading to this increase in the beta to be fulfilled and represented.

In short, adding another point here or there has the potential to have a psychological impact large enough to cause the unbacktested or partially hypothetical betas to actually self-fulfill the values set out by the analyst who calculated the beta.

In essence, I say the beta of an asset is higher than it's baseline beta, so in periods of market worry private investors move from this asset to an asset with a lower beta, thus fulfilling in practical terms my increased beta calculation.


I think that the message here is to question figures you're ever given and check that they're in tandem with your investment strategy.


The masked AIM Trader.

Understanding and Rethinking Financial Beta Trends

I'm going to cheat here and begin by directing people to a MoneyWeek video on Beta, because not only is it fantastic, but it will also help to cover a lot of ground much faster than I could by writing about it:

https://www.youtube.com/watch?v=etlv7qTQUSY

For those who don't know, the man in that video is Tim Bennett - a truly wonderful teacher who's videos should be mandatory for anyone seeking financial knowledge - and for those who can't be bothered to watch the video, beta is basically a numerical illustration of the risk of an asset in relation to the wider market.

I'm now going to write with the assumption that you've watched that video:

One of the problems I have with regression betas in portfolio management is that we take our regression calculation and get a slope. We then take the gradient of this slope and it becomes a beta. The issue is that when we take the gradient of this slope we get a level of standard error. Now, in the US for example, the typical standard level of error for equity betas is between 0.2-0.25. 

This is a bit of a problem, because it means that if Apple has a beta currently of 0.90, it may actually have a beta anywhere between 0.65-1.15 and it gets worse because depending on how you calculate your regression beta, you can end up with a cornucopia of betas and then pick and chose which ever one you want to believe. 

Regardless of this single issue, the much bigger fall back with betas for me is that only about 20-25% of the risk in a company (according to Aswath Damodaran - an expert on betas) is market risk and a beta will only ever capture that portion of risk. This then can get even more fiddly, because you then have to analyse the extent to which company specific risk becomes market risk. The example that Aswath Damodaran uses was the whole Lehman Brothers debacle, which began with internally odd CDS bets (a company specific risk), but ended with a global financial collapse. 

Leading on from this, betas are often calculated for whole investment portfolios, which we will assume have been diversified. This leads us to another issue, which is that diversification (ironically) only works as a hedging method when it's required least. So, in periods of crisis this iconic hedging method blows up because all of the global markets begin to move in tandem with each other. For those who don't believe me, this can be proved:

If you run a regression of stock returns against market returns you get R² and as I've said this averages at about 20-25% in the US, but between September 12th and December 31st of 2008 the average R² for US stocks rose to 70%, meaning that stocks were moving primarily with the market rather than with company specific news and events.

All is not lost though, because with a simple trick we can reduce not only this inherent statistical standard error significantly, but also get a better idea of the real levels of risk to our portfolios when we see rapidly declining markets and R² levels of 70% or more. 

This certainly isn't an original idea, but if we get multiple regression betas (the more the better) under industry sectors and average them out, we get a much better picture of what levels of risk we're really seeing, in part because that level of standard error is reduced with the more sample regressions we take, but also because of the potential to use of many years of data in a beta calculation. 

What's really good about this, is that sector average betas generally stay the same over long periods of time, which helps us personal portfolio managers to have a much better idea of our "actual beta" in periods of market turmoil. Although, this being said they do change, with the financial industry seeing large changes in average sector betas in the 2008 financial crisis, so it's not fool proof, but it is significantly better. 

To conclude, I would say that it's worthwhile having a rethink regarding how you calculate and think about the betas you see for portfolios and stocks that you're viewing. The sector based beta calculation is certainly not a magic wand to success in risk calculating, but I feel that it does a much better job of giving realistic figures than standard regression betas do.

Enjoy,

The Masked AIM Trader