^GSPC Forecast for May 5-15, 2009

Chart and data table: ^GSPC forecast for May 5..15

Table shows ^GSPC closing price on each trading day from May 5 to May 15. Results calculated using experimental tool NNSPP-2 (Neural Network). Data presented for testing purpose only.

Comparative Analysis (added on 2009-05-15):

date         actual  predicted        difference
2009-05-05   903.80   904.17        0.37   0.04%
2009-05-06   919.53   905.04       14.49   1.58%
2009-05-07   907.39   915.63        8.24   0.91%
2009-05-08   929.23   910.45       18.78   2.02%
2009-05-11   909.24   906.58        2.66   0.29%
2009-05-12   908.35   912.47        4.12   0.45%
2009-05-13   883.92   915.30       31.38   3.55%
2009-05-14   893.07   913.80       20.73   2.32%
2009-05-15   882.88   918.73       35.85   4.06%
                                 average:  1.69%

^GSPC forecast for May-July 2009

^GSPC forecast for May-July using Neural Network
^GSPC forecast for May-July using Neural Network

This is an approximate ^GSPC forecast for May-July (using Neural Network from experimental tool NNSTP-2).

Major points in numbers:

  • May 4-8: up to almost 900 (evidently if bad news affects investors’ mood, it can go down instead)
  • May 25-29: down to 870..800
  • June 22-26: up to around 930
  • July: massive selloff

Reminder: Forecast built on statistical methods may fail because of redistribution of driving factors – news, investors’ sentiments, regulatory impact, etc.

Important to notice: The recent forecast by pattern similarity shows earlier selloff.

^GSPC for May-June 2009, predicted by pattern similarity

^GSPC for April-July 2009, predicted by pattern similarity
^GSPC for May-June 2009, predicted by pattern similarity

The algorithm of InvAn prediction using pattern similarity is the following. It searches for the best match from the internal database by scanning all historical data. The ranking of all possible matches is calculated on the basis of minimum deviation and maximum correlation within given historical period (number of candlesticks). Pattern matching is performed using open, high, low, and close prices and volume data. When scanning is completed, it composes forecast using several best matched patterns (top ranked). The composite result is built as a weighted average with weights proportionally patterns’ ranks. More details can be found at www.addaptron.com

Major points in numbers:

  • May 4-8: High 935
  • June 15-19: Low 548

Case Study

Case Study: Making Investment Decision

Making an investment-related decision involve gathering data, analysis, and prediction. As a rule, at any moment, two groups of factors exert influence on the decisions – positive and negative. To minimize the investment risk, all factors should be properly evaluated. This article shows an example of a real time experiment of making investment decisions. The experiment started in February 2009. The initial amount of fund for investing was around USD 2000. Below are some inputs for making the first buying decision at that time:

  1. The US and global economies were in a bad shape. Although the stock market showed some weak signs of recovering, most investors considered the market as bearish.
  2. In addition, there was some risk for the market to decline in the short term mostly due to expected disappointing financial reports for the fourth quarter of 2008.
  3. On the other hand, normally, month February is a annual cyclical minimum for energy sector stocks. It was confirmed by the chart of the indexes that represents the energy sector (XLE and XEG.TO). The result was calculated by SMAP-2 using the recent 12-year period.
  4. Statistical research may not be accurate if something new suddenly appears.
  5. Another positive factor was the fact that the equity markets are leading indicators of economy. As a rule, the stock market starts recovering in around six months in advance before the economy.
  6. Calculated composite rating (by InvAn-4) and comparative analysis of sectors showed a favorable position for energy sector stocks. OII was one of the stocks from energy sector with high composite rating. OII has all good three components of composite rating – fundamental, technical, and timing ratings.
  7. On February 6th, the five-day forecast by NNSTP-1 showed some uptrend so that the entry point (buying) was chosen correctly. The stock OII was purchased.

After purchase the overall stock market moved slightly up and then went down more erasing previous gain. The lesson – a market correction (decline) can be used as an opportunity to maximize return. However, eventually the stock moved up much more with solid ROI – more than 25% in April.