Step by step instructions to Be Rich in Securities exchange: An information mining approach

How to be rich in stock market a data mining approach l.jpg
1 / 37
856 days ago, 244 views
PowerPoint PPT Presentation

Presentation Transcript

Slide 1

The most effective method to Be Rich in Stock Market: An information mining approach Wei Pan Umang Bhaskar

Slide 2

Standard&Poor's 500 Elementary Analysis Clustering and Leading Stocks. Foreseeing.

Slide 3

Data Source 06-07 Standard Poor's stock, 253 trade days, free on the web. Kill all stocks that splitted amid 06-07. 387 stocks remain. Standardized costs.

Slide 4

The Stock (100 out of 387)

Slide 5

Investigate arbitrarily, 0 returns

Slide 6

Every day

Slide 7

It's difficult to win cash in a securities exchange

Slide 8

Variance and Classifications After we standardize stocks, we figure the subordinate of the day by day cost of the stock. At that point we figure changes at the subsidiaries of the cost of every stock.

Slide 10

Slightly stocks that have a bigger fluctuation have a superior change of positive return. (frail) => Risk runs with Potential Profit.

Slide 11

Standard&Poor's 500 Elementary Analysis Clustering and Leading Stocks Predicting

Slide 12

Clustering Why? "Amass" stocks Better expectation Says something about the stocks How? Preprocess the information kmeans bunching We attempt to locate an "ideal" number of groups

Slide 13

Clustering: Preprocessing For every stock: Normalize the stock value Price on day d for stock i p(i,d) = p(i,d) - µ(i)/σ 2 (i) Calculate the 7-day moving normal

Slide 14

Clustering: what number bunches? Ideal bunching We attempted to utilize chi-square test for Mahalanobis separate Too few stocks, excessively numerous ascribes Other strategies to get non-solitary lattice additionally did not work We saw that around 30 groups is great

Slide 15

Clustering: Results

Slide 16

Prediction utilizing Clustering Objective: To foresee conduct of gathering for next 7 days Find a "gathering pioneer" Find stock with most extreme relationship with "future qualities" of different stocks Is this connection is superior to present-day relationship? This technique is not ideal

Slide 17

Prediction: Group Leader

Slide 18

Prediction: Group Leader

Slide 19

How great is this expectation? Address: what amount of cash would we be able to make? Calculation: Start with 100 stocks on day 1 If driving stock goes up by 10%, purchase on the off chance that you can If driving stock goes around 10%, offer in the event that you can How much is return?

Slide 20

How much cash would we be able to make? Group 1: Investment: $8051 Returns: $14044 Market: $6477 Cluster 2: Investment: $10518 Returns: $12883 Market: $8878

Slide 21

How much cash would we be able to make? Over every one of the groups, we have the accompanying returns: Total Investment: $142297 Total Returns: $158693 Market: $148884 We have made $9809 over the market!

Slide 22

Prediction with isolated preparing set We isolate the preparation and test information sets We acquire the groups and the "pioneer" in light of the initial 100 days We then purchase 100 stocks on the 101 st day, and after that purchase or offer in view of forecast of the "pioneer" stock

Slide 23

Prediction with particular preparing set Most stocks go down in the last 150 days, yet the execution is still great in a few bunches. We can in any case win cash in this sort of market by taking after the main stock notwithstanding when mean of the bunches goes down inevitably. We show the great bunches

Slide 24

Prediction with partitioned preparing set For group 1: Investment: $5403 Returns: $5839 Market: $5214 For bunch 2: Investment: $1990 Returns: $2069 Market: $1557 Rising Interval (take after driving and profit) By taking after driving stocks, you can win cash inside a little interim in which the stock goes up, while all stocks in the end go down in the bunch.

Slide 25

Prediction with independent preparing set The issue with this approach is that from day 101 onwards, most stocks go down In our calculation, we implement that 100 stocks are purchased on day 101 (to be rational with past tests) Hence, the profits and in addition advertise esteem go down Total speculation: $94154 Total returns: $89732 Total market esteem: $89426

Slide 26

Prediction with discrete preparing set A superior methodology is not purchasing any stock until driving stocks go up. Along these lines we can abstain from losing cash even all stocks go down.

Slide 27

Standard&Poor's 500 Elementary Analysis Clustering and Leading Stocks Predicting

Slide 28

Predictions We test ARIMA on every one of the groups.

Slide 30

ARIMA is bad.

Slide 31

Simplify the question We simply anticipate whether it is going up or down, instead of the cost. It's a double indicator. In software engineering research, we have a pack of double indicators.

Slide 32

A (2,2) indicator 4 DFAs for indicators, pick the DFA as per the past two numbers in the parallel time arrangement. We need to foresee Pt, (Pt-2, Pt-1) => (0 , 0) DFA 1 => (0, 1) DFA 2 => (1, 0) DFA3 => (1,1) DFA4

Slide 33

Each indicator is a DFA For a (2,2) indicator, each DFA has 4 states, and upgrade its states by the real result; every states has one forecast.

Slide 34

Benchmark For 387 stocks, we prepare ARIMA and our paired indicator with value information of the initial 252 days. Furthermore, we need to see which one predicts better on the stock cost of the 253th day. ARIMA: 52% wrong; Binary indicator: 38% off-base.

Slide 35

Error In Predicting: Training Set lengths don't influence much on ARIMA. Neither do AR arrange.

Slide 36

What about foreseeing different days? We utilize parallel to anticipate costs of different days: The mistake rate is around (37%- - 43%). Nonetheless, at times, the blunder rate increments to half (33% of all the test we do.) We trust it is superior to anything ARIMA since it can recollect late state.

Slide 37

Acknowledgment Thanks Eugene for this term and for all the valuable aptitudes he showed us. Much obliged to you to every one of you and cheerful Christmas.