The Finance Book Club

Chapter 10 - Deception and Charts

1. Deception is ubiquitous. Every lifeform is a master at it, from virii to homo sapiens
2. To ignore deception is a ticket to disaster
3. Sensible sounding advice like
   1. Long-term charts are paramount to gaining the proper perspective
   2. Look for repetitive cycles
   3. Make a note of key highs and lows
   4. Measure the time between highs and lows
   5. Markets go through inactive and active periods; trending and non-trending
   6. Momentum shows trends
   7. Take a consensus approach with different indicators

   
   

4. VN didn’t make these up they are quotations from best-selling books on technical analysis

5 major lessons showing things are seldom what they seem

1. The Natural Order
   1. Artie (VN’s father) would take him for walks through the wetlands (back when they existed) around Brighton Beach Baths
   2. Nature is beautiful and you can learn a lot from it
   3. Concealing, decoys, distraction, playing dead, Trojan Horses, key to winning in all sports
   4. The various tactics of nature applied to sports, feinting, fading back, suddenly springing to life, faking handoffs, etc.

   
   

2. Even Chess Players Cheat (decades before Neimann!)
   1. Writing down a blunder on the scoresheet before playing a different move and changing it at the last second, to throw off opponents who might be reading the sheet
   2. Poker face in poker and chess

   
   

3. By Hook or by Crook
   1. Hook serve in tennis
   2. Borotra playing dead on the court

   
   

4. Primates and Deception
   1. The purpose of a big primate brain might be to deceive mates
   2. Only lemurs use no trickery in the primate family; they have the smallest brains

   
   

5. Deceptive Technical Patterns
   1. VN has a conversation with Magee (of the famous Edwards & Magee) but it’s not clear if it’s real or not. He seems to be mocking the naivety of simple trendlines and moving averages
   2. If monkeys and insects can deceive, it seems that speculators should pay more attention to it than does Magee
      1. Viruses invade bodies
      2. Ants enslave other ants
      3. Moths evade birds
      4. General defeat generals
      5. Girls and boys, con men, magicians, financiers, poker players, economists, mystery writers, painters using trompe l’oeil, actors, novelists adding twists to plots, etc, etc

      
      

   3. Weak hands get shaken out at just the wrong (or right, depending on your view) time

   
   

Ecological Theories of Deception

1. Energy used for deceptive behaviour crowds out other life-enhancing features; balance must be struck
   1. Camo is better than running fast, because it works without expenditure; if a zebra escapes from a lion there could be a second lion around that he won’t be able to escape; better not to be seen at all

   
   

Circumspection and Distrust; An Economic Theory of Deception

1. Oliver E. Williamson’s theory
2. Businesses act the same as plants/animals in a jungle
3. ‘Transaction Cost Economics’ - economizing on transaction costs is mainly responsible for the choice of one capitalist organization or another
4. Institutions achieve efficacy
   1. Institutional environment - rules gives rise to
   2. Governance - of contractual relationships within parameters dictated by beliefs and behaviour of
   3. Individuals

   
   

5. Madison, in the Federalist Papers
   1. “As there is a degree of depravity in mankind which requires a certain degree of circumspection and distrust, so there are other qualities in human nature which justify a certain portion of esteem and confidence.”
   2. Williamson calls this depravity - ‘opportunism’; the use of guile in pursuing self-interest

   
   

6. Organizations develop sanctions for governance, that maximize profits while mitigating Machiavellian opportunism
7. Buy or sell decisions according to Transaction Cost Economics
   1. Price
   2. Hazards (guile)
   3. Safeguards - norms, customs, rights, laws, courts all make safeguards less expensive; in societies where these are lacking safeguards are expensive; this can include creating a hierarchical organization where internal matters can be settled internally; pre-emptive; can make new rules to control employees by fiat; endogenous preferences - propaganda to control thinking and respect the governance structure

   
   

Principles of Deception

1. VN studies war, sports and nature as a preliminary to studying deception in markets
2. Predator and Prey
   1. Much energy is lost in direct battles because both are so adept at their respective offensive and defensive tactics
   2. Indirect approaches saves energy for both parties
   3. Deceptive techniques emerge
   4. One side gains the upper hand and their tactic is passed on by genes, learning or sentences
   5. Then the other side counters this new tactic; the balance is restored
   6. This is the arms race

   
   

3. BH Liddell Hart
   1. After studying a long series of military campaigns he became aware of the superiority of the indirect approach
   2. A direct assault of new ideas provokes stubborn resistance; intensifying difficulty in changing outlooks
   3. The indirect approach is fundamental to politics, sex, commerce
   4. Suggesting there is a bargain to be secured is more persuasive than urging someone to buy
   5. The surest way to get a superior to accept a new idea is to convince him that it was his idea

   
   

1. The aim is to weaken resistance before overcoming it
2. Draw the other party out of their defenses

1. The Decoy
   1. Example from the Old Testament
   2. Sisera attacks Israel with 900 iron chariots
   3. Warrior priestess Deborah advises Barak of Kedesh to raise a force of 10k Israelites and hide them on the slopes of Mount Tabor
   4. A smaller force led by Deborah, lures Sisera’s army along the river to a narrow marshy plain below Tabor where the chariots were bogged down
   5. Then the 10,000 swooped down from the mountain and decimated the Canaanites (also the first ambush?)

   
   

2. The False Gift
   1. The Trojan Horse story
      Attached Image (click to enlarge)

      
      
   2. The horse was designed with beautiful carvings to look like a trophy to entice the Trojan’s greed (Laocoon - beware of Greeks bearing gifts)
   3. The doors were concealed with cunning bolts
   4. Sinon - a double agent was left behind by the Greeks. He was paid to tell lies about why the horse was left behind and the consequences if it was refused
   5. The horse was intentionally built so large as to be impossible to bring inside the gates without removing part of the wall; the mainstay of their defence
   6. The Greek ships were hidden miles away in Tenedos
   7. When Laocoon hurls a spear at the horse to prove that it’s hollow, the gods send a serpent to eat his sons and make it seem they were angry with him for his lies. (when the gods are not on your side, there isn’t much you can do)

   
   

3. The Holdup
   1. A variation of playing dead
   2. May 1940; British ship Scientist is bound for Liverpool carrying tons of chromium and maize.
   3. The Kasii Maru, a rusty freighter is flying the colours of Japan, neutral at the time, and approaching them
   4. The Japanese sailors are lounging aimlessly ,a woman pushes a pram on the deck
   5. Suddenly the swastika flag is raised and a shell is fired as a warning shot
   6. The crew is hostaged and the ship is scuppered

   
   

VN really pads out this chapter with detailed descriptions of mimicry, playing dead, but he rarely brings it back to the marketplace as he’s having so much fun.

 

1. The numbers in the major economic announcements are completely meaningless, subject as they are to revision, errors
2. Sometimes the move can happen day before or day after the announcement - an ambush
3. Stationary prices seem to be a favourite trait of bulls rather than bears
4. Long-term basing (ranging) followed by a fantastic move is as common as flies
5. Soft commodities are notorious for playing dead - the long growing cycle magnifies the influence of price quirks
6. However then they can suddenly quadruple rapidly and wipe out the complacent; even the opossum will bite after playing dead
   Attached Image (click to enlarge)

   
   
7. VN seems obsessed with ants; they can only detect each other by smell, and so many other species have developed the ability to mimic their scent and prey, enslave or parasitize colonies
8. VN’s Cloaking Conjecture (my name for it)
   1. Moves will be inversely correlated with those that occurred following the one most similar day the previous year
   2. April 17, 1995 - USDCHF - up from 87.6 to 89.08; a gain of 2.48
   3. The most similar rise over the past year was March 31, 1995 when it rose 2.97 points from 85.91 to 88.88
   4. April 3, 1995 (the next trading day) it rose further
   5. So the April 18 prediction is for a decline
   6. This doesn’t work (why use it as an example then???)
   7. However VN does an 8 year test that confirms the hypothesis (I guess I should test this myself. Any interest in hearing about the results?)
   8. Correlation between the most similar move and actual move the next day is -0.10 which has only a 2% chance of occurring by chance (but what about time frames less than a year? And how is ‘similarity’ being defined?)
   9. The Swiss franc is a particularly deceptive market
      1. Of 100 different similarity relations tested, analyzing the most similar x days out of the last y days, only one showed a positive correlation with subsequent moves (the one he used as the example above??)

      
      

   10. “Considering the prevalence of trading techniques that use similarities as their base (nearest neighbour, neural networks, clustering), it is no wonder that dealers consistently make billions a year in trading profits by coppering their customers.”

   
   

Disruptive Behaviour

1. The most spectacular form of deception
   1. Quails feigning injury
   2. Squid spraying ink
   3. Skunks spraying skunk…musk?
   4. Ants (again!) spray pheromones mimicking the slave targets ‘danger’ signal which puts the whole colony into a panic and allows them to carry off victims leisurely

   
   

2. Dow
   1. Small warning crash of 503 points on Oct. 19, 1987
   2. Bigger crash on Oct. 20, 1987; futures halted
   3. Scares bulls out
   4. 250% rise over the next nine years

   
   

The 2000s, (although technically this slide should be going up)

1. Grain markets are worst for this

Tin: hold my beer?

Trapping

1. Angler fish
2. Trap door spider
3. The most common deceit in markets. Too many examples to list.
4. British pound September 1992;
   1. After some ranging
   2. Breaks the five year high
   3. Trap is set
   4. Shorts rush to cover
   5. New buyers surge in
   6. The pound plummets to 1.40 in four months; a 60 cent decline, $40k/contract
   7. Worse than being eaten alive, says VN

   
   

Tempo Fakery

1. VN’s fencing instructor has something to say about this incident
2. This is an example of a Tempo Fake
3. Start with a small jot down; then close up unexpectedly at 1.965
4. The December pound changed its tempo by delaying its actual move
5. This causes speculators to misread the market or withdraw
6. Curiously, the rest of the examples VN uses are all currencies
7. Redoublement - a renewed attack in fencing
8. A pause causes anticipation of a reversal but it just continues
9. We are perhaps seeing a textbook example of that in CHFJPY

This was a textbook Sperandeo-123 with the breaking bar not retracing even a little bit. Many shorts piled in roughly at the arrow. Despite being a model of technical analysis perfection, it didn’t hold, the trend resumed and bears are getting ground to bits. This trap is also enticing new shorts who will prolong the agony. Copper the public’s bets.

Markets vs. Moths

1. VN advises that these examples we’ve seen have no predictive power
2. The market is rich enough to provide counterfactuals for all simple phenomena
3. Arguments by evolution are often circular
   1. Attribute X is beneficial for survival.
   2. How do we know?
   3. Because it exists.
   4. Why does it exist?
   5. Because the organism survived.

   
   

4. What appears to be deception might be pure chance.
5. Lowly moths can survive bird attacks. Imagine what Mr. Market can do.
6. Market life is hazardous.

You went to a lot of effort here Clemmo, well done, sir.

I am a speed reader and even the condensed version here took me time, so your time & energy spent from the original text is well noted by me.

A terrific read, again thank you.

Thanks for your kind comments.

I sometimes think I should condense even more, but I have two reasons for not doing it.
1. Laziness. It would require an initial reading and then asking myself what's really useful? Sometimes I don't know until later. That ends up being more work so instead I just jot down whatever strikes me as interesting/important or that we haven't already covered.
2. When I look back over these notes I'm glad to have the extra detail rather than just the spirit of the instruction. It keeps things memorable and prevents ambiguity.

In the case of this book, I can assure the public that I'm still trimming about 90%; the book is an epic history of everything in Niederhoffer's life and he has a prodigious memory and no lack of ambition. What makes it bearable is that it's mostly entertaining and witty, contrast this with Du Toit's book which was less so.

Chapter 11 - Sex

1. When the Fed reverses course it’s a great time to go with [it].
2. The average run of contracting (raising rates) or expanding (lower rates) lasts about 3 years and involves seven consecutive tightenings or easings
3. VN usually finds himself fighting the Fed more often than not
4. He collaborates with Bloomberg’s Caroline Baum on an article about Hillary Clinton’s mysterious year of cattle trading. https://www.nationalreview.com/2016/06/hillary-clinton-cattle-futures-windfall/
   1. They make a fairly compelling case. Lock her up. But put Trump in the cell beside hers.

5. This chapter has a (bodice-ripping) story featuring John Law (introduced by Sperandeo). I have no idea how I’d not heard of him before.
   Attached Image (click to enlarge)

   
   

LOL, as the kids used to say.

This chapter is truncated, not because it’s uninteresting or I’m being prudish but simply that it has hardly anything to do with markets.

Chapter 12 - Returns and Randomness: Academic Style

1. VN says the only worthwhile things he learned at Harvard, were squash, and the ability to use the scientific method to “uncover and integrate regularities”
2. James Lorie and Larry Fisher use a computer to show that stocks are a great investment in the 1950s (we take it for granted, but it’s nice to have proof)
3. Engel and Hecht: How to Buy Stocks
   1. If you invest on a time horizon of ten years using a portfolio of stocks it’s actually hard to lose money
   2. There were only 72 negative rates of return out of 2,211 year-to-year combinations between 1925-1991
   3. If you extend your horizon to seven years, only five losing ones

   
   

4. Bob Gwinn
   1. VN gets a $25k stake from him to short a printing equipment manufacturer
   2. He invests more money in the short as the stock price rises
   3. Gwinn invests his own money
   4. Together they lose nearly $100,000 as the bears get squeezed
   5. A month later the company goes bankrupt

   
   

5. Wilton Jaffee
   1. VN bumps into him while moping on Broadway
   2. Tells him about the short squeeze
   3. “Oh yeah, we had some fun squeezing the shorts on that one. Really took some of those midwestern hayseeds to the cleaners.”

   
   

6. If anyone offers you a bet on a sure thing, take your wallet and run in the opposite direction
7. VN now never will short without a hedge; this saves him when he tries to short silver and his long gold hedge saves him
8. The 10% annual return in stocks is the best reason not to go short

Random Walk Theory

1. Teaches that all info is in prices
2. A firm’s capital structure or dividend has no effect on its value
3. Price movements generated by random processes look just like real charts
4. Analysts and funds have random performance
5. Market-timing is inferior to holding
6. This was the academic line for decades
7. VN notes that among six or so students in his classes who espoused that theory all of them have gone on to careers with firms whose ‘raison d’etre’ was to find anomalies in prices
8. VN paints a dramatic picture of himself defending his ideas against the intractable resistance of the old guard at the Ph.D. Finance Seminar.
   1. His argument is basically that there is no methodology to prove that prices are random because you can’t prove a negative
   2. He isn’t allowed to continue because they didn’t want to hear about things they’d already read
   3. His statistics are challenged based on the grounds that stocks have infinite variances, his samples were small, unrepresentative, and even if they were correct they couldn’t overcome transaction costs so no value to anyone but specialists and floor traders

   
   

9. 30 years later VN still refutes the theory but has updated
   1. The strong version of the theory is supported by myopic academic research full of biases
   2. He finds that earnings are the key to individual stock performance

   
   

1. However earnings forecasts are inaccurate; price performance of terrible forecasted earnings is superior to that of companies with excellent forecasted earnings

Transactions and Regularities

1. Limit orders to buy tend to cluster below the market
2. Limit orders to sell tend to cluster above the market
3. Market orders to buy must be sufficient to overcome the limit sell orders before consecutive rises can occur (and vice versa)
4. A dull market where prices range can go up and down hundreds of times before a continuation takes over
5. This produces four non-random properties
   1. There is a general tendency for price reversal between trades
   2. Reversals are relatively more concentrated at integers where stable slow-moving participants place their limit orders (smart money?)
   3. Floor traders knowing the locations of these limit orders, take positions at nearby prices, pocketing the spread (safe arb)
   4. After two price changes in the same direction, the probability of a third change in the same direction as the second is > two changes in opposite directions (proven to my satisfaction earlier in the book club)
   5. These tendencies are now well documented says VN
   6. Odds ratios of reversals vs. continuations is 5:1 says VN (something I couldn’t prove)

   
   

6. Days of the week
   1. Monday might be better for some reason I don’t get
   2. The action on a Monday might be particularly bearish after a Friday that was sub-par; VN Says this basic idea has made his customers 9 figures (the Blue Monday effect)
   3. Chances of a rise in the last four days of the week run 13 points higher than on a Monday
   4. However these discrepancies are not enough to rise above the transaction costs

   
   

7. Results that appear significant in one period can evaporate in another
   1. If something is there to be found it will be found and exploited and killed

   
   

8. Academics don’t consider that shrewd operators might know something and keep it secret
9. Members of ‘the trade’ hire smart graduates to keep abreast of new developments
10. Even if it’s a delusion they can front-run those who believe in it.
11. VN checks the daily discrepancies years later and finds that they have reversed from when he tested it
    1. However that doesn’t stop academic publications from publishing the exact opposite findings
    2. This is good news for contrarians

    
    

1. I would just say be suspicious of statistically significant effects for which the cause is not known or cannot be surmised
2. Attached Image (click to enlarge)

   
   

An Ounce of Testing

1. Turn of the Month Effect
   1. Robert Ariel divided the months into two halves
   2. He computes returns in both halves but adds an academic twist - using equal-weighted and value-weighted indexes
   3. He finds returns are greater in the first half of the month

   
   

2. VN says buying a stock based on the first letters of its name is all you need to know in many cases
   1. During biotech bubbles, any stock with the word bio in its name will rise
   2. When the value of such stocks is measured in price/patents its near time to get out
   3. Same with companies that have ‘tech’ in their name
   4. ‘Onics’
   5. ‘Computers’

   
   

3. If a country’s rate of return is due to high interest rates its neighbours are likely ready to feel the same effect
4. The story of the rabbi whose wine cellar burned down
   1. The townspeople plan to restore it but the cost is too high
   2. So they agree to each bring a bottle of wine to his house and pour it into a vat
   3. When the rabbi turns the tap out comes pure water
   4. They had all said to themselves. What does my contribution matter? It will all end up the same.

   
   

Chapter 13 - Connections to Monitor

1. Darwin
   1. Cats eat mice
   2. Mice devour bee nesting sites
   3. Bees pollinate clover
   4. Cattle eat clover
   5. The presence of cats in one area could determine the numbers of cattle in that same area by a series of hops

   
   

2. The Domino Effect / Butterfly Theory
3. El Nino and anchovies
   1. El Nino is a weather pattern
   2. Anchovies need cold water
   3. El Nino reduces cold water
   4. Reduced anchovies means poultry and cattle feed needs to be supplemented with soybeans
   5. Soybean price goes up

   
   

4. In the 1960s there was a strong correlation between Hong Kong stocks and the US stock exchanges; by 1995 the relation vanished
5. Hot sector was now tech
6. High tech stocks crater near the end of 1995 as Fidelity/Magellan shed its holdings “shortly after touting their eternal value”
7. Hong Kong then rallied 15 percent in 2 months in 1996 as money shifted
8. Emerging markets now dance to the Hong Kong tune
9. March 11, 1996 - HK has the third largest decline ever, falling 7.5%; not due to fears of war in the Taiwan Strait but fears that Greenspan will tighten
10. Feedback loops can be mindboggling
11. Emerging market portfolios base exposure on US monetary policy
    1. When the Fed tightens they ‘rush like lemmings’ to Hong Kong, Singapore, Mexico because they export a lot of GNP to USA
    2. When the Fed eases they jump to India, Korea, South Africa, because the connections to USA are more distant

    
    

12. Jesse Livermore preferred to speculate out of London to be closer to the action
    1. 1907 during the panic he hopped the first trans-Atlantic steamer to inspect the carnage
    2. Every speculator ‘worth his salt’ must track the moves of the US stocks in Tokyo and London, the Asian markets, European markets after NY, all to get a handle on what’s in store for the USA

    
    

13. Futures on foreign stock markets traded in Chicago simply copy the Dow and USD during the NY session. “Emerging Markets Quiet Awaiting US Data” is the perennial headline.

Reasons for Interrelations

1. Many varied reasons
   1. Shared language
   2. Physical laws of conservation, especially of energy
   3. Common genetic heritage
   4. Societal ties
   5. Substitutable goods
   6. Attached Image (click to enlarge)

      
      

   
   

Bogus Interrelations

1. Sunspots
2. Generalizations about long-term moves (overlapping graphs) (and yet this so common that even VN has done it in this book)
3. Most charts aren’t adjusted for cost of carry
   1. Production numbers increase with GNP
   2. Price series move up with inflation
   3. A five year chart has only 10 semi-annual data points; there won’t likely be more than 3 turning points; so naturally it looks like one series moves up/down or before/after or with the other
   4. Stigler’s chart on German birthrates correlating with declines in stork populations (the irony here is they could well be linked, but inversely correlated; decreasing birthrates == greater standards of living == greater natural resource extraction == greater loss of habitat == reduced stork biodiversity)

Humility in Interrelations

1. Change one thing in nature and everything else is affected
2. Fragmented, shifty, random is the nature of market interrelations (ever-changing cycles, Vic!)
3. VN goes into the reasons behind the 1987 crash, differential between dividend yields and fixed-income yields, foreign investors reluctant to hold US debt, James Baker going on TV to say the Germans need to buy more US goods or they’ll allow the dollar to decline; Pohl blames Baker for the crash (the problem is a reason will always be found after the fact even when no one would be able to connect the dots beforehand)
4. The following day the feedback process was at work to contain the calamity
5. Yield differential between stocks and bonds moved almost the entire distance back to year-end 1986 levels in 1 day; dollar recovered 2% ‘signalling that Baker would not do it again’ (how can you know?)
6. Copper and silver declined 12% the next day, signalling US industrial activity would be subdued for the future
7. Stocks recovered 114 Dow points, about ⅕ on the way to a 100% gain over 8 years
8. VN ignores his customer and his wife’s advice to avoid stocks on the Monday of the crash and he gets wiped out; in fact he was ‘goaded’ by their advice. (never give or take advice/tips!)

Recency Effect

1. You remember the last place you parked your car but you tend to forget the other places
2. This survival trait is useful in finding a book, thought, ring, but it’s a killer in the markets
3. The memory of big stock market crashes lives on in the collective consciousness
   1. On the crash day the Fed intervened to buy treasuries
   2. Fixed-income prices gained 10+ percentage points in one day
   3. Since then, stocks have pretended to crash at least 50 times
   4. Speculators rush in to buy bonds and sell stocks on every fakeout
   5. More money has been lost by bears in such manoeuvres than in the Oct. 19 crash

Intermarket Correlations

1. VN waxes philosophical about global economies and their complexities but I’ll just summarize by saying - economics is hard
2. Economists can’t even agree on the determinants of exchange rates; there are 3 theories.
   1. Balance of payments approach - focus on short-term and long-term flows of money in/out of countries
   2. Portfolio-asset-management approach - focuses on expectations about returns and risks of holding currencies within an efficient portfolio of other assets
   3. Technical analysis approach - favoured by 90% of practitioners (practitioners of what? TA? Then he’s saying 10% of technical analysts do it but don’t believe in it??) MAs, filters, EWT, Fibs, mumbo jumbo that this book has hopefully cautioned against

Japanese Case Study

1. VN describes his experience trading the yen in ‘94 and ‘95.
2. 1994
   1. US bonds, small stock, dolar:mark and dollar:yen markets dropped by 10+% wiping out trillions in US wealth
   2. Popular explanation was foreigners decreased holdings of US assets because: fear of USD declines
   3. The more USD declined the more it seemed the decline would continue, leading to further selling of USD assets like stocks and bonds
   4. There was history behind this fear
   5. 1986, April
      1. It would cost a JPN investor 185,000 Yen to buy $100k of the 8% US bond in futures
      2. End of April 1995, the falling dollar reduced the value of the 8% bond to 84k yen; 55% decline on principal
      3. However end of year 1995 the bond had advanced 50% to 125k yen

      
      

   6. JP Morgan research group developed benchmarks for predicting the value of the USDJPY. VN knows no quantitative studies done to test the validity of these methods. But he calls them ‘the laureates’

   
   

3. The yen rises in 1994
4. US fixed income cratered in 1994
5. VN makes ‘good money’ by considering feedback relations
   1. ‘They’ will be on alert to halt USD declines
   2. ‘They’ will prop up the dollar around Tokyo lunchtime for the NY opening
   3. VN buys the dollar
   4. 9/10 times he does this the central bank leaked news of intervention
   5. VN figures either the central banks were following him, brokers confused his action for theirs, or it was just dumb luck, but interventionists were making their moves at the same he did
   6. This made him the most successful fund manager of 1994

   
   

6. Correlation between USD changes and US stocks was positive in 1994; about 30% daily
   Attached Image (click to enlarge)

   
   
7. Easy ways of making money disappear in time
8. 1995
   1. US stock and bond markets rose continuously as the USD drops
   2. March 5 - 7, 1995 USD drops from 93.50 to 88 yen in 36 hours after having declined four big figures in the previous five days
   3. VN figures the carnage must be over
   4. Newspaper headlines foretold imminent disaster if the decline is not halted
   5. USD status and future viability of Japan are questioned
   6. Rumour of MITI and MOF - Japanese industrial and financial planning authorities
      1. Urging JPN institutions to sell their $50B of US bond holdings for deutschmarks

      
      

   7. VN figures ‘they’ will step in again
   8. But when he buys USDJPY this time he stands in front of an avalanche alone; the yen moved down 2.5% in an hour
   9. At 10x leverage he loses 25% of his assets in 1 hour
   10. At 4PM March 7, the Japanese, through counterparts in central banks in NZ, AU, and HK started buying the dollar; reversing the entire move before dollar bears knew what hit them
   11. VN rushes in to buy JPN stocks
   12. A trade deal is reached
   13. But instead of rising the JPN stock market drops 5% as often happens when good news is confirmed; dollar rose 2% agains the yen on euphoria
   14. VN can’t take another 25% drubbing so he dumps his holdings
   15. On cue, the JPN stock market marches up 40% from his bailout point
   16. Selling out when fear is greatest is the worst time to sell

   
   

Japanese Field Trip

1. VN goes to find out the cost of a big mac (the big mac index)
2. He sees 299/300 office workers wearing white buttoned-down shirts; the nail that sticks out gets hammered; he concludes the Japanese market participants are followers rather than leaders
3. The story of Hachiko, the dog whose story is possibly the inspiration for Seymour in Futurama
4. When VN suggests that sake-fuelled Kobe beef revels allow dissemination of economic numbers like the US trade balance hours or days before traders in the US can profit from it, a former Fed governor angrily retorts, “we’re not a sieve like the Japanese!”
5. The Fed governor senses that VN is insincere and doesn’t hold him in reverence like those in the command sector have come to expect, and he hangs up.
6. He still finds that the moves in JPN markets on the day before important numbers are announced in the US are invariably on the mark
7. He especially likes the 0:00GMT - 06:00 GMT move in the USDJPY because this ‘sings like a canary’
8. After visiting Japan VN vows never to buy into news about a crisis of this 2700 year old dynasty

1. As JPN economic activity rises, demand for USD increases to finance imports of US goods
2. Value of USD goes up; value of JPY goes down
3. As dollar rises, JPN exporters become more competitive; JPN stocks go up

International Correlations

1. 78 separate daily correlations between domestic equity, interest rate, and currency markets
2. US and Euro fixed-income markets are highly correlated
3. Euro equity and fixed-income markets move closely but opposite to their currencies
4. Highest correlations are between Euro fixed-income markets
5. US stock market is correlated about 0.5 with English and French markets; only 0.13 with the Nikkei
6. All currencies are negatively correlated with their respective stock and bond markets.

This is very easy to check at a glance, at least for stocks. Let’s consider the USD and the SPX500 vs the EUR and DAX30 over the past 10 years.

I’ll use the US dollar index to gauge the value of the USD. I could use USDJPY just as readily.

USDIDX

Attached Image (click to enlarge)

USDJPY

You see they are very closely correlated. As you’d expect.
Now let’s examine SPX500

SPX500
Fairly homogeneous.

Just eyeballing this we can see the charts are broadly similar. They both go up and have dipped down in some common spots.

Now let’s make a table comparing their correlations using a simple up/down/flat matrix.



At least on an annual basis the results are not convincing; some years they are correlated and some they are inversely correlated, while others are inconclusive.

What about the Euro?

EURUSD
Unfortunately I don’t have something like a Euro index, but the EURUSD is basically, by definition, the anti-dollar and the standard expression of Euro strength or weakness. More so than EURJPY, but please correct me if you feel I’m reaching.


German DAX
A much more interesting chart than the SPX. Why am I using DAX rather than EuroStoxx or CAC? Well, I could, but the economic news that most affects the EUR is usually the German news, as it remains the economic powerhouse of Europe.

Again we use a simple matrix using a simple determination split up by year.



Again, the results are not at all convincing, though perhaps a bit closer than the first comparison. What I do see is that EUR to USD are inversely correlated as you’d expect, and SPX and DAX are fairly closely correlated, as you might expect from two equity indices.



Now that’s correlation. What else can we say about all this?

Breaking things up by year, forces a ‘trading horizon’ on the forecast so that we don’t have forever to wait for things to line up just as we expect; just as you’d be dealing with if you were actually trading. Over an infinite time span, eventually EVERYTHING will correlate. This makes paying too much attention to correlations, if not pointless, at least something that should be confined to a particular horizon. Clearly this well-known ‘correlation’ isn’t very useful for trading MN or even W1, at least in the modern era. Maybe it’s more useful intraday? I doubt it though.

Since VN goes on and on about other types of correlations, but I have shown that even the most obvious correlation possibly isn’t, I’m going to skip the rest. This is something you’d have to investigate on your own if you believe correlations are a useful trading tool.

 

1. If a regularity became too glaring it would immediately be washed away by all the hawks.
2. What matters isn’t the impact of moves in local markets, it’s what participants in one market expect to happen based on those impacts.
3. No wonder it’s hard to make a profit, due to the complexity
4. VN believes he is the first to have analyzed these interconnections systematically

This joy of discovery is tarnished by five sobering factors

1. Interrelations are always changing (because they are cyclical, and possibly aperiodic?)
2. His students have started their own businesses exploiting the same ideas
3. Books and articles about intermarket analysis and their ideas are now commonplace
   1. VN points out that the level of analysis hasn’t advanced much; like astrology or alchemy vs. modern medicine

   
   

4. Computerized black boxes tracking all major market interrelations (and now, everything)
   1. One group selling a product similar to the one VN developed for his own use said they had sold 400 to a bank for their traders

   
   

5. Top traders have clubs where they can discuss the day’s interrelations (and how they’re going to collude to fix prices)

Chapter 14 - Music and Counting

1. VN compares the markets to music; later on a report for the London Times makes fun of him for it.
2. A frequent work he hears in the market is this one. https://youtu.be/VOLy6JxEDLw Don’t turn it up too loud or you’ll blow something.
3. https://www.youtube.com/watch?v=NtzEdLS6IQA Artie’s favorite tune (the playful trout!)
4. https://www.youtube.com/watch?v=0fOiCgdwLaw The LoBagola pattern (back and forth) is compared to Treasury futures from 1976 to 1992
   Attached Image (click to enlarge)

   
   
5. https://www.youtube.com/watch?v=5OHBP3iS4D0 which VN describes as ‘scary’ and compares to this chart
   Attached Image (click to enlarge)

   
   
6. Memories of violent soybean futures movements, up and down between $5 and $10 every couple years.
7. The music of market crashes is Beethoven’s first movement of the 9th symphony. The music of the end of the world. https://www.youtube.com/watch?v=ke6SatPLzaw (Awesome! I wonder why this doesn’t get used more often in movies?)
8. Swiss drug companies hire math champions to trade options which they consider as important as finding new drugs
9. A simple algorithm for translating chart notation into musical notation
   1. Take the total range of the previous day (or 50 points around yesterday’s close)
   2. Divide it into eight intervals or notes, starting with middle C as the previous close
   3. Take each half-hourly price, round it, and assign it a note

   
   

10. What you get is often something like a nursery rhyme (three blind mice)
11. Eastern markets show more ‘continuity’ than Western ones
12. VN argues that much of Western music and fiction follows an A-B-A form, with the A being the melody, the B, providing contrast; this A-B-A form dominates the market, he says
13. B-A-B happens often enough to stop anyone from making too much money by betting consistently on the A-B-A form.
14. VN hires a floor trader who believes there is a 1:1 correspondence between score notes and stock market moves for the year. VN gives him ‘six figures’ in initial capital and he loses 100% in 3 days, leaving town ‘as poor as Berlioz’.
    Attached Image (click to enlarge)

    
    
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15. If you’re a Bach, don’t worry about the rules
16. Mysticism, describes everything, predicts nothing (much like this chapter)
17. Counters know their opponent’s likely holdings and the remaining cards at all times. Quite an advantage.
18. After a big rout on a previous day VN has seen markets open like Beethoven’s fifth (ta-ta-ta-tum); three quick notes, and a long unmeasured lower
    1. Hope springs eternal so the next day is likely to begin with a few short small spastic trades slightly above the close, then the big boys lower the hammer of reality

    
    

Chapter 15 - the Ecology of Markets

1. VN spends several pages comparing the markets to a natural ecosystem, which they surely are.
2. Losses take the form of food and energy
3. The primary consumers of these are the FX and fixed-income dealers - banks and investment brokers; Bankers Trust, Goldman, Citi, Morgan Stanley, Salomon, UBS
4. Secondary consumers who feed on the banks are large hedge funds, Quantum, Tiger, Omega
5. At each step orders and volatility are broken down into chunks and recycled among the decomposers such as floor traders, and telephone brokers
6. The public is made of futures traders, stock flippers, market timers, system clubs, tape watchers
   1. Their mortality is high; they are always in flux
   2. Characterized by inflexibility, ignorance, arrogance, myopia, hesitancy, undercapitalization, overconfidence, spendthrift, hopefulness
   3. VN admits he sometimes plays the part of the public, taking bad gambles
   4. The role of the public is to be eaten
   5. Stanley Kroll - futures broker in the 1980s; was successful but got discouraged because none of his clients made any money; no other brokers at his brokerage house had any winning clients either
   6. Public futures traders are therefore rare

7. The public participation is declining and replaced by managed systems traders
8. One public for futures is easy to define
   1. The group responding to advanced, mechanical, space-age, effortless systems traded by supertraders who ‘won't’ let you make critical mistakes’
   2. They attend intense seminars learning to find exact highs and lows
   3. Tuition refunds if the return is less than 200 percent or 5 percent drawdown
   4. The ratings reports that these managed futures returns are greater than stocks by a few points, but VN doubts the figures
   5. For one thing only the successful funds will self-report their figures
   6. For the same reason the first year of reporting results is usually the strongest
   7. Results are reported by the trading advisers themselves (no accountability)
   8. Improper weighting by initial value, selective reporting, fattening up contenders
   9. Computing prospective performance by a preselected group shows sharply lower returns

9. Teweles - The Commodity Futures Game - the best book on Futures says VN
   1. Only the brokers make money

10. John Allen: Who in their right mind would invest in managed futures if they knew they would need to make a 34% return in one year just to equal the yield of a T-bill?
11. Flipping investments with high vig is asking to be devoured
12. The public has unrealistic expectations about feasible returns
13. Horace Lorimer: You’ve got to learn not to overwork a dollar
    1. 3% is a small load
    2. 6% is safe
    3. 10% is hard work, watch to see that it doesn’t buck
    4. 20%; you own a blame good critter or a mighty foolish one and you want to make dead sure which
    5. 100%; you’re playing the races and the horse is destined for the glue factory

14. Selling debt provides the market ecosystem with an important source of energy
    1. Gov dealers agree by tradition to bid on debt without commission
    2. But the inventory has carrying costs
    3. If the debt value doesn’t go up the markets will grind to a halt
    4. 30 year debt marketing day is usually the most volatile

15. Robert Aliber
    1. FX profit of money center banks is inordinately large relative to the capital they employ
    2. These profits compensate dealers for the persistent needs of importers to buy FX and for hedgers to reduce risk

16. Paul DeRosa
    1. FX dealer bonuses are large but only ¼ that paid to fixed-income
    2. Bank managers know it’s the system that creates the profits
    3. Wide bid/ask and the use of limit and stop orders by slow-moving participants
       Attached Image (click to enlarge)

       
       
       Attached Image (click to enlarge)

       
       

17. Dealers play the role of the house in the casino
18. FX volume in 1995 was 1.2 trillion
    1. ⅓ is the public with the bank
    2. ⅔ is directly between banks in the interbank market
       Attached Image (click to enlarge)

       
       

19. Amazingly, reporting banks only make $8-10B a year from FX business
20. FX holders don’t bother to hedge unless they expect at least 3% move against their holdings
21. Bank profits are enhanced by a lack of a central clearing mechanism
    1. Customers must exit from the same house they got in
    2. Therefore banks always know which way a customer will go
    3. Money must be deposited in advance; most customers only want to leave enough to cover their positions
    4. This allows the bank to know which way the customer will go once it has used up its available margin funds
       1. Assume a customer with $10M on deposit as a 5% margin against the value of FX holds long positions of $200M
       2. The bank knows he has to sell dollars if he asks for a quote so they will bias the spread to a low dollar bid
       3. It’s like a roach motel - easy to check in, very tough to check out

1. Banks can also invest customer money in the Fed funds rate
2. 
   
   Attached Image (click to enlarge)

   
   
3. Attached Image (click to enlarge)

   
   
4. Banks supplement the info they have from dealers and brokers by maintaining data about the trading styles of their customers
   1. Trend-follower
   2. Pyramider
   3. Stop-loss operator

5. Dealing banks regularly talk with each other to keep track of who is long and who is short (and in this age I bet it’s practically all done with a few apps or neural nets)

Next: Fixed income herbivores

Fixed-Income Herbivores

1. Dealers make comparable bid/ask and volume to FX
2. Government dealers don’t need a ‘security deposit’
3. Government securities can be delivered against each other at various clearing banks or brokers, so you’re not locked into a particular position with a bank
4. As a result they lack the edge of FX banks, and refuse to quote a two-way market
   Attached Image (click to enlarge)

   
   
5. To maintain balance customers would get several dealers on the phone simultaneously and get quotes ‘before they could react’. But the dealers quickly adapted.

Carnivores - Hedge Funds

1. Mainly raised from wealthy individuals
2. At the top of the heap is/was Quantum returning 35% for 35 years

Sometimes VN is annoyingly colloquial instead of explicit. For example this sounds interesting but I’m not really sure what’s going on here:

1. Advantages they might have
   1. Big holdings
   2. Former high-ranking government officials as advisors
   3. A spouse who works at a central bank or a good friend at the UN
   4. A regular tennis game with a central banker or a country president

2. During a rising market they borrow short and invest long in equities and debt
3. Dealers have uncertainty about the variation in value of the inventories they hold
   1. Uncertainty about optimum quantities to hold and prices to pay and charge
   2. To maintain credit access, it is helpful for dealers to lay off risk by trading with speculators
   3. Large hedge funds are willing to accept these risks in exchange for a profit
   4. Dealers maintain inventories, speculators buy goods from dealers; dealers charge a price
   5. Dealers conduct their business while remaining insulated from fluctuations in the value of their inventories
   6. Dealers can then specialize in the secure capturing of spreads instead of speculating on value
   7. Banks and suppliers are more willing to supply credit to dealers who insulate themselves from price fluctuation
   8. Dealers can engage in higher volume than if they didn’t lay off the inventory risk
   9. Dealers total reward and reward per unit of risk is enhanced by large hedge fund activities

Decomposers

1. Brokers
2. Break orders into digestible sizes
3. Commision house brokers solicit the order; floor traders digest it; bankruptcy specialists recycle the contract

1. VN reveals the cause of his 1987 misadventure
   1. After the crash he sold some bonds short against futures he owns
   2. Futures closed limit up several days in a row, preventing him from offsetting his cash position with the matching futures
   3. The bank mercilessly demands immediate covering despite all the tens of billions that were traded to their mutual profit over the years
   4. He pays off his debt in a couple of days but by cheque after which the bank blackballs him. Despite this, years later salespeople are still trying to get him to open accounts there. (institutional memories are as short as individuals)

Rules and Regulations

1. Low margin requirements (2-10%) means large amounts of money can be made swiftly in futures
2. In contrast minimum margin in stock markets is 50%
3. The margins need to be low enough
   1. to encourage the public to turn over its trades fast enough to provide the vig that fuels the machinery;
   2. To allow exchange members to feed on slippage
   3. To encourage new membership

4. Margins must be high enough:
   1. To prevent overtrading; Bankruptcies that would stop them from returning to the table
   2. To protect members against customer bad debts; chasing debts is not usually worth their time
   3. If margins were 1%; then a 1% move would double your money your wipe you out; too much risk for a one day bet

5. 50% margin makes most stock holders long-term; NYSE turnover is less than 100% a year; institutions have higher turnovers than the public; this protects them from margin liquidation, gambler’s ruin, excessive trading, bandwagon psychology, broker tricks

Maintaining Rent

1. VN confirms what I have long suspected; many of the traditional rules given to novice traders are actually beneficial to brokers by limiting their credit losses and encouraging maximal turnover
   1. Limit losses with stops
   2. Bulls and bears make money, pigs get slaughtered
   3. Cut losses short
   4. Don’t fight the trend
   5. Always trade at the market

2. Using stops has some commons sense but it allows the reducers on the floor to make money by moving prices to levels where stop orders will be triggered; “running the stops”
3. When you get stopped out you take your losses at an ‘acceptable’ ‘predetermined’ level so your brokers are protected against your credit risk
4. VN has never used stops even to bail himself out (though maybe he should have?)
5. Having a fixed rule to exit provides my adversaries too great an advantage
6. Instead trade with a low enough percentage of money at risk to avoid disaster

Public Bullishness

1. The public is psychologically predisposed to take the bullish side of a trade; especially new market entrants
   1. Optimistic bias
   2. Predisposition to ownership
   3. Going short is high friction, with borrowing costs, dividend payouts, difficulty of finding sufficient stock
   4. Seems arcane and sinister to the newbie; “selling something you don’t own”
   5. Broker limitations on shorting

2. VN tells the story of the Hunt brothers silver corner once again
   1. He lost millions and would have been entitled to some compensation from the lawsuits that followed but he didn’t keep his records (7 years after)
   2. His broker had gone out of business and so they didn’t have records either
   3. Keep your records for a long, long time, advises VN, especially if you have a big loss as you never know when the dust will settle

3. In 1993 there was a similar story with the copper market
   1. A rumor arises that a Japanese trading firm was cornering copper
   2. The LME puts a restriction on the difference in price between the near month and the distant month contract
   3. David King, chief of LME: It’s not about protecting anybody, just ensuring that prices are what people expect them to be.
   4. This works until May 1996 when several ‘large carnivores’ short copper at $1.30 a pound
   5. Sumitomo corporation is shown to have lost $2.6B from a rogue trader named Hamanaka, and copper falls 40% next month. Before that Sumitomo had said Mr. Copper (Hamanaka) was “the most honest person I have ever met.”
   6. Reforms designed to prevent this type of thing from happening again may cripple trading, VN fears

Competitive Exclusion

1. No 2 species with similar styles can occupy the same niches, nor can two species that are completely competitive co-exist.
   1. Fruit flies and paramecia
   2. Chicago and NY gold
   3. CBOT stock futures and IMM stock futures

2. Coexistence is possible by separating into niches
   1. Giraffes (eat leaves in tall trees) and Wildebeests (eat grass) in the Serengeti
   2. CBOT Treasury bonds (3 month rate) and Eurodollar futures on IMM (30 year rate) traded just 7 blocks apart

3. A constant stream of new money going into the system is needed to keep it going
   1. Net money coming in must at least equal net money going out
   2. The public is the basic producer that must provide this new energy
   3. There must be a constant stream of new members coming back to take up the slack caused by those who have fallen
   4. Net contributions must provide for the costs of running the system and the profits that the higher levels take out
   5. The consumers (banks, hedge funds, brokers) must not get greedy because if they take out more of the public’s resources than can be refreshed from new entrants, the system will diminish its own food supply and hinder itself through excessive competition, which can lead to extinction
   6. There is therefore ‘close commonality of interest’ between predators and prey

4. Homeostatic Functions
   1. Homeostasis == negative feedback
   2. Keeps organisms in balance, internally and externally
   3. CFTC and NFA == white blood cells
   4. Price moves in markets take on the role of temperature stabilizer
   5. Nobody knows what the equilibrium level is until the morning after
   6. Interacting markets, activities of contrarians, value investors are the main forces for market homeostasis
   7. VN condescends to dismiss Soros idea of a supranational body to govern markets

-Charles Darwin

Next: the somewhat scattered final chapter.

Finale
VN chooses to finish his book with a fairly eclectic and seemingly unrelated series of topics.

Lessons from Fishing

1. Take account of ecology. Where does the other side live and what do they eat? Consider their rhythms; synch your activities with the speed of the currents.
2. Everything is hooked together; “A man may fish with the worm that hath eat of a king and eat of the fish that hate fed of that worm.” - Hamlet by Shakespeare
3. To know how good you are, enter a contest; but keep in mind that completely different techniques are appropriate here from those that win in the normal fray.
4. Be a contrarian, above all; once you hit a winner, you’re unlikely to hit again in the same place.
5. The weather affects everything; easiest pickings at full moons
6. The weakest prey are the easiest to catch; when the battlefield is red, prepare to reel in the biggest winners.
7. Stay calm. Keep emotions in check. The time for exhilaration is after you’ve bagged a winner. Reflect on what you did right or wrong; Adapt a scientific approach, keep records; Change something if you’re doing badly; Learn from the legends
8. Cycles are always changing

Steve Stigler

1. You get different results when the trend followers have made money for a few months in a row
2. Attached Image (click to enlarge)

   
   
3. The chart shows the results of different groups of large-mouth bass, one group that is susceptible to being caught with artificial lures, and the other not
4. When the two groups spawned, the ‘wary’ offspring were caught half as often as the ‘vulnerable’ fish
5. Do you get what he’s saying here? I’m not sure I get it.
6. VN says Martin, his grandfather, and Artie, his dad, were bad bargainers; so they’re in the ‘vulnerable’ fish group?
7. Artie was uncomfortable with haggling and VN feels that has also failed to ask for his fair share in business
8. However now he bargains for everything and it carries over into his personal life
   1. If the speculator cannot bargain for the best deal they must ask a trusted friend to ‘carry the ball’
   2. When asking for a market quote he contacts several dealers to see which way they are leaning
   3. At a certain point prices converge

Dealing with Dealers

1. Dealers like to trade against VN once they know which way he’s leaning
2. If he’s planning to sell now, he leaves a limit order to buy on the books
3. If current bond price is 101.08 and he intends to sell at 101.31, he will leave an order to buy at 101.01
4. He then calls several dealers to get a market; asking each of them to tighten the quotes, never accepting too wide a spread
5. Then he chooses the best one, a process which takes 2 seconds
6. The extra 1/100 of 1% he saves in face value good for at least 50% of his total profits over the course of an average year

Modern Panic

1. VN tells the story of selling in the July 16, 1996 flash crash
2. VN disparages (rightly) Japanese candlestick patterns
3. VN disparages advanced mathematical treatises, catastrophe theory, genetic algorithms, chaos theory, Fourier analysis, nonlinear differential equations, and neural networks (all this in 1997. What is the big new tech that hedge funds have been exploring in the past 25 years??)
   1. The techniques were great retrospectively but invariably had nothing to say on a predictive basis

4. VN brings up LoBagola again.
   1. As soon as he gets out at the top of a price move it goes right back where it came. The story of LoBagola is an epic and unlikely tale, but the gist is that elephants always go right back the way they came just like that price move.
   2. One problem is that LoBagola is a complete fraud. Can we trust his statements about elephants? Did VN not have access to the internet before he wrote this book? A little research does suggest they do follow the same migratory routes.
   3. VN calls his analysis for dealing with sudden catastrophic prices moves LoBagola analysis.
   4. VN reasons that if he could just catch one of these giant moves with a reasonably sized position it would achieve his lifetime’s ambition for wealth
   5. He gets clues by listening to the floor
      1. Monkeys chattering: refrain from trading for a while
      2. If the floor sounds like a 7th inning stretch at a baseball game, a trade is warranted
      3. When the elephants are trampling: sits on his hands
      4. Wait for the funnel, the eye of the storm to pass
      5. After elephants destroy an area, other species descend to pick up the debris
      6. He plays the fixed-income and commodity markets of a currency that just ‘LoBagolaed’

5. When interviewed in 1994 after his annus mirabilis, he attributes his success to LoBagola analysis
6. But there is a problem
   1. Each of these LoBagola moves that retrace have numerous countermoves that start to retreat, but then turn around in the direction of the original roll
   2. Fakeouts
   3. Some moves are so abrupt they are over before you can follow them
   4. There appears to be no systematic way to profit from them
   5. The one thing we know about elephants on a roll is that they will return by the same path at some time
   6. The patient native sets his traps where he knows they will retrace their steps

Valuing the Goods

1. In the most roundabout way possible, VN tells us that he wishes his approach to buying stocks was based more on fundamentals;
2. He has a tendency to ‘buy copper in London when Hong Kong stocks go down 3 days in a row” or sell wheat in Minneapolis when the price of rice is down in Tokyo.
3. He finds that buying stocks based on the ValueLine price/book ratio gives mixed results and prefers his ‘futures trading based on the numbers’
4. Value stocks look good on paper but if the distressed merchandise doesn’t sell, it’s good to fall back on a reliable money-making program

Chinese handball

1. Requires a bounce on the ground before hitting the wall
2. Using a limit order slows down the game in the market too
3. A market order in a fast-moving instrument ‘frequently’ leads to a fast rebound against
4. A limit order doesn’t win as fast but slows down losses
5. VN doesn’t trade NASDAQ stocks because of the wide spreads $0.40 on a $20 security.
6. Giving up 2% to get in/out of a trade before commissions is ‘loathsome’
7. Using a limit order to capture the spread in his favour doesn’t work “because limit orders for the public are not given priority, even if at more favorable prices than dealers’ bids and asks.”
8. It turns out the reason for these wide spreads is fear of bullying and humiliation by fellow dealers
   1. Attached Image (click to enlarge)

      
      

9. On his dad’s deathbed he reassures him that he has everything (that is to say, unknown unknowns) under control in his business. Heartbreaking on the page, but even more so knowing what we know in the future!
10. VN disparages commodity trading seminars; nobody with a system worth selling would sell it; and even if it did work, the knowledge that makes it work would be stale by the time you got wind of it

Trading Susan

1. If VN gets out of synch with the market his trading rhythm goes awry
2. So he must keep in touch with prices 24h/ day
3. He even brings in a portable screen while his wife is in labour, trying to trade out of positions on a cell phone
4. “Successful speculation is nothing more than a scientific, systematic, and economical way of hastening the inevitable. There is no secret to it.”
   1. Any techniques he could share would be outdated by the time we read them

5. Basing trading strategies on blind faith in a guru is high on the list of mistakes
6. However VN relies heavily on an in-house guru, Steve “Bo” Keeley, who is also a hobo
   1. Hobos ride the railways illegally moving from job to job
   2. Tramps don’t work but also ride the rails illegally

7. VN and Bo use railroad car loading statistics, an indicator that has fallen into disuse
   1. The hobo:tramp ratio is a good measure of the employment situation
   2. Bo sends VN a bulletin in Feb. 1996 that freight cars were going through key locations at double the usual rate; VN decides to remain short the bond
   3. Shortly after, bonds drop 3 points

8. Bo is also a veterinarian
   1. When dog owners anticipate good times they buy richer food for their dogs
   2. This causes cavities and gum problems
   3. Again based on Bo’s intelligence that vet business is brisk he shorts bonds

9. Soup kitchen lines length; long lines == bad times
10. Amount of food in dumpsters
11. A shift from coal to oil cars says that energy is on a roll
12. Auto carrier trains; sticker prices show the direction of inflation
13. The size of cigarette butts on the ground; the longer they are the better things must be as fewer people are picking up discarded butts to smoke the remaining tobacco

Code of Brighton

1. VN returns once again to the topic of racquet sports, this time to argue for the libertarian ideal of lower or no taxes
2. Politicians are professional gangsters; their goal is to pluck as many chickens without too much squawking
3. With this philosophy, during the Peso Crisis VN anticipates that US politicians will not be interested in further devaluation of the peso and he buys Mexican T-bills to great effect

The book concludes with a masterful paraphrasing of the chaos experienced in a long two minutes of a ‘typical’ market panic at his home office. I can’t do it justice; you’ll just have to read it.

Review:
Despite its problems, this is a wondrous book. Written nearly 7 years before ‘Fooled by Randomness’ but with at least as much wit and clever insight. This is a damn fine read if not a masterpiece of speculation education. The only reason you might not think so is if, like me, you’ve already read ‘Fooled by Randomness’ and a bunch of similar trading books which have given us much of the same background, but assuredly not in as charming a package.

This is good news for us, because it means we are making progress in our journey and can start to dismiss books that go over the basics. We are looking for specialist knowledge now. More technical knowledge.

One criticism I have is that the book was written with goals other than to educate. For Niederhoffer it’s clear his main goals were

1. To write an entertaining story (fine by me)
2. To show off his knowledge not just of markets but of everything
3. To honour his family

It’s mostly in b) and to some extent c) where the reader might wish he had cleaved closer to brevity.

Actually there is one other reason you might doubt the value of this book. Niederhoffer blew up not just one hedge fund, but two. His own fund in the ‘97 mini-crash, by investing in risky Thai stocks and somehow, (actually Taleb told us but I forgot) Matador partners, in September 2007 (before the frenzy of the economic crisis?) with a loss of 75% of its value. This is what he’s remembered for, unfortunately, more than his brilliance.

https://www.newyorker.com/magazine/2...blow-up-artist
https://en.wikipedia.org/wiki/Victor_Niederhoffer

So what do we make of this? Does it mean VN doesn’t know what he’s talking about? I don’t think so. I think there is a big difference between knowing a thing and doing it. VN knew he shouldn’t gamble. He also knew he couldn’t become a legend without plunging. The era of plungers may sadly be behind us. What made Soros great? Maybe his back, but also he was early; he was a pioneer before the computer age. VN possibly knew this but made the attempt anyway, to his peril.

I also think that some personalities simply have to live close to the edge, touch the burning stove, see how underwater they can get without drowning, and eventually they find out.

The other lesson, maybe even more important than anything we get from the book, is that spurious correlations should not be trusted. VN might have had too much faith that what he saw in the past was a generalization of what would happen in the future. Recall Taleb: empiricism is not the same as deduction.

Finally, recall the lesson of Solon: Count no man lucky, until he is dead. VN isn’t; so there’s still time for a comeback.

Footnotes

Next: Cycle Analytics for Traders by John Ehlers

Thanks for doing VN. His was one of the first "serious" books I read when it first came out.

Hey, we're at 50 pages and just a few posts shy of 1,000! Should we celebrate? Should I have a contest? I don't know how to do stuff like this. I'm not very good at promotions or managing a community.

Well, let's wish everyone a Merry Christmas, Happy New Year and see you probably during the after-Christmas lull.


ps.
Without a shadow of a doubt, Santa Claus is a master trader. It's probably how he funds his whole operation.

hi, i struggle to understand opening hours in forex, like here https://www.markethours.net/forex-market-hours , since it is open all the time. From what you have read, what are the opening hours of the forex market?

As a side note, if you continue to review books, can you add at the top of each post, the author + title of the book, otherwise we don't know what post refers to what book

Opening hours
You would think it should be a simple topic, but it's not. One problem is that there is no central authority to decide when the market is to open. It's all up to your broker. The major brokers have a loose convention but there are many exceptions. Also for traders there are always at least 3 times to consider.

1. Local time (depends on where you live)
2. Server time (depends on what your broker decides, and is not usually the local time of your trading server)
3. Some standard reference time. For example I like to keep track of GMT (UTC) time and EST (Eastern Standard Time) because then I know what time the New York Session closes and often it is considered the last session of the day and of the week.

When you add in daylight savings time it gets even worse. And then all brokers like to prohibit trading in some markets at certain 'closing' times even though there is no official consensus, but metals usually close at different times than currencies, and during holidays some pairs are not available. Also much of what you read on the internet about Forex times is outdated or just plain false. Be careful.

Here is my advice.

1. First, obviously, know your local time and time zone.
2. Know the offset of your local time zone from GMT, for example GMT+2 is the 'Bravo' time zone or when it's noon in London then it's 14:00 your time.
3. Get used to the 24h clock if you don't already use it. It's much easier to calculate time offsets than trying to convert to a 12 hour clock.
4. Know your trading server time. If you use MT4 it's in the MarketWatch
   Attached Image (click to enlarge)

   
   
   This tells you how much time is left in the trading 'day'. Most brokers like to set their time to GMT+2 because then the Friday New York close ends at close to 23:59 at the end of the week. However I also have a broker that opens five minutes later than everyone else. Why? I would guess they are trying to counter some hourly/daily/trading strategies?
5. Use the 'sessions' widget right here on Forex Factory.
   
   Attached Image (click to enlarge)

   
   
   It tells you exactly where you are in relation to the Forex sessions, as long as you set it with your local time zone. You do that here:
   Attached Image (click to enlarge)

   
   
   
   See the little clock beside my username: I set it to GMT for this example to hopefully make things simpler.
6. There are no official sessions or timings for Forex. Some instruments are 24/5 and some are 24/7 (mostly crypto). Every broker makes up their own rules based on what they think is best but there is no definite standard. Check out everything yourself.
7. As for the best 'time' to trade, as that link you posted says, there is no best time, it depends on how you trade, and what you trade, but trading during holiday periods can be very tricky. I suggest not bothering until you think you know better.

"can you add at the top of each post, the author + title of the book, otherwise we don't know what post refers to what book"
I used to do that but then I thought it creates too much repetition and if you aren't sure you can always just scroll a page or two back to find the start of the book. If you don't find it there is also a table of contents on the main page where you can click the link to the first page for any book. Or you can just ask; I don't mind answering a simple question with a simple answer. It is a refreshing change.

Cycle Analytics for Traders by John Ehlers

1. John Ehlers is an electrical engineer. He has a doctorate degree. He is smarter than me. He is probably too smart to be a good trader.
2. He is a pioneer in digital signals processing, although he isn’t the first person to compare markets to digital signals or harmonics with a noise vs. signal ratio.
3. He developed MESA (maximum entropy spectrum analysis) and various other authors have applied it to other cycle market systems, notably JM Hurst’s cyclical methodology.
4. I had planned to write a more extensive warning/caution about the material in this book but I realized that would introduce an unfair bias. I will just warn you that this book is highly technical; probably the most technical book we've examined yet.

Preface

1. Any technique must first work on theoretical waveforms before testing against real-world data is attempted.
2. Futures trading has lost popularity because of scandals.
3. Buy-and-hold is no longer a valid investment strategy.
4. Major brokerage houses have responded by including technical analysis tools in their trading platforms.
5. This is a technical resource book written for self-directed traders who want to understand the scientific underpinnings of the filters and indicators they use in their trading decisions rather than to use the trading tools on blind faith.
6. Cycles are unique because they are one of the few characteristics of market data that can be scientifically measured.
7. Code is in EasyLanguage, a dialect of Pascal.
8. Cycle Analytics for Traders will allow traders to think of their indicators and trading strategies in the frequency domain as well as their motions in the time domain.
9. This allows for selecting efficient filter lengths
10. What you’ll ‘learn’:
    1. Spectral Dilation, and how to eliminate it or to use it to your advantage.
    2. Automatic gain control (AGC) to normalize indicator amplitude swings.
    3. Thinking of prices in the frequency domain
    4. All indicators are statistical rather than absolute
    5. ‘Cookbook’ filters
    6. Methods of estimating market spectra
    7. Sifting out the dominant cycle, with the autocorrelation periodogram preferred
    8. How to use transforms to improve the display and interpretation of indicators.
    9. Ehlers, mercifully promises to be brief in his style

Chapter 1 - Unified Filter Theory

1. Linear systems are the heart of simplicity
2. One input and one output
3. The system in between can be as complex as desired
4. Output divided by input = transfer response = action of the system
5. Responses in time domain and frequency domain are related
6. No filter is predictive because they are computed on the basis of historical data samples (and so what good are they? What is predictive?)

Transfer Response

1. A four bar moving average has four samples of price as input data
2. Filter output is ¼ of the most recent price data + ¼ of the data sample delayed by one bar + ¼ of the data delayed by 2 bars + ¼ of the data delayed by 3 bars
3. The ¼ values are the ‘coefficients of the filter’; they generally sum to 1
4. So the ratio of input to output = 1 if the input is a constant
5. Ehlers now goes off the rails and rewrites the equation for the general moving average as an algebraic polynomial and then as the ratio of two polynomials (mathy! But why?)
6. Supposedly the resultant equation completely describes the transfer response of any filter.
7. The only thing that differentiates filters is the selection of the coefficients of the polynomials
8. The fancier a filter gets the more data it needs; this is bad because it causes them to lag and it is implicitly assumed that this is bad for trading.
9. Attached Image (click to enlarge)

   
   
   Ok here’s an equation because it’s the simplest one you’ll find in the book. This is a simplified version of the equation that describes all filters.
   
10. Attached Image (click to enlarge)

    
    
    And if we cross-multiply that one we get this one, which is useful for programming
11. This has two parts. The first is the numerator; composed of the input
12. If that is the only term used in the filter then that’s a nonrecursive filter
13. Filters that use previously computed values of the output are recursive
14. It is difficult to create recursive filters for some programming languages, claims Ehlers

Nonrecursive Filters

1. Moving average filters
2. Finite impulse response (FIR) filters
3. Tapped delay line filters
4. Transversal filters
5. Traders care about filter lag
6. A nonrecursive filter whose coefficients are symmetrical about the center of the filter always has a lag equal to the degree of the filter divided by two.
7. Lag is directly related to filter degree
8. Filters therefore usually have low degree
9. Ehlers mentions the fundamental theorem of algebra
10. Again he goes off the rails, talking about roots of polynomials, complementary and imaginary roots. COME ON
11. Half the sampling frequency is the highest frequency that is allowable in sampled data systems without aliasing, and is called the Nyquist frequency.
    1. so the highest possible frequency we can filter is 0.5 cycles per bar, or a period of two bars. (which I think is intuitive?)

    
    

12. Then it just gets crazier
13. The important conclusion from this discussion is that we can think of the transfer response with equal validity in the time domain or in the frequency domain. (gotta take your word for that)
14. “The concept of thinking of how a filter works in the frequency domain as well as how it works in the time domain is central to the understanding of the indicators that will be developed. Low frequencies near zero are passed from input to output with little or no attenuation. Since higher frequencies are blocked from being passed to the output, the SMA is a type of low-pass filter—passing low frequencies and blocking higher frequencies.” (fair enough)
15. When higher frequencies are passed, and the lower frequencies are severely attenuated by the filter, that's a high pass filter.
16. With arcane complexities and calculus, the waveform of a filter can offer a predictive output into the future; it can also normalize output amplitude to the amplitude of the input data (ok?)

Recursive Filters

1. Exponential moving average filters
2. Infinite impulse response (IIR) filters
3. Ladder filters
4. Autoregressive filters
5. At one point Ehlers tries to simplify things by making an analogy to a tent with poles, but it’s hilariously outmatched by the depth of the material. This is no tent and these are not poles!

Generalized Filters

1. A generalized filter uses both the numerator and denominator to achieve a wider range of responses other than low-pass filtering and high-pass filters.
2. Autoregressive moving average (ARMA) filters
3. Autoregressive integrated moving average (ARIMA) filters

Programming the Filters

1. For lag reasons, we’ll consider only filters of degree two; this simplifies our general equation
   
   Attached Image (click to enlarge)

   
   
2. Good luck. Ehlers then discusses mixing and matching different filters to achieve other types of filters.
3. At one point Ehlers laments that “Market cycles tend to be ephemeral (oh j’accuse) and have very fuzzy bandwidths, making them difficult to reject by filtering. Even more sophisticated band-stop filters with relatively wide stop bands do not seem to be effective in removing cyclic components from the data. This is indeed unfortunate because removing cyclic components to isolate the trends is a philosophically interesting digital signal processing approach.”
4. You want to filter the trend out from cycles? Just stand back from the chart and squint, Poindexter!

Wave Amplitude, Power, and Decibels (dB)

1. “The difference between power and wave amplitude is analogous to describing a lightbulb as using 60 watts rather than using 115 volts. Power is proportional to the square of the wave amplitude, and to better examine a wide range of power levels, relative power is usually measured in a logarithmic ratio called decibels.”

Key points

1. Common filters useful for traders have a transfer response that can be written as a ratio of two polynomials.
2. Lag is very important to traders. More complex filters can be created using more input data, but more input data increases lag. Sophisticated filters are not very useful for trading because they incur too much lag.
3. Filter transfer response can be viewed in the time domain and the frequency domain with equal validity.
4. Nonrecursive filters can have zeros in the transfer response, enabling the complete cancellation of some selected frequency components.
5. Nonrecursive filters having coefficients symmetrical about the center of the filter will have a delay of half the degree of the transfer response polynomial at all frequencies.
6. Low-pass filters are smoothers because they attenuate the high-frequency components of the input data.
7. High-pass filters are detrenders because they attenuate the low-frequency components of trends.
8. Band-pass filters are both detrenders and smoothers because they attenuate all but the desired frequency components.
9. Filters provide an output only through their transfer response. The transfer response is strictly a mathematical function.
   Do we need to know any of this? Is Ehlers simply showing off, and if so, what is he showing off besides his own knowledge of digital signal processing? Anything?

Hahaha, my readership of about 4 will decline to zero before this is done. I must carry on for the sake of the Quest! I won’t let you down Mr. Frodo!!