Predicting the Next Move of the Market by Elliott Wave

The Elliot Wave model is based on a crowd psychology that changes between optimistic and pessimistic states creating patterns that can be fitted to natural sequences. These days, the Elliott Wave principle can be improved and used in stock market forecast. Combining this method with Neural Network (NN) helps to eliminate the subjectivity in counting waves.

Instead of assuming that waves obey only the sequence of Fibonacci, harmonic, or fractal ratios, a more general approach can be used to process all extracted waves. Besides, employing NN enables identifying both the price and date of extremes. Although the Elliott Wave does not generate always accurate and consistent forecasts, its result can be successfully used as an additional input for making a trading or investing decision in modern market conditions.

The chart and the portion of output result below can give a clue for a possible next move of S&P-500 for the next week:

Symbol: ^GSPC
Total waves number: 126
Cases number: 108
Target: 1,382.35$ at 2012-02-21 (change 3.10% from 2012-02-14)
     No        Date                $           %   up/down  period
    115  2012-01-02         1,284.62        2.88    100.00     3
    116  2012-01-04         1,265.26       -1.51      0.00     2
    117  2012-01-11         1,296.82        2.49    100.00     5
    118  2012-01-12         1,277.58       -1.48      0.00     1
    119  2012-01-20         1,322.28        3.50    100.00     5
    120  2012-01-24         1,306.06       -1.23      0.00     1
    121  2012-01-26         1,333.47        2.10    100.00     2
    122  2012-01-30         1,300.49       -2.47      0.00     2
    123  2012-02-09         1,354.32        4.14    100.00     8
    124  2012-02-10         1,337.35       -1.25      0.00     1
    125  2012-02-13         1,353.35        1.20    100.00     1
    126  2012-02-14         1,340.83       -0.93      0.00     1

Chart and calculation results by SMFT-1 (a version of FTA-2 sub-system Waves module interface)

Automated Recognition of Candlestick Patterns

The idea of using the chart with candlesticks (or candles) for predicting market prices is very old. Two centuries ago, Japanese rice trader found that the candlesticks pattern chart could be used as a tool to predict future prices in a free market with a natural demand-supply balance. The method was improved later by others and today it is successfully used by many traders and investors in the stock market.

A candlestick is presented using high, low, opening, and closing prices during a certain trading period, for example, trading day. A regular candlestick figure consists of Real Body, Upper Shadow, and Lower Shadow. The Real Body size is proportional to the difference between opening and closing prices. Real Body can be two types - white (green) for uptrend and black (red) for downtrend. Upper Shadow size is proportional to the difference between either high price and closing price in case of uptrend or high price and opening price in case of downtrend. Similarly, Lower Shadow size is proportional to the difference between either low price and opening price in case of uptrend or low price and closing price in case of downtrend.



The number of candlesticks that is normally used for predicting can range within 1..12. Evidently, the number of different combinations of several candlesticks in a row can be big. Some believe that there are only 12 major candlestick patterns, others consider this number is 70 or even more. Anyway, in case of chart analysis, it is necessary to remember at least major patterns and process many charts in order to make forecast successful.

Apparently, statistical methods combined with computer power can be a good solution to make the candlestick patterns recognition work less time-consuming and more effective. For example, Neural Network (NN) can help to automate a candlestick patterns recognition task. NN should be properly trained in order to be able to recognize and predict further movements. One of the obvious problems of implementing a candlestick pattern NN predicting system is a formalization of inputs, i.e., the way how to express each candlestick shape and relative position of all candlesticks in numerical values.

Preparing Data for Neural Network. The idea is simple - look at several candlesticks, recognize pattern, and predict the next candlestick. But how to convert a candlestick shape in numerical values? For simplicity, let's consider one major characteristic of each candlestick. In case of using six candlesticks to predict the performance within the next seventh one (actually the value of Real Body can be considered as an equivalent of performance), the data row for training neural network would be presented by the following:



In reality we need to use more input parameters including shadows, relative position of each candlestick, etc. so that it can be, for example, 60 inputs for each row:



The candlesticks pattern can be formalized in different ways. Which one is the best? It may depend on the type of NN that is used and statistical characteristic of input data. Evidently, only practical testing (out-of-sample test) can indicate which formalization can give the best result for most cases. Logically, all numbers that describe candlestick shape should be expressed in relative units. For example, Real Body size can be converted using the following formula:

RB = 100% * (C - O) / O

where RB - Real Body relative size, C - closing price, O - opening price.

Two more numbers can express the Upper an Lower Shadows relatively to Real Body. The following figure shows distances that used for calculation relative Upper and Lower Shadows:


US = 100% * c / a
LS = 100% * c / b

where US, LS - relative Upper and Lower Shadows correspondingly. US and LS can have values within 0..100%; minimum value equals 0 if Real Body size equals 0, and maximum value equals 100 if Shadow size equals 0.

Therefore, we can use these three numbers for formalizing one candlestick. The number of candlesticks that can be used for historical period can be up to 12. So that the number of inputs for NN can be equal 3 * 12 = 36.

Two More Inputs for Candlestick Pattern Recognition. As it was discussed above, we can use three major numbers to describe the pattern of one candlestick - relative size of Real Body, relative size of Upper Shadow and Lower Shadow. Also we can use 12 candlesticks with these three parameters for each as inputs. However, it would be insufficient to use only these parameters since each candlestick can have different position and their relative position traditionally is used for the analysis and prediction.

The simplest position parameter would be a percentage deviation from an average of all candlesticks position. It could be just closing prices of each period of candlestick. The formula for calculation:


CP_i = 100% * (C_i / C_aver - 1)

where CP_i - relative position of i-candlestick; C_i - closing price of i-candlestick; C_aver - average of all closing prices (all 12 candlesticks).

Except above introduced parameter, it could be reasonable to add one more parameter to distinguish negative and positive candlesticks (black and white) since it makes a significant difference to investors' psychology. So that each black candlestick would have 0 value, white - 1. To summarize, 5 parameters multiplied by 12 (the total number of candlesticks) give 60 inputs.

Numerous tests show many possibilities of improving NN candles patterns recognition abilities. For example, output result can be composed from selected optimized calculations based on different historical periods. As well as, there are many other different ways to formalize the shapes and relative positions of candlesticks.

Optimal Solution. There is an automated tool FTA-2 (free use of fully-functional version for one month). It has module which enables using Neural Network to recognize typical candles patterns and predict future prices. This module predicts only one next candle but the candles pattern prediction can be successfully used for different widths of candle, i.e., the number of trading days in one candle. The module is enhanced to calculate result that is composed from different historical periods that allows making the forecast more accurate. Also it can perform comparative forecast analysis for many symbols.

Useful resources:

• Candlestick basics - major signals
  
• Neural Network basics - introduction
  
• The software which enables using Neural Network to recognize typical candles patterns and predict future prices - about FTA-2
  

The Stock Market Remains Weak Despite Positive Reports

In general, the stock market demonstrates weakness despite better-than-expected US economic reports and news. The US unemployment rate dropped to 8.6% in November from 9% in October. It is a lowest level in more than 2.5 years, since March 2009. October US retail sales were 7.2% higher than the same month a year ago. Furthermore, the retail sales have been raising for 5 straight months.

Improvement in consumer spending was one of the reasons why the economy grew at the best of the year annual rate of 2.5% in the 3^rd quarter. US auto sales increased in 14% in November. Also during the 4-day Thanksgiving weekend consumer spending reached a record 16% in total sales from a year ago. Although some of these numbers might be reevaluated and revised in the future, in overall, they indicate improvements.

The Stock Market Outlook Would Be More Positive

After a few years of struggle to find an alternative to over-leveraged financial models, the transition to a new era of a deleveraged economy has begun. Recently, the necessity of this transition is confirmed by rising European and Global financial uncertainties. Evidently, these are negative factors that still keep investing risk at higher than normal level. On the other hand, there are positive signs of recovery.

The third quarter US GDP almost doubled to 2.5%. The unemployment rate has improved from 9.1% to 9.0% in September. These two numbers cause some debates concerning how they are calculated and if they are able actually to reflect the reality of economics, however, other numbers also look good. The most important numbers that push the shares prices are corporate earnings. They are relatively healthy. Therefore, the possibility of a double recession now seem less likely than before.

Furthermore, S&P-500 is trading below historical price-earning ratio. Many stocks look cheap and attractive. Since most investors are still scared by the market volatility of the recent years, they keep a lot of cash and potentially able to lift the demand and prices. If the future outlook of the stock market stays strong, further uptrend can be just a matter of time.

Better Expectations Drive the Stock Market

A sluggish economical recovery, weak job market, financial crisis in Europe, and the fear of global recession kept the major stock market indexes in a choppy pattern during 3^rd quarter. However, October started with some good news and better-than-expected statistics. These were able to lift S&P-500 from 1075 to 1225 (almost 14%) for two weeks. Among good economic news: September was one of the best months of the year for the US automobile industry and retails. Also according to the latest GDP forecasts, the 3^rd quarter can be the best quarter in a year.

The slow economy, high unemployment, Euro-zone crisis, and the threat of global recession might not find a quick solution but as old news all these have been priced already in a market equilibrium. The question is what will be the next. As a rule, the future expectation is the thing that drives the stock market, not the past performance. Statistically, November and December are bullish months of a year. In addition, this time the annual cycle may be propelled by positive economic projections. Therefore, if no more bad news wakes up the pessimism again, 2011 has a chance to end on a positive note.

October: Is It Time for Bull?

Lately, the economists re-evaluated their previous estimates and significantly lowered the predictions for the US economic growth this year and in 2012. This fundamental equilibrium reset, as one-time event, coincidentally has combined with a cyclical September-October low market season. A high volatility, remaining macroeconomic risks, and crowd fears persist. A lot of confusions still dominate the stock market. What is next to expect?

Europe troubles might continue to affect the global markets. The unemployment rate and housing market might not improve quickly. However, the US corporate reported earnings continue to exceed the profitability estimates in most cases. A lot of stocks are now cheaper than during the 2008-2009 market calamity. The future expectation is the thing that drives the market, not the past performance. That is why many investors and traders hope for rallies.

Crashes Always Come Unexpectedly

What did exactly cause the recent sell-off ? There are different opinions among experts - debt-ceiling standoff, US credit rating downgrade, disappointing economic growth statistics, Europe troubles, or all combined together. As usually, it happened suddenly and quickly. There might be at least two ideas why recent declines happen faster and faster. The first one is that investors reaction to uncertainty and tolerance for risk have changed; it takes less and less to scare investors.

The second reason is a fast electronic trading. It allows programmed computers to sell under particular circumstances in milliseconds that develops a temporal disconnection between the actual factors and reasonable market prices - the market overreacts and makes irrational movements before reaching an after-crash equilibrium. As a matter of fact, the latest huge swings with changing directions every day have never been seen before in S&P-500 history.

In new era of globalization, mutual dependencies, and markets interconnection, the system crises turned out to be a new threat to our global well-being. Nonetheless, it seems the investors' expectation now has changed - the US and global economic growth is likely to be less healthy in the near future than previously estimated.

Some resources: Trading Strategies for a Stock Market Crash,
How to Escape from a Bear