There are 3 contexts of laws required in trading . The appropriate LAWS OF THINKING for trading, the appropriate LAWS OF FEELINGS for trading , and the appropriate LAWS OF ACTIONS for trading.
The Successful trading is based according to these three laws on
1) POWER OF COLLECTIVE SCIENTIFIC THINKING: A GREAT AND SIMPLE SCIENTIFIC PERCEPTION OF THE FUNCTION OF THE ECONOMY THROUGH SOME GLOBAL STATISTICAL LAW. E.g. The law of Universal attraction in economy: that big money attracts more big money in the capital markets, and this by the balance of demand and supply makes securities indexes of the companies , that are indeed the big money, to have mainly stable ascending trend, whenever one can observe such one. Valid statistical deductions can be obtained with simple statistical hypotheses tests about the existence or not of a trend, with sample size half the period of a dominating cycle). (STABLE GREAT SCIENTIFIC THOUGHT-FORM OR BELIEF FACTOR IN TRADING. )
2) POWER OF COLLECTIVE PSYCHOLOGY: A LINK WITH THE POSITIVE COLLECTIVE PSYCHOLOGY.(E.g. that the growth of security indexes also represent the optimism of the growth and success of real business of the involved companies. And we bet or trade only on the ascension of the index, whenever an ascending trend is observable). (STABLE GREAT POSITIVE COLLECTIVE EMOTIONAL OR PSYCHOLOGICAL FACTOR IN TRADING. )
3) POWER OF INDIVIDUALS SIMPLE , CONSISTENT AND EASY TO CONDUCT PRACTICE. (e.g. a trading system with about 80% success rate that utilizes essentially only one indicator in 3 time frames, simple risk management rules of stop loss, take profit, trailing and escalation, and time spent not more than 20 minutes per day. In this way there are not many opportunities of human errors in the conduction of the trading practice. Failed trades are attributed to the randomness and are not to blame the trader). (STABLE SIMPLE AND EASY PRACTICAL FACTOR IN TRADING)
We may make the metaphor that successful trading is the ability to have successful resonance with the activities of top minority of those who determine the markets.
In trading there are 3 components in the feelings that must be dealt with. 1) The feeling of MONEY itself, 2) The feeling of the UTILITY of the money 3) The feeling of the RISK of the money each time. What is called usually money management in trading is essentially RISK MANAGEMENT.
VALID STATISTICS AND PREDICTABILITY
We must make here some remarks about the robust application of statistical predictions in the capital markets.
1) The theory that the efficient markets and in particular that they follow a pure random walk is easy to refute with better statistical experiments and hypotheses tests. The random walk would fit to a market where the sizes of the economic organizations are uniformly random. But the reality is that they follow a Pareto or power distribution, therefore this is inherited in the distribution of the volumes of transactions and also in the emerging trends or drifts.
2) The statistical models of time series are more robust , when they apply to the entity MARKET as a whole and are better as non-parametric , and not when they apply to single stocks and are linear or parametric. The reasons is that a time series as a stochastic process , requires data of a sample of paths, and for a single stock is available only a single path. While for all the market the path of each stock or security is considered one path from the sample of all paths of all the stocks.
3) The less ambitious the statistical application the more valid the result. E.g. applying a statistical hypothesis test, or analysis of variance to test if there is an up or a down trend (drift) or none, is a more valid statistical deduction , than applying a linear model of a time series and requiring prediction of the next step price.
4) Multivariate statistics, like factor analysis, discriminant analysis , logistic regression, cluster analysis , goal programming e.t.c., are possible to utilize for a more detailed theory of predictability and of portfolio analysis, and sector analysis of the market and not only H. Markowitz theory.
5) In applying of the above applications of statistics, the researcher must have at first a very good "feeling" of the data, and should verify rather with statistics the result rather than discover it.
6) The "Pareto rule of complexity-results" also holds here. In other words with less than 20% of the complexity of the calculations is derived more than 80% of the deduction. The rest of the 20% requires more than 80% more complexity in the calculations.
The Successful trading is based according to these three laws on
1) POWER OF COLLECTIVE SCIENTIFIC THINKING: A GREAT AND SIMPLE SCIENTIFIC PERCEPTION OF THE FUNCTION OF THE ECONOMY THROUGH SOME GLOBAL STATISTICAL LAW. E.g. The law of Universal attraction in economy: that big money attracts more big money in the capital markets, and this by the balance of demand and supply makes securities indexes of the companies , that are indeed the big money, to have mainly stable ascending trend, whenever one can observe such one. Valid statistical deductions can be obtained with simple statistical hypotheses tests about the existence or not of a trend, with sample size half the period of a dominating cycle). (STABLE GREAT SCIENTIFIC THOUGHT-FORM OR BELIEF FACTOR IN TRADING. )
2) POWER OF COLLECTIVE PSYCHOLOGY: A LINK WITH THE POSITIVE COLLECTIVE PSYCHOLOGY.(E.g. that the growth of security indexes also represent the optimism of the growth and success of real business of the involved companies. And we bet or trade only on the ascension of the index, whenever an ascending trend is observable). (STABLE GREAT POSITIVE COLLECTIVE EMOTIONAL OR PSYCHOLOGICAL FACTOR IN TRADING. )
3) POWER OF INDIVIDUALS SIMPLE , CONSISTENT AND EASY TO CONDUCT PRACTICE. (e.g. a trading system with about 80% success rate that utilizes essentially only one indicator in 3 time frames, simple risk management rules of stop loss, take profit, trailing and escalation, and time spent not more than 20 minutes per day. In this way there are not many opportunities of human errors in the conduction of the trading practice. Failed trades are attributed to the randomness and are not to blame the trader). (STABLE SIMPLE AND EASY PRACTICAL FACTOR IN TRADING)
We may make the metaphor that successful trading is the ability to have successful resonance with the activities of top minority of those who determine the markets.
In trading there are 3 components in the feelings that must be dealt with. 1) The feeling of MONEY itself, 2) The feeling of the UTILITY of the money 3) The feeling of the RISK of the money each time. What is called usually money management in trading is essentially RISK MANAGEMENT.
VALID STATISTICS AND PREDICTABILITY
We must make here some remarks about the robust application of statistical predictions in the capital markets.
1) The theory that the efficient markets and in particular that they follow a pure random walk is easy to refute with better statistical experiments and hypotheses tests. The random walk would fit to a market where the sizes of the economic organizations are uniformly random. But the reality is that they follow a Pareto or power distribution, therefore this is inherited in the distribution of the volumes of transactions and also in the emerging trends or drifts.
2) The statistical models of time series are more robust , when they apply to the entity MARKET as a whole and are better as non-parametric , and not when they apply to single stocks and are linear or parametric. The reasons is that a time series as a stochastic process , requires data of a sample of paths, and for a single stock is available only a single path. While for all the market the path of each stock or security is considered one path from the sample of all paths of all the stocks.
3) The less ambitious the statistical application the more valid the result. E.g. applying a statistical hypothesis test, or analysis of variance to test if there is an up or a down trend (drift) or none, is a more valid statistical deduction , than applying a linear model of a time series and requiring prediction of the next step price.
4) Multivariate statistics, like factor analysis, discriminant analysis , logistic regression, cluster analysis , goal programming e.t.c., are possible to utilize for a more detailed theory of predictability and of portfolio analysis, and sector analysis of the market and not only H. Markowitz theory.
5) In applying of the above applications of statistics, the researcher must have at first a very good "feeling" of the data, and should verify rather with statistics the result rather than discover it.
6) The "Pareto rule of complexity-results" also holds here. In other words with less than 20% of the complexity of the calculations is derived more than 80% of the deduction. The rest of the 20% requires more than 80% more complexity in the calculations.
There are mainly three types of people making money in the financial markets:
1) The buy-and-holders. They like to buy index funds (all the market) they keep them forever and make (as of the last 50 years) an average of 10% annually (but maybe with a 30% annual standard deviation). If organized well they can make an independent income, without having to work. Their golden and unbeatable rule is: Buy when you have the money, keep them at least 10 years (possibly adding when you have additional money) and find an opportunity to sell from the 11th to 25th year, so that you have made a lot more, compared to a bank deposit. The mathematical-statistical assumption behind this strategy is that only the long term trend is predictable all other fluctuations unpredictable.
2) The portfolio adjusters (the optimal separation of portfolio theory) . These people dare to buy and sell in a relatively seasonal or annual horizon. Their optimal strategy does not include forecasting the market! A good example is the seminar of Michael LeBoeuf "Beat the time money trap" that can be bought from the www. nightingale.com (It has been applied for many years by professor Michael LeBoeuf in his investments (see https://en.wikipedia.org/wiki/Michael_LeBoeuf and http://www.nightingale.com/beat-time-money-trap-mp3.html) They utilize a back office accounting method as follows: E.g. if they have 1,000,000$ to invest, they split them, to 3 parts the annual spending part which is one years spending and should correspond in to no more than 50% of the average annual profit of the portfolio. Given that the markets the last half century have rate of return of 10% this would mean 5% of the total value of the portfolio or 50,000$. These 50,00 are allocated to money market funds in other way easily liquidated and very low fluctuating in value funds, It could even be in bank deposits. Then the next part is 6 years of spending also called liquidity part or here 300,000$ which is allocated to bond index funds, that expire during these 6 years, so that there is the certainty of expiring at the nominal value. In this way this part of the portfolio may be losing a bit but it is safe that we know in advance the value that it will expire during the next 6 years, so losses are lower bounded. In total the annual spending and 6 years sending (in total 7 years of spending which corresponds to a bit over the half of the 11 years cycle of economy) part of the portfolio is about 350,000$ or about 1/3 of the total value of the portfolio. This 1/3 may be considered risk-free liquidity that is used to sustain the rest 2/3 of the value of the portfolio which is the growth part of the portfolio. The growth part is allocated to national, international stocks and bonds index funds. . After a fixed period e.g. annually (or every 10% change in this allocation), they check the value of their growth part of the portfolio against the liquidity part and if the percentages of 65% and 30% deviate they act accordingly. . If e.g. their investment has lost value to 600,000$ (from 650,000$) , them they withdraw X$ (about 25,000 or more precisely after solving the (350,000-X)/(600,000+X)=350000/650000 ) from the 300,000$ liquidity part to buy more index funds and increase the percentage of the value of the growth part again to 65%, and 30%. If the investment has gained to value to 700,000$ , they liquidate 25,000$ from the growth part and put it to the liquidity part (or more precisely after solving the (350,000+X)/(700,000-X)=350000/650000 ), so that both have the percentages of 65% and 35% . They keep on doing it in other words adjusting to the appropriate constant percentages the not invested liquidity part with the invested growth part . This does not involve forecasting of the markets . It is a posterior adjustment. Every year they transfer 5% of the value of the portfolio (or 50,000$ which is about the 50% of the average profits of the portfolio) ) to the annual spending part of the portfolio, so as to make a living. . This optimal strategy may make at least 2-3 times more than the rate of return of buy and holders, on the same stocks, and the same time period. In the mathematics of the stochastic optimal control there is an exact formula to compute the percentage of the total funds that will be exposed to the markets (stocks and bonds etc) compared to the rest of the funds that are in a risk-free way stored. The formula of this optimal percentage is p=(R-r)/a^2 , where R is the rate of the return of the period of the portfolio , r is the risk free rate and s^2 is the variance of R. (See also post 33 and "Stochastic Differential equations" by B. K. Oksendal (Springer editions) page 223, example 11.5 . The optimality criterion for this formula is called the Kelly criterion and is to maximize at the end of finite time interval the average value of the logarithm of the value of the total investment or wealth. Here the logarithm function of the wealth plays the role of the utility of the wealth for the investor ) E.g. if the average the annual rate of return of the index-funds in the american market from 1950 till 2000 is 10% and the risk free rate 2%, and the standard deviation of R s=34% then p=(0.1-0.02)/(0.34)^2= 0.66=66%=2/3 that is the percentage of exposed funds that Michael Leboeuf. If this p>1, then the optimal is that 100% are exposed. E.g. for s=25% this is the case. On the other hand for s=30% p=88% and for s=50% p=32% that is about 1/3 exposed funds. On the other hand if we want to see how thsi would be if we would do a daily adjustment of the portfolio, then also the R, and a^2 should be daily measured quantities. And we may convert the annual value of a^2 to the daily value by the n^(-2) rule as in the post 12. In other words we divide with (365)^(1/2), and this makes the annual exposure 66% to daily exposure of about 3.45% ! The mathematical or statistical assumption behind their strategy, is the same with the buy-and-holders, in other words that only the long term trend is predictable and all other fluctuations are neutral and unpredictable.Their difference with the buy and holders is that they make the unpredictable fluctuations their friend that benefits them, while buy and holders suffer for the fluctuations. See for the mathematical base of the optimal adjustments as in this paragraph the post 33. An example of such speculator is Warren Buffet, that is both a buy and holder, and speculator of the above type. He is also utilizing talented fundamental analysis of stocks to select his investment portfolio. He has succeed in an average 20% annually for the last 50 years which made him for some years the richest man in the world. We should remark also that the above protocol of portfolio adjustments that keeps constants the percentages of the portfolio, is profitable not only when there is a long-term constant trend in the instruments that make the portfolio, but also, when there is no trend at all , but there is an average constant equilibrium level of prices, that prices return on this level, and a random constant distribution with zero average, of fluctuations around this level (notice that such constant distribution fluctuations around the equilibrium is different from a random walk around this equilibrium level). This has applications as well in currencies exchange cross rates instruments (forex) or even commodities that for some time interval are assessed to be around an equilibrium level
The previous considerations lead to a withdrawal rule even for trading and not only buy and hold investments
CONSTANT ADJUSTMENT RATIO WITHDRAWAL RULE . We may divide the funds to 2/3 of them that we trade, and 1/3 that we do not trade. The exact percentage should be defined by the ratio (f=R/a^2) (that we mention above from the book "Stochastic Differential equations" by B. K. Oksendal (Springer editions) page 223, example 11.5) where R is the average per period rate o return of the trading on the used funds, measured on a sample of periods and a^2 is the variance of this rate of return on this sample of periods. E.g. of the rate of return is 10% per period and the standard deviation a of it is 34%, then the percentage f is 2/3. Each period we re-adjust the total funds so that this ratio f applies as division of the funds. As the sample of measurement of this ratio is not small, usually it remains rather constant. We withdraw e.g. from the 1/3 non-trading funds ,each period never more than half of the average profits per period of the other 2/3 of the funds that are traded.
3) Finally there are the traders (sometime called also speculators). They dare further to buy and sell in a faster horizon (e.g. of a few days) and to be successful they must have a method to forecast the market in the horizon of interest, based on the laws of demand-supply, that they encompass to what it is called technical analysis. There are two old long term examples the http://www.commodex.com/ (of P. Gothelf) , and http://www.profitunity.com/ (of B. Williams) that are successful in a systematic way the last 50 years, with annual returns of 130% and 300% respectively. Both utilize a single universal algorithm of forecasting, and trading, for all markets, stocks, commodities, bonds, currencies simultaneously . Although their returns may seem high, their methods are by far the most difficult and sophisticated compared to speculators and buy-and-holders. The mathematical-statistical assumptions behind their strategies are not well stated, but essentially contain the assumption that both the trend (drift) and the fluctuations are predictable and varying , in many different scales, based on laws of Demand-Supply and they make them their friend to benefit from them.
And you nay ask of course why then only a 20% annual (of Warren Buffet) made him the richest man in the world, and a 150% not? I think the answer is obvious: It is easy to enforce by publicity a 20% annual, but not a 150% annual. Furthermore, metaphorically speaking, having the fastest airplane , does no mean that you want to have the largest fleet of such airplanes too.
Obviously the majority, of successful investors, are the buy-and-holders. A minority only are the successful speculators, and a tiny minority (probably of less than 0.5% ) are the successful traders. And it is traditional that buy-and-holders despise as unethical the speculators, and the speculators as unethical the traders. Exactly as very often real estate investors, and traditional businessmen, despise and accuse the financial market investors of any type!
Obviously the majority, of successful investors, are the buy-and-holders. A minority only are the successful speculators, and a tiny minority (probably of less than 0.5% ) are the successful traders. And it is traditional that buy-and-holders despise as unethical the speculators, and the speculators as unethical the traders. Exactly as very often real estate investors, and traditional businessmen, despise and accuse the financial market investors of any type!
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