Posts Tagged ‘Statistical Data’

Fed Policy: Bernanke Is Warming Up His Helicopter

Wednesday, March 28th, 2012

 

This article originally appeared in the Daily Capitalist.

Economists cling to statistical data like barnacles in order to have some kind of anchor to explain what is going on in the world. They will try to cram the square data peg into the round holes of economic ”laws” rather than abandon them when they are obviously wrong. Which is not a very satisfactory way to explain things. You might begin to think the data you measure is just coincidentally correlative for the period measured if it falls apart at some point. Instead of trying to stretch the data into what you think it should be, then maybe you might think that you’ve got it all wrong.

Chairman Ben Bernanke is not letting a data inconsistency get in the way of his prior conclusions about unemployment. In his speech today, he says that he’s not sure why we have such high rates of unemployment, some may be just cyclical, some may be structural, but whatever it is the Fed will be available to print money to “support demand and for the recovery.” Somehow QE1 and QE2 were not enough.

In his speech Bernanke tries to explain why Okun’s law (a correlation between GDP and employment/unemployment) is still valid yet doesn’t explain the current situation. Perhaps GDP “growth” Bernanke sees is really a figment of money steroids, something the Fed has unleashed, and that unemployment is still high because of long-term capital/savings destruction caused by QE and ZIRP.

If you look at the rate of employment to the working population, you might wonder why it crashed so disproportionately to past cycles:

The blue line is the ratio of employment to population; the red line shows population growth. The population is still growing but the ratio fell off a cliff. The ratio of employed to population is back to 1977 levels. This is not something Chairman Bernanke wishes to hear or believe. He would rather adhere to the outdated ideas of the mainstream rather than question the dogma.

This is apropos of a conference sponsored by the Fed last Friday on, among other topics, the efficacy of quantitative easing. The conclusion coming from a Fed conference should be pretty obvious: QE is successful. The paper presented by economist Mark Gertler of NYU, a close collaborator with the former Professor Bernanke, concludes that QE works and he has an econometric model to prove it. What he does is look at what he considers to be the proper data and concludes that there is cause and effect and then he builds a mathematical model around this and believes it works. If his interpretation of the data is incorrect then the model is incorrect.

Here is a portion, out of context, of Gertler’s model:

Etcetera. You would have to be an econometrician to understand this. But it is just a way to disguise false ideology by cloaking it in mathematical formula. See Hayek and Mises on this topic. Because the formula “works” doesn’t mean it is right. Believe what you want to believe.

I think this is mostly an ad hoc ergo propter hoc (A happened and then B happened, thus A caused B) kind of analysis and that his conclusions are based on incorrect theory and fail to explain anything. But it doesn’t matter if he’s right or wrong for our purposes because Bernanke and most of the people at the Fed believe it. We can thus be assured that the Fed will unleash another round of QE when the economy stagnates (as I have forecast that it will).

It matters if he’s right or wrong for our purposes as investors though, because QE distorts the entire economy, gives an illusion of growth, destroys capital, causes more unemployment, and leaves the economy structurally impaired for a considerable time. One of the nasty little side-effects of QE that is most recognizable to investors and consumers is price inflation. It is probably higher than it is reported but it would grow much higher with more money printing. It destroys your wealth. It is possible, as we found out in the 1970s that you can have economic stagnation and high inflation at the same time.

What we are seeing now in the data is an effect from QE1 and QE2 and Operation Twist. It cannot last and it won’t because you can’t create wealth out of thin air as they are attempting to do.

Bernanke’s speech is another example of Mr. Bernanke’s admission that he does not understand the fundamentals underlying our economic problems. Otherwise Fed policies he unleashed would have cured the problem long ago.

Just last Thursday in an econ class at George Washington University he said the Fed’s low-interest-rate policies in the early 2000s didn’t cause the housing boom and bust:

“There’s no consensus on this,” Mr. Bernanke told a class of college students at George Washington University. “But the evidence I’ve seen suggests that monetary policy did not play an important role in raising house prices during the upswing.”

The housing boom-bust must have been caused by “irrational exuberance” which was Alan Greenspan’s “animal spirits” Keynesian explanation for the Dotcom bubble. Greenspan has also denied that he caused the housing bubble.

What can one say about this? There is actually very good evidence that the Fed’s easy money policy was the fountainhead of the bubble. But readers of the Daily Capitalist are well aware of that.

These speeches are further confirmations of disastrous Fed monetary policies that won’t end until the Fed raises interest rates and stops printing money. I’m betting on stagnation, more QE, and higher price inflation.

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How to Make Money in Microseconds

Sunday, May 15th, 2011

How to Make Money in Microseconds

by Donald Mackenzie (excerpt)

Human beings can, and still do, send orders from their computers to the matching engines, but this accounts for less than half of all US share trading. The remainder is algorithmic: it results from share-trading computer programs. Some of these programs are used by big institutions such as mutual funds, pension funds and insurance companies, or by brokers acting on their behalf. The drawback of being big is that when you try to buy or sell a large block of shares, the order typically can’t be executed straightaway (if it’s a large order to buy, for example, it will usually exceed the number of sell orders in the matching engine that are close to the current market price), and if traders spot a large order that has been only partly executed they will change their own orders and their price quotes in order to exploit the knowledge. The result is what market participants call ‘slippage’: prices rise as you try to buy, and fall as you try to sell.

In an attempt to get around this problem, big institutions often use ‘execution algorithms’, which take large orders, break them up into smaller slices, and choose the size of those slices and the times at which they send them to the market in such a way as to minimise slippage. For example, ‘volume participation’ algorithms calculate the number of a company’s shares bought and sold in a given period – the previous minute, say – and then send in a slice of the institution’s overall order whose size is proportional to that number, the rationale being that there will be less slippage when markets are busy than when they are quiet. The most common execution algorithm, known as a volume-weighted average price or VWAP algorithm (it’s pronounced ‘veewap’), does its slicing in a slightly different way, using statistical data on the volumes of shares that have traded in the equivalent time periods on previous days. The clock-time periodicities found by Hasbrouck and Saar almost certainly result from the way VWAPs and other execution algorithms chop up time into intervals of fixed length.

The goal of execution algorithms is to avoid losing money while trading. The other major classes of algorithm are designed to make money by trading, and it is their operation that gives rise to the spasms found by Hasbrouck and Saar. ‘Electronic market-making’ algorithms replicate what human market makers have always tried to do – continuously post a price at which they will sell a corporation’s shares and a lower price at which they will buy them, in the hope of earning the ‘spread’ between the two prices – but they revise prices as market conditions change far faster than any human being can. Their doing so is almost certainly the main component of the flood of orders and cancellations that follows even minor changes in supply and demand.

‘Statistical arbitrage’ algorithms search for transient disturbances in price patterns from which to profit. For example, the price of a corporation’s shares often seems to fluctuate around a relatively slow-moving average. A big order to buy will cause a short-term increase in price, and a sell order will lead to a temporary fall. Some statistical arbitrage algorithms simply calculate a moving average price; they buy if prices are more than a certain amount below it and sell if they are above it, thus betting on prices reverting to the average. More complicated algorithms search for disturbances in price patterns involving more than one company’s shares. One example of such a pattern, explained to me by a former statistical arbitrageur, involved the shares of Southwest Airlines, Delta and ExxonMobil. A rise in the price of oil would benefit Exxon’s shares and hurt Delta’s, while having little effect on Southwest’s (because market participants knew that, unlike Delta, Southwest entered into hedging trades to offset its exposure to changes in the price of oil). In consequence, there was normally what was in effect a rough equation among relative changes in the three corporations’ stock prices: Delta + ExxonMobil = Southwest Airlines. If that equation temporarily broke down, statistical arbitrageurs would dive in and bet (usually successfully) on its reasserting itself.

No one in the markets contests the legitimacy of electronic market making or statistical arbitrage. Far more controversial are algorithms that effectively prey on other algorithms. Some algorithms, for example, can detect the electronic signature of a big VWAP, a process called ‘algo-sniffing’. This can earn its owner substantial sums: if the VWAP is programmed to buy a particular corporation’s shares, the algo-sniffing program will buy those shares faster than the VWAP, then sell them to it at a profit. Algo-sniffing often makes users of VWAPs and other execution algorithms furious: they condemn it as unfair, and there is a growing business in adding ‘anti-gaming’ features to execution algorithms to make it harder to detect and exploit them. However, a New York broker I spoke to last October defended algo-sniffing:

I don’t look at it as in any way evil … I don’t think the guy who’s trying to hide the supply-demand imbalance [by using an execution algorithm] is any better a human being than the person trying to discover the true supply-demand. I don’t know why … someone who runs an algo-sniffing strategy is bad … he’s trying to discover the guy who has a million shares [to sell] and the price then should readjust to the fact that there’s a million shares to buy.

Continue Reading

 

Donald MacKenzie is a professor of sociology at Edinburgh University. His books include An Engine, Not a Camera: How Financial Models Shape Markets and Material Markets: How Economic Agents Are Constructed.

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What is Seasonal Investing? (Brooke Thackray)

Thursday, February 3rd, 2011

Brooke Thackray, CFP, CIM, Research Analyst, JovInvestment Management Inc. explains, 1) seasonal investing, 2) three trades used in the Horizons Alphapro Seasonal Rotation ETF (HAC:TSX), reveals 3) how decisions are made in the fund, and finally 4) his 2011 outlook in the following 4 short videos, which you can watch here, or here.


This additional information is courtesy of Don Vialoux’s TimingTheMarket.ca. Don Vialoux and Brooke Thackray are advisors to the Horizons AlphaPro Seasonal Rotation ETF (HAC-TSX)
By definition, seasonal investing includes:

  • A start date for an investment
  • An end date
  • Either price strength or weakness between the start and end dates for the chosen equity, sector, index or commodity.

A seasonality study preferably uses at least 10 years of data. Most of our studies use 15-20 years of data However, data may not always be available for 10 years. Studies using less than ten years of data can be used, but they tend to be less reliable. Results of shorter term studies have a higher chance of being skewed by a single data point.

Results using at least ten year of data tend to be stable for long periods of time, particularly when annual recurring fundamental reasons causing seasonality are unchanged. However, “statistical” slippage can occur. For example, the U.S. high tech sector has a period of seasonal strength from the end of September to a time between the end of December and the end January. On average, the sector peaks between start of the annual Las Vegas consumer electronics show in the second week in January and start of fourth quarter earnings reports near the end of January. Optimal time to own high tech securities for a seasonal trade based on month end statistical data over a 10 year period frequently flips back and forth from the end of December to the end of January. Seasonality studies on equity indices, sectors and commodities need to be re-examined once a year to see if slippage has occurred.

Time length for intermediate periods of seasonal strength or weakness ranges from five weeks to seven months. In addition, special short term periods often related to holidays have been identified. Examples include strength just before and after U.S. Thanksgiving and strength from just before Christmas until just after the New Year. Also, longer term “cyclical” periods lasting several years have been identified. Most notable is the four year economic or “presidential” cycle. Data for longer term cyclical periods frequently can be overlaid with annual data to refine seasonal entry and exit points.

Some sectors and commodities have more than one period of seasonal strength. A good example is the Canadian financial services sector. Its periods of seasonal strength are from the end of September to the end of December and from the end of February to the end of May. Investors frequently will combine the two periods. Traders with a shorter time horizon may choose one or both periods based on fundamental and technical considerations.

Most periods of seasonal strength are NOT followed by a periods of seasonal weakness. In most cases, periods of seasonal strength are followed by a period of random performance. Markets moving from a period of seasonal strength to a period of seasonal weakness are rare.

Measuring Seasonality

Seasonality is measured in three ways:

  • Average return during the chosen period expressed as a percent
  • Reliability expressed by the number of profitable periods out of at least the past ten periods.
  • Performance relative to a major equity index such as the S&P 500 Index or the TSX Composite Index.

A seasonal investment by definition is profitable more than 50% of the time. If frequency of profitable trades is 50% and frequency of unprofitable trades is 50%, results are random. Confidence in a seasonal trade increases with the frequency of profitable trades. A confidence level for a seasonal trade exceeding is 70% is preferred. A confidence level of 80% frequently is available. A confidence level of 90% is relatively rare. A confidence level of 100% is extremely rare.

Primary Factors Influencing Seasonality

Seasonality happens because of a series of annual recurring events. The job of a seasonality analyst is to examine if the annual events are likely to recur prior to a period of seasonal strength. If annual recurring events are less likely to occur, the seasonality analyst will avoid recommending a seasonal trade.

The classic example is a series of recurring events that trigger the annual period of seasonal strength in the Canadian equity market. The TSX Composite Index has an historic period of seasonal strength from the end of September to the end of April. The strategy is known as the “Buy when it snows, sell when it goes” strategy: Canadian equity markets historically start to move higher near the end of September when the first snowfalls frequently appear in many parts of southern Canada. Equity markets tend to reach a seasonal peak near the end of March/ middle of April when last of the snow melts away. U.S. equity markets as well as almost all equity markets in developed nations have a similar seasonal pattern.

Securities Suitable for Seasonal Equity Investing

Security suitability depends on the knowledge level achieved by the investor:

Investors with the least amount of investment knowledge should focus on Exchange Traded Funds (ETF) that track the seasonality of well known equity indices and sectors. A wide variety of ETFs currently are available. Over 800 equity ETFs currently are listed on North American exchanges. ETFs hold a basket of equities that track an index. Reasons to own ETFs include their diversification, low cost, tax efficiency and ease to buy and sell. Better known Exchange Traded Funds include DIAMONDS (i.e. Dow Jones Industrial Average tracking units), SPYDRS (i.e. S&P 500 Index Deposit Receipts), Qubes (i.e. NASDAQ 100 tracking units) and i60s (i.e. TSX 60 Index units).

Investors with access to reliable fundamental analysis sources can choose individual equity securities that track a period of seasonal strength. Top choices are individual equities with encouraging news making potential during the period of seasonal strength.

Similarly, investors with access to reliable technical analysis sources can choose individual equity securities that are developing favourable technical patterns during a period of seasonal strength.

Investors with greater investment knowledge can apply sophisticated strategies including various conservative listed option strategies that tie into periods of seasonal strength.

Combining Seasonality with Technical and Fundamental Analysis

Using seasonality as a “stand alone” tool to make investment decisions is NOT recommended. Seasonality is a useful analytical tool, but only when used in conjunction with fundamental and technical analysis. Trades based on seasonality alone are profitable in say seven or eight times out of 10, but are unprofitable in two or three times out of ten.

The same can be said for investment based on technical analysis. Reliable technical patterns such as head-and-shoulders patterns are accurate approximately 75% of the time. However, they are not accurate 25% of the time.

Trades based on fundamental analysis alone also are not recommended. Fundamental analyst picks may be profitable most of the time. However, results from a stock picking contest during 2006 run by the Globe and Mail showed that even the best fundamental analysts are far from perfect. The contest requested each participant to choose one stock to buy at the beginning of 2006 and to hold until the end of the year. Participants included a college student, a financial journalist and seven of Canada’s top fundamental analysts. You guessed it! The winner and only person to choose a stock that appreciated in 2006 was the college student.

Chances of a choosing a profitable seasonal trade are greatly enhanced if all three methods of analysis are combined. Of equal importance, chances of losing capital are greatly reduced.

Seasonality analysis is the bridge between fundamental and technical analysis:

  • Fundamental analysis tells us what to buy and sell
  • Technical analysis tells us when to buy and sell.
  • Seasonality analysis tells us what and when to buy and sell.

Identifying Seasonal Trades

Several methods are available to identify periods of seasonal strength:

  • Comments on seasonality made by fundamental analysts can be confirmed by completing a seasonality report based on data for 10 years or more. Fundamental analysts are notorious for commenting on seasonal trends based on 2-5 year data. Ten year studies will confirm or not confirm their comments. A few fundamental analysts on Bay and Wall Street are well aware of long term seasonal trends and base the timing of their recommendations at least partially on seasonality. They usually are analysts who have been in the financial service industry for 10 years or more.
  • Recurring spikes can be examined on monthly price charts using 10 or more years of data. Recurring spikes at the same time each year either on the upside or downside can suggest the possibility of a seasonal trend.
  • Companies and sectors can be examined when they have at least one quarter per year when revenues, earnings, cash flow and/or Earnings Before Interest, Depreciation and Amortization (EBITDA) are seasonally strong. Examples include retail merchandising and consumer electronic companies in the fourth quarter or airline companies in the summer. Seasonal strength in their share price normally begins just prior to their period of seasonal financial strength and ends just prior to the end of their seasonal period of financial strength.
  • Data for 10 years or more can be screened to identify equities and sectors showing periods of above average strength relative to their benchmark index. Preferred benchmarks are the S&P 500 Index for U.S. equities and sectors and the TSX Composite Index for Canadian equities and sectors.

Seasonality Myths

One of the greatest myths on Wall Street and Bay Street is that North American equity markets usually experience a “summer rally”. Traders frequently start talking in May about the possibility of a rally in the stock market in the June to August period. Talk by traders normally escalates during a period when North American equity markets are experiencing a short term correction. The message is “Don’t worry, be happy. The market will come back”. A long term study of the TSX Composite Index and S&P 500 Index confirms that a rally lasting three weeks or more inevitably happens during the three month summer period. However, traders fail to mention that the three week rally period has no consistency. Timing of the appearance of the three week rally is random and can appear at any time during the three month period. Of greater importance, traders fail to mention that virtually all three month periods during the year record at least one period of recovery lasting three weeks or more regardless of season.

Another myth is the expression “Sell in May and go away”. The myth originated from an actual period of seasonal strength in the base metal sector. Base metal prices as well as base metal equity prices tended to peak early in May and bottom near the end of September. The main reason was the annual operating shut down by base metal smelters in Europe in July and August for Europe’s extended holiday season. Demand by smelters for base metal concentrates slowed in May and recovered in September. Currently, base metal prices continue to show this seasonal pattern, but the pattern has been muted over the years. Market share of base metal smelter capacity in Europe has declined while market share in the Far East and South America has increased. Over the past decade, the “Sell in May and go away” phrase became adopted by the media, but with a slightly different twist. The phrase was transformed into expectation for weakness by broadly based North American equity indices such as the S&P 500 Index and the TSX Composite Index from the end of May to the end of September. The myth is not supported by fact. The S&P 500 Index and the TSX Composite Index has gained in five of the past ten periods from the end of May to the end of September. Unlike the period of seasonal strength by North American equity markets from the end of September to the end of

April, performance in the May to September period is random. This period does not have a sufficient number of annual recurring events to influence equity markets.

Another myth is that the month of October is a weak and dangerous month for North American equity markets. The myth is based on the fact that substantial downdrafts in North American prices have occurred in the month of October. October 1929 and October 1987 are seared into the minds of traders. However, data during the past ten years suggests that fears of weakness in October no longer are founded. The S&P 500 Index has advanced in five of the past 10 periods and the TSX Composite Index has gained in seven of the past 10 periods. On the contrary! October frequently is the month of the year when important seasonal lows frequently are reached.

Identified Periods of Seasonal Strength for Equity Indices, Equity Sectors, Industrial Commodities and Selected Stocks as of June 1st 2009

Lots of changes this year due to the big downdraft in equity markets during the past year! Most seasonal trades showed diminished returns based on data for the past 10 years. In addition, some seasonal trades experienced slippage (e.g. data showing that the optimal period for entering a seasonal trade moved from September to October). Seasonal trades that generated an average return of 5% were eliminated. New seasonal trades were added (e.g. Gold equities, Platinum). Following is the annual report:

Sectors

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Exchange Traded Funds are available on all of the above sectors.

Following is seasonality for equivalent Canadian sectors:

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** Exchange Traded Funds are available.

Equity Indices

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** Exchange Traded Fund available

Commodities

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** Exchange Traded Fund or Note available

Selected Periods of Seasonal Strength in Sectors Based On Identified Annual Recurring Events

U.S. Energy

  • Strongest quarter for cash flow and earnings: First quarter
  • Influenced by favourable seasonality in the price of crude oil and natural gas from February to May and by favourable seasonality in natural gas from August to December.

Information Technology

  • Strongest revenue and earnings quarter: fourth quarter in response to consumer electronic sales prior to Christmas
  • Climax often associated with the Las Vegas Consumer Electronic show in the second week in January

Health Care

  • Key health care conferences usually are in September (Oncology conference) and January (JP Morgan health conference). The sector has a history of reaching a seasonal peak when the JP Morgan health care conference is held in mid January.
  • A higher frequency of drug approval in the U.S. usually occurs just prior to year end

Philadelphia Gold and Silver (XAU)

  • Strength in the July to September period corresponds to strength in gold. Gold strengthens when gold fabricators are buying gold to make jewelry for the Christmas and Dhaliwal seasons.
  • Gold stocks and ETFs tend to be contra-cyclical. They move higher during periods of stock market weakness (particularly in summer months).

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