Basics of Stock Markets

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Basics of Stock Markets
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Basics of Stock Markets

• Stocks are shares in the ownership of a company,
  or investments on which a fixed amount of
  interest will be paid.
• A companys shares are the many equal parts into
  which its ownership is divided. Shares can be
  bought by people as an investment.
• Shareholder is a person who owns shares in a
  company. Share index is an indicator of the
  state of a stock market. It is based on the
  combined shares prices of a set of companies (The
  FT 30 share index was up 16.4 points to 1,599.6).

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Basics of Stock Markets

• Stock markets such as those in New York, London,
  Frankfurt, Bombay. Bond markets, which deal in
  government and other bonds. Currency markets or
  foreign exchange markets, where currencies are
  bought and sold commodity markets, where
  physical assets such as oil, gold, copper, wheat
  or electricity are traded. Futures and options
  markets, on which the derivative products are
  traded.

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Basics of Stock Markets

• Roughly speaking, a company that needs to raise
  money for example, to build a new factory or
  develop a new product, can do so by selling
  shares in itself to investors. The company is
  then owned by its shareholders. If the company
  makes a profit, part of this may be paid out to
  shareholders as a dividend of so much per share.
  If the company is taken over or otherwise wound
  up, the proceeds (if any) are distributed to
  shareholders.

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Basics of Stock Markets

• Shares have a value that reflects the views of
  investors about the likely future dividend
  payments and capital growth of the company this
  value is quantified by the price at which they
  are bought and sold on stock exchanges (In
  practice, companies may have a much more complex
  structure, for their equity called index point).
  We have, then, a collection of markets on which
  assets of various kinds are bought and sold.

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Basics of Stock Markets

• A random walk is one which future steps or
  directions cannot be predicted on the basis of
  past actions. When the term is applied to stock
  market, it means that short-run changes cannot be
  predicted.

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Basics of Stock Markets

• Stock Indices
• A stock index is a mathematical measurement of
  the performance of a number as a group. The most
  widely known indices are
• The major market Index, produced by American
  Stock Exchange, which follows 30 of the most
  important industrial shares quoted on the New
  York Stock Exchange.
• The Standard and Poors 500 Index (SP 500)
  follows 500 shares quoted on the New York Stock
  Exchange, American Stock Exchange and the
  over-the-counter market in the United States.

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Basics of Stock Markets

• The FTSE 100 (Financial Times Stock Exchange 100
  Share Index) measures 100 of the largest
  companies quoted in London and was introduced
  principally for options and futures trading.
• The FT Actuaries Indices which examine the
  performances of different industrial sectors so
  one can judge the relative performance of, say,
  shipping and energy.

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Basics of Stock Markets

• There are more than 60 countries, including Saudi
  Arabia, and other Gulf countries where stocks are
  traded.
• The biggest stock exchange, where about
  one-third of the total value of the worlds
  shares is traded, is in New York at New York
  Stock Exchange (NYSE).
• The main index used is the Dow Jones Industrial
  Average, which is the arithmetic mean of the
  shares price movements of 30 important companies
  is listed on the NYSE.

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Gulf Region Stock Market

• The New York Times of October 15th 1997 wrote
  Two North American Scholars won the Nobel
  Memorial prize in Economic Science yesterday for
  work that enables investors to price accurately
  their bets on the future, a break-through that
  has helped power the explosive growth in
  financial markets since the 1970s and plays a
  profound role in economics of every day life.
  These two scholars are Myron Scholes and Robert
  Merton. In fact, Fischer Black and Myron Scholes
  had initiated the study of the model, which is
  nothing but the heat equation or diffusion
  equation, generally known by the name
  Black-Scholes equation.

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Gulf Region Stock Market

• Theory and applications of this equation is
  called the Black-Scholes world. Prior to the
  Black-Scholes model, pricing of financial
  derivatives was a mysterious task. The basis of
  derivative pricing is the Black-Scholes model and
  its extensive usage is likely to influence the
  market itself. In particular, it has been shown
  that this is a factor in the rise in
  volatilities.

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Gulf Region Stock Market

• In Arab News of April 18 and 19, 2000, Dr. Henry
  T. Azzam, Chief Economist and Managing Director
  of Middle East Capital Group in a series of two
  articles has nicely presented New Challenges and
  Rising Potential of Arab World Stock Markets.
  He writes The Saudi stock market, the largest in
  Arab World recorded gains of 43 in 1999,
  following a 28 decline in 1998 and a rise of 28
  in 1997, founded in 1991 following the 83 rise
  recorded in 1993. He has also mentioned in this
  article that

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Gulf Region Stock Market

• the Saudi Government is in the final stages of
  preparing a new set of capital market
  regulations, including the setting up of an
  independent regulator. He predicts a rise in
  Saudi Stocks. This article has provided
  motivation to study the behavior of different
  stocks as listed on 1, April 2000 in the Saudi
  Stock market (Siddiqi Firozzaman). By taking
  into consideration the value of the volatility
  for this period, prediction of stocks values
  applying Black-Scholes model is examined.
  Similarity of volatility with its value
  determined by inverse problem method is looked
  into.

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Gulf Region Stock Market

• Saudi Stock Market
• References
• Salah Jameal Malaikah, Saudi Arabia Kuwait A
  Study of Stock Market Behavior and its Policy
  Implications, Michigan State University, 1990.
• A.H. Siddiqi and M. Firozzaman, Proc. The First
  Saudi Science Conference, KFUPM, 9-11, April
  2001, pp. 291-311.

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Detecting Long Term Evoluation

• The purpose of this example is to show how
  analysis by wavelets can detect the overall trend
  of a signal. The signal in this case is a ramp
  obscured by "colored" (limited-spectrum) noise.
  (We have zoomed in along the x-axis to avoid
  showing edge effects.)

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Detecting Long Term Evoluation

• There is so much noise in the original signal, s,
  that its overall shape is not apparent upon
  visual inspection. In this level-6 analysis, we
  note that the trend becomes more and more clear
  with each approximation, A1 to A6. Why is this?
• The trend represents the slowest part of the
  signal. In wavelet analysis terms, this
  corresponds to the greatest scale value. As the
  scale increases, the resolution decreases,
  producing a better estimate of the unknown trend.
  
• Another way to think of this is in terms of
  frequency. Successive approximations possess
  progressively less high-frequency information.
  With the higher frequencies removed, what's left
  is the overall trend of the signal.

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Detecting Self-Similarity

• The purpose of this example is to show how
  analysis by wavelets can detect a self-similar,
  or fractal, signal. The signal here is the Koch
  curve -- a synthetic signal that is built
  recursively.
• This analysis was performed with the Continuous
  Wavelet 1-D graphical tool. A repeating pattern
  in the wavelet coefficients plot is
  characteristic of a signal that looks similar on
  many scales

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Wavelet Coefficients and Self-Similarity

• From an intuitive point of view, the wavelet
  decomposition consists of calculating a
  "resemblance index" between the signal and the
  wavelet. If the index is large, the resemblance
  is strong, otherwise it is slight. The indices
  are the wavelet coefficients.
• If a signal is similar to itself at different
  scales, then the "resemblance index" or wavelet
  coefficients also will be similar at different
  scales. In the coefficients plot, which shows
  scale on the vertical axis, this self-similarity
  generates a characteristic pattern.

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Identifying Pure Frequencies

• The purpose of this example is to show how
  analysis by wavelets can effectively perform what
  is thought of as a Fourier-type function -- that
  is, resolving a signal into constituent sinusoids
  of different frequencies. The signal is a sum of
  three pure sine waves.

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Suppressing Signals

• The purpose of this example is to illustrate the
  property that causes the decomposition of a
  polynomial to produce null details, provided the
  number of vanishing moments of the wavelet (N for
  a Daubechies wavelet dbN) exceeds the degree of
  the polynomial. The signal here is a
  second-degree polynomial combined with a small
  amount of white noise.

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Suppressing Signals

• Note that only the noise comes through in the
  details. The peak-to-peak magnitude of the
  details is about 2, while the amplitude of the
  polynomial signal is on the order of 105.
• The db3 wavelet, which has three vanishing
  moments, was used for this analysis. Note that a
  wavelet of the Daubechies family with fewer
  vanishing moments would fail to suppress the
  polynomial signal. For more information, see the
  section Daubechies Wavelets dbN.

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Suppressing Signals

• Here is what the first three details look like
  when we perform the same analysis with db2.
• The peak-to-peak magnitudes of the details D1,
  D2, and D3 are 2, 10, and 40, respectively. These
  are much higher detail magnitudes than those
  obtained using db3

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De-Noising Signals

• The purpose of this example is to show how to
  de-noise a signal using wavelet analysis. This
  example also gives us an opportunity to
  demonstrate the automatic thresholding feature of
  the Wavelet 1-D graphical interface tool. The
  signal to be analyzed is a Doppler-shifted
  sinusoid with some added noise.

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SP500 Data Analysis
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SP500 Data Analysis
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BSE Data Analysis
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BSE Data Analysis
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DOW Data Analysis
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DOW Data Analysis