Value Line Geometric Index

The american market (I focus on S&P 500) has recuperated 40% of the value since March where it touch the minimum value.

It’s an impressive “come back” while the macro economy data is showing terrible numbers about unemployment, consumption, industrial production….

The economy and the market are driving themselves in the opposite direction. So, so many questions come to my mind:

  • Is the market crazy?
  • is the market completely disconnected from the economy?
  • are we looking at the right numbers?

Well, Mr. Market does what he wants, and we cannot do anything about it. Disconnected from the economy? I do not think so, maybe there is a bubble, but sooner or later it will adjust. Well, my answers are poor, and it’s basically because I have not a concrete answer to these questions.

The last question: “are we looking at the right numbers?” makes me to go to Value Line Geometric Index

Value Line Geometric Index

This index includes all the american market. For more information the Wikipedia.

“All companies in the Value Line Composite Index are publicly listed on one of the major exchanges listed below. The number of companies in the Value Line Composite Index fluctuates based on factors including: the addition or delisting of the companies on the exchanges themselves, mergers, acquisitions, bankruptcies, and the coverage decisions made by Value Line for the Value Line Composite Index. Value Line’s decisions as to which companies to include are undertaken with the intention to create a broad representation of the North American equity market.

Exchanges in The Value Line Composite Index are:

  • American Stock Exchange
  • NASDAQ
  • New York Stock Exchange
  • Toronto Stock Exchange

Well, comparing the VALUG (blue) with SPX (red), you can see that Value Line Geometric Index is more closed to the reality that I had in mind that is that we still have not recuperated.

If we look the comparison of the indexes from February 12th, the result is that draw-down is bigger for VALUG, and that the recuperation is below than S&P:

To do: I will add this index to my reviews, I have first to learn from it.

Spanish market issue: script dividend as extended policy

One of the big issues of the stock market in Spain is the one represented in this picture.

Dividends? No, “papers”

Some of the popular investment methods is based on dividends, some others at least recognize that dividends is an incentive to invest on a company.

The main companies in the IBEX-35 market, so many times, instead of cutting the dividend or ask for money to the market, they have created a mechanism to do two things at the same time: script dividend.

They define a process with different steps that you have to follow. Mainly there are 2 options:

  • you grab the dividends and run.
  • you grab a set of new stocks (a new amount of stocks published to inject extra money into the company).

The process to do it

Once that is declared the “script dividend” you have to learn how to go through the process of claiming the new stocks or to take the dividend itself. This process is not straight forward, it’s a very complex process that requires you time to understand it, and then you have to pay attention the key days where you have to make a decision.

In a nutshell, so many small investors fall in the trap and they capture more stocks that they really do not want, diluting the value of the company.

My opinion

is that an incentive turns into an obligation to the investor, making him to decide on something that is not initially their responsibility. The result in the long term is not working well for the companies that are following this policy.

 

 

 

Design Thinking Process

A process cycle based in a 5 stages:

  • Empathize
  • Define
  • Ideate
  • Prototype
  • Test

Empathize

Objective:

  • Understand customers’ needs
  • Deep insight into what they need and expect from the solution/product

Activities

  • Engage with stakeholders
  • Use innovative methods to collect information
  • Understand the problem/need
  • Record observation to answer what, how, and why

Guidelines to follow:

  • Adopt appropriate method to gather relevant information
  • Engage stakeholders positively
  • Experience the problem to understand the impact

Define

Objective:

  • To create a well-defined and meaningful problem statement to focus on

Activities

  • Information analysis
  • Collaborating observations that help define the core problem
  • Provide clarity and scope to the problem

Guidelines to follow:

  • Focus on the positive aspects of the customers’ needs
  • Eliminate negative aspects identified in their needs
  • Identify connectivity between problems by identifying similar context
  • Break down the problems into sub-problems
  • Define each problem to create meaningful problem statement

Ideate

Objective:

  • To generate and define multiple ideas

Activities:

  • Collect as many ideas as possible
  • Think, think, think…
  • Brainstorming
  • Use tools like charting, mind maps, boards
  • Gather all solutions suggested by the team of thinkers

Guidelines to follow:

  • Don’t get into details, feasibility, and viability during this phase
  • Don’t evaluate ideas
  • Focus on generating ideas
  • Always create visualization of ideas

Prototype

Objective:

  • To testify an idea
    • Verifies whether an implemented solution is successful
    • Identify further problems with the implemented idea to refine
  • Gathering feedback from the users about their experiences with the product

Activities:

  • Identify the type of prototype needed to illustrate the ideas
  • Capture all the key aspects of the idea and solution with the prototypes

Guidelines to follow:

  • Don’t spend too much time on the prototype always build prototypes with the intended users in mind
  • Checking feasibility and collecting feedback is the primary objective of prototypes.
  • Don’t focus on internal problems

Types of prototypes:

  • Basic prototype model: provide limited tested features, it’s inexpensive, may not be useful for some cases, you will get limited feedback.
  • Operational prototype model: operates like finished products, they are expensive, users can visualize how they will operate, you will get deep feedback.

Test

Objective:

  • Return to your users and test the prototype for obtaining feedback

Activities:

  • Engage with stakeholders
  • Understand if the needs are covered
  • Collect as much feedback as possible

Guidelines to follow:

  • Focus on assertive and defined feedback
  • structure the test activities with the user stories.
  • explain what was implemented and not implemented in the prototype (set expectations)
  • Don’t focus on internal problems

Discourse

I am participating on different forums and some of them are built with this open source software called: Discourse.

From usability point of view it’s the best one I am using. It’s not only the look and feel, it’s the sequence of information offered, the access to the already read threads, and many little details that makes you feel comfortable using it. You feel you save a lot of time when reading a blog using this software.

This behavior is on the desktop and mobile, which makes quick reviews of updates very easy. The keep you engaged on the web.

On the .org page you can implement it for your own forum with different pricing options.

Tribe.so

You can use https://tribe.so/ too for installation of an environment.

 

 

 

The Fifth risk

I bought this book in 2019 but I was not able to read it till now, with the quarantine I remembered I had this one on a shelf.

Very interesting to understand how the different governmental organizations work and how the transitions were completed (or not in this case).

I love the personal stories that Michael Lewis give us in this book.

Fundsmith annual shareholder meeting 2020

I discovered this shareholders meeting last year and I was willing to review it in detail. Below some notes.

First 33 minutes is the formal presentation, after that Q&A.

The link to the letter to shareholders: https://www.fundsmith.co.uk/docs/default-source/analysis—annual-letters/annual-letter-to-shareholders-2019.pdf?sfvrsn=6

Minute #10:

Why they sold 3M on the one hand, 3M sold its ceramic bulletproof business unit at a price of 1.1x sales and acquired Acelity, which was a private company, for 11x EBITDA. Acelitywas actually going public and according to the prospectus, the opening price was was going to be 15x EBITDA. When asked about the difference between 11 and 15, they claimed that they announced the multiple of 11 EBITDA for the expected synergies. Fundsmith interpreted this as a lie.

In the shareholder letter: “we were acting on growing doubts about the current management’s capital allocation decision”.

minute #37:

Sub-estimate the impact of Covid-19.

minute #48:

Replying about why they don’t buy Apple or Alphabet.

About Google : ROE in 2019 is about 17%, which is average. Capital allocation is poor: they acquire small potential companies that could become competence in some niche and they dissolve these projects.

About Apple: sales and cash flow are flat in the last 4 years and the stock price has tripled. They saw the same behavior in companies as Nokia.

Comment about Sortino Ratio Versus Sharpe Ratio

The Sortino ratio is an adaption of the Sharpe ratio, and in my view an improvement. Whereas the Sharpe ratio estimates risk by the variability of returns, the Sortino ratio takes into account only downside variability as it is not clear why we should be concerned about upside volatility (i.e. when our Fund goes up a lot) which mostly
seems to be a cause for celebration.

Market Opening checklist

This is a checklist of things to review once just before and after the market is opened.

Before market opens (9:15 ET)

  • Check S&P and Nasdaq futures indexes.
  • How is Europe going on? Check DAX.
  • Check DIX and GEX Index in squeeemeetrics.
  • EUR/USD trend of the day.
  • Market breadth check.
  • Any major news to take into account?

After market opens (9:35 ET)

  • Check S&P trend in comparison with the futures.
  • Check the swing portfolio.
  • Check the potential new longs I have in the list.

System: Carter MA 8 – 21

Abstract

In his book “Mastering the trade”, John Carter proposes a simple system based on the moving averages of 8 and 21.

I have implemented the basis of this system and I have changed some behaviors:

I have build this system on Tradingview and I have done some back-testing in 3 periods to see how it works.

The system

The system code is this one:

// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © joapen

//@version=4
strategy("Carter EMA(8/21)", overlay=true)

// === BACKTEST RANGE ===
FromMonth = input(defval = 1, title = "From Month", minval = 1)
FromDay   = input(defval = 1, title = "From Day", minval = 1)
FromYear  = input(defval = 2020, title = "From Year", minval = 2000)
ToMonth   = input(defval = 12, title = "To Month", maxval = 12)
ToDay     = input(defval = 31, title = "To Day", maxval = 31)
ToYear    = input(defval = 2020, title = "To Year", maxval = 2020)

//
// SQZMOM
//
length = input(14, title="BB Length")
mult = input(2.0,title="BB MultFactor")
lengthKC=input(20, title="KC Length")
multKC = input(1.5, title="KC MultFactor")

useTrueRange = input(true, title="Use TrueRange (KC)", type=input.bool)

// Calculate BB
source = close
basis = sma(source, length)
dev = multKC * stdev(source, length)
upperBB = basis + dev
lowerBB = basis - dev

// Calculate KC
ma = sma(source, lengthKC)
range = useTrueRange ? tr : (high - low)
rangema = sma(range, lengthKC)
upperKC = ma + rangema * multKC
lowerKC = ma - rangema * multKC

sqzOn  = (lowerBB > lowerKC) and (upperBB < upperKC)
sqzOff = (lowerBB < lowerKC) and (upperBB > upperKC)
noSqz  = (sqzOn == false) and (sqzOff == false)

val = linreg(source  -  avg(avg(highest(high, lengthKC), lowest(low, lengthKC)),sma(close,lengthKC)), lengthKC,0)

// lime green  red marron
sqzLong = iff( val > 0, iff( val > nz(val[1]), true, false), iff( val < nz(val[1]), false, true))
// Plots
plot(upperBB, title="BB", color=color.green)
plot(ema(close,8), title="EMA(8)", color=color.orange)
plot(ema(close,21), title="EMA(21)", color=color.red)

longCondition = (ema(close, 8)> sma(close, 21)) and (close < ema(close, 8) and sqzLong) and time > timestamp(FromYear, FromMonth, FromDay, 00, 00) and time < timestamp(ToYear, ToMonth, ToDay, 23, 59)
alertcondition(longCondition, title='Long', message='long!!!')
if (longCondition)
    strategy.entry("My Long Entry Id", strategy.long)

shortCondition = (close > upperBB ) or (ema(close, 8)< sma(close, 21) or val<0) and time < timestamp(ToYear, ToMonth, ToDay, 23, 59)
alertcondition(shortCondition, title='Short', message='short!!!')
if (shortCondition)
    strategy.entry("My Short Entry Id", strategy.short)

Back-testing conditions

  • Amount = 100.000$
  • Commission = 0$
  • List of companies: fix list of companies I know with different behaviors, volatility and industries: BEN, TROW, CTVA, AEMD, GMRE, WBA, CVS, MO, DT, GE.

Quality of the samples:

  • Minimum around 200 trades to consider a good amount of cases.
  • Share Ratio > 1
  • Profit Factor > 2

Back-testing results for first date range

  • Date range = 1/1/2020 – 13/05/2020
  • Time frame = 5 minutes

Volatility during this period have been so high:

Back-testing results for second date range

  • Date range = 1/1/2019 – 13/05/2019
  • Time frame = 15 minutes (Tradingview does not enable me to do it in 5 minutes)

Back-testing results for bio companies in 2020

  • Date range = 1/1/2020 – 13/05/2020
  • Time frame = 5 minutes

Conclusions

15/05/2020

  • I have to improve or discard this system.
  • Check when the market trend is better.
  • Check when volatility is lower.

Backtesting tips

I do this list of tips so I review before I do some back-testing analysis.

main questions (from John Bogle)

  • What is the rationale or hypothesis?
  • What is the empirical evidence?
  • What are the implementation results?

Tips to remind

  1. Do a test of your system with 2008.
  2. If you look for correlation, you probably will find it, it does not mean that it will work in future.
  3. Test in not the best conditions and compare with other systems of
  4. Check volatility, tests on periods with low and high volatility is useful to get conclusions.
  5. Track the results in a word document with screenshots of the work done, the variables you touched and the specific results.
  6. Prepare set of 5 to 10 stocks for the back-test, track the results of all these stocks.

Typical pitfalls

  1. Survivor bias: you are not adding the companies that have not survived.
  2. Look-ahead Bias: you have all the data in front of your eyes and can make trades according to that data, this provokes you get a false understanding about the behavior of your system.
  3. In sample bias: you use a sample data that is always the same or similar, so then your system adjust to this sample and lose effective.