8123-6261507824613586-72687-16215158-17-130112124-8850018825518551410637214313151-1030300296-49693328107358
573725543325668050100424257332833100336033663045661008033100333340756033337510050
79
45
238.98
1. Assumes commission of $40 per round trip trade2. Average initial margin requirements existing during time period as charged by Lind-Waldock205
The Complete Guide to Point-and-Figure Charting
Table 5.4 – Comparison of profitability research results for point-and-figure commoditytrading techniques
Study
Time Period
No ofHoldingCommodities Period (days)
Reliability(%)
Average ProfitPer Trade ($)
Davis-Thiel
1960-69
15
50
53
311
Zieg
1974
22
12
40
306
Zieg
1997
38
52
45
234
206
Profitability Analysis
Profitability of P&F stock trading 1994-1996The next research study by Dr. Zieg involved analysing the profitability of point-and-figuretrading in common stock.First testStudy parameters
•
The first evaluation covering calendar years 1994-96,
•
Examined the performance of 150 stocks selected at random for the universe on the NewYork Stock Exchange, American Stock Exchange and in the over-the-counter markets.Commission costs were set at $40 per 100 shares of stock
•
A round lot was traded at each initial signal.
Table 5.5 – Profitability analysis for randomly selected stocks, for the 3 years endingDecember 31st, 1996
Number of stocks analysed
150
Selection method
Random
Total number of trades
1730
Commission rate per 100 shares
$40
Total number of profitable trades (after commissions)
710
Reliability (profitable trades to total trades)
41%
Average stock price
$30.25
Average net profit per trade
$28.32
Average duration of trades
95 days
Average net profit per long position
$146.33
Reliability for long positions
52%
Average net profit (loss) per short position
($99.63)
Reliability for short positions
28%207
The Complete Guide to Point-and-Figure Charting
Key points
•
During the 36 months, 1730 trades were executed.
•
710, or 41%, of them were profitable.
•
The average stock price was $30.25, or $3,025 per 100 shares.
•
The average profit per trade was $28.32.
•
Trades were open on average 95 days.
•
This was only a 3% return on the invested capital.
The 1994-96 period was a very bullish time, but this first study assumed that all long and shorttrades were taken. Because the short sales in a rapidly rising market were big losers, the resultsof only taking buy (long) positions, and ignoring all short signals was examined. The resultswere much improved. The average net profit per long position was $146.33 or 9% annualisedprofit on a cash investment. Better, but still not a decent return.The test group of 150 stocks were certainly not a real winner for this period. It seems highlyunlikely that any investor or trader would randomly select such a portfolio of stocks. Hopefully,most would utilise some selection criteria to bias the portfolio toward stronger fundamental andtechnical issues. With this in mind, another test was undertaken.
Second test
In this new test, 20 stocks were selected at random each January from the list of 100 Value Linestocks ranked 1 (highest) for performance in the next 12 months as found in The Value LineInvestment Survey.The results are in the table opposite
208
Profitability Analysis
Results — long and shortTable 5.6 – Profitability analysis of point-and-figure charting for common stock with ValueLine timely ranking 1 for the three years ending December 31, 1996
Number of stocks analysed - 20 per year
60
Selection method
Random 20 from VL rankingin January each year
Total number of trades
178
Commission rate per 100 shares
$40
Total number of profitable trades (after commissions)
82
Reliability (profitable trades to total trades)
46%
Average stock price
$24.25
Average net profit per trade
$129.10
Average duration of trades
123 days
Average annual profit per security taking all signals
$382.99
Annualized profit per security taking all signals(cash account)
15%
Average net profit per long position
$452.48
Reliability for long positions(take only long positions or out of market)
65%
Average annual profit per security
$648.55
Annualized profit per security taking only longpositions (cash account)
26%
If all original long and short signals were selected for these 20 securities, the average profit pertrade was $129.10 with a duration or 123 days. The average price of the selected stocks was$24.25. But this is a list of securities expected to perform well in the forthcoming year, so whygo short on such potentially bullish stocks?Results — only long
If only buy signals were selected, the average net profit per long position increased to $452.48.The average annual profit was $648.55, representing a 26% profit on a cash transaction. If 50%margin was employed, the annualised return jumps to a respectable 52%.The selection criteria of the stocks was based on the rating by Value Line.209
The Complete Guide to Point-and-Figure Charting
Third test — using support and resistance lines
An alternative strategy for the chartist is to employ the point-and-figure 45 degree support andresistance lines in addition to simple buy and sell signals. Thus a study was undertaken to determinetheir impact on the profitability of the 150 randomly selected stock for the 3 years of 1994-96.Using support and resistance lines makes p&f charting more complicated, but theirimplementation had a dramatic impact on the results. Many of the whippy or losing transactionswere eliminated while keeping the trader in a winning position for the major portion of a trend.Table 5.7 – Profitability analysis of point-and-figure charting for common stock utilizingsupport and resistance lines in conjunction with simple signals for the three years endingDecember 31, 1996
Number of stocks analysed
150
Selection method
Random
Total number of trades
1080
Commission rate per 100 shares
$40
Total number of profitable trades (after commissions)
507
Reliability (profitable trades to total trades)
47%
Average stock price
$30.25
Average net profit per trade
$261.38
Average duration of trades
152 Days
Average annual net profit per security
$627.65
Average annual net profit per security (cash account)
20%
Key points
•
The results indicate that there were considerably fewer transactions (1080 versus 1730).
•
Reliability (profitable trades as a percentage of total trades) increased from 41% to 47%.
•
Average profit per trade showed a significant jump, increasing from $28.32 to $261.38.
•
Duration of open trades increased from 95 to 152 days.
•
The annualised return on an unleveraged or cash account also increased from 3% to 20%,or from 6% to 40% if a 50% margin were employed.
210
Profitability Analysis
Fourth test — selection criteria plus trend lines
As in the third test, resistance and support lines were used. Additionally a selection criteria wasintroduced, namely the Value Line ranking as already explained in the second test. Incomparison with the earlier tests, the results are positive, though the difference is small.Table 5.8 – Profitability analysis for stocks with Value Line timely ranking 1 utilizing supportand resistance lines in conjunction with simple signals for the 3 years ending December 3st1,1996
Number of stocks analysedSelection method
60Random 20 from VL rankingin January each year
Total number of trades
63
Commission rate per 100 shares
$40
Total number of profitable trades (after commissions)
37
Reliability (profitable trades to total trades)
58%
Average stock price
$24.25
Average net profit per trade
$651.36
Average duration of trades
323 Days
Average annual net profit per security
$677.41
Annualized profit per security (cash account)
27%
211
The Complete Guide to Point-and-Figure Charting
Weber-Zieg study on European stocks 1988-2002When we, the authors, joined forces to run extensive studies on the European markets, we againtried to go a step further than in the previous studies. As explained in the chapter coveringoptimisations, we have implemented some concepts to make the tests as realistic as possible,such as optimising over entire portfolios and using subsequent data for the simulations.We have also extended the strategies we have been analysing. We have applied the modernconcepts of simulations normally used for the pricing of exotic derivative instruments. We havebeen looking at the influence of volatilities of the parameter sets, trying to answer the question,‘If we experience an increase of the volatility of the underlying, should we change thepoint-and- figure parameters?’Summary of study results
Our studies show:•
Cyclical optimisation and simulation, as described above, is an excellent concept and itworks. It clearly improves profitability.
•
Distributions of signals on the European markets are different from the distributionsfound in the Davis Study. For example, one third of the signals are a Simple Buy or SimpleSell (B1, S1), whereas in the Davis Study they account for less then one seventh.
•
The level of accuracy of the signals of recent price history of European stocks is wellbelow the level of accuracy found in the original Davis Study.
•
The performance of the parameter sets depend on the volatility of the stock or security.Davis did not take into account different levels of volatility of different stocks.
Strategies involving the early realisation of profits increase overallprofitability
We have looked at the frequency of point-and-figure signals and we have examined whetherthey were wrong. The criteria for being wrong was established as generating the signal on thetop or bottom of a column. We also checked the frequencies of the signals B1 to B4 and S1 toS4. The frequencies show to be different for the FTSE 100 stocks than for the US stock portfolioused in the Davis study.
212
Profitability Analysis
Table 5.9 – FTSE 100 Signal Frequencies, 1988-2002
Total Signals
9,527
100%
Total False
2,715
28.50%
Total False Long
1,387
14.56%
Total False Short
1,328
13.94%
Avg in %
St Dev in %
B1
34.54
7.01
B2
19.80
8.22
B3
19.80
7.24
B4
29.22
7.63
S1
35.42
7.51
S2
17.42
6.33
S3
17.54
7.64
S4
26.24
6.16
The results shown above are highly valuable and encouraging for the trader as the results clearlysuggest that in more than 70% of the cases the point-and-figure signals are correct. Such a highnumber of accuracy has not been found – to the authors’ knowledge – in any other technicaltrading system. Moreover, the time horizon of 15 years is very long and therefore the results arestatistically relevant. which translates directly into higher confidence while applying the p&fmethod for your own account. The long-term analysis makes clear that a 70% chance exists tobe correct entering a position based on a p&f signal.Precise studies like the FTSE example obviously need clean price data and a fully automatedprocess to calculate all relevant values. Such a computer-based approach was not common untilthe 1980s and it has to be said that the original studies of Prof. Davis still included a lot ofmanual work and therefore had a bigger probability of carrying errors. Today, once we have ourdata audited, no possible errors are imaginable. Parameters can be chosen differently, likecommission rates, slippage, bid-ask spread etc. but the resulting figures will always be correct.In order to conduct further research on the UK market, we have chosen a subset of the FTSE100 Index as of 25 October 2002. The subset consists of the 80 stocks that have a price historyof at least 6 years. Specifically the portfolio was as outlined in the table overleaf.213
The Complete Guide to Point-and-Figure Charting
Table 5.10 – FTSE 100 test stocks
214
ABF.L
CBRY.L
JMAT.L
SDR.L
AHM.L
CPI.L
KGF.L
SDRt.L
ALLD.L
CS.L
LAND.L
SFW.L
ANL.L
CW.L
LGEN.L
SGE.L
AUN.L
DGE.L
LLOY.L
SHEL.L
AV.L
DMGOa.L
MKS.L
SHP.L
AVZ.L
DXNS.L
MRW.L
SMIN.L
AZN.L
EMG.L
NXT.L
SN.L
BA.L
EXL.L
PRU.L
SPW.L
BAA.L
GAA.L
PSON.L
SSE.L
BARC.L
GKN.L
RB.L
STAN.L
BATS.L
GSK.L
REL.L
SVT.L
BG.L
GUS.L
REX.L
SXC.L
BLND.L
HAS.L
RIO.
TOMK.L
BNZL.L
HG.L
RR.L
TSCO.L
BOC.L
HNS.L
RSA.L
ULVR.L
BOOT.L
HSBA.L
RTO.L
UU.L
BP.L
ICI.L
RTR.L
VOD.L
BSY.L
III.L
SBRY.L
WOS.L
BT.L
ISYS.L
SCTN.L
WPP.L
Profitability Analysis
We have used this portfolio – which we call FTSE_6year – for several simulations. What wehave seen is that:the approach to optimise parameters repetitively increases the profitability significantly.We have seen that over the most simple optimisation/simulation cycle, (whereby the new settingof parameters was done once a year, always January 1, and only 4 basic strategies were tested),we achieved an increase of profitability over the traditional 3 box reversal system, and a veryhigh percentage of gainers.The first optimisation was done over the period beginning 1995 to end 1996, and the first tradingsimulation – based on the parameters found during the first optimisation – was conducted on thesubsequent year, 1997. Then this cycle was repeated 6 times, the last optimisation being fromthe beginning of 2000 to the end of 2001 and the last simulation 2002. The result was verysatisfactory. In 69% of all stocks the approach worked and showed profitability. This result isvery promising, because the sample size is big, calculated as 480 trading years, being 80 stockstimes 6 years, and therefore the result is statistically relevant.This optimisation was done exactly as it could be done with a standard software package.(We continuously do optimisations/simulations on many model portfolios. Whether sectors ornational markets. The detailed results of the studies will be published soon.).The main conclusions from this most recent research are:1.
Run optimisation / simulation cycles periodically.
2.
Volatility matters, however, it is taken into account with each optimisation cycle.
3.
Use a portfolio of at least 3 stocks in order to reduce the risk of 31% (100% less 69%)that the stock does not perform.
4.
Be patient during sequences of whippy trades, chances are high that the P&L of thestocks recuperate.
5.
There is no better or more scientific way to improve technical analysis than to runoptimisation / simulation cycles. The timing of such runs is probably a field which couldbe investigated in order to find optimal criteria to decide upon the timing of newoptimisations.
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The Complete Guide to Point-and-Figure Charting
SummaryResearch period: 1914-1964
It has been shown historically for the period of 1914 through 1964 that the traditional point-andfigure charting technique is an extremely reliable and profitable method for trading stocks onboth the long and short sides. Typically, over 80% of all transactions were profitable, and profitsaveraged in the mid-20% range. Losses were rare, occurring less than 20% of the time, weresmall, generally around 15%, and were realised quickly in a matter of several months.Research period: 1961-1969
It was also shown that for the period of 1961 through 1969, using optimal values for box sizesand reversal distances, the point-and-figure technique as applied to commodities was profitable53% of the time, the average profit per transaction was $311, and the average duration of tradeswas 49.8 days.Research period: 1974
The later studies of profitability results covering a period of 135 trading days ending May 17,1974, using non-optimised box and reversal values (the generally accepted box sizes and thethree-box reversal criterion in use during the study period), showed that 40.0% of all commoditytrades were profitable, trades remained open for an average of 12.4 days, the average profit pertransaction was $306.48, and the net return was a healthy 178.46% earned in just over six months.Still later, a 1974 research has revealed that while sizable commodity returns were possible bytaking positions on simple signals using the non-optimised approach, even larger returns andhigher reliability occurred when an optimised technique was employed.Research period: 1997
Subsequent research for both futures and stocks show a continuation of profitability for pointand-figure trading. For the 12 months ending January 10, 1997, trading in just one deliverymonth of 38 commodities, 45% of all trades were profitable, the average duration was 52 daysand the average per trade profit was $238.98. This represents a 79% annual return on retailbrokerage initial margin requirements. These figures are very similar to those found in all of theprevious research studies.Research period: 1994-96
Stock profitability of a randomly selected portfolio for the 3 years 1994-96 showed a smallannual profit of 3% on cash. When support and resistance lines were utilized in addition tosimple buy and sell signals, the profit increased to 20% on a cash investment. Where portfolio
216
Profitability Analysis
selection techniques were employed, such as randomly selecting stocks from the list of ValueLine’s most timely stocks, and only simple signals were used, the annualised profit on cash was26% if only long positions were traded, and 15% if all signals were employed. If 50% marginwas employed, the returns increased to 52% and 30% respectively.The Davis, Davis-Thiel, and the Zieg studies covered different time spans, different securitiesor commodities and employed different research methodologies, but the conclusions areobvious. Point-and-figure charting is a profitable trading tool, yielding a substantial return pertransaction while permitting the users the satisfaction of understanding and participating in thedecision-making process.Research period: 1988-2003
In 2003 Zieg and Weber have conducted extensive studies on European stock portfolios.Especially in the context of using volatility as the major indicator to choose parameter sets.Once again, point-and-figure has shown to be an excellent approach to orchestrate one’s trading.Over a large sample size, 15 years, false trading signals averaged below 30% of the total numberof signals.The conclusion is that few, if any, trading systems are so inexpensive to maintain, require solittle time to keep current, are so readily comprehensible, provide such personal satisfaction, useso little mathematics and are so profitable.
Table 5.11 – Summary of major P&F research
Study
Underlying
Optimized
Profitability
Davis 1914-1964
US-Stocks
No
Excellent
Davis-Thiel 1961-1969
Commodities
Yes
Good
Zieg 1974
Commodities
No
Fair
Zieg 1994-1996
US-Stocks
No
Fair
Zieg 1997
Commodities
No
Fair
Weber-Zieg 1988-2003
UK-Stocks
Yes
Good
217
Chapter
6
Technical notes
•
The conflict between technical analysis andacademic research
•
More about scaling
•
Description of the program environment used
The Complete Guide to Point-and-Figure Charting
The conflict between technical analysis andacademic research
IntroductionIt is common knowledge that the financial industry devotes considerable resources to the studyof technical analysis. At the same time there is a respected academic theory called the EfficientMarket Theory which reject the entire basis of technical analysis. We would like to comment onthis obvious conflict and long-standing paradox.History of quantitative financeQuantitative finance and financial econometrics has come a long way since its beginning. Itshistory could be defined by this list of groundbreaking ideas:1938 – Macaulay: theoretical problems regarding price movements1959 – Markowitz: portfolio selection1965 – Fama: behaviour of stock prices1973 – Black, Scholes, Merton: option pricing1976 – Box-Jenkins: ARIMA models for time-series analysis1977 – Boyle: Monte Carlo approach to value options1986 – Bollerslev: GARCH, a forecasting method for volatilitiesFrom 1986 onwards there has been an explosion of quantitative finance and financialeconometrics. Only time will tell which theories work and will be regarded as outstanding in thefuture. If you want a detailed synthesis of the latest developments, we strongly recommend PaulWilmott’s book, Paul Wilmott on Quantitative Finance.In quantitative finance many statistical parameters can be calculated, always based on certainassumptions. The cornerstone of the discipline is the calculation of risks based on assumedprobabilities and correlations. The original ideas of random walk and normally distributed stockprices that ignited the animosity between followers of technical analysis and theoretical financehave now been replaced.The micro-structure of marketsToday we study the micro-structure of the markets, including technical levels and microsqueezes. Volatility clustering has become an accepted feature of the behaviour of asset prices.
220
Technical Notes
Volatility clustering means that after violent moves, there follow violent moves. Leptokurtosis,or so-called fat tail behaviour, of the return distributions are scientifically described. Theseconcepts have been used for a long time by market technicians. They just called thephenomenon by a different name – break-outs. What we also know from science – behaviouraleconomics – is that technical analysis works because a high percentage of market participantsuse it. The majority of traders base their daily trading decisions fully or partly on technicalanalysis. Technical levels are continuously scrutinized and when they break, moves getamplified and thus make them significant and therefore give a justification to technical analysis.Today we are in a situation where technical analysis has lost its flavour of astrology and gainedacceptance in the scientific world. This acceptance is highly important. The crucial problem isa certain misunderstanding between the two worlds of technical analysis and theoretical finance.Technical analysis and subjectivityIn technical analysis we look at charts and interpret them. There is always some subjectivenessto this interpretation which is hard to model.Mathematical finance tries to model market behaviour, calculates risks based on given returndistributions, and estimates derivatives prices based on simulated asset paths. These are allexcellent tasks for mathematicians. However, even the most straightforward trading systems,such as point-and-figure, are difficult to value from the perspective of mathematics.Let us tell you why: point-and-figure is adapted by traders to individual trading styles and isused like a map in order to find the way to a location, and not like an autopilot. Mathematics,however, can only evaluate autopilots, systems that totally exclude human interaction.Technical analysis has not been popular within the academic world. Why? Because it alwaysincluded and will always include a lot of subjectivity. Relationships in technical analysis are fartoo complex to be described with calculus. Technical analysis is furthermore not based on ascientific methodology and is therefore often difficult to grasp. We are convinced, though, thatquantitative or econometric finance and technical analysis are both important.Technical analysis catalogues repeating patterns in price moves, whereas quantitative financemodels the behaviour of price moves. Both have a strong raison d’être.Today, the situation in a trading room is usually as follows: the trader makes his decisions basedon technical analysis, incorporates obviously important fundamentals, and uses quantitativemodels to help value the derivative instruments that he has decided to buy. Later, the riskmanagers will use quantitative models to parameterise the risks the trader has taken.We think that this explanation of the intertwining of both schools of thought makes clear thatquantitative methods are not a threat to technical analysis, but complement it.
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The Complete Guide to Point-and-Figure Charting
Quantitative financeQuantitative finance, roughly speaking, started off with the model of the random walk of assetprices and the arbitrage-free world. Traders always knew that stock returns are not normallydistributed and that arbitrage exists. Today, after a couple of decades of intensive research, wehave seen quantitative finance maturing. The research in high-frequency data, especially, showsnon-random behaviour as well as the development of the various GARCH models whichdemonstrate a certain predictability of the variation of returns as opposed to the concept ofrandom walk .Specific applications from Artificial Intelligence, so called artificial neural networks, beingconsidered the state-of-art in econometrics, are used to predict future prices. Such artificialneural networks are searching for patterns in the price history of one or several financial values(stock-prices, interest-rates, economic growth etc), test those patterns on the past and oncevalidated, use them for forecasts or trading decision support. This process which, from a globalview, resembles classic technical analysis and especially the way we propose to optimise pointand-figure.What we like in the scientific approach of quantitative analysis is that the researchers have thecourage to abandon concepts which are shown to be erroneous and work on new ones. And, asin all science, the drive is towards better, more realistic, models and not towards defending anestablished concept.Both technical analysis and quantitative finance have profited a lot from the decrease in theprice of computing power. In quantitative finance, particularly, the calculation performance ofmodern computers has shifted the focus on simulations. In quantitative finance the mostcomplicated derivatives can now be priced in simulations. Difficult stochastic integrals can beresolved the same way. Huge amounts of high-frequency data can be analysed.But one thing always remains the same: the data input is the market data. And markets move bygreed and fear, two of the most prominent characteristics of human behaviour. We know thathuman behaviour leads to markets which are complex chaotic systems, still very far from beingmodelled by even the most sophisticated calculus.We are convinced that in the near future more and more research will give credit to technicalanalysis, and therefore to point-and-figure, its most aesthetic representative in our opinion. Andbooks like this one will give it more and more credibility as well.But again, in both schools it is important to understand what one is doing. A trader should neverget carried away by backtracking software, nor should he sub-estimate the fat-tails of an assetreturn-distribution.The understanding of each others’ endeavours is really crucial in order to advance this subject.Even today, it is often difficult for an engineer to understand an econometrician, as they use‘regress’ and mean ‘approximate’, or ‘error’ and mean ‘difference between calculation and222
Technical Notes
observation’. The difficulty experienced by an engineer talking to a technical analyst ismaximal. Hopefully, it will be possible to develop a common language for all people, whetherpractitioners or academics, interested in forecasting prices and risks.We are also aware of the fact that so far technical analysis lacks a structured scientific approachand thus allows charlatans into its ranks, who harm the serious scholar of technical analysis.Some concepts are very helpful. Others dangerous. We urge the reader to use logical conceptssuch as point-and-figure. We warn the reader against systems that are a cocktail of variousindicators so complex that they are impossible to understand and most importantly without alogical background. What we mean, if you take the same time series, and based on it you do allsorts of arithmetic or geometric operations (oscillators, indicators, fans, angles), you havesimply over-analysed the same time series over and over again. It is then obvious that one of themyriad of combinations of technical indicators lead to a terrific past performance. However,such a combination is so closely fitted to the time series that it is not much more than statingthat over the sample period it would always have been wonderful to buy before an up-move andsell before a down move. Unfortunately, in the media there are loud proponents of such a wrongand over-simplistic concept, and the result is that they harm the serious technician while makingthis method an easy target for scientific attacks.We are convinced that a system using more than five parameters applied to the same data-seriesshould be looked at very critically and most probably avoided.We include good arguments for the use and concept of the best of all technical analysismethodologies – point-and-figure – in order to bridge a gap between quantitative finance andtechnical analysis. And again point-and-figure does not only work but is a mature and coherentphilosophy about how to interpret price moves. Science is an art, the most progressive ideashave always sprung from creative processes. It is the same with point-and-figure, science andart.
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The Complete Guide to Point-and-Figure Charting
More about scalingSummarising what we have already explained:•••
the point-and-figure chart is drawn on a structure of squareseach square on the chart represents a unit of pricethe values of the boxes change based on the price of the security being tracked
The Classic Scale
A steady evolution of the system has led to the traditional scale for US stocks, which we call theclassic scale. The classic scale works the following way