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Editorial Board


The Indian Journal of STEAM invites technical papers or case studies or review article from authors of various streams of innovation and research in the areas within the scope of the STEAM. Manuscripts are accepted throughout the year and there is no deadline for submission. Manuscript should be sent by email to: ijsteam@dypatiluniversitypune.edu.in.

Once the manuscript is accepted after review, it will be considered for publication in the subsequent issue.

About The Journal

About the Journal


D Y Patil Technical Campus was established in the year 2006 with aim of providing UG/PG education in the fields of Engineering & Technology, Pharmacy, Agriculture, Management and Architecture. More than 7000 students are pursuing their higher education in various disciplines within the campus. In a span of 13 years, the campus is transformed into “D Y Patil University”, a state private university approved by Government of Maharashtra and all statutory bodies of respective programs.

In providing a unique platform facilitating researchers and students of multidisciplinary areas, D. Y. Patil University Pune Ambi hosts publication of Indian Journal of STEAM. It is an ideal platform for “Innovation & Research” in various disciplines under one umbrella. It is strongly believed that removing barriers to access technical papers online greatly influence the transfer of knowledge worldwide at a greater scale. It also serves as a value added input to the potential researchers to present their innovative ideas to the aspiring researchers in science and technology, arts and management disciplines. Thus STEAM represents excelling the growth of technology in various disciplines.


Aim & Scope

Aim & Scope


The Indian Journal of STEAM, provides a broad-based platform for Innovations & Researches in various streams. The Journal is the initiative of the students and research scholars of D. Y. Patil University Ambi Pune to have the broad based platform for publishing research work or case studies or review article of different disciplines and it is a journey towards consilience in a way of interdisciplinary approach.

The focus of Journal aims:

I) To publish the most exciting research areas and sub areas in the field of STEAM

S Computer and information sciences/Physical sciences/Chemical sciences/ Environmental sciences/ Pharmaceutical Sciences .
T Agricultural Technology/Energy and Power /Construction Technology/ Manufacturing Technology/Transportation Technology/Information and Communication Technology .
E Computer Engineering/Mechanical Engineering/Automobile Engineering/ Industrial Engineering/Civil Engineering/Electrical Engineering.
A Arts: painting, architecture, sculpture.
M Strategic Management, marketing, finance, human resources

II) To provide easy accessibility for publishing with rapid reviewing to disseminate the articles for teaching, research, and reference purposes. In today's world of quick information availability globally at the click of the button. The Indian Journal of STEAM gives an open access worldwide to reach readers on larger scale than that of any other subscription-based journal. The published papers in Indian Journal of STEAM increases the visibility and impact factor.

Objective

Objective


  • To create awareness of knowledge, its quality and effectiveness of various innovative ideas.
  • To stimulate further research on the effects of innovations for exploring the scope of further research.
  • To contribute to an overview of areas that lacks interventions.

Editorial Board

Editorial Board


Editor In- Chief

Dr. Vijay Patil, Chancellor and President, DYPU, Pune, Ambi

Prof. Dr Sayalee Gankar, Vice Chancellor, DYPU, Pune, Ambi

Associate Editors
Prof. Dr. Roshanlal Raina
Vice Chancellor, JKLU

Prof. Shrihari Honwad
Vice Chancellor,
Sir Padmapat Singhania University

Prof. Ramadevi Pani
Deputy Director, AIU

Prof. Dr. Sudhir Gavahane
Vice Chancellor, MGM

Prof. Dr. Shihsnak
Director BCU-CCEG
Birmingham City University Birmingham , UK

Co Editor Board
(Col ) Dr. Jyothi Sathpathy
Prof. Dr. Laxman Kambale
Prof. Dr. Rajesh Kherde
Prof. Dr. Abhay Pawar
Prof. Dr. Uma Jadhav
Dr. Ravindra Deshmukh
Dr. Shylesha Channapattana
Dr. Sumant Kulkarni
Dr. M K Nighot
Dr. Ashish Chimbalkar

Research Paper Coordination Committee
Prof. Dr. Kiran Devade
Prof. Dr. Nilesh Bhosale
Ar. Dhanashree Mirajkar
Prof. Jayakar Jadhav

Guidelines for Authors

Guidelines for Authors


1. Type of Papers published: Paper should be of sufficient novelty and not published or under consideration of publication anywhere else. It may be of research article or case studies or review type in nature. The Author Guidelines and paper template is provided in journal webpage.
2. Plagiarism: Manuscript is checked for plagiarism. Similarity index should not be more than 10%.
3. No. of Issues: It is Published monthly, there are 12 issues published in a year.
4. Publication charges: Open access publication charges are 150 Rupees and 500 Rupees for UG and PG/Ph.D scholars respectively. The payment of fees is accepted only if the paper is accepted for publication after single blind review process. If the authors demands print version of the issue, it is chargeable extra as per the prevailing expenses incurred.
5. Call for Papers: The manuscripts are accepted throughout the year. Manuscripts are called by editorial board members through email and Journal web page.
6. After submitting manuscript to the Journal : The chief Editor will assign Editorial board members to the manuscript received based on its scope. All the manuscripts submitted undergoes single blind review process. If any conflict of interest, corresponding author should clearly mention while submission. The manuscript goes through initial screening by the editor about its novelty. If it lies within the scope of the Journal, suitable reviewers are assigned. The manuscript is forwarded to the reviewers (mostly two) with instructions of the review process. Within Fifteen days, reviewer submits his/her comments to the editor. Based on the reviewer comments, editor decides about publishing it in the current issue or next issue. Before the paper being published, authors should submit copyright duly signed.
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Paper Template

Paper Template Download


Paper Title (24pt, Times New Roman, upper case, line spacing: Before: 8pt, after: 16pt)

Subtitle if needed (14pt, Italic,line spacing: Before:8pt, after:16pt)

1*Name of 1st Author, 2Name of 2nd Author, 3Name of 3rd Author

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Abstract :  This study has been undertaken to investigate the determinants of stock returns in Karachi Stock Exchange (KSE) using two assets pricing models the classical Capital Asset Pricing Model and Arbitrage Pricing Theory model. To test the CAPM market return is used and macroeconomic variables are used to test the APT. The macroeconomic variables include inflation, oil prices, interest rate and exchange rate. For the very purpose monthly time series data has been arranged from Jan 2010 to Dec 2014. The analytical framework contains.

Index Terms - Component, formatting, style, styling, insert.


    I. Introduction

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Font: Use Only Times New Roman for whole paper

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All manuscripts must be in English. These guidelines include complete descriptions of the fonts, spacing, and related information for producing your proceedings manuscripts. Please follow them.

This template provides authors with most of the formatting specifications needed for preparing electronic versions of their papers. Margins, column widths, line spacing, and type styles are built in; examples of the type styles are provided throughout this document and are identified initial type, within parentheses, following the example. PLEASE DO NOT READJUST THE SEMARGINS. Some components, such as multileveled equations, graphics, and tables are not prescribed; although the various tablet Ext styles are provided. The formatter will need to create these components, incorporating the applicable criteria that follow.

   II. Experimental/Analytical/Numerical Method

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The template is used to format your paper and style the text. All margins, column widths, line spaces, and text fonts are prescribed; please do not alter them. You may note peculiarities. For example, the head margin in this template measures proportionately more than is customary. This measurement and others are deliberate, using specifications that anticipate your paper as one part of the entire proceedings, and not as an independent document. Please do not revise any of the current designations.

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Abbreviations and Acronyms(Heading2)

Define abbreviations and acronyms the first time they are used in the text, even after they have been defined in the abstract. Abbreviations such as IEEE and SI do not have to be defined. Do not use abbreviations in the title or head sun less they are unavoidable.

3.1Population and Sample

                KSE-100 index is an index of 100 companies selected from 580 companies on the basis of sector leading and market capitalization. It represents almost 80% weight of the total market capitalization of KSE. It reflects different sector company’s performance and productivity. It is the performance indicator or benchmark of all listed companies of KSE. So it can be regarded as universe of the study. On-financial firms listed at KSE-100 Index (74 companies according to the page of KSE visited on 20.5.2015) are treated as universe of the study and the study have selected sample from these companies.

                The study comprised of non-financial companies listed at KSE-100 Index and 30 actively traded companies are selected on the bases of market capitalization. And 2015 is taken as base year for KSE-100 index.

3.2 Data and Sources of Data

                For this study secondary data has been collected. From the website of KSE the monthly stock prices for the sample firms are obtained from Jan 2010 to Dec 2014. And from the website of SBP the data for the macroeconomic variables are collected for the period of five years. The time series monthly data is collected on stock prices for sample firmsand relative macroeconomic variables for the period of 5 years. The data collection period is ranging from January 2010 to Dec 2014. Monthly prices of KSE -100 Index is taken from yahoo finance.

3.3 Theoretical framework

                Variables of the study contains dependent and independent variable. The study used pre-specified method for the selection of variables. The study used the Stock returns are as dependent variable. From the share price of the firm the Stock returns are calculated. Rate of a stock salable at stock market is known as stock price.

                Systematic risk is the only independent variable for the CAPM and inflation, interest rate, oil prices and exchange rate are the independent variables for APT model.

Consumer Price Index (CPI) is used as a proxy in this study for inflation rate. CPI is a wide basic measure to computeusualvariation in prices of goods and services throughout a particular time period. It is assumed that arise in inflation is inversely associated to security prices because Inflation is at last turned into nominal interest rate and change in nominal interest rates caused change in discount rate so discount rate increase due to increase in inflation rate and increase in discount rate leads todecreasethe cash flow’s present value (Jecheche, 2010). The purchasing power of money decreased due to inflation, and due to which the investors demand high rate of return, and the prices decreased with increase in required rate of return (Iqbal et al, 2010).

Equations

Theequationsareanexceptiontotheprescribedspecificationsofthistemplate.YouwillneedtodeterminewhetherornotyourequationshouldbetypedusingeithertheTimesNewRomanortheSymbolfont(pleasenootherfont).Tocreatemultileveledequations,itmaybenecessarytotreattheequationasagraphicandinsertitintothetextafteryourpaperisstyled.

Numberequationsconsecutively.Equationnumbers,withinparentheses,aretopositionflushright,asin Eq. 1,usingarighttabstop.Tomakeyourequationsmorecompact,youmayusethesolidus(/),theexpfunction,orappropriateexponents.ItalicizeRomansymbolsforquantitiesandvariables,butnotGreeksymbols.Usealongdashratherthanahyphenforaminussign.Punctuateequationswithcommasorperiodswhentheyarepartofasentence,asin

Notethattheequationiscenteredusingacentertabstop.Besurethatthesymbolsinyourequationhavebeendefinedbeforeorimmediatelyfollowingtheequation.UseEq.1or “Equation1, not “(1)”, especially atthebeginningofasentence:Equation1is...

I.                    RESEARCH METHODOLOGY

The methodology section outline the plan and method that how the study is conducted. This includes Universe of the study, sample of the study, Data and Sources of Data, study’s variables and analytical framework. The details are as follows;

3.1Population and Sample

                KSE-100 index is an index of 100 companies selected from 580 companies on the basis of sector leading and market capitalization. It represents almost 80% weight of the total market capitalization of KSE. It reflects different sector company’s performance and productivity. It is the performance indicator or benchmark of all listed companies of KSE. So it can be regarded as universe of the study. On-financial firms listed at KSE-100 Index (74 companies according to the page of KSE visited on 20.5.2015) are treated as universe of the study and the study have selected sample from these companies.

                The study comprised of non-financial companies listed at KSE-100 Index and 30 actively traded companies are selected on the bases of market capitalization. And 2015 is taken as base year for KSE-100 index.

3.2 Data and Sources of Data

                For this study secondary data has been collected. From the website of KSE the monthly stock prices for the sample firms are obtained from Jan 2010 to Dec 2014. And from the website of SBP the data for the macroeconomic variables are collected for the period of five years. The time series monthly data is collected on stock prices for sample firmsand relative macroeconomic variables for the period of 5 years. The data collection period is ranging from January 2010 to Dec 2014. Monthly prices of KSE -100 Index is taken from yahoo finance.

3.3 Theoretical framework

                Variables of the study contains dependent and independent variable. The study used pre-specified method for the selection of variables. The study used the Stock returns are as dependent variable. From the share price of the firm the Stock returns are calculated. Rate of a stock salable at stock market is known as stock price.

                Systematic risk is the only independent variable for the CAPM and inflation, interest rate, oil prices and exchange rate are the independent variables for APT model.

Consumer Price Index (CPI) is used as a proxy in this study for inflation rate. CPI is a wide basic measure to computeusualvariation in prices of goods and services throughout a particular time period. It is assumed that arise in inflation is inversely associated to security prices because Inflation is at last turned into nominal interest rate and change in nominal interest rates caused change in discount rate so discount rate increase due to increase in inflation rate and increase in discount rate leads todecreasethe cash flow’s present value (Jecheche, 2010). The purchasing power of money decreased due to inflation, and due to which the investors demand high rate of return, and the prices decreased with increase in required rate of return (Iqbal et al, 2010).

                Exchange rate is a rate at which one currency exchanged with another currency. Nominal effective exchange rate (Pak Rupee/U.S.D) is taken in this study. This is assumed that decrease in the home currency is inversely associated to share prices (Jecheche,2010).  Pan et al. (2007) studied exchange rate and its dynamic relationship with share prices in seven East Asian Countries and conclude that relationship of exchange rate and share prices varies across economies of different countries. So there may be both possibility of either exchange rate directly or inversely related with stock prices. Oil prices are positively related with share prices if oil prices increase stock prices also increase (Iqbal et al, 1012).Ataullah (2001) suggested that oil prices cause positive change in the movement of stock prices. The oil price has no significant effect on stock prices (Dash & Rishika, 2011).Six month T-bills rate is used as proxy of interest rate. As investors are very sensitive about profit and where the signals turn into red they definitely sell the shares. And this sensitivity of the investors towards profit effects the relationship of the stock prices and interest rate, so the more volatility will be there in the market if the behaviors of the investors are more sensitive. Plethora (2002)has tested interest rate sensitivity to stock market returns, and concluded an inverse relationship between interest rate and stock returns. Nguyen (2010) studies Thailand market and found that Interest rate has an inverse relationship with stock prices.

                KSE-100 index is used as proxy of market risk. KSE-100 index contains top 100 firms which are selected on the bases of their market capitalization. Beta is the measure of systematic risk and has linear relationship with return (Horn, 1993). High risk is associated with high return (Basu, 1977, Reiganum, 1981 and Gibbons, 1982). Fama and MacBeth (1973) suggested the existence of a significant linear positive relation between realized return and systematic risk as measured by β. But on the other side some empirical results showed that high risk is not associated with high return (Michailidis et al. 2006, Hanif, 2009). Mollah and Jamil (2003) suggested thatrisk-return relationship is not linear perhaps due to high volatility.

3.4Statistical tools and econometric models

                This section elaborates the proper statistical/econometric/financial models which are being used to forward the study from data towards inferences. The detail of methodology is given as follows.

3.4.1 Descriptive Statistics

                Descriptive Statics has been used to find the maximum, minimum, standard deviation, mean and normally distribution of the data of all the variables of the study. Normal distribution of data shows the sensitivity of the variables towards the periodic changes and speculation. When the data is not normally distributed it means that the data is sensitive towards periodic changes and speculations which create the chances of arbitrage and the investors have the chance to earn above the normal profit. But the assumption of the APT is that there should not be arbitrage in the market and the investors can earn only normal profit. Jarque bera test is used to test the normality of data.

3.4.2 Fama-Mcbeth two pass regression    

                After the test statistics the methodology is following the next step in order to test the asset pricing models. When testing asset pricing models related to risk premium on asset to their betas, the primary question of interest is whether the beta risk of particular factor is priced. Fama and McBeth(1973)develop a two pass methodology in which the beta of each asset with respect to a factor is estimated in a first pass time series regression and estimated betas are then used in second pass cross sectional regression to estimate the risk premium of the factor. According to Blum (1968) testing two-parameter models immediately presents an unavoidable errors-in-the variables problem. It is important to note that portfolios (rather than individual assets) are used for the reason of making the analysis statistically feasible.Fama McBeth regression is used to attenuate the problem of errors-in-variables (EIV) for two parameter models (Campbell, Lo and MacKinlay, 1997).If the errors are in the β (beta)of individual security are not perfectly positively correlated, the β of portfolios can be much more precise estimates of the true β (Blum, 1968).

                The study follow Fama and McBeth two pass regression to test these asset pricing models. The Durbin Watson is used to check serial correlation and measures the linear association between adjacent residuals from a regression model. If there is no serial correlation, the DW statistic will be around 2. The DW statistic will fall if there is positive serial correlation (in worst case, it will be near zero). If there is a negative correlation, the statistic will lie somewhere between 2 and 4. Usually the limit for non-serial correlation is considered to be DW is from 1.8 to 2.2. A very strong positive serial correlation is considered at DW lower than 1.5 (Richardson and smith, 1993).

                According to Richardson and smith(1993) to make the model more effective and efficient the selection criteria for the shares in the period are: Shares with no missing values in the period, Shares with adjusted R2 < 0 or F significant (p-value) >0.05of the first pass regression of the excess returns on the market risk premium are excluded. And Shares are grouped by alphabetic order into group of 30 individual securities (Roll and Ross, 1980).

3.4.2.1 Model for CAPM

In first pass the linear regression is used to estimate beta which is the systematic risk.

                     (3.1)

Where Riis Monthly return of the security, Rf is Monthly risk free rate, Rm is Monthly return of market and βis systematic risk (market risk).

                The excess returns Ri - Rf of each security is estimated from a time series share prices of KSE-100 index listed shares for each period under consideration. And for the same period the market Premium Rm - Rfalso estimated. After that regress the excess returns Ri - Rf on the market premium Rm - Rfto find the beta coefficient (systematic risk).

Then a cross sectional regression or second pass regression is used on average excess returns of the shares and estimated betas.

(3.2)

Where ƛ0= intercept, ȒIis average excess returns of security iIisestimated be coefficient of security I and Є is error term.

3.4.2.2 Model for APT

In first pass the betas coefficients are computed by using regression.

Where Ri is the monthly return of stock i,Rf is risk free rate, βi is the sensitivity of stock i with factors and is the error term.

Then a cross sectional regression or second pass regression is used on average excess returns of the shares on the factor scores.

WhereȒ is average monthly excess return of stock I, ƛ = risk premium, β1 to β4 are the factors scores and εi is the error term.

3.4.3 Comparison of the Models

                The next step of the study is to compare these competing models to evaluate that which one of these models is more supported by data.This study follows the methods used by Chen (1983), the Davidson and Mackinnon equation (1981) and the posterior odds ratio (Zellner, 1979) for comparison of these Models.

3.4.3.1 Davidson and MacKinnon Equation

                CAPM is considered the particular or strictly case of APT. These two models are non-nested because by imposing a set of linear restrictions on the parameters the APT cannot be reduced to CAPM. In other words the models do not have any common variable. Davidson and MacKinnon (1981) suggested the method to compare non-nested models. The study used the Davidson and MacKinnon equation (1981) to compare CAPM and APT.

This equation is as follows;

WhereRi= the average monthly excess returns of the stock i, RAPT= expected excess returns estimated by APT, RCAPM= expected excess returns estimated by CAPM and α measure the effectiveness of the models. The APT is the accurate model to forecast the returns of the stocks as compare to CAPMif α is close to 1.

3.4.3.2 Posterior Odds Ratio

                A standard assumption in theoretical and empirical research in finance is that relevant variables (e.g stock returns) have multivariate normal distributions (Richardson and smith, 1993). Given the assumption that the residuals of the cross-sectional regression of the CAPM and the APT satisfy the IID (Independently and identically distribution) multivariate normal assumption (Campbell, Lo and MacKinlay, 1997), it is possible to calculate the posterior odds ratio between the two models. In general the posterior odds ratio is a more formal technique as compare to DM equation and has sounder theoretical grounds (Aggelidis and Maditinos, 2006).

The second comparison is done using posterior odd radio. The formula for posterior odds is given by Zellner (1979) in favor of model 0 over model 1.

The formula has the following form;

                WhereESS0iserror sum of squares of APT, ESS1iserror sum of squares of CAPM, Nisnumber of observations, K0is number of independent variables of the APT and K1 isnumber of independent variables of the CAPM.As according to the ratio when;

R> 1 means CAPM is more strongly supported by data under consideration than APT.

R < 1 means APT is more strongly supported by data under consideration than CAPM.

IV. RESULTS AND DISCUSSION

               

4.1 Results of Descriptive Statics of Study Variables

Variable

Minimum

Maximum

Mean

Std. Deviation

Jarque-Bera test

Sig

KSE-100 Index

-0.11

0.14

0.020

       0.047

5.558

0.062

Inflation

         -0.01

0.02

0.007

0.008

1.345

0.510

Exchange rate

-0.07

0.04

0.003

0.013

1.517

0.467

Oil Prices

-0.24

0.11

0.041

0.060

2.474

0.290

Interest rate

-0.13

0.05

0.047

0.029

1.745

0.418

Table 4.1: Descriptive Statics


Table 4.1 displayed mean, standard deviation, maximum minimum and jarque-bera test and its p value of the macroeconomic variables of the study. The descriptive statistics indicated that the mean values of variables (index, INF, EX, OilP and INT) were 0.020, 0.007, 0.003, 0.041 and 0.047 respectively. The maximum values of the variables between the study periods were 0.14, 0.02, 0.04, 0.41, 0.11 and 0.05 for the KSE- 100 Index, inflation, exchange rate, oil prices and interest rate.

The standard deviations for each variable indicated that data were widely spread around their respective means.

Column 6 in table 4.1 shows jarque bera test which is used to check the normality of data. The hypotheses of the normal distribution are given;

H0 : The data is normally distributed.

H1 :The data is not normally distributed.

Table 4.1 shows that at 5 % level of confidence, the null hypothesis of normality cannot be rejected. KSE-100 index and macroeconomic variables inflation, exchange rate, oil prices and interest rate are normally distributed.

The descriptive statistics from Table 4.1 showed that the values were normally distributed about their mean and variance. This indicated that aggregate stock prices on the KSE and the macroeconomic factors, inflation rate, oil prices, exchange rate, and interest rate are all not too much sensitive to periodic changes and speculation. To interpret, this study found that an individual investor could not earn higher rate of profit from the KSE. Additionally, individual investors and corporations could not earn higher profits and interest rates from the economy and foreign companies could not earn considerably higher returns in terms of exchange rate. The investor could only earn a normal profit from KSE.

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Use10 point Times New Roman for figure labels. Use words rather than symbols or abbreviations when writing figure axis labels to avoid confusing the reader. As an example, write the quantityMagnetization, orMagnetization, M, not justM.

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   V. Acknowledgment

The preferred spelling of the wordacknowledgmentin America is without aneafter theg.Avoid the stilted expression,One of us (R.B.G.) thanks...  Instead, try R.B.G.thanks. Put applicable sponsor acknowledgments here; DO NOT place the month first page of your paper or as a footnote.

References

[1] Ali, A. 2001.Macroeconomic variables as common pervasive risk factors and the empirical content of the Arbitrage Pricing Theory. Journal of Empirical finance, 5(3): 221–240.

[2] Basu, S. 1997. The Investment Performance of Common Stocks in Relation to their Price to Earnings Ratio: A Test of the Efficient Markets Hypothesis. Journal of Finance, 33(3): 663-682.

[3] Bhatti, U. and Hanif. M. 2010. Validity of Capital Assets Pricing Model.Evidence from KSE-Pakistan.European Journal of Economics, Finance and Administrative Science, 3 (20).

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