ShaveA good thing to do at the beginning of the day or the beginning of a week is to shave and study what happened to clear the mind — indeed to write it down.

Five up days in a row start the retrospective but these five just move us a tiny bit above where it was before the five started on the Thursday at 1062. And we are half way between the x day high and x day low at 1090 and 1033. Note that the fixed incomes are at lows, even below a round number or two and that C is above V and BAC above 15 by a hair.

The Nas is within a percent of a two-year high. VIX has declined by some 25% in the last week. Crude is within a day or two's move of a one-year high as is the value of a euro and a yen. Gold is at an all time high and stands at 60 times the value of silver. The grains are in — dare I say it – a trading range of 15% from low to high over the last six months above 5% from the bottoms.

If only someone would provide a summary of the markets like we get for baseball, it would be so much easier to describe, and then to shave. Indeed, I might be tempted to provide such averages and standings and results oneself for those lonely readers…





Speak your mind

6 Comments so far

  1. Steve Leslie on November 9, 2009 7:48 am

    Recently some have commented how this site now caters to political commentary instead of financial and market discussions. In this world unfortunately both are inextricably tied together. this weekend House passes "historic health bill" however it will die in the Senate. Certain parts are not palatable to the Senate. Lieberman denounces "public option" Last week major Gov races went Elephant. Confidence for Congress is at all-time lows. My point is that despite the well-documented love affair the mainstream media (NBC,CBS,ABC,CNN,NYT) has for Prez the public is slowly getting things clear that old style Chicago politics will not work on a national level. Tea party moviements and town hall meetings are working. The peoples voice is being heard if Congressmen and women dont get it right 2010 will be treacherous waters for Donkeys Results are being seen in the markets. Now question remain what will holiday season which starts in two weeks look like. What effect will 10 percent unemployment have on these numbers. New jobless claims are down to 514,000. What does this number signify.

  2. Craig Bowles on November 9, 2009 10:28 am

    I got blasted last time I mentioned it but everything is pretty normal if you look at it priced in gold. Wheat is near the previous lows of the 1980s, oil would range $75-150 with $1000 gold if similar to the last decade. Stocks are weakening relative to gold similar to what followed last summer. If we were still on a gold standard, it’d be a pretty dull looking world. My wife remembers 25c a slice pizza in Brooklyn before the 1970s. That would mean my daughter would pay $30/slice when she’s our age and probably $50 with the additional stimulus programs being introduced. Bob Prechter showed $1.00 from 1910 is now worth 4c. Using the 1980 CPI calculation, it’s probably closer to 1c. Folks from the depression say about everything, “it only cost 5c by who had 5c.” Wonder what we’ll say? Mises said you can either accept or postpone a collapse of a boom brought about by credit expansion. He said accepting means abandoning further credit expansion, while postponing brings on a final and total catastrophe of the currency system involved. The crazy thing is that everyone thinks it’s political to voice concern but both parties caused it. The 1960s programs couldn’t be continued without Nixon taking us off the gold standard. Reagan spending went overlooked. Every President seems to be exponentially worse than the previous. In real terms, the stock market may be turning down again. So, do we use the stock market as an economic indicator or stocks/gold? They give us opposite directions for an economic outlook.

  3. Diego Joachin on November 9, 2009 11:09 am

    I will implement a closing system for Fridays and I'll post it at my blog. I'll name it "Mkt Standings". Thanks for the idea.

  4. Rod Clifton on November 9, 2009 4:56 pm

    But what if one has a beard?

  5. Anonymous on November 9, 2009 7:51 pm

    Mr. Niederhoffer said:
    “Indeed, one might be tempted to provide such averages and standings and results oneself for those lonely readers..”

    To which this lonely reader replies:

    Please do.

  6. douglas roberts dimick on November 10, 2009 5:59 am

    What, When, and How to Report Statistical Summary?

    What are the optimal rules governing quantification of input for functions and indicators when producing that statistical summary?

    It seems that a graduated scaling based on a timing correlation (markets open/close) inclusive of valuation, liquidity, and frequency of exchanges would provide calculations for answering “pre-formulated questions” (see Context hereinbelow) – or selected strategies.

    If so, then a primary concern may be how to calculate key market and issuance data (see James reference Sabermetrics hereinbelow) so as to distinguish between historical context and predictive applications?

    I like the batter/pitcher format (see hereinbelow) for constructing a long/short organization of issuances per exchange. Perhaps one could embed this data into an assimilation of markets by sequencing according to time or size correlations?

    Someone does not already offer this type of report on the markets?

    As to baseball, long ago being at the mound and first base, I wandered about for a survey…

    Template ( )

    Baseball statistics

    Batting average • On-base percentage • Slugging percentage • On-base plus slugging • Hits • Doubles • Triples • Home runs • Grand slam • RBI • Game-winning RBI • Bunt • Sacrifice bunt • Sacrifice fly

    Run • Stolen base • Caught stealing

    Win–loss record • Pitchers of record • Save • Hold • Earned run • ERA • Complete game • Shutout • No-hitter • Perfect game • Wild pitch • Passed ball • Strikeout • WHIP

    Assist • Putout • Error • Catcher’s ERA

    Team stats
    Sports league ranking

    Baseball Stats ( )
    At the middle-school level, students can build on previous experience to delve into statistics in greater detail. Students should be focused on the entire process, including formulating key questions; collecting and organizing data; representing the data using graphs and summary statistics; analyzing the data; making conjectures; and communicating statistical information in a meaningful and convincing way.

    In this lesson, students will use baseball data available on the Internet to develop an understanding of the different ways in which data can be analyzed. First, they will practice selecting data to perform calculations in response to pre-formulated questions. Then they will use the data to formulate and answer their own questions.

    New baseball statistics seem to be invented every year, measuring another variable in the game and attempting to measure value in a different (sometimes better) way that takes the human element out of the argument.

    Many of these stats can be attributed to “sabermetrics,” which were born in the 1980s, grew in the 1990s, and really gained traction in the 2000s as many of baseball’s front-office decision makers became disciples of some of these statistics as an alternative objective way to evaluate players.

    Sabermetrics is derived from the acronym SABR, which stands for the Society for American Baseball Research.

    Sabermetrics was coined by renowned baseball author and researcher Bill James. James and others created new statistics with which to measure players’ productivity other than the traditional batting averages and ERA. It’s often used to measure future productivity.

    ( )

    Major league baseball stats are what led to baseball being considered a “thinking man’s sport”. In this day and age, nearly every sport relies heavily on numbers to determine strategy, but the math produced by baseball players is perhaps the most accessible and recognizable to its fans. So to make sure you’re not left out of the loop at the next cook out, let’s learn to calculate statistics.

    This refers to a player’s batting average—meaning, the percentage of the time he gets a hit. This number is calculated by dividing a player’s total number of hits by at-bats. An at-bat refers to a plate appearance that does not result in a base on balls (a walk) or a hit-by-pitch. Generally speaking, .300 and above is considered to be the standard for “very good,” making baseball one of the few professions in which an individual can fail 70% of the time and be considered one of the best in the business. Conversely, .200 and below is considered to be downright terrible for position players—with this limbo-like space of terribleness being referred to as the “Mendoza Line” (so named after former short stop Mario Mendoza, a full-time short stop who hit .198 in 1979).
    Human resources. Psyche! Of course, HR in baseball is an abbreviation for “home run.” This takes into account the total number of “taters” hit by a player, as well as any and all inside-the-park home runs he may achieve.
    RBI stands for “Runs Batted In,” and may often be referred to as “ribbies” in baseball slang. As the name would suggest, every time a player scores on a batter’s hit, the batter is credited with an RBI. Accordingly, players who achieve a high number of home runs and/or RBIs are generally thought of as “power hitters”—meaning their at-bats generally lead to exceptional run production.
    Now we’re getting fancy. The slugging percentage is a rough measurement of a hitter’s power. The figure represents the total number of bases achieved divided by a player’s at-bats. A player who hits a lot of doubles or home runs will have a slugging percentage much higher than his batting average. A slap hitter who racks up singles all year long will have a slugging percentage that is very similar to his batting average. Barry Bonds broke Babe Ruth’s record for slugging percentage in 2001, turning in an incredible (and possibly artificially enhanced) .863 SLG.

    An ERA is a pitcher’s Earned Run Average—which refers to the number of earned (error-free) runs allowed per nine innings. As no pitcher routinely pitches all nine innings in modern baseball, the statistic is generally hypothetical, but comparatively useful in terms of how reliable he is at keeping the offense from scoring. Generally speaking, pitchers with an ERA lower than 2.00 are considered to be the best in the game, while ERAs over 5.00 indicate a weaker pitcher.
    No, we’re not talking presidents, we’re talking “wins.” In order to get a W, a pitcher must pitch a minimum of five complete innings, and leave with his team in the lead. A good starting pitcher should have well over 10 wins in a season, with elite starters earning around 20.
    No, we’re not talking “losers”—well, actually, we are. As it does so often in life, “L” stands for “loss” in baseball. Pitchers are “on the hook” for a loss when they allow the opposing team to take the lead while they are pitching. In other words, pitchers who generally pitch poorly earn the most losses. Makes sense, eh?
    This is an abbreviation for a “save.” This is a distinction reserved for the final relief pitcher of a winning team. To earn a save, a relief pitcher must enter the game when his team is already winning and record at least one out. Furthermore, he must meet one of the following conditions: he enters with a maximum lead of three runs and pitches at least one full inning; he enters the game, with the potential tying run either on base, at bat or on deck; or he pitches for at least three innings in relief. In other words, a pitcher can come in with a 10 run lead in the 7th, pitch three innings of relief and earn a save.

    A Baseball Statistics Course ( )
    An introductory statistics course is described that is entirely taught from a baseball perspective. Topics in data analysis, including methods for one batch, comparison of batches, and relationships, are communicated using current and historical baseball data sets. Probability is introduced by describing and playing tabletop baseball games. Inference is taught by first making the distinction between a player’s “ability” and his “performance”, and then describing how one can learn about a player’s ability based on his season performance. Baseball issues such as the proper interpretation of situational and “streaky” data are used to illustrate statistical inference.

    Report for Input Data ( )

    Number of Games Reported
    Total runs scored of winning teams
    Total runs scored of losing teams
    Total Time of games (do not average) Hours Minutes
    Total number of Extra Inning games

    Total number of player at-bats
    Total number of Hits
    Total number of BATTERS HIT BY PITCH
    Total number of BASES ON BALLS
    Total number of STOLEN BASES
    Total number of RUNNERS LEFT ON BASE
    Total number of DEFENSIVE ERRORS
    Total number of DEFENSIVE APPEALS
    Total number of STRIKE OUTS
    Total number of ILLEGAL PITCHES
    Total number of PARTICIPANTS EJECTED

    Diego, I am curious how you will do this… good luck.



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