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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.

IndexTerms - Component,formatting,style,styling,insert.
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I. INTRODUCTION
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INTRODUCTION
With time, computers have become increasingly significant tools that are also getting cheaper. The goal of the personal virtual
assistant is to provide a trustworthy, affordable, and simple to use helper. The term "virtual assistant" (VA) refers to computer-
simulated environments that can approximate physical presence in both real-world and fictional settings. A real-time and
interactive technology is a virtual assistant. It implies that the computer can instantly alter the virtual reality in response to user
input. The user's perception of being a part of the action in their environment is enhanced through interaction and its gripping
power. A high-level encounter can be had by utilising all human sensory pathways. The majority of virtual assistant environments
today are primarily visual, shown on a computer screen, but some simulations also contain extra sensory data, such sound through
speakers or headphones. The development of virtual assistants has shown promise in a number of fields, including training
simulators, medicine and health care, rehabilitation, education, engineering, scientific visualisation, and the entertainment sector.
The software functions similarly to Siri and Google Assistant. Yet, the primary focus of the application is the computer. A voice
assistant is a digital assistant that helps people through gadgets and voice recognition software by using speech synthesis, natural
language processing, and voice recognition. The foundation of this research is speech recognition, one of the fundamental ideas in
artificial intelligence.

APPLICATION OF PERSONAL DESKTOP VOICE ASSISTANT

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Voice assistants can do the following basic functions: o Web search o Play music or videos o Set reminders and alarms o Launch
any programme or application o Receive weather updates o Send emails, WhatsApp, etc. These are only a few instances of the
jobs that voice assistants can complete; we can conduct a great deal more depending

on our needs. Voice assistants' capabilities and advancements are always growing day by day to give users improved
performance. Our desktop-based voice assistant is built using Python modules and libraries, allowing it to function quickly and
efficiently on the desktop. The fundamental premise of our project is that the user asks the voice assistant to complete their task
using the device's microphone, and the command is subsequently translated into text. The text request is processed after that, and
a text response is provided along with any voice assistant work. In addition to fundamental daily functions, we are attempting to
integrate the idea of face identification for security purposes in our voice assistant to give it more versatility and personality. Our
application uses the fewest system resources, which decreases the need for expensive systems and lessens the risk to your system
because it doesn't communicate with servers directly. There are many reasons that make this vocal voice command application
necessary in practical settings. These are a few of them: -
To make it possible for a very engaging user experience: Like no other interface, voice help keeps users' attention. Users can
ask for anything they want by speaking naturally to the programmes.
To eliminate user annoyance with the application: With the current machine system, we must touch, type, and use a mouse to
complete our task, which occasionally causes user frustration. Users can ask their desired task directly utilizing a voice assistant.
Voice assistants are truly able to answer for each user: based on their location, language, and preferences. This allows you to
customize your app experience for each user

TECHNOLOGY USED IN PERSONAL DESKTOP VOICE ASSISTANT

Users of the desktop voice assistant can provide voice commands to carry out a variety of tasks. The system must be able to
reliably recognise voice instructions, react quickly, and carry out the specified activities effectively.
Python: Python is a well-liked programming language for creating personal voice assistants on desktop computers. For
implementing speech recognition, natural language processing, and machine learning, it provides a number of libraries and
frameworks.
APIs for speech recognition: Google Cloud Speech-to-Text API, Amazon Transcribe, and Microsoft Azure Speech Services are
a few well-known speech recognition APIs. These APIs offer the ability to convert speech to text and can be incorporated into
voice assistant software.

Natural Language Processing (NLP) libraries: There are several NLP libraries available for Python such as Natural Language
Toolkit (NLTK), spaCy, and Stanford CoreNLP. These libraries help with tasks such as sentiment analysis, named entity
recognition, and part-of-speech tagging.

Text to Speech (TTS) engines: Popular TTS engines include Google Text-to-Speech, Amazon Polly, and Microsoft Speech
Services. These engines can be used to generate human-like speech output from text input.

Machine learning frameworks: Popular machine learning frameworks include TensorFlow, PyTorch, and Scikit-learn. These
frameworks can be used to train machine learning models for speech recognition and NLP tasks.

Graphical User Interface (GUI) libraries: GUI libraries such as PyQt and Tkinter can be used to create a visual interface for
the voice assistant application. The GUI can be used to display information such as weather updates, news articles, and reminders
.
Web APIs: Web APIs such as OpenWeatherMap, NewsAPI, and Spotify Web API can be used to integrate the voice assistant
with external services. These APIs allow the voice assistant to access weather forecasts, news articles, and music streaming

Functional Requirements: The desktop voice assistant should have the following functionalities:
• Wake word detection: The system should be able to detect a wake word such as "Hey, assistant" to activate the assistant.
• Voice recognition: The system should be able to accurately recognize and interpret voice commands from the user.
• Natural language processing: The system should be able to understand the user's intent and respond accordingly.
• Task execution: The system should be able to execute tasks such as setting reminders, playing music, sending emails, and
searching the web.
• Multi-language support: The system should be able to recognize and interpret voice commands in multiple languages

Non-functional Requirements
The desktop voice assistant should meet the following quality attributes:
• Accuracy: The system should recognise voice commands with a high degree of accuracy.
• Speed of response: The system must react quickly to user commands.
• Security: The system must maintain user privacy and be secure.
• Usability: The system ought to be simple to operate and have an intuitive user interface
• Constraints: Windows, Mac, and Linux operating systems should all be compatible with the desktop voice assistant. Moreover,
a variety of microphones and audio input devices should work with the system.

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• For some operations, like sending emails and conducting web searches, the desktop voice assistant expects that the user has a
dependable internet connection. For several features like weather updates and music streaming, the system also relies on third-
party APIs.

Requirements:
1. Software Requirements:
• Windows OS.
2. Hardware Requirements:
• Minimum Requirement – 2 Gb RAM, Microphones.
• Recommended – 4 Gb RAM, Microphones.

Other Requirements:
● Internet Connection.

RESEARCH METHODOLOGY
We are going to use python language and google text to speech API for this project, speech recognition module can be
used to recognize the voice of used, and based on its query will be fired. Many different modules i.e., web browser, YouTube,
Wikipedia, etc. are used to interact with the internet. the OS module is used to interact with operating system related queries. For
Learning purposes, users can search any information related to a certain topic on Wikipedia, Google or in text documents. We are
using some concepts related to AI and NLP for the processing of text into voice. Our project's main goal is to develop a virtual
voice assistant for blind people so they may use it to communicate with emerging technologies, manage their devices, and learn
from them.

Problem statement

Build a virtual voice assistant that will enable users to interact with emerging technologies, manage their devices, and utilize
technology for learning. It serves as a voice assistant for visually impaired people and is a cutting-edge system. By utilizing
distinct custom layouts and speech to text, this solution improves system quality while enabling visually challenged users to
access the desktop's most crucial functionalities. The user's speech will be the basis for all actions taken by the system. The
system assists the user based on voice note, meaning that it follows instructions provided by the user. Because the user cannot see
the action going place on the desktop, the system speaks out if the user needs to receive a response.

• The blind applicant will also sense independence.

• Because the system is a machine, it will execute without error.
• Your smartphone will be controlled solely by voice commands, and the assistant will recognize the situation and respond to the
user appropriately.
• Although many seniors are unable to utilize desktop computers, they can still benefit from this. These assistive technologies will
enable users who are blind or visually handicapped to learn from, compete with, and interact with their sighted counterparts.

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 detailsare 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.Non-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

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Variables of the study contains dependent and independent variable. The study used pre-specified method for the
selection ofvariables. 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 lastturned into nominal interest rate andchange in nominal interest
rates caused change in discount rate so discount rate increase due to increase in inflation rate and increase in discount rateleads
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 inverselyassociated 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 concludethat relationshipof exchange rate and share prices varies across economies of different countries. So there
may be both possibility of either exchange rate directly or inverselyrelated 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 arevery 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 thatInterest rate has aninverse 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 alinear 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 notlinear 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 regressionto 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, thestatistic 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).

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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 R 2 < 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.
Ri−R f =( Rm−R f ) β (3.1)
Where RiisMonthly return of thesecurity, Rf isMonthly risk free rate, Rm isMonthly 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 periodthe market Premium R m - 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.
Ȓi=γ 0+ γ 1 β 1+ є(3.2)
Where ƛ0= intercept, ȒIis average excess returns of security i,βIisestimated 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.
Ri−R f =βi f 1+ β i 2 f 2 + β i 3 f 3 + β i 4 f 4 + ϵ (3.3)
Where Ri is the monthly return of stock i,Rf is risk free rate, βi is the sensitivity of stock i with factors and ϵ 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.
Ȓ=γ 0+ γ 1 β 1+ γ 2 β 2+ γ 3 β 3 +γ 4 β 4 +ϵ i (3.4)
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;
Ri=α R APT + ( 1−α ) RCAPM +e i (3.5)
WhereRi= the average monthly excess returns of the stock i, R APT= expected excess returns estimated by APT, R CAPM= 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 assumptionthat 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;

N /2
R=[ ESS0 / ESS1 ] N K −K /2 (3.6)
0 1

WhereESS0iserror sum of squares of APT, ESS1iserror sum of squares of CAPM, Nisnumber of observations, K 0is
number of independent variables of the APT and K 1 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

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4.1 Results of Descriptive Statics of Study Variables

Std. Jarque-Bera test Sig
Variable Minimum Maximum Mean Deviation
KSE-100 Index
-0.11 0.020 0.047
0.14 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 checkthe 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.

Table 1 Table Type Styles

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II. ACKNOWLEDGMENT
Thepreferredspellingoftheword “acknowledgment” inAmericaiswithoutan “e” afterthe “g”.Avoidthestiltedexpression,
“Oneofus(R.B.G.)thanks...”
Instead,try“R.B.G.thanks”.Putapplicablesponsoracknowledgmentshere;DONOTplacethemonthefirstpageofyourpaperorasafootnote.
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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