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Share Market Sectoral Indices Movement Forecast with Lagged Correlation and Association Rule Mining

TL;DR: This paper analyses the correlation between two different sectoral indices in a time lagged manner to identify the level of dependence among two different sectors and considers only those sectors having higher value of correlation for association rule mining.
Abstract: This paper analyses the correlation between two different sectoral indices (eg between Automobile sector index and between Metal sector index, between Bank sector index and IT sectoral index etc) in a time lagged manner Lagging period is varied from 1 day to 5 days to investigate if any selected sector has lagged influence over any other sectoral index movement If any upward/downward movement of a sectoral index (sector A) is correlated with similar upward/downward movement of another sectoral index (Sector B) with a time lag of ‘d’ days, then with association rule mining support and confidence is calculated for the combination If d is the lag for which support and confidence is maximum then depending on the higher correlation as well as higher support and confidence value it is possible to forecast future (d days ahead of current day) movement of sector B based on present day movement of sector A This model first uses correlational analysis to identify the level of dependence among two different sectors, then considers only those sectors having higher value of correlation for association rule mining Those sector are not considered for which combination correlation is very low or 0

Summary (3 min read)

1 Introduction

  • The basic idea behind technical analy- sis is that current stock price of a company incorporates impacts and effects of eco- nomic, financial, political and psychological factors.
  • In any Stock market listed companies are categorized into different sectors depending on the business domain the company belongs to.
  • In the next sub section the authors briefly discuss about Indian share market as well as sectoral indices that are considered in this case study.

An Overview of Indian Share Market

  • Two most important stock exchanges in India are BSE and NSE.
  • The Bombay Stock Exchange (BSE) is one of the oldest stock exchanges in India and one of the top stock exchanges globally with respect to number of listed companies and market capitalization.
  • These are some of the largest and most actively traded stocks, hence it is con- sidered as representative of various industrial sectors of the Indian economy.
  • It is published since 1st January 1986 and regarded as the pulse of the domestic stock markets in India [9].
  • The NIFTY 50 index is n tion l stock exch nge of Indi ‘s benchmark stock market index for Indian equity market [10].

Statistical Correlation

  • Let Xt and Yt are two given time series closing prices for N days.
  • Cross correlation between them is defined as- Where x , and Y ; Sx , Sy being the sample standard deviations of series X and Y.
  • Any positive r values between 0 and +1 indicates that the relationship between x and y variables are such that with increase in values of X, Y value also increases.
  • R value closer to 0 signifies that there is no linear correlation or a very weak correlation, also known as No correlation.
  • Gener lly with noisy data less threshold values are considered.

Association Rule Mining

  • Data mining, an important part of knowledge discovery in databases (KDD) pro- cess employs many different techniques for knowledge discovery and prediction such as classification, clustering, sequential pattern mining, association rule mining and analysis.
  • From the frequent item-sets a set of strong rules are calculated.
  • Not all frequent item- sets are considered as strong, only those with a minimum support and confidence are considered for the next step.
  • It indicates if people are buying bread and sugar then they may also buy butter.

Research Framework

  • The research framework of this study is shown in Fig.
  • It involves collecting in- dex values of 6 industrial sectors from NSE.
  • Each trading days closing prices are used as the raw input data for their analysis.
  • Initial time series plotting of sectoral indices of selected sectors gives a basic graphical visualization of the raw data about their co-movement pattern.
  • Raw dataset is then processed into proper format to be used in association rule mining and for correlation analysis.

Correlation Analysis

  • The authors data set consists of day wise closing prices of 6 different sectoral indices of 2015.
  • The authors then calculated pairwise correlation for all the possible pair of sectors with a lag of 0 day to 5 days.
  • A delay of 0 day means same day correlation between the two sectoral indices.
  • Microsoft excel spreadsheet based statistical tools has been used to derive the re- sults shown in table 1 and Fig.
  • In this study a correlation value of r >=0.8 has been considered as good correlation nd correl tion v lue of r<=0.5 h s been neglected s ‗the authors k or no correl tion‘.

Data Preprocessing and Encoding

  • Whole dataset contains such sectoral index closing prices of 6 sectors.
  • For their experiments the authors have considered ∆t =0.2 as it gives good results.
  • Step 1: change in index values are calculated for each sector as follows: ∆pi = pi+1 -pi Step 2: Different sectoral index values has different base and movement amount in absolute values so to normalize all sectors the authors consider percentage change.
  • Ci value may be positive or negative depending on the price movement of the sec- toral index.
  • Here vi becomes +1 if change is in positive direction i.e. sectoral index moves upwards.

Rank the Generated Association Rules

  • Rank all the derived association rules as per there support and confidence value.
  • Value of K depends on the investors risk profile and preferences.

4 Results and Analysis

  • The authors have used open source java based frequent pattern mining library SPMF [7] for deriving association rules with apriori and suitably modified to incorporate other required changes.
  • Fig. 2 shows the initial time series plotting of different sectors where co-movement patterns can be visually seen.
  • Different correlation values are plotted against delay in Fig. 3 to show the positive, negative as well as no-correlation between different sectors with varying delays.
  • Finally table 2 shows the top 15 association rules that re mined using the bove mentioned method nd r nked s per rules‘ support nd confidence measure.
  • IT and pharma index also shows similar pattern.

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Share Market Sectoral Indices Movement Forecast with
Lagged Correlation and Association Rule Mining
Giridhar Maji
1
, Soumya Sen
2
and Amitrajit Sarkar
3
1
Department of Electrical Engineering, Asansol Polytechnic, India
Giridhar.Maji@gmail.com
2
AK Choudhury School of Information Technology, University of Calcutta, Kolkata, India
iamsoumyasen@gmail.com
3
Ara Institute of Canterbury, NZ
Amitrajit.Sarkar@ara.ac.nz
Abstract. This paper analyses the correlation between two different sectoral in-
dices (e.g. between Automobile sector index and between Metal sector index,
between Bank sector index and IT sectoral index etc.) in a time lagged manner.
Lagging period is varied from 1 day to 5 days to investigate if any selected sec-
tor has lagged influence over any other sectoral index movement. If any up-
ward/downward movement of a sectoral index (sector A) is correlated with sim-
ilar upward/downward movement of another sectoral index (Sector B) with a
time lag of d days, then with association rule mining support and confidence
is calculated for the combination. If d is the lag for which support and confi-
dence is maximum then depending on the higher correlation as well as higher
support and confidence value it is possible to forecast future (d days ahead of
current day) movement of sector B based on present day movement of sector A.
This model first uses correlational analysis to identify the level of dependence
among two different sectors, then considers only those sectors having higher
value of correlation for association rule mining. Those sector are not considered
for which combination correlation is very low or 0.
This model has been tested with Indian share market data (NSE sectoral in-
dex data of 6 sectors) of 2015. Result shows it is possible to predict in short
term (1 to 5 days in future) price movement of sectoral indices using other
lagged correlated sector price index movement.
Keywords: stock indices prediction, lagged correlation, association rule mining
1 Introduction
Predicting the future stock prices are the most important queries for the investors in
share market. Many different techniques, mathematical formulation, genetic algorithm
(GA) based models, neural network models, machine learning based techniques etc.
have been proposed and tested with mixed success[1,2,3,15]. Predicting the future
price of some stock is inherently difficult as the price movement depends on large
number of i of macro-economic, micro-economic, technical parameters
as well as a lot of unknown parameters which come in to the context all of a sudden.
Future stock price of a company becomes stochastic due to difference in perception
about the future of the company among investors. A group of investor foresees a fu-

ture uptrend or good earnings for the company and they expect its stock price to go up
in near future. Therefore they buy at current price to sell at some higher price in future
and earn profit. At the same time some other groups of investors with a perception

they sell with current price with a view to latter buy the same or more quantity of
shares with lower price in future to earn profit. The basic idea behind technical analy-
sis is that current stock price of a company incorporates impacts and effects of eco-
nomic, financial, political and psychological factors. It studies the historical stock
prices and assumes that the future trend will follow the past behavior. The technical
analysis offers information about the possible future evolution of the stock market.
Technical analysis is done based on a lot of different technical indicator parameters
  -    n      -days
 MACD, relative strength index, momentum etc. along with price-
to-earnings ratio, dividend yield, profit margin , return on investment etc.[2,3,12,15].
  erception also depends on rumors & market speculation and some
unforeseen sudden big events and their unknown reaction towards stock prices of

a stochastic random event but due to the technical parameters it is also not totally
unpredictable.
In any Stock market listed companies are categorized into different sectors depend-
ing on the business domain the company belongs to. We have considered the follow-
ing six sectors for our study: Banks, Automobiles, IT & Software, Metals, Pharma-
ceuticals and FMCG (Fast Moving Consumer Goods). These different sectors have
sectoral index to represent their aggregated trends in a stock exchange. It is similar to
the stock exchange index (for example SENSEX, NIFTY in BSE and NSE). These
sectoral indices react with different external and internal events differently and hence
their movement. Same external event may affect different industry sector differently.
Depending on a many different factors some sectoral index moves in positive direc-
tion while in the same time some other sector moves into the negative zone (or may
remain neutral). As an example when dollar value increases with respect to Indian
Rupee (INR) almost all export companies of India gains and IT sectors majorly get
most of the benefits as they earn in dollar and spend in INR. At the same time import-
ers incur losses.
This is a very complex relationship to measure. In this research work we aim to fo-
cus on this in terms of following issues:
1. If these reactions with the external factors are correlated between the sectors.
2. Identifying how different sectors are related? They may be highly correlated, corre-
lated, neutral or not co-related at all.
3. Among the highly correlated sector pairs which are positively correlated and which
are negatively correlated.
4. Is there any correlation among the highly correlated sector pairs with some days
al index movement of sector-A is correlated with sectoral

index movement of sector-B on d days in future. If we find a high correlation
among two different sectors with a time lag of d days then we can forecast sectoral
index movement of Sector-B days ahead.
In the next sub section we briefly discuss about Indian share market as well as sec-
toral indices that are considered in this case study. Then we will discuss about Asso-
ciation rule mining techniques along with support and confidence that will be used in
our analysis.
An Overview of Indian Share Market
Two most important stock exchanges in India are BSE and NSE. The Bombay
Stock Exchange (BSE) is one of the oldest stock exchanges in India and one of the
top stock exchanges globally with respect to number of listed companies and market
capitalization. The 30 company index from BSE is known as SENSEX or BSE30 is a
stock market index of 30 well established and financially sound companies listed on
BSE. These are some of the largest and most actively traded stocks, hence it is con-
sidered as representative of various industrial sectors of the Indian economy. It is
published since 1st January 1986 and regarded as the pulse of the domestic stock
markets in India [9]          
benchmark stock market index for Indian equity market [10]. It covers 22 sectors of
Indian economy. As SENSEX and NIFTY is used to understand average trend and
movement of BSE and NSE for almost all financial purposes, each stock exchange
has industry sectors and each sector has many sectoral index(s) that reflect the behav-
ior and performance of the concerned sector. In this study following 6 sectors are
considered: Auto, Bank, Pharma, FMCG, IT and Metal. All index values are taken
from NIFTY industrial sectors. Different sectoral index(s) consists of different num-
ber of representative company stocks. For example NSE Auto Index consists of 15
stocks and NIFTY bank index comprises of 12 banking sector stocks.
Statistical Correlation
Let Xt and Yt are two given time series closing prices for N days. If we consider a
lag of d days between them then co-variance between the two series is defined as-
󰇛󰇜
󰇛

󰇜󰇛

󰇜


󰇛󰇜
w
X
and
Y
are the sample means of the time series X and Y.
Cross correlation between them is defined as-

󰇛
󰇜


󰇛󰇜
Where󰇛󰇜, and 󰇛󰇜 ;

Sx , Sy being the sample standard deviations of series X and Y.
The value of r varies between +1 to -1. Depending on the sign of r following can be
inferred:
Positive correlation: r value closer to +1 signifies strong positive correlation be-
tween the variables. An r value of exact +1 indicates a perfect positive fit. Any
positive r values between 0 and +1 indicates that the relationship between x and y
variables are such that with increase in values of X, Y value also increases.
Negative correlation: If x and y have a strong negative linear correlation, r is close
to an r value of exactly -1 indicates a perfect negative fit. Negative values indicate
a relationship between x and y such that as values for x increase, values for y de-
crease.
No correlation: r value closer to 0 signifies that there is no linear correlation or a
very weak correlation. In other words x and y values are completely un-correlated
and there is a random, relationship between the two variables x, y.
A perfect correlation of ± 1means that all the data points are lying on a straight
line. Correlation coefficient r does not have a dimension; hence it does not depend
on the units used. Generally an r value of greater than 0.8 is considered as highly
correlated and less than 0.5 is considered weakly correlated. A point to remember is
           
data less threshold values are considered.
Association Rule Mining
Data mining, an important part of knowledge discovery in databases (KDD) pro-
cess employs many different techniques for knowledge discovery and prediction such
as classification, clustering, sequential pattern mining, association rule mining and
analysis. Nowadays it is used in almost all the data driven decision models such as
business analysis, strategic decision making, financial forecasting, future sales predic-
tion etc. Agrawal [13] first introduced association rules for frequent pattern mining
among items in large transaction dataset. They introduced the Apriori principle which
says: Any subset of a frequent itemset must be frequent. Hence it can also be said in
another term as: No superset of any infrequent itemset should be calculated for further
processing. From the frequent item-sets a set of strong rules are calculated. Strength
of a rule is measured based on support and confidence values. Not all frequent item-
sets are considered as strong, only those with a minimum support and confidence are
considered for the next step. This Aprori n-
 computations feasible. Let us consider an association rule :{ bread,
sugar} => {butter} It indicates if people are buying bread and sugar then they may
also buy butter. Association rule mining (ARM) is used here to show the relationship

between different item-sets. It is also known as market basket analysis. An association
rule is expressed in the form of an implication as:
X Y, where X and Y are disjoint item-sets, i.e. .
Support and confidence measures the strength of an association rule. Support is used
to find how frequently a rule is applicable, whereas confidence finds how frequently
items in itemset Y also appear in transactions containing itemset X. The formal defi-
nitions of these metrics are:
Support is the fraction of the total transactions that matches the rule. It is defined for
rule R as the ratio of the number of occurrence of R, given all occurrences of all rules
[3].
Support (X Y) = P (X U Y) =


󰇛󰇜
Support of the rule {tire, auto accessories} i-
fies that 98% of people who purchase tires and auto accessories also get automotive
services done.
Confidence signifies the strength of the rule. The confidence of a rule X -> Y, is the
ratio of the number of occurrences of Y given X, among all other occurrences given
X. [3].
Confidence (X -> Y) = P (Y|X), the probability of Y given X =

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
󰇛
󰇜
A minimum support threshold value (min_sup) is generally defined to select the
point of interest. It is used to discard those itemsets with support less than min_sup as
that may not be interesting from business perspective. Confidence gives an idea of the
conditional probability of Y given X. It is a measure of reliability of the inference
made by a rule. Higher value of confidence implies that it is more likely for Y to be
present in transactions that contain X.
One important point to consider is that not all strong rules (based on support and
confidence values) are necessarily interesting. As we can see support-confidence
framework can be misleading; it can identify a rule (A=>B) as interesting (strong)
when, in fact the occurrence of A might not imply the occurrence of B. Correlation
Analysis provides an alternative framework for finding interesting relationships and
allows to improve understanding of meaning of some association rules . Measure of
interest or Lift is one of such correlational measure of association rules. Lift is de-
fined as [19] :
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Citations
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Proceedings ArticleDOI
26 Sep 2019
TL;DR: A framework using LSTM (Long ShortTerm Memory) model and companies’ net growth calculation algorithm to analyze as well as prediction of future growth of a company is proposed.
Abstract: Predicting stock market is one of the most difficult tasks in the field of computation. There are many factors involved in the prediction – physical factors vs. physiological, rational and irrational behavior, investor sentiment, market rumors,etc. All these aspects combine to make stock prices volatile and very difficult to predict with a high degree of accuracy. We investigate data analysis as a game changer in this domain.As per efficient market theory when all information related to a company and stock market events are instantly available to all stakeholders/market investors, then the effects of those events already embed themselves in the stock price. So, it is said that only the historical spot price carries the impact of all other market events and can be employed to predict its future movement. Hence, considering the past stock price as the final manifestation of all impacting factors we employ Machine Learning (ML) techniques on historical stock price data to infer future trend. ML techniques have the potential to unearth patterns and insights we didn’t see before, and these can be used to make unerringly accurate predictions. We propose a framework using LSTM (Long ShortTerm Memory) model and companies’ net growth calculation algorithm to analyze as well as prediction of future growth of a company.

37 citations


Cites methods from "Share Market Sectoral Indices Movem..."

  • ...in [5] has used RNN-LSTM model on NIFTY-50 stocks with 4 features (high/close/open/low price of each day)....

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Book ChapterDOI
TL;DR: A new methodology for efficient implementation by forming lattice on query parameters helps to co-relate the different query parameters that in turn form association rules among them.
Abstract: This research work is on optimizing the number of query parameters required to recommend an e-learning platform This paper proposes a new methodology for efficient implementation by forming lattice on query parameters This lattice structure helps to co-relate the different query parameters that in turn form association rules among them The proposed methodology is conceptualized on an e-learning platform with the objective of formulating an effective recommendation system to determine associations between various products offered by the e-learning platform by analyzing the minimum set of query parameters

8 citations

Journal ArticleDOI
TL;DR: In this paper, a data mining based approach employs a curve fitting/regression technique to forecast the individual stock price, based on the above analysis, a framework to diversify the investment of the capital fund is proposed.
Abstract: Investment in the share market helps generate more profit than the other financial instruments but has the threat of market risk that might lead to a high loss. This risk factor refrains many potential investors from investing in the share market directly. Instead, they invest in different mutual funds that are being managed by experienced portfolio managers. To avoid the risk factors and increase the gain, they put the accumulated capital in multiple stocks. They need to perform many calculations and predictions to overcome the uncertainties and unpredictability and need to ensure higher gains to the investors of that mutual fund. In this research work initially, a data mining based approach employs a curve fitting/regression technique to forecast the individual stock price. Based on the above analysis, we propose a framework to diversify the investment of the capital fund. This method employs buy and hold strategy using both statistical features and basic domain knowledge of the share market. The proposed framework distributes the capital first, by distributing sector-wise, and then for each sector, investing company-wise, as a diversified approach among different stocks for higher return but maintaining lower risks. Experimental results show that the proposed framework performs well and generates a good yield compared to some benchmark and ranked mutual funds in the Indian stock market.

6 citations

Journal ArticleDOI
01 Apr 2021
TL;DR: In this paper, the authors proposed a new association rule mining technique for quick decision-making and it gives better performance over Apriori algorithm which is one of the most popular approaches for Association rule mining.
Abstract: Recommendation systems are now inherent for many business applications to take important business decisions. These systems are built based on the historical data that may be the sales data or customer feedback etc. Customer feedback is very important for any organization as it reflects the view, sentiment of the customers. Online systems allow customers to purchase products at a glance from any e-commerce website. Generally, the potential buyers check the review of the products to take informed decision of purchase. In this work, we attempt to build a recommendation model to find out the influence of a product on another product so that if a user purchases the influential product then the recommender system can recommend the influenced products to the users. In this paper, the recommendation system has been built based on association rule mining. We proposed a new association rule mining technique for quick decision-making and it gives better performance over Apriori algorithm which is one of the most popular approaches for association rule mining. The entire framework has been developed in Neo4j graph data model for doing the data modelling from raw text file and also to perform the analysis. We used real-life customer feedback data of amazon for experimental purpose.

6 citations

References
More filters
Proceedings ArticleDOI
01 Jun 1993
TL;DR: An efficient algorithm is presented that generates all significant association rules between items in the database of customer transactions and incorporates buffer management and novel estimation and pruning techniques.
Abstract: We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer management and novel estimation and pruning techniques. We also present results of applying this algorithm to sales data obtained from a large retailing company, which shows the effectiveness of the algorithm.

15,645 citations

Journal ArticleDOI
TL;DR: An efficient algorithm is presented that generates all significant transactions in a large database of customer transactions that consists of items purchased by a customer in a visit.
Abstract: We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant assoc...

3,198 citations


"Share Market Sectoral Indices Movem..." refers background in this paper

  • ...Agrawal [13] first introduced association rules for frequent pattern mining among items in large transaction dataset....

    [...]

Proceedings ArticleDOI
01 Jun 1997
TL;DR: A new algorithm for finding large itemsets which uses fewer passes over the data than classic algorithms, and yet uses fewer candidate itemsets than methods based on sampling and a new way of generating “implication rules” which are normalized based on both the antecedent and the consequent.
Abstract: We consider the problem of analyzing market-basket data and present several important contributions. First, we present a new algorithm for finding large itemsets which uses fewer passes over the data than classic algorithms, and yet uses fewer candidate itemsets than methods based on sampling. We investigate the idea of item reordering, which can improve the low-level efficiency of the algorithm. Second, we present a new way of generating “implication rules,” which are normalized based on both the antecedent and the consequent and are truly implications (not simply a measure of co-occurrence), and we show how they produce more intuitive results than other methods. Finally, we show how different characteristics of real data, as opposed by synthetic data, can dramatically affect the performance of the system and the form of the results.

2,149 citations


"Share Market Sectoral Indices Movem..." refers methods in this paper

  • ...We adapt the association rule mining using Apriori from [11] and used lift value[19] as a measure of interest of the mined rules....

    [...]

Journal ArticleDOI
TL;DR: A neural model is built for the financial market, allowing predictions of stocks closing prices future behavior negotiated in BM&FBOVESPA in the short term, using the economic and financial theory, combining technical analysis, fundamental analysis and analysis of time series, to predict price behavior.
Abstract: Predicting the direction of stock price changes is an important factor, as it contributes to the development of effective strategies for stock exchange transactions and attracts much interest in incorporating variables historical series into the mathematical models or computer algorithms in order to produce estimations of expected price fluctuations. The purpose of this study is to build a neural model for the financial market, allowing predictions of stocks closing prices future behavior negotiated in BM&FBOVESPA in the short term, using the economic and financial theory, combining technical analysis, fundamental analysis and analysis of time series, to predict price behavior, addressing the percentage of correct predictions of price series direction (POCID or Prediction of Change in Direction). The aim of this work is to understand the information available in the financial market and identify the variables that drive stock prices. The methodology presented may be adapted to other companies and their stock. Petrobras stock PETR4, traded in BM&FBOVESPA, was used as a case study. As part of this effort, configurations with different window sizes were designed, and the best performance was achieved with a window size of 3, which the POCID index of correct direction predictions was 93.62% for the test set and 87.50% for a validation set.

181 citations


"Share Market Sectoral Indices Movem..." refers background or methods in this paper

  • ...along with priceto-earnings ratio, dividend yield, profit margin , return on investment etc.[2,3,12,15]....

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  • ...Several researches have been done over the period on predicting future stock price or price movement direction (upward or downward) along with trend analysis based on mainly different statistical modeling [3,4,6,7,8,14,15]....

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  • ...have been proposed and tested with mixed success[1,2,3,15]....

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  • ...It is defined for rule R as the ratio of the number of occurrence of R, given all occurrences of all rules [3]....

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Journal ArticleDOI
TL;DR: Ten different techniques of data mining are discussed and applied to predict price movement of Hang Seng index of Hong Kong stock market and experimental results show that the SVM and LS-SVM generate superior predictive performances among the other models.
Abstract: Ability to predict direction of stock/index price accurately is crucial for market dealers or investors to maximize their profits. Data mining techniques have been successfully shown to generate high forecasting accuracy of stock price movement. Nowadays, in stead of a single method, traders need to use various forecasting techniques to gain multiple signals and more information about the future of the markets. In this paper, ten different techniques of data mining are discussed and applied to predict price movement of Hang Seng index of Hong Kong stock market. The approaches include Linear discriminant analysis (LDA), Quadratic discriminant analysis (QDA), K-nearest neighbor classification, Naive Bayes based on kernel estimation, Logit model, Tree based classification, neural network, Bayesian classification with Gaussian process, Support vector machine (SVM) and Least squares support vector machine (LS-SVM). Experimental results show that the SVM and LS-SVM generate superior predictive performances among the other models. Specifically, SVM is better than LS-SVM for in-sample prediction but LS-SVM is, in turn, better than the SVM for the out-of-sample forecasts in term of hit rate and error rate criteria.

133 citations

Frequently Asked Questions (13)
Q1. What are the contributions in "Share market sectoral indices movement forecast with lagged correlation and association rule mining" ?

This paper analyses the correlation between two different sectoral indices ( e. g. between Automobile sector index and between Metal sector index, between Bank sector index and IT sectoral index etc. ) in a time lagged manner. 

This correlation can be exploited to predict the future index movement direction with a forecast horizon of d days where d is the number of day lag considered. 8. Future work will include analysis considering all sectors at a time instead of only a single sector predicts another. Hence this model can be used by different investors in balancing their portfolio to minimize risk as well as in deciding which sector to invest next. This model can be considered for short term investment as only prediction of next few d ys is possible using current d y ‘ s sector l index movements. 

Artificial neural network models can also be considered in combination with association rules to predict the sectoral index movement. 

The BombayStock Exchange (BSE) is one of the oldest stock exchanges in India and one of thetop stock exchanges globally with respect to number of listed companies and marketcapitalization. 

Data mining, an important part of knowledge discovery in databases (KDD) pro-cess employs many different techniques for knowledge discovery and prediction suchas classification, clustering, sequential pattern mining, association rule mining andanalysis. 

Technical analysis is done based on a lot of different technical indicator parameterssuch s ‗n-d ys moving ver ge‘ (where ‗n‘ c n be 5/10/20/50 etc. d ys), ‗n-daysweighted ver ge‘, MACD, relative strength index, momentum etc. along with price-to-earnings ratio, dividend yield, profit margin , return on investment etc.[2,3,12,15]. 

Rusu et al. discussed stock forecasting [14] methods used by classical approaches such as fundamentalists and chartists and at the same time discussed various recent stochastic methods like white noise, random walk, auto-regressive models etc. 

The basic idea behind technical analy-sis is that current stock price of a company incorporates impacts and effects of eco-nomic, financial, political and psychological factors. 

If the authors consider alag of d days between them then co-variance between the two series is defined as-∑where μX and μY are the sample means of the time series X and Y. 

Auto index movements were very low during the year 2015 than all other sectors and it again can be attributed to non-reduction of car loan interest rates during the year. 

Results shows that some sectors are completely un-correlated but some are highly correlated (positively or negatively) with correlation coefficient values more than 0.8. 

Correl tion with del y of ‗d‘ d ys is c lcul ted s the correl tion between the twodata arrays X,Y as below:xy = Correlation between X {p1,p2…pn} and Y{p1+d, p2+d,… pn+d}So the authors have a total of 15 sector pair from 6 sectors considered and for each pair wehave a total of 6 correlation values (with 0 day to 5 days of lag). 

Future stock price of a company becomes stochastic due to difference in perceptionabout the future of the company among investors.