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Presentation Etalon12 12 Last Presentation Etalon12 12 Last Presentation Transcript

  • Financial market crises prediction by multifractal and wavelet analysis. Russian Plekhanov Academy of Economics Romanov V.P., Bachinin Y.G., Moskovoy I.N., Badrina M.V .
    • It is well known, that financial markets are essentially non-linear systems and financial time series are fractals.
    • That’s why prediction of crash situations at finance market is a very difficult task. It doesn’t allow us to use effectively such well-known methods as ARIMA or MACD in view of their sluggishness.
    • Multifractal and wavelets analysis methods are providing more deep insight into the nature of phenomena..
    • The main aim of this research is to find out the predictors or some kind of predicting signals, which may warn as about forthcoming crisis
    The aim of the research
  • a ) Changing of ruble/dollar exchange rate at period 01.08.1997-01.11.1999 ( Default in Russia ) ‏ b ) American Index Dow Jones Industrial at “Black Monday” 1987 at period 17.10.1986-31.12.1987 Examples of analyzed financial market crisis situations(1)
  • с) Dow Jones Industrial Index e) Nasdaq d) RTSI 07.10.1999 - 06.10.2008 07.10.1999 - 06.10.2008 07.10.1999 - 06.10.2008 Examples of analyzed financial market current crisis
    • E fficient M arket H ypothesis  ( EMH ) asserts , that financial markets are "informationally efficient", or that prices on traded a ssets, e.g., stocks, bonds, or property, already reflect all known information. The efficient-market hypothesis states that it is impossible to consistently outperform the market by using any information that the market already knows, except through luck. Information or  news  in the EMH is defined as anything that may affect prices that is unknowable in the present and thus appears randomly in the future.
    • Capital Asset Pricing Model (CAPM) is used to determine a theoretically appropriate required rate of return of an asset, if that asset is to be added to an already well-diversified portfolio, given that asset's non-diversifiable risk. The model takes into account the asset's sensitivity to non-diversifiable risk (also known as systemic risk or market risk), often represented by the quantity beta (β) in the financial industry, as well as the expected return of the market and the expected return of a theoretical risk-free asset.
    • Arbitrage pricing theory ( APT ), in finance, is a general theory of asset pricing, that has become influential in the pricing of stocks. APT holds that the expected return of a financial asset can be modeled as a linear function of various macro-economic factors or theoretical market indices, where sensitivity to changes in each factor is represented by a factor-specific beta coefficient.
    Financial Market Models
  • Efficient Market Hypothesis versus Fractal Market Hypothesis
    • Efficient market hypothesys ( EMH ) ‏
    • Assumption of normal distribution of prices increments
    • The weak form of EMH from a purely random distribution of prices has been criticized
    • Semi-strong form of EMH, in which all available information is reflected in the prices used by professionals
    • Changing prices in the long run does not show the presence of «memory»
    • Fractral market hypothesys
    • (FMH)
    • Prices shows leptoexcess effect for prices probability distribution ( “fat tails” ) ‏
    • The prices plot looks similary for the period of time in the day, week, month (fractal pattern) ‏
    • Reducing the reliability of predictions with the increase of its period
    • Prices shows short-term and long-term correlation and trends (the effect of feedback) ‏
    • Chaotic activity of the market
    • Fractals – The term fractal was introduced in 1975 by Benoît Mandelbrot, from the Latin fractus , meaning "broken" or "fractured".
    • A shape that is recursively constructed or self-similar, that is, a shape that appears similar at all scales of magnification.
    • A geometric object that has a Hausdorff dimension greater than its topological dimension.
    • The second feature that characterizes fractals is the fractional dimension.
    • The word fractal came from “fractional values” – partial values, which may take the fractal dimension of objects
    Fractal definition
  • Chaos and dynamics of fractal market
    • Market prices tend to level the natural balance within the price range
    • These levels or ranges can be described as «attractors»
    • However, the data within those ranges remain casual
  • Fractal attractors and financial markets
    • Stocks and futures - classic examples of securities. Profit from buying and selling comparable with fluctuations in the pendulum
    • Each security or futures contract are located in its own phase space
    • Long-term forecasting is heavily dependent on accurate measurement of initial conditions of the market
  • Fractals on capital market
    • Financial markets describes a nonlinear function of active traders
    • Traditional methods of technical analysis based on linear equations and Euclidean geometry are inadequate
    • Market jumps growth and recession are nonlinear
    • Technical analysis methods are poor indicators of the relationship trend and trading decisions
    • Fractals can describe the phenomena that are not described in Euclidean geometry
  • Point attractors
    • The simplest form of the attractor. In theory, compatible with the balance of supply and demand in the economy or the market equilibrium.
    •   Represent market volatility on balance, or "market waves "
    • Displays multiple chaotic,varying the amplitude fluctuation, which are contained within the set limit cycle attractor, called «phase space».
    Limit cycle attractors Strange or fractal attractors Attractors types
  • Serpinsky Triangle
  • Fractals examples
  • Dynamic systems fractals June 10, 2011
  • Crisis prediction technique
    • Because our goal is the prediction of crises, we are trying to first find out the best indicator, using methodologies of fractal, multifractal and wavelet analysis.
    • First of all we looking for several different predictors
    • Then we test various types of pre-processing the original time series to find the best indicator.
  • Definition the Fractal Dimension
    • Fractal dimension :
    • where N A (1/ n ) – the number of blocks with length of the sided , equals 1/ n , which necessary to cover a set А.
    • For S – Serpinsky triangle:
  • Hurst exponent (H) as one of predictors Depending on the value of Heurst exponent the properties of the process are distinguished as follows: When H = 0.5, there is a process of random walks, which confirms the hypothesis EMH. When H > 0.5, the process has long-term memory and is persistent, that is it has a positive correlation for different time scales. When H < 0.5, time-series is anti-persistent with average switching from time to time.
  • Fractal Dimension Index(FDI = 2-H)
    • Defines the persistence or antipersistence of market. Persistent market weakly fluctuated around the market trend
    • Antipersistent market shows considerable volatility on the trend
    • Antipersistent market is more rugged pricing schedule and more frequently show a change trends
  • Stochastic process {x(t)} is called Multifractal , if it has stationary increments and satisfies the condition , when c(q) – predictor , E- operator of mathematical expectation , , – intervals on the real axis . Scaling function , which takes into account the impact of the time on the moments q . Multifractal spectrum of singularity as the second predictor . Multifractal spectrum of singularity is defined by Legendre transform :
  • Multifractal spectrum of singularity width as crash indicator
    • Multifractal may be composed of two or infinite number of monofractals with continuous varying α values. Width of α spectrum may be estimated as difference between maximum and minimum values of α:
    • Δ  =  max -  min ,
    • By carrying out Legendre transform we are trying using our program by estimating Δ  to find differences in its values before and after crash.
    • Roughly speaking f(  ) gives us number of time moments, for which degree of polynomial, needed for approximation f(  ) equals  (according to Lipshitz condition).
  • Five steps of multifractal spectrum of singularity estimation: The First step: time series partitioning
    • Time series: { x t } ; t  [0, T ].
    • Compute : Z ={ z t }, z t = lnx t+1 -lnx t ; t  [0, T ] ;
    • Divide interval [0, T ] into N subintervals , 1 ≤ N ≤ Nmax .
    • Each subinterval contains int ( T/N )= A values Z;
    • For each subinterval K; 1 ≤ K ≤ N current reading number l K ; 1 ≤ l K ≤ A; t = ( K -1) А+ l K
    • As soon as we are looking for the best indicator of a coming default, we will use several variants of a preliminary processing.
  • The second step: Time series preprocessing
    • 1. The original time series itself: Z ={ z t } ;
    • 2. Preprocessed time series Z 1 ={ }, K =1,2,… N ,
    • where
    • 3. Preprocessed time series
    • where
    • 4. Preprocessed time series Z 3 ={ }
  • The third step: Partition functions computing For each preprocessed time series compute partition function for different N and q values :
  • The fourth step: Scaling function computing
  • The fifth step: Multifractal spectrum of singularity estimation 1. Lipshitz – Hoelder exponent estimation : : where, i = 1, 2, 3, 4. 2 . Multifractal spectrum of singularity estimation by Legendre transform
  • Scaling function Non-linear scaling function  (q) ( Multifractal process )‏ Changes in currency for the Russian default of 1998
  • Multifractal spectrum of singularity at period 09.07.96-21.07.98 Multifractal spectrum of singularity at period 18.11.96-30.11.98 Multifractal spectrum of singularity
  • Dow Jones Industrial Index, pre-crisis situation 19.12.2006-06.10.2008 Scaling functions Non-linear scaling-function  (q) ‏ ( multifractal process ) ‏
  • RTSI index, pre-crisis situation 19.12.2006-06.10.2008 Non-linear scaling-function  (q) ‏ ( multifractal process ) ‏ Scaling functions
  • Scaling functions linear scaling-function  (q) ‏ (monofractal process ) ‏ Multifractal spectrum of singularity RTSI at period 16.05.2000 - 30.05.2002
  • Multifractal spectrum of singularity for analyzed situations Multifractal spectrum of singularity DJI at period 19.12.2006-08.10.2008 Multifractal spectrum of singularity RTSI at period 16.12.2003-10.01.2006
  • Russian default 1998 and USA Black Monday 1987 analysis Plot of the august 1998 Russian default currency exchanging data Plot of width of fractal dimension spectrum ( Δ (t)= α max - α min ) for different time periods US Dow Jones index for Black Monday 1987 for period 17.10.1986-31.12.1987 Plot of width of fractal dimension spectrum ( Δ (t)= α max - α min ) the Black Monday
  • Indexes DJI , RTS.RS , NASDAQ , S&P 500 falling at 2008 crisis period 1 month S eptember 15,2008 – O ctober 17, 2008 The collapse in the stock markets the analysts linked to the negative external background. U.S. indexes have completed a week 29.09 - 6.10 falling, despite the fact that the U.S. Congress approved a plan to rescue the economy. Investors fear that the attempt to improve the situation by pouring in amount of $ 700 billion, which involves buying from banks illiquid assets will not be able to improve the situation in credit markets and prevent a decline in the economy. 3 months July 1 7 ,2008 – O ctober 17, 2008 When Asian stock indices collapsed to a minimum for more than three years. The negative news had left the Russian market no choice – its began to decline rapidly. 6 months April 1 7 ,2008 – O ctober 17, 2008
  • &quot;Needles“, that determine the expansion of Multifractal spectrum at hourly schedule 5.2008-11.2008
  • Graph of Multifractal spectrum singularity width ( Δ (t)= α max - α min ) at Russian index RTSI at period 07 .10.19 99 - 07 .1 1 . 2008 interval Qmin Qmax N ∆ 1-512 07.10.1999 –18.10.2001 -2 6 47 0,964 151-662 16.05.2000 - 30.05.2002 -2 6 103 0,495 301-812 15.12.2000 -31.12.2002 -2 6 129 1,62 451-962 25.07.2001 - 11.08.2003 -2 5 31 0,81 601-1112 28.02.2002 - 17.03.2004 -2 6 170 1,77 751-1262 03.10.2002 - 19.10.2004 -2 6 129 2,17 901- 1412 15.05.2003 - 02.06.2005 -2 6 129 1,927 1051-1562 16.12.2003 - 10.01.2006 -2 5 43 0,952 1201-1712 26.07.2004 - 15.08.2006 -2 5 21 0,868 1351-1862 04.03.2005 - 26.03.2007 -2 5 22 0,89 1501-2012 06.10.2005 - 25.10.2007 -2 5 23 0,848 1651-2162 19.05.2006 - 07.06.2008 -2 5 40 0,927 1801-2246 19.12.2006 - 06.10.2008 -2 7 145 2,133 1 765 -22 77 25.09.2006 - 0 7 .1 1 .2008 -2 7 161 2,1 77
  • Experimental results (RTSI) ‏ Graph of Multifractal spectrum singularity width assessment ( Δ (t)= α max - α min ) at russian index RTSI at period 07 .10.19 99 - 07 .1 1 . 2008 Over 4 years outstanding mortgage loans in Russia rose more than 16 times - from 3.6 billion rubles. in 2002 to 58.0 billion rubles. in 2005. In quantitative terms - from 9,000 loans in 2002 to 78,603 in 2005. Why mortgage evolving so rapidly? Many factors. This increase in real incomes and the decline of distrust towards mortgage, as from potential buyers, and from the sellers, and a general reduction in the average interest rate for mortgage loans from 14 to 11% per annum, and the advent of Moscow banks in the regions, and intensifying in the market of small and medium-sized banks. Pre-crisis situation:   July 2008 - the beginning of september 2008
  • Graph of Multifractal spectrum singularity width ( Δ (t)= α max - α min ) at Russian index RTSI at period 07 .10.19 99 - 09 .1 2 . 2008
  • Graph of Multifractal spectrum singularity width ( Δ (t)= α max - α min ) at American index Dow Jones Industrial at period 07 .10.19 99 - 07 .1 1 . 2008 interval Qmin Qmax N ∆ 1-512 07.10.1999 –18.10.2001 -2 5 164 1,84 151-662 16.05.2000 - 30.05.2002 -2 4 5 0,717 301-812 15.12.2000 -31.12.2002 -2 5 134 1,77 451-962 25.07.2001 - 11.08.2003 -2 5 65 1,01 601-1112 28.02.2002 - 17.03.2004 -2 5 74 1,108 751-1262 03.10.2002 - 19.10.2004 -2 4 11 0,791 901- 1412 15.05.2003 - 02.06.2005 -2 4 38 0,803 1051-1562 16.12.2003 - 10.01.2006 -2 4 50 0,815 1201-1712 26.07.2004 - 15.08.2006 -2 4 53 0,884 1351-1862 04.03.2005 - 26.03.2007 -2 4 57 0,973 1501-2012 06.10.2005 - 25.10.2007 -2 4 29 0,864 1651-2162 19.05.2006 - 07.06.2008 -2 4 11 0,836 1801-22 63 19.12.2006 - 06.10.2008 -2 5 151 2,324 1 765 -22 84 25.09. 2006 - 0 7 .1 1 .2008 -2 5 1 74 1,984
  • There was a sharp drop in the index and 9 october 2002 DJIA reached an interim minimum with a value of 7286,27. Dow Jones Industrial index of 15 september 2008, fell to 4.42 per cent to 10,917 points - is the largest of its fall in a single day since 9 october 2002, reported France Presse. World stock markets experienced a sharp decline in major indexes in connection with the bankruptcy Investbank Lehman Brothers. Graph of Multifractal spectrum singularity width assessment ( Δ (t)= α max - α min ) at american index Dow Jones Industrial at period 07 .10.19 99 - 07 .1 1 . 2008 Experimental results(DJI) 3 May, 1999, the index reached a value of 11014.70. Its maximum - mark 11722.98 - Dow-Jones index reached at 14 January 2000. Pre-crisis situation:   July 2008 - the beginning of september 2008
  • Graph of Multifractal spectrum singularity width ( Δ (t)= α max - α min ) at American index Dow Jones Industrial at period 07 .10.19 99 - 09 .1 2 . 2008
  • Graph of Multifractal spectrum singularity width assessment ( Δ (t)= α max - α min ) at american index NASDAQ Composite at period 07 .10.19 99 - 07 .1 1 . 2008 interval Qmin Qmax N ∆ 1-512 07.10.1999 –18.10.2001 -2 6 47 0,91 151-662 16.05.2000 - 30.05.2002 -2 6 57 0,935 301-812 15.12.2000 -31.12.2002 -2 6 86 1,092 451-962 25.07.2001 - 11.08.2003 -2 5 25 0,74 601-1112 28.02.2002 - 17.03.2004 -2 5 31 0,821 751-1262 03.10.2002 - 19.10.2004 -2 5 129 1,385 901- 1412 15.05.2003 - 02.06.2005 -2 4 9 0,726 1051-1562 16.12.2003 - 10.01.2006 -2 4 13 0,765 1201-1712 26.07.2004 - 15.08.2006 -2 4 19 0,78 1351-1862 04.03.2005 - 26.03.2007 -2 4 19 0,792 1501-2012 06.10.2005 - 25.10.2007 -2 4 15 0,778 1651-2162 19.05.2006 - 07.06.2008 -2 4 5 0,772 1801-22 63 19.12.2006 - 06.10.2008 -2 5 77 1,185 1 765 -22 84 25 . 09 .2006 - 0 7 .1 1 .2008 -2 6 20 7 1, 067
  • Experimental results(NASDAQ) Graph of Multifractal spectrum singularity width assessment ( Δ (t)= α max - α min ) at american index NASDAQ Composite at period 07 .10.19 99 - 07 .1 1 . 2008 In August 2002 the first NASDAQ closes its branch in Japan, as well as closing branches in Europe, and now it was turn European office, where for two years, the number of companies whose shares are traded on the exchange fell from 60 to 38. After that happened result in a vast dropIn 2000, he reached even five thousandth mark, but after the general collapse of the market of computer and information technology is now in an area of up to two thousand points. The index of technology companies NASDAQ Composite reached its peak in March 2000. Pre-crisis situation:   July 2008 - the beginning of september 2008
  • Graph of Multifractal spectrum singularity width ( Δ (t)= α max - α min ) at American index NASDAQ Composite at period 07 .10.19 99 - 09 .1 2 . 2008
  • Wavelet analysis and crisis prediction
    • где   ,  (t) – where   ,  (t) – function with zero mean centered around zero with time scale  and time horizon  .
    • Family of wavelet vectors is created from mother function by displacement and scaling
  • Time series f(t) representation as linear combination of wavelet functions where j o – a constant, representing the highest level of resolution for which the most acute details are extracted .
  • WA crisis detection (experiment – 1 )
    •   In experiment-1 of our study we used D au be c hi es wavelet functions decomposition (db-4 и db-12).
    • The goal was the detection of the signal, which could predict the sudden changes. Data on exchange rates (USD) to the ruble were taken from the site www.rts.ru for the period 1.09.1995 - 12.02.1999
    • The total number of numbered in the order several times in the interim for the period 1.09.1995 - 12.02.1999 was 862 value.
  • Graph of changing RTS indexes at period 1.09.1995 – 12.02.1999
  • The division time series on the ranges
    • To achieve the goal of this time series was divided into 7 overlapping intervals located unevenly, so that the interval 4 (242-753) immediately preceding the time of default and subsequent intervals captured the moment of default.
    • Each interval consisted of 512 values: 1-512, 101-612, 201-712, 242-753, 251-762, 301-812, 351-862.
  • Predicting the crisis with the help of wavelet analysis Changes difference of maximum values of decomposition of Dobeshi-12 for the period 19.09.1997 -12.02.1999.
  • The difference of maximum coefficients of D au be c hi es -12 (17.10.1986-31.12.1987)
    • Here we can see the positive peak earlier 01.10.87 and negative peak before 15.10.87.
    • This is more than 4 days before the «Black Monday».
    • Sharp line connects the two peaks. Obviously, this information can serve as a detector impending crisis.
  • 42 days prior to the default
    • Of the figure shows that the start of trading, the corresponding spike in the dollar may be adopted point 742 (21.08.1998), a peak corresponds to 754 points (07.09.1998). As we can see from the previous slide in the event of data processing by the Russian default, if we use the average of the indicator is the intervals difference, then we can find that the sharp increase occurring 18.09.1998, that is delayed by at least 11 days. At the same time schedule for the coefficients of wavelet functions shows us that the beginning of dramatic changes difference wavelet coefficients of expansions is a point 712 (10.07.1998). We can, apparently, to predict the onset of default at least 42 days (10.07.1998 - 21.08.1998). At the same time increase the maximum value of this indicator in the starting time was 74.5 times (initial value = 0.15; following value = 11.23) ‏
  • Wavelet Analysis for Crisis Detection ( experiment – 2)
    • In our experiment, number 2, we used D au be c hi es wavelet functions decomposition (db- 4 ).
    • The goal was the detecting the signal, which could predict the sudden changes in the index DJI (Dow Jones Index - Dow Jones). Data on DJI were taken from the site http://finance.yahoo.com for the period 7.10.1999 - 24 .1 1 .2008
    • The total number of numbered in the order several times in the interim for the period 7.10.1999 - 24 . 11 .2008 at 22 99 values.
  • Graph DJI change 7.10.1999- 8 .1 1 .2008
  • Change the values of Hurst exponent said that the market in anticipation of becoming antipersistent crisis: H <0,5 Changing detailing factors wavelet decomposition of db-4 show conversion market (antipersistent)
  • Changing detailing factors wavelet decomposition of db-4 suggest crossing a market for the period 07.07.2005 - 24.11.2008
  • Financial market model FIMASIM
    • The main functional modules are:
    • FMSWorld, which contains virtual world classes and relationships,
    • FMSStandardRoles, which contains financial market classes, and others.
    • Standard classes of the system are:
    • Trader ( TFMTrader )
    • Broker (TFMSBroker)
    • Company (TFMCompany)
    • Market, stock exchange (TFMSMarket)
    • Strategy ( TFMSStrategy )
    • Plan ( TFMSPlan )
    • Order , transaction request ( TFMSShareTransactionRequest )
    • Transaction ( TFMSShareTransactiont )
    June 10, 2011
  • Virtual market program interface June 10, 2011
  • The experiments were made with aim to find out at which values of parameters the market instability arises. Experiment 1:
    • Overall parameters:
    • MARKET_MAKER_TRADER_COUNT = 2;
    • RANDOM_TRADER_COUNT = 0;
    • FUNDAMENTAL_TRADER_COUNT = 500;
    • BROKER_COUNT = 5;
    • MARKET_COUNT = 1;
    • COMPANY_COUNT = 10;
    • CLASSIFICATORS_COUNT = 31;
    • Companies:
    • COMPANY_MAX_ASSETS = 50000000; // 50Mbyte
    • COMPANY_MIN_ASSETS = 1000000; // 1Mbyte
    •  
    • Brokers:
    • MIN_BROKER_MARKET_ACCOUNT_MONEY = 100000; // 100k.
    • MAX_BROKER_MARKET_ACCOUNT_MONEY = 150000; // 300k.
    • BROKER_MONEY = 10000; // 10k.
    • Broker and market:
    • MAX_COMMISION_PLANS = 3;
    June 10, 2011   Market maker trader parameters: MIN_MM_TRADER_CHANGE_PERCENT = 0.1; MAX_MM_TRADER_CHANGE_PERCENT = 0.5; Random Trader parameters: MIN_RANDOM_TRADER_PORTFOLIOS = 0; MAX_RANDOM_TRADER_PORTFOLIOS = 5;   MIN_RANDOM_TRADER_MONEY = 50; MAX_RANDOM_TRADER_MONEY = 2000;   MIN_RANDOM_TRADER_ACCOUNT_MONEY = 200; MAX_RANDOM_TRADER_ACCOUNT_MONEY = 1000; MIN_RANDOM_TRADER_PORTF_ITEM_PRICE = 20; MAX_RANDOM_TRADER_PORTF_ITEM_PRICE = 3000;   MIN_RANDOM_TRADER_RISK_AMOUNT = 0.01; MAX_RANDOM_TRADER_RISK_AMOUNT = 0.25;
  • Program realization June 10, 2011 Real price and fundamental price distributions Minimum, maximum and average price distributions
  • Experiment 2:
    • Overall parameters:
    • MARKET_MAKER_TRADER_COUNT = 2;
    • RANDOM_TRADER_COUNT = 0;
    • FUNDAMENTAL_TRADER_COUNT = 500;
    • BROKER_COUNT = 20;
    • MARKET_COUNT = 1;
    • COMPANY_COUNT = 10;
    • CLASSIFICATORS_COUNT = 31;
    • Companies:
    • COMPANY_MAX_ASSETS = 15000; // 50Mbyte
    • COMPANY_MIN_ASSETS = 10000; // 1Mbyte
    •  
    • Brokers:
    • MIN_BROKER_MARKET_ACCOUNT_MONEY = 100000; // 100k.
    • MAX_BROKER_MARKET_ACCOUNT_MONEY = 150000; // 300k.
    • BROKER_MONEY = 10000; // 10k.
    • Broker and market:
    • MAX_COMMISION_PLANS = 5;
    June 10, 2011 Market maker trader parameters: MIN_MM_TRADER_CHANGE_PERCENT = 0.5; MAX_MM_TRADER_CHANGE_PERCENT = 0.7; Random Trader parameters: MIN_RANDOM_TRADER_PORTFOLIOS = 0; MAX_RANDOM_TRADER_PORTFOLIOS = 3;   MIN_RANDOM_TRADER_MONEY = 10; MAX_RANDOM_TRADER_MONEY = 200000; MIN_RANDOM_TRADER_ACCOUNT_MONEY = 200; MAX_RANDOM_TRADER_ACCOUNT_MONEY = 1000; MIN_RANDOM_TRADER_PORTF_ITEM_PRICE = 20; MAX_RANDOM_TRADER_PORTF_ITEM_PRICE = 3000; MIN_RANDOM_TRADER_RISK_AMOUNT = 0.01; MAX_RANDOM_TRADER_RISK_AMOUNT = 0.5;
  • Price time series. Experiment 2 June 10, 2011
  • Experiment 3: June 10, 2011 Overall parameters: FUNDAMENTAL_TRADER_ MARKET_MAKER_TRADER_COUNT = 2; RANDOM_TRADER_COUNT = 250; COUNT = 250; BROKER_COUNT = 5; MARKET_COUNT = 1; COMPANY_COUNT = 10; CLASSIFICATORS_COUNT = 31; Companies: COMPANY_MAX_ASSETS = 50000000; // 50Mbyte COMPANY_MIN_ASSETS = 1000000; // 1Mbyte   Brokers: MIN_BROKER_MARKET_ACCOUNT_MONEY = 100000; // 100k. MAX_BROKER_MARKET_ACCOUNT_MONEY = 300000; // 300k. BROKER_MONEY = 10000; // 10k. Broker and market: MAX_COMMISION_PLANS = 3; Market maker trader parameters: MIN_MM_TRADER_CHANGE_PERCENT = 0.1; MAX_MM_TRADER_CHANGE_PERCENT = 0.5; Random Trader parameters: MIN_RANDOM_TRADER_PORTFOLIOS = 0; MAX_RANDOM_TRADER_PORTFOLIOS = 2; MIN_RANDOM_TRADER_MONEY = 500; MAX_RANDOM_TRADER_MONEY = 5000; MIN_RANDOM_TRADER_ACCOUNT_MONEY = 2000; MAX_RANDOM_TRADER_ACCOUNT_MONEY = 7000; MIN_RANDOM_TRADER_PORTF_ITEM_PRICE = 2000; MAX_RANDOM_TRADER_PORTF_ITEM_PRICE = 4000; MIN_RANDOM_TRADER_RISK_AMOUNT = 0.01; MAX_RANDOM_TRADER_RISK_AMOUNT = 0.10;
  • Price time series. Experiment 3 June 10, 2011
    • THANK YOU
    • ANY QUESTIONS?
    June 10, 2011
  • Fundamental analysis
      • Fundamental analysis is based on an assessment of market conditions in general and assessing the future development of a single issuer.
      • Fundamental analysis is a fairly laborious and a special funding agencies.
      • Fundamental analysis depends on the news of factors. By random and unexpected news include political and natural, as well as war.
      • How to conduct a fundamental analysis can be divided into four separate units, correlating with each other.
  • Fundamental analysis technology The first unit - is a macroeconomic analysis of the economy as a whole. The second unit - is an industrial analysis of a particular industry. A third unit - a financial analysis of a particular enterprise. A fourth unit - analyzing the qualities of investment securities issuer. Fundamental analysis technology includes an analysis of news published in the media, and comparing them with the securities markets.
  • Analysis Method Keyword extraction, characterizing the market: boost or cut, the increase / decrease. Automatic analysis using the terminology the ontology. Processing time series (filtering, providing trends, the seasonal components). Using neural networks to classify the flow of news and processing time series.
    • Examine what news articles relevant to the company, Yahoo uses profiling to establish consistency between articles and companies.
    • For each trend formed a temporary window to explore how art relates to the trend.
    • It is believed that there is a match, if the article appeared a few hours before the trend.
    News analysis target
  • The intensity of the flow of news data The joint processing of digital and text data Digital data Time series The movement of financial instruments (price / volume) ‏ Flow intensity: 5Mb/day, on the tool Text data Text flows Various types: News, financial reports, company brochures, government documents Flow intensity: 20 Mb / day
  • Idea of system Past articles with news Past data pricing securities market Building model Model New arcticles with news Prediction results System exit