Leading Indicators

Even though the below article focuses on trading frequencies, timesteps, they mentioned some interesting variables tested to forecast market moves.


From STEVEN KRAWCIW AND IRENE ALDRIDGE at Wealth Manager,

"
... Now, let’s talk about ways to trade the S&P 500 on the basis of the U.S. Leading Indicators Index. The index is a composite of ten indicators that historically preceded peaks and troughs in the U.S. economy. The composition of the Index changes through time, and may include the following indicators: interest rate spread between a U.S. 10-year note and the Fed funds rate, average weekly initial claims for unemployment insurance, average weekly manufacturing hours, index of supplier deliveries (vendor performance), stock prices, and manufacturers' new orders for non-defense capital goods.

Next, to estimate the impact of the announcement on the price changes in the S&P 500, we conduct a regression analysis. This analysis can be performed using the regression functionality in the Excel. We regress the changes in the S&P 500 on different aspects of the event announcements.

Monthly
From the regression results, we find that at the monthly portfolio rebalancing frequency, the S&P 500 moves in tandem with the previous month’s change in the leading indicator index with probability of 87%. Thus, if the leading indicator index increased in the previous month, S&P 500 is expected to rise this month.

Daily
At daily trading frequencies, such a relationship does not hold: neither prior nor concurrent changes in the leading indicators have any bearing on the daily changes in S&P 500.

Hourly
At hourly frequencies, however, the price of the S&P 500 moves with the unexpected component of the announcement: if the actual figure announced is higher than the consensus, S&P 500 rises with 90% probability; else, if the actual leading indicator index shows lower values than the expected value, then S&P 500 is likely to fall. Our results are consistent with several academic studies written on the topic, including one published in 2003 in the American Economic Review, volume 93, "Micro Effects of Macro Announcements: Real-Time Price Discovery in Foreign Exchange," by T.G. Andersen, T. Bollerslev, F.X. Diebold and C. Vega.

"

US job-count failures

Caroline Baum on effects of the US Healthcare reform, with respect to the Obama administration's claims of job creation. (Bloomberg)


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How many small businesses are holding off on hiring additional employees until they know what additional burdens Congress will impose on them in the name of health-care reform? How are expectations of higher health-care costs affecting consumers’ current spending, which in turn affects output and employment? (Only in government la-la land can you provide more health care for less money.)

In the face of a 10.2 percent unemployment rate and growing doubt about government claims of jobs created, the administration is standing by its 640,329. After all, Devaney has “no doubt that there’s a lot of jobs being created.” It’s just a question of how many.

One million? 640,329? It’s close enough to zero for government work.

"

Having the above coupled with the coming "Climate-Gate" carbon-tax policies, no actual economic recovery would likely commence until the wrongs are put right. Here too, politics in NZ presently has largely followed American, Euro-centric influence.

Recent revelations on Climate-Gate,

Somebody must address these tough issues, and voice hard questions! Truth conquers all.

Textbook Theories vs. Reality

Real world experience often brings to light academic fallacies, especially when it comes to the financial markets. Right off the bat, many business students here (Auckland) are taught that inefficiencies do not exist, or impossible to exploit; which completely disengages from reality.

Textbook theories vs. empirical reality

Just because an idea shows up in a textbook does not make it absolute truth. A widely known phenomenon, while Black-Scholes PDE presents an elegant option pricing formula, the “volatility smile” noticeably points out the formula’s inaccuracy and hence unreliability. Some may say “well, it’s all I got, better than nothing right?” No, a wrong solution is often WORSE than nothing.

Remember how the stochastic finance stock pricing model completely ignores credit risk and actual rate of inflation? Models like this have simply no practicality in actual trading.

Textbook contradictions (efficiency vs. inefficiency)

They do not even agree among each other. While some academics keep pushing Efficient Market Hypothesis, top business schools like Wharton use texts specifically on exploiting market inefficiencies like Understanding Arbitrage: An Intuitive Approach to Financial Analysis. What is up with that?!

Searching for truth

Knowledge requires actual experience, not just sometimes subjective ideas off some random textbook. It takes work, like everything else in life.

US unemployment 10.2%, "economy is rebounding"

"The economy is rebounding" What the heck, right? Do people really fall for this stuff?

Unemployment climbs in New Zealand

6.5%
Just a few weeks ago they tried to sell the "recession's over..." rhetoric to Kiwis (New Zealanders), like their American counterparts. What is the point of this blatant deception? More importantly, how does one exploit this situation for a profit?

Interest rates will likely lower in the near term future in NZ, to uphold the "recovery" facade. Real estate will likely fall some more, as increasing number of folks will default on mortgage payments, etc. Low-profile listed companies will experience reduced earnings, obviously, if they are willing to sacrifice staff (profit producing capacity). Short selling high credit risk businesses sounds quite viable at this time.

ARMA(p,q) forecasting

ARMA, or Autoregressive Moving Average, offers a relatively simple time series forecasting model. So what about non-stationary financial time series without much autocorrelation, would it perform well?

ARMA(p,q) basics

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Forecasting model or process in which both autoregression analysis and moving average methods are applied to a well-behaved time series data. ARMA assumes that the time series is stationary-fluctuates more or less uniformly around a time-invariant mean. Non-stationary series need to be differenced one or more times to achieve stationarity. ARMA models are considered inappropriate for impact analysis or for data that incorporates random 'shocks.' See also autoregressive integrated moving average (ARIMA) model.

“

Source: Business Dictionary

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Autoregressive Model, AR(p)

where are the parameters of the model, c is a constant and e is white noise. The constant term is omitted by many authors for simplicity.

Moving Average Model, MA(q)

where the θ₁, ..., θ_q are the parameters of the model, μ is the expectation of X_t (often assumed to equal 0), and the e, e,... are again, white noise error terms.

“

Source: Wikipedia

Some thoughts off empirical findings

As ARMA was created to address stationary processes, ARMA forecasting resulted much more reliably with percentage returns (of equal length time steps) instead of raw financial time series. Practical application for trading strategies could surface with more analysis of conditional error distributions. Reliability however remains an issue until conditional volatility management. Over all, this simple method presents some promising capabilities!