Logistic regression analysis to know the factors affecting the financial knowledge in decision of investment non riil assets at university investment gallery

The large population of Indonesia is not an indication of the increasing number of investors in Indonesia, especially investors in non-real assets. This study aims to investigate the variable causes of non-real asset investment decisions.

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LOGISTIC REGRESSION ANALYSIS TO KNOW THE FACTORS AFFECTING THE FINANCIAL KNOWLEDGE IN DECISION OF INVESTMENT

NON RIIL ASSETS AT UNIVERSITY

INVESTMENT GALLERY

Isfenti Sadalia

Dr., Faculty of Economics and Business, Universitas Sumatera Utara, Indonesia

Fahmi Natigor Nasution

Iskandar Muda

ABSTRACT

The large population of Indonesia is not an indication of the increasing number of investors in Indonesia, especially investors in non-real assets This study aims to investigate the variable causes of non-real asset investment decisions The population used in this research is university students who have an investment gallery and registered as an investor at university investment gallery in Aceh, North Sumatera, West Sumatera and Pekan Baru, Riau using Lemeshow formulation for the sample size Dependent variable in this research is non asset investment decision, while independent variable are gender, education level, and availability of financial advisory This research uses binary logistic regression approach The samples used in this study were 384 samples selected by the Lemeshow formulation method The test results with binary logistic regression on the designed model, ie there is one predictor variable that significantly affects the non-real asset investment decision is the variable availability of financial advisors

Keywords: Financial Knowledge, Investment Knowledge, Educational Level, Non-real asset investment decision, binary logistic regression

Cite this Article: Isfenti Sadalia, Fahmi Natigor Nasution, Iskandar Muda, Logistic

Regression Analysis to Know the Factors Affecting the Financial Knowledge in Decision of Investment Non Riil Assets at University Investment Gallery,

International Journal of Management (IJM), 11 (2), 2020, pp 147–162

http://www.iaeme.com/IJM/issues.asp?JType=IJM&VType=11&IType=2

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1 INTRODUCTION

The large population of Indonesia is not an indication of the increasing number of investors in Indonesia, especially investors in non-real assets Investment is sacrificing something in the present to get something in the future with hope of course better (Abdul, 2005) Investment is essentially a placement of funds in the hope of making a profit in the future Investment plays

an important role in driving economic growth and employment in Indonesia On capital market has a strategic position in national economic development Growth in the non-real sector plays an important role in the process of economic growth Therefore, the growth of the non-real sector requires investment to maintain the sustainability of economic growth itself The purpose of this study is aimed to investigate the variable causes of investment decisions

on non-real assets seen from several variables namely, gender, education level, and availability of financial advisors Through this research is expected to yield the best investment decisions

Financial knowledge is now an integrated part of financial literacy (Forster et al., 2019 and Saurabh and Nandan, 2019) Current financial knowledge needs to be known by students since my lecture This is because financial knowledge will have a personal impact on students

in the form of individuals' ability to utilize financial knowledge to make decisions Financial knowledge occurs when individuals have a set of skills and abilities that make these individuals able to utilize existing resources to achieve expected goals Financial knowledge involves not only knowledge and ability to deal with financial problems but also other attributes Financial knowledge will improve financial skills and mastery of financial tools Financial skills as a technique for making decisions in financial management behavior, such

as preparing a budget, choosing investment, choosing an insurance plan, and using credit are examples of financial skills in students so that the entrepreneurial spirit of students is getting stronger

College graduates are also expected to be experts or professionals to plan finances with markets in Indonesia that are still extensive as population growth develops Financial education programs must be carried out to the community and students This program is in line to educate the public about the benefits of financial planning This activity also helps students understand and begin planning and managing finances Financial Management is all activities or company activities related to how to obtain, use and manage company finances Financial management is a management activity that aims to manage funds and assets owned

by the company to be used on things or activities that help achieve the company's main goal, namely profit In a company or business, financial management has 3 main activities carried out by financial managers, namely the acquisition of funds, activities for using funds and managing assets These three things are related to internal and external funding sources that students need to know Working capital and share ownership also include tasks in financial management

Research by Xiao el.al, (2008); Mandell, (2007) and Klein, (2009), Nababan and Sadalia (2013) concluded that the best way to improve behavior in adulthood is to teach good behavior from childhood, including financial behavior While in Indonesia alone personal finance education is still rarely found either in elementary schools to universities Developed countries such as the United States, Canada, Japan and Australia are incessantly providing financial education to their communities, especially students with hopes of literacy Financial behavior of the community is increasing Several institutions were formed, as well as various researches and programs conducted to measure and improve the financial literacy of their people Research by Nababan and Sadalia (2013) concluded that the average respondent was only able to answer half of the 27 questions correctly, which amounted to 56.11%, this means that the level of personal financial literacy of the student strata one overall was included in the

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low category (<60%) In addition, other results concluded that the characteristics of respondents with relatively high financial literacy were male students, economic development study programs, 2008 stambukes (seniors), GPA ≥3, and self-residence, while the characteristics of respondents with financial literacy tendencies were relatively low are female students, management study programs, stambuk 2011 (junior), GPA <3.00, and stay with parents

2 THEORETICAL BACKGROUND

2.1 Financial Knowledge

Financial knowledge is everything about finance that is experienced or that occurs in everyday life (Lusardi, 2019) Financial knowledge as someone's mastery of various things about the world of finance, which consists of your financial skills financial tools Financial knowledge consists of knowledge of financial management, knowledge of financial planning, knowledge of expenditure and income, knowledge of money and assets, knowledge of interest rates, knowledge of credit, basic knowledge of accounting standards, insurance, basic knowledge of investment, deposits, stocks, investments on bonds, and property knowledge Financial knowledge is an integrated part of financial literacy, but financial literacy still includes the ability of individuals to utilize financial knowledge to make decisions Financial literacy occurs when individuals have a set of skills and abilities that make the individual able

to utilize existing resources to achieve the expected goals (Momtalto et al 2019 and Totenhagen et al., 2019) Financial knowledge involves not only knowledge and ability to deal with financial problems but also other attributes Important financial knowledge is owned

by individuals to develop their ability to manage their assets Financial knowledge does not only make individuals able to use assets smartly and wisely, but through financial knowledge will provide added value economically The higher the level of financial knowledge a person will be the better the financial behavior he shows With increasing knowledge, behavior patterns shown by individuals will also increase

2.2 Investment Knowledge

Investing is sacrificing something nowadays to get something in the future with hope of course better (Abdul, 2005) Investment is essentially a placement of funds in the hope of making a profit in the future Investment plays an important role in driving economic growth and employment in Indonesia The growth of the non-real sector requires investment to maintain the sustainability of economic growth itself Irman (2018) explains that the learning process is very important in the process of knowledge formation in each student The understanding associated with having the role necessary to improve the economic environment is very diverse today It is hoped that students with better knowledge can have a more prosperous life in the future

Margaretha and Pambudhi (2015) uses the level of financial literacy on undergraduate students from the faculty of economics, shows gender, and parental income significantly affects students' financial literacy Kristanto and Andreas (2015) also stated that information

is very important A good level of financial literacy has an important role to play in investing The exposure made in the preceding paragraph indicates that education, gender and the availability of financial advisors are considered capable of influencing the outcome of investment decisions to be made by investors, where in this study the investor is a student who becomes the sample

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2.3 Educational Level

Lee and Widyaningrum (2019) states the there stages or levels of formal education Each has

a different education time The following is the level of education :

2.4 Basic Education

The first formal education is basic education Primary education is in the form of elementary

schools and Madrasah Ibtidaiyah as well as junior high schools and Madrasah Tsanawiyah

(Demina et al., 2019) Basic education is the beginning of a child's education starting from training children to reading well, sharpening numeracy and thinking skills The aim of this level of basic education is to lay the foundation for knowledge, personality, noble character and also the skills to live independently and to pursue further education

2.5 Middle Education

Secondary education is an advanced education from basic education These forms of secondary education are high school, Islamic schools, vocational high schools, and vocational

Aliyah madrasas (Eliza et al., 2019) The general purpose of secondary education is to

improve intelligence, knowledge, personality, noble character and skills to live independently and to attend further education The general purpose of vocational secondary education is to improve intelligence, knowledge, personality, noble character, and skills to live independently and follow further education in accordance with their vocational

2.6 Higher Education

Higher education is a continuation of secondary education Higher education includes a variety of diploma, bachelor, master, doctoral and specialist education programs organized by universities This university is obliged to organize education, research, and community service At this level of higher education students are required to be more active in practicing and directly involved in each learning activity because the ultimate goal of this level of education is that students are expected to become human beings who are beneficial to others (Heiman and Olenik, 2019) Colleges can hold academic, professional or vocational programs

3 STAGES IN THE CONSUMER PURCHASE DECISION PROCESS

Basically, consumer decision making for a product varies depending on the type of purchasing decision Complex and expensive purchases may involve more consideration of buyers and more participants but in certain purchasing processes Consumers must go through several stages, known as the "stage model" This model shows that consumers must go through 5 (five) stages in the process of buying a product According to Armstrong et al., (2014) the stages of the process of consumer purchasing decisions as shown below:

Sources : Principles of marketing Armstrong et al., (2014)

Figure 1 Five Stages of Decision Making Process

The more detailed explanation of the image stages of the consumer decision making process are as follows:

Introduction

to problems

Information search

Evaluate alternatives

Buying decision

Post-purchase behavior

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3.1 Need Recognition

The purchase process begins when the buyer recognizes a problem or decision, the buyer feels the difference between the actual state and the desired condition, these needs can be caused by

an internal stimulus such as hunger, thirst, sex, reaching a certain point of the occurrence of a stimulus caused by stimulation externally, for example, someone passes a pastry shop and sees fresh bread that stimulates their hunger

4 ALTERNATIVE SEARCH (EVALUATION OF ALTERNATIVES)

Stimulus consumers try to find more information The amount of search done depends on the strength of the drive, the amount of additional information and satisfaction in searching for the information

4.1 Evaluation of Alternatives

Certain methods view cognitive-oriented processes, namely they assume consumers form judgments on products primarily based on and ratios Some basic concepts in understanding consumer evaluation processes First, consumers try to make ends meet Second, consumers seek certain benefits from product solutions Third, consumers perceive each product as a set

of attributes with different abilities in providing benefits used to satisfy needs

4.2 Purchase Behavior

In the evaluation phase, consumers form preferences for brands, brands in a collection of choices Consumers may also form an intention to buy the product they like best, but two factors can be between purchase intentions and purchasing decisions, namely: (Panda et al., 2019)

 The establishment of other people, the more aggressively the negative attitude of others, the closer the other person is to the consumer, so the greater the consumer will adjust the purchase intention and in the opposite condition also applies

 Factor situation, which is not anticipated, this factor can arise and can change the purchase intention of a consumer

4.3 Post Purchase Behavior

After buying a product, consumers will experience satisfaction or dissatisfaction Satisfaction and dissatisfaction will influence subsequent behavior If consumers feel satisfied, there will

be a repeat purchase, if not then the consumer will switch to another store until the consumer

is satisfied

5 METHODS

Sources of data used in this study are college students who have an investment gallery and registered as an investor in the university investment gallery in Aceh, North Sumatra, West Sumatra and Pekan Baru, Riau, Indonesia This population is not known exactly how many students are investors in each investment gallery The next step uses the Lemeshow formulation to determine the total sample or population not known for certain (Hosmer and Lemeshow, 2000) calculated using the formula:

N = Z2 P(1-P) : d2 Information :

Z = 1.96

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P = Maximum estimation = 0.5 (a standard for research that has not been done before, if it has been done then used the standard in accordance with previous research or use the number

P = 0.4)

d = Alpha 5%

Then the total required sample is:

N = Z2 P(1-P) : d2

N = (1,962)(0,5)(1-0,5) : (0,052)

N = 384 sample

In this study response variable (Y) used has 2 categories, namely:

Y = 1 for investment decisions in non-real assets

Y = 0 for non-investment decision

The predictor variable (X) used is:

The Description of variable as a follows :

Table 1 Description of Variable

0=Female

0=Not S1 Availability of financial advisor (X3) 1=Has a financial advisor

0=Do not have a financial advisor

Binary logistic regression is a method of data analysis used to find the relationship between binary response or outcome or dependent variables, with predictor or explanatory or independent variables (Hosmer and Lemeshow, 2000) The response variable Y consists of 2 categories, investment in non-real and non-investment assets denoted by Y = 1 (investment) and Y = 0 (not invested) In such circumstances, the variable Y follows the Bernoulli distribution for every single observation

5.1 Simultaneous Test

The simultaneous test is conducted to find out the significance of β parameter to the response variable as a whole Testing the significance of these parameters using G test statistic, where the test statistic G follows the distribution of Chi-Square (Hosmer and Lemeshow, 2000) Hypothesis used :

H0 : β1= β2 βp =0

H1 : at least one βj ≠ 0, with i = 1, 2, …,p

Test statistics :

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Area of rejection : Reject Ho if G > X2

5.2 Partial Test

Individual test results will show whether a predictor variable is eligible to enter the model Hypothesis used :

H0 : βj = 0

H1: βj ≠ 0 with j= 1, 2, 3,…, p

Test Statistics: Wald Test Statistics

Area of rejection : reject Ho if

6 RESULTS AND DISCUSSION

Iteration History

The iteration history as a follows :

Table 2 Iteration History Iteration Historya,b,c

Iteration -2 Log likelihood

Coefficients Constant

a Constant is included in the model

b Initial -2 Log Likelihood: 522,574

c Estimation terminated at iteration number 2 because parameter estimates changed by less than, 001

Source: Processed SPSS test calculator results (2019)

H0 = model before entering the independent variable is FIT with data

H1 = model before entering the independent variable is NOT FIT with data

Accepting H1 if Value -2Loglikelihood > Chi square table

In block 0 when the independent variable is not included in the model with the total sample N

= 384 got the value of log-2 likelihood 522,574

Degree of Freedom (DF) = N-1 = 384-1 = 383

Chi square table on DF 383 and probability 0.05 = 429,6324881

Value -2Loglikelihood (522,574) > Chi square table (429,6324881), so reject H0, it’s mean show model before entering independent variable is NOT FIT with data Clasification Table (Frequency of expectation based on the empirical data of the dependent variable) as a follows :

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Table 3 Classification Table

Classification Table a,b

Observed

Predicted Investasi Aset Non Riil

Percentage Correct

tidak investasi

investasi aset non riil

Step 0 investment in non-real

assets

not investment feel

investment in

a Constant is included in the model

b The cut value is ,500

Classification Table is a contingency table 2 x 2 that should occur or also called the expected frequency based on the empirical data of the dependent variable, where the number

of samples that have the category of dependent variable reference or decision of investment of non real assets (code 1) is 170 Meanwhile, invested as much as 210 Total sample 384 people So the overall percentage value before the independent variable is included in the model of 55.3% Variables in the equation as a follows :

Table 4 Variables in the Equation

Variables in the Equation

Table 4 explianed in the equation when before the independent variable is inserted into the model, this means there is no independent variable in the model yet The value of Slope or Coefficient Beta (B) of Constant is equal to -0.211 with Odds Ratio or Exp (B) of 0.810 The significance or p value of the Wald test is 0.041 Variable not in Equinning Stage Beginning (Interpretation of Logistic Regression with SPSS) as a follows :

Table 5 Variable Not In Equinning Stage Beginning

Variables not in the Equation

Overall Statistics 6.958 3 073 Source: Processed SPSS test calculator results (2019)

Table Variables not in the Equation shows the variables that have not been included in the regression model, namely variables X1, X2, and X3 Where X1 is the gender variable, X2 is the education level variable and X3 is the variable availability of the financial advisor Entry Stage Variable (Interpretation of Logistic Regression with SPSS) as a follows :

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Table 6 Entry Stage Variable Iteration History a,b,c,d

Iteration -2 Log likelihood

Coefficients

a Method: Enter

b Constant is included in the model

c Initial -2 Log Likelihood: 522,574

d Estimation terminated at iteration number 4 because parameter estimates changed by less

than, 001

When the independent variable is included in the model with N = 384

Where :

H0 = model by entering independent variable is FIT with data

H1 = model by entering an independent variable is NOT FIT with data

Accept H0 if Value -2 loglikelihood < Chi Square table

Degree of Freedom (DF) = N-number of independent variables - 1

= 384-3-1 = 380

Chi-Square Table On DF 380 and Prob 0.05 = 426,4537305

The value of -2 loglikelihood (515.213) < Chi Square table (426,4537305), thus receiving H0, which shows the model by entering the independent variable is FIT with the data

Table 7 Omnibus Test Omnibus Tests of Model Coefficients

Hypothesis used:

H0 = the addition of independent variables does NOT give a real effect on the model

H1 = addition of independent variables GIVE a real effect on the model

Accept H0 if Chi square value < Chi square table or

sig value > alpha 0,05

In the omnimbus table, it can be seen that Chi square value 7,361 < Chi square table at df

3 (7,8147) or sig value 0.061 > 0,05 so it can be concluded that the received result is receiving

H0 which indicates that the addition of independent variable does NOT give effect real to the model

Interpretation of Logistic Regression with SPSS (Hypothesis Answers)

In OLS to test the simultaneous significance using F test, whereas in logistic regression use the Chi-Square value of the difference between -2 Log likelihood before the independent variables enter the model and -2 Log likelihood after the independent variable enter the model This test is also called Maximum likelihood testing

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Hypothesis used:

H0 = no significant influence simultaneously level of education, sex, and availability of financial advisor to investment decision on non real asset

H1 = there is significant influence simultaneously education level, gender, and availability of financial advisor to investment decision on non real asset

Accept H0 if Value p value Chi-Square > Alpha 0,05 or

Chi square < Chi square table

Table 8 Omnibus Test

Omnibus Tests of Model Coefficients

Based on the Omnibus Test table it is known that the answer to the hypothesis of independent simultaneous influence on the dependent variable is to accept H0 and reject H1 or meaning no significant influence simultaneously level of education, sex, and availability of financial advisors to investment decisions on non-real assets, value of p value Chi-Square equal to 0.061 > Alpha 0,05 or Chi square value 7,361 < Chi square table at df 3 (7,8147)

Table 9 Psudo R Square

Model Summary Step

-2 Log likelihood

Cox & Snell R Square

Nagelkerke R Square

a Estimation terminated at iteration number 4 because parameter estimates changed by less than, 001

Table Summary Model to see the ability of independent variables in explaining the dependent variable, used the value of Cox & Snell R Square and Nagelkerke R Square These values are also called Pseudo Square or if the linear regression (OLS) is better known as R-Square

The value of Nagelkerke R Square is 0.026 and Cox & Snell R Square 0.019, indicating that the ability of independent variable in explaining the dependent variable is 0,026 or 2,6% and there are 100% - 2,6% = 97,4% other factor outside model that explains the dependent variable The value of 2.6% can also mean the ability of education, gender, and availability of financial advisors to explain investment decision variables on non-real assets of only 2.6% and 97.4% explained other factors outside the model

Table 10 Hosmer and Lemeshow Test Hosmer and Lemeshow Test

Hosmer and Lemeshow Test is a test of Goodness of fit test (GoF), which is a test to determine whether the model is correct or not (whether the model is appropriate) It is said that if there is no significant difference between the model and the observation value, there can be no difference between the observation result and the possibility of the model prediction

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