proceeding october 31 2019ugefic.gunadarma.ac.id/file/proceeding+ugefic 2019.pdf · media (case...

PROCEEDING

UG ECONOMICS FACULTY

INTERNATIONAL CONFERENCE 2019

Adopting Human-Centered Technology for Social

Innovation of Economics and Environment

Sustainability

October 31th 2019

Campus J6 - Universitas Gunadarma

Jaka Mulya, Cikunir

Bekasi – Indonesia 17146

PROCEEDING

UG ECONOMICS FACULTY

INTERNATIONAL CONFERENCE 2019


Innovation of Economics and Environment

Sustainability

October 31th 2019


Jaka Mulya, Cikunir


ISSN : 9772654887009

Copyright @2019 by Gunadarma Publications

Gunadarma Publications

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Phone: +62-21-78881112

Fax: +62-21-7872829

PROCEEDING UG Economics Faculty

International Conference 2019


Innovation of Economics and Environment Sustainability

October 31st 2019


Jaka Mulya, Cikunir


Cover: P.J. Slameto & Team

Copyright @2019 by Gunadarma Publications

ISSN : 9772654887009

Co Host :

Sponsors :

Scientific Board

1. Prof. Dr. E.S. Margianti, SE., MM, Gunadarma University, Jakarta, Indonesia 2. Prof. Suryadi H.S., Ssi., MMSI, Gunadarma University, Jakarta, Indonesia 3. Prof. Dr. Alexandru Stratan, National Institute for Economic Research, Republic of

Moldova

4. Prof. Dr. Didin Mukodim, Gunadarma University, Jakarta, Indonesia 5. Ir. Toto Sugiharto, M.Sc., Ph.D, Gunadarma University, Jakarta, Indonesia 6. Prof. Dr. Ercan Uygur, Turkish Economic Association, Ankara, Turkey 7. Prof. Dr. Euphrasia Susy Suhendra, Gunadarma University, Jakarta, Indonesia 8. Prof. Dr. Budi Hermana, Gunadarma University, Jakarta, Indonesia

9. Prof. Dr. Dharma Tintri Ediraras, SE., Ak., MBA, Gunadarma University, Jakarta, Indonesia

10. Y. C. Paya HSU, Ph.D, Duy Tan University, Da Nang, Viet Nam 11. Dr. Ing. I Made Wiryana, M.Sc., Gunadarma University, Jakarta, Indonesia 12. Dr. Peni Sawitri, SE., MM, Gunadarma University, Jakarta, Indonesia 13. Iman Murtono Soenhadji, Ph.D, Gunadarma University, Jakarta, Indonesia 14. Dr. Imam Subaweh, SE., MM, Ak., CA, Gunadarma University, Jakarta, Indonesia 15. Dr. Misdiyono, SE., MM, Gunadarma University, Jakarta, Indonesia 16. Dr. Himanshu Dutt, DMI Finance Ltd., New Delhi, India 17. Prof. Dr. Ikramov Murat Akramovic, Tashkent University of Economy, Uzbekistan

Editorial Board

1. Dr. Sri Murtiasih 2. Dr. Emmy Indrayani 3. Dr. Sundari, SE., MM 4. Dr. C. Widi Pratiwi 5. Dr. Ida Astuti, SKom., MMSI

http://duytan.edu.vn/

Foreword from

The Rector of Universitas Gunadarma

Indonesia

First of all, on behalf of Universitas Gunadarma, I would like to welcome all speakers and

participants of the UG Economics Faculty International Conference 2019 in Campus J6

Universitas Gunadarma, Jakarta-Indonesia. The topic of this conference is “Adopting Human-Centered Technology for Social Innovation of Economics and Environment

Sustainability”, and this topic is one of the current issues mainly in the global economy as it

has challenged the economy during the era of Society 5.0.

As one of the biggest university in Indonesia who’s based in information technology,

Universitas Gunadarma always attempts to have a golden opportunity in taking parts of

increasing the abilities and competition of Indonesia economics. After passing quite a

long journey of history, currently Universitas Gunadarma has been existing in Indonesia

for more than a quarter of century, which has no less than 30,000 students as well as more

than 75,000 graduates. Gunadarma University has succeeded in achieving the pinnacle of

its career by having a good reputation as a prominent university in Indonesia as well as

globally.

Based on the conference theme, Human Centered Technology is the most influence factors

which is needed particularly in the global economics. In this conference, various Digital

Economics problems will be investigated by the involvement of researchers across the

globe who are in the developing countries. These researches eventually act as a bridge of

the dominance of thought of researchers in developed countries and developing countries.

It is also a provision of platform in exchanging management thoughts in this new era of

globalization.

The main topic is determined due to the current economy condition from the whole world

which continuously keeps the pressure on the society condition which becomes the

essential needs from the whole world’s components. In the different sight, the continuity

and development of a particular economy must always be maintained in order to hold up

the value of a particular country. Hence, society 5.0 will help country‘s welfare in

globalization era.

As the medium for knowledge sharing, a proceeding is published and distributed. In these

publications, all valuable articles which are presented on the conference can be found. The

articles cover a broad spectrum of topics of human centered technology as well as

environment sustainability. The articles provide an overview of critical research issues

reflecting on past achievements and future challenges.

In this occasion, I would like to thank our keynote speaker, H E Ulugbek Rozukulov, the

ambassador of the Republic of Uzbekistan, and also to our distinguished speakers, Dr. Ir.

Hermanto Dwiatmoko, MSTr., IPU, ASEAN Eng., Dr. Paya Y.C. Hsu, and Dr. -Ing.

Joewono Prasetijo

In addition to the efforts of all those people, the success of the conference was due to the

financial support from Universitas Gunadarma Indonesia, as well as our sponsors PT. Bank

DKI and BPJS Ketenagakerjaan, and PT. Maskapai Reasuransi Indonesia during this event.

Finally, we owe gratitude to all the conference participants for their contributions to the

intellectual discourse during the conference.

As closing remarks, let us say thank you to the Lord Almighty God for all His blessing on

us. Ultimately, I hope that this conference will produce a wide range of formulation forms

which can be used by many parties in order to increase competition, and the ability of

Indonesia in particular as well as other countries.

Jakarta, 31st October 2019

Prof. Dr. E.S. Margianti, SE. MM

Rector of Universitas Gunadarma

Acknowledgement

Thank God for the blessing and grace without which the UG Economics Faculty

International Conference 2019 would have not been accomplished. The topic of this

conference is “Adopting Human-Centered Technology for Social Innovation of Economics

and Environment Sustainability”, and this topic is one of the current issues mainly in the

global economy as it has challenged the economy during the era of Society 5.0.

We would like to express our gratitude that our ideas were positively responded by the

speaker and participants from such different countries as Indonesia, Uzbekistan, Vietnam

and Malaysia. Our gratitude hereby specially goes to:

1. Prof. Dr. E.S., Margianti, SE., MM, the Rector of Gunadarma University

2. Prof Suryadi Hs., SSi., MMSI, the Vice Rector II

3. Ir. Toto Sugiharto, MSc. Ph.D, the Dean of Economic Faculty

4. Prof. Dr. Euphrasia Susy Suhendra

5. The Speaker

6. The moderator

7. The presenter

8. The reviewers

9. The organizing committee

We are equally thankful for the great support and sponsorship from PT. Bank DKI, BPJS

Ketenagakerjaan, and PT. Maskapai Reasuransi Indonesia. We are thanks for Taskent

Branch.University, Bukhara State University, Tashkent State Economics, STIE Nusa

Megar Kencana, Bina Insan Lubuk Linggau University, and STMIK Jakarta STIK as the

Co Host. We certainly acknowledge the possible mistakes or imperfection in either the

presentation or the content of the book which may result from our limited knowledge and

capacity. Accordingly, constructive suggestion and correction are welcome. We expect that

this work will contribute much to the improvement of our scientific knowledge and insight.

Finally, our infinite thanks are extended for the time shared by the families and friends. We

are deeply indebted to their understanding and support in completing this work. Hopefully,

this book will bring benefit to us.

Editor

Proceeding UG Economic Faculty-International Conference Gunadarma University – Campus J6,Oct 31th 2019 ISSN: 9772654887009

Table Of Content xi

Table of Content Abstract

NO Title Page

1. Modern Technical Analysis Using Relative Strength Index Indicators, Stochastic Oscillator, And MACD In Foreign Exchange Trading.

Yunus Patty, Henny Medyawati

1-9

2. The Effect of Return on Assets, Return on Equity and Earning Per Share on Stock Price: An Empirical Study of Public Firms Listed on LQ45 Period 2009-

2018

Kurnia Nugraha , Toto Sugiharto

10-16

3. The Role of Fintech P2P Lending in Growing The Potential of Financial Activities In Indonesia

Jessica Barus, Cicilia Erly Istia

17-22

4. The Effects of Corporate Social Responsibility on Brand Equity and Firm Performance

Hendri Rahmayani Asri, Hantoro Arief Gisijanto

23-29

5. The Analysis of Accounting Information Systems Quality on Financial Performance at PT. Raflesia Madani Propertindo

Dian Wulan Sari

30-37

6. Decision Support System Based on Consumer Needs for The Product

Detty Purnamasari, Dini Aprillia, I Made Wiryana

38-42

7. Service Quality Price Perception And Customer Relationship Ojek Online Transportation : Case Study On Gojek

Arliesza Mutiara P.A, Sri Rakhmawati, Budiasih, Komsi Koranti

43-59

8. The Influence of Product Quality, Price Perception, and Promotion of Fashion Products Purchase On E-Commerce Zalora.co.id

Waseso Segoro, Puspa Arum Mustikaloka

60-63

9. Improving Purchase Decisions of Shopee Online Stores in Tangerang City

Lies Handrijaningsih, Septi Mariani, Panji Chaerul Alam

64-71

10. The Effect of Youtube Beauty Vlogger Toward Purchase Intention for Indonesian Female (Case Pixy Cosmetics )

Siti Nurfaizah, Titi Nugraheni

72-78

11. Purchasing Behaviour Through The Internet in The Usage of Shopee Online Shop Application

Christera Kuswahyu Indira, Tanti Arfianti Dewi, Budi Utami

79-84

https://ssl.microsofttranslator.com/bv.aspx?from=&to=en&a=ZALORA.CO.ID

Proceeding UG Economic Faculty-International Conference ISSN: 9772654887009 Gunadarma University – Campus J6,Oct 31th 2019

xii Table Of Content

NO Title Page

12. Quality of E-Commerce Website, Perceived Ease of Used, Perceived Usefulness, to Usage Decision of Bukalapak Merchant Website

Desti Dirnaeni, Irfan Ardiansyah, Christera Kuswahyu Indira

85-94

13. Influence Perceived on Benefits, Risk Perceived, Perceived Easy, Perceived Compliance, and Word of Mouth Against Interests Using E-Wallet Application

OVO

Anisah, Septi Mariani TR, Lies Handrijaningsih

95-106

14. Effect Service Features, Attractiveness of Advertising, Perception of Benefits, Perceived Usefulness, Attitude, Security, and Risk Towards Repurchase

Interest of E-Money Based on OVO

Komsi Koranti, Wahyulie Anggraini Putri

107-114

15. The Influence of Capability of Financial, Social Influence, and Perceived Benefits on Interest in Using E-Wallet (DANA)

Angga Putri Ekanova, Anisah, Dewi Anggraini Puspa Hapsari

115-124

16. The Effect of Celebrity Endorser on Brand Switching With Perceived Value as Intervening Variables in Emina Cosmetics

Mega Pratiwi, Nenik Diah Hartanti

125-131

17. Impact Quality of Service And Price to Customer Satisfaction in Tokopedia

Tia Chisca Anggraini, Diah Aryati P, Early Armein

132-143

18. The Influence of Digital Payment on The Growth Of Culinary Business in Depok City

B. Sundari, Ary Natalina, Moh. Andhika Pratama

144-151

19. Online Communication Strategy and Business Marketing on Facebook Social Media (Case Study On BBH-Online)

Bertha Meyke Waty Hutajulu, Winda Widya Ariestya

152-159

20. The Effectiveness of Electronic Advertising Indomie Ayam Geprek Using The Customer Response Index (CRI)

Rendi Agi Prabowo, Caecilia Widi Pratiwi

160-167

21. Research Systematic Empirical Review of Financial Distress Determinant: Meta Analysis

Trisnawati Taswin

168-175

22. The Impact Of Competency, Workload, And Work Environment To Work Stress And The Employee Performance Of Bank BJB S. Parman

Indyra Dwi Cahyaningtyas, Sri Setya Handayani

176-183


Table Of Content xiii

NO Title Page

23. Influence of Company Size, Liquidity, Profitability, Assets and Structure Business Risk Against Capital Structure (Empirical Study on Companiesfood

and Drink Registered in Indonesia Stock Exchange 2013-2017 Period)

Maria Cici Puspita Sari, Sri Sapto Darmawati

184-191

24. The Effect of Entrepreneurial Spirit and Product Innovation on Business Performance of Furniture Business at Plaza Mebel Fatmawati


192-197

25. The Impact of Organizational Culture, Motivation and Competency on Employee’s Performance in Lubuklinggau, South Sumatra

Sutanta, Pramalia Wishuda

198-207

26. The Analysis of Product Marketing Role Strategy on Kelompok Usaha Bersama (KUBE) in Increasing Family Income (Case Study of KUBE In

Lubuklinggau)

Fitria, Suyadi

208-217

27. The Influence of The Leadership Style and Motivationtoward Employee Performance at The Department of Livestock And Fisheries Lubuklinggau

Yulpa Rabeta, Irma Idayati

218-224

28. Analysis of Determinants of Human Development Index Factors in East Java Province

Sri Setyorini, Edi Pranoto

225-235

29. Social Impact of Community-Based Tourism: Study of Tourism Villages Dolandeso Boro "Culture For Nature”

Dhiana Ekowati, Winanto Nawarcono

236-246

30. The Effect of Exclusive Breastfeeding and Complementary Feeding of The Breast Milk on The Incidence of Stunting in The Working Area of The

Community Health Center in The West Pendopo Sub-District of West Pendopo

District 4 Lawang, South Sumatera

Yohanes Susanto, Dian fitryah anwar, Ahmad Basri

247-258

31. Study of Environmental Friendly Support Management Based on Adiwiyata Program in Musi Rawas District (Case Study in Terawas State High School)

Betti Nuraini, Mutiara Rahma

259-268

32. The Influence of Profitability, Company Sizes, Efficiency, and Liquidity of Sustainability through Adequacy of Investment and Solvability of General

Insurance in Indonesia

Rianto

269-275

33. Information Technology Governance for Research Department Management in STMIK Jakarta STI&K With Control Objectives in Information and Related

Technology

Ire Puspa Wardhani, Irfan , Melani dewi Lusita, Sunny Arief Sudiro

276-282


xiv Table Of Content

NO Title Page

34. The Effect Of Islamic Business Ethics and Religiosity Towards Merchant Behavior on The Depok Jaya Market

Anggi Pratiwi, Stevani Adinda Nurul Huda

283-288

35. Evalution of Tax Billing With Forced Letters on KPP Cibinong for Years 2015-2017

Tommy Kuncara, Abednego Priyatama, Early Armein

289-295

36. Effect of Convenience, Trust, Conformity, Usability, Credibility, and Risk in The Decision to Use Mobile Banking of Muamalat Indonesia Bank

Komsi Koranti, Fernando Wastian

296-302

37. Conceptual Basics Of “Territory Image” Forming

Eshmatov Sanjarbek

303-307

38. Intermodal Transportation Of Goods From Central Asian Region to Europe

Dadabev Q. A, Shermukhamedov A.N, Mamatqulov Sh.T

308-311

39. Development of The Market of Electronic Commerce in Uzbekistan

Ikramov Murat Akramovich, Shermukhamedov Abbas Tairovich, Alimov

Gayratjon Abduraxmonovich

312-314

40. Factors Affecting on Going Concern Audit Opinion of Food and Beverages Firms Registered in The Indonesia Stock Exchange

Echa Hamalta Putir , Toto Sugiharto

315-323

41. Improvement of Light Industry Enterprises and Competitiveness of Management System

Ikramov Murat Akramovich, Mamajonov Hamidjon Nasriddinovich,

Toshpulatov Ikboljon Adiljonovich

324-329

42. The Effect of Leverage, Profitability, Liquidity and Firm Size on Earnings Management and Its Impact on Tax Avoiding on Consumer Goods Sector

Companies in Indonesia Stock Exchange In 2014-2018

Sherly Indria Maharani, Sugiharti Binastuti

330-337

43. Marketing Research iIn Solving Problems of Inclusive Sports in Educational Institutions in Uzbekistan

Usmonova Dilfuza I, Nabieva Nilufar Muratovna

338-343

44. Foreign Experience of Using Marketing Strategies to Increase The Attractiveness of The Regions' Investment Climate

Sharipov Ixtiyor Baxtiyorovich, Nabieva Nilufar Muratovna

344-347

45. Comparison Study of Return on Asset of PT. Indofood Sukses Makmur Tbk Using The Dupont Method and Conventional Method

Merin Pradita Sari, Toto Sugiharto

348-355


Table Of Content xv

NO Title Page

46. Issues of The Individual Brand in Economy

Nosirova Nargiza, Mardonov.A.B, Xoljigitov.M.G.

356-358

47. International Customs and Trade Standards

Safarov Bahtiyor Djuraqulovich, Fattahov Adiham Azizovish,

Samadov Asqarjon Nishonovich

359-363

48. The E-Commerce Frame in Business Environment

Ergashkhodjaeva Shakhnoza Djasurovna, Aliev Abdulaziz Ismailovich

364-366

49. The Underlying Factors of Tax Avoidance: an Empirical Study on Food and Beverage Firms Listed in The Indonesian Stock Exchange Period 2014-2018

Fetta Ferena, Toto Sugiharto

367-372

50. The Effect of Price Reception, Product Quality, and Service Quality in Customer Loyalty Through Satisfaction as an Intervening Variables on Richeese Factory

Kelapa Dua Restaurant Customers, Depok

Mohammad Ramdan Effendi, Sri Setya Handayani

373-381

51. Digital Identity and Digital Signature as a New E-Bussiness Innovation in Indonesia (System Found by Privy.ID Indonesia)

Gesty Ernestivita, Subagyo

382-388

52. Using Customer Relationship Management as a Strategy to Win the Market in the Software House Business

Susi Widayati, Maria Sri Wulandari, Kokoy Rokoyah, Ire Puspa Wardhani,

Sunny Arief Sudiro

389-395

53. Market Overview of Indonesia Copper Export Commodity (Case of Indonesia, Thailand and Japan Copper Exporting Countries in 2004-2018)

Greatty Claudia, Iman Murtono Soenhadji

396-402

54. Analysis of Location Effect, Store Atmosphere, Word of Mouth, and Social Media on Purchasing Decisions and The Implication on Sate Taichan ‘Goreng’

Consumer Satisfaction

Nabella Aprilia , Rina Sugiarti

403-409

55. Descriptive Analysis of Determinants of Abnormal Return In Indonesian Islamic Capital Market

Feny Fidyah, Dharma Tintri Ediraras, Iman Murtono Soenhadji

410-414

56. On Comparison of Stock Indexes Performance in Indonesian Stock Exchange 2014 – 2019

Ratna Pertiwi, Riskayanto

415-422


xvi Table Of Content

NO Title Page

57. Descriptive Anaylises On Environmental Factor And Financial Performances Study At Smes In Java Island

S. Tiwi Anggraeni, Emmy Indrayani, Dharma Tintri Ediraras

423-431

58. The Effect Perception of Price and Promotion on Purchase Decisions and The Impact of Loyalty of Lifebuoy Soap Customers: Case Study in The Citizens of

East Cilebut Bogor Regency

Muhamad Mirzan Hasan Bisri, Izzati Amperaningrum

432-442

59. Optimization of the 4P-UMKM Sector through Sharia Small Medium Enterprise (SSMe) Investment and Restructuring Fund (S2IRF) Based on Financial

Technology as Supporting the Indonesian Economy

Mulyadi, Maulana Syarif Hidayatullah

443-449

60. The Effect of Security Variable, Electronic Word of Mouth, Ease of Use to Consumer Purchase Intention With Trust as an Intervening Variable (Empirical

Study on Bukalapak Sites)

Basilius Kevin, Rina Sugiarti

450-455

61. Beneish M-Score Analysis Model in Detecting Fraudulent Financial Statements (Case Study: Construction Subsector Company Listed on The Indonesia Stock

Exchange)

Diva Viona Leonita, Supiningtyas Purwaningrum

456-462

62. Medical Services for Patients According to Minimal Service Standards in Emergency Services Dr. Hospital Sobirin, Musi Rawas Regency

Sutanta, Mulyadi, Evi Damayanti

463-481

63. The Effect Of Asset Returns, Equity Returns And Current Ratio On Stock Price Of Telecommunication Companies Listed In Indonesia Stock Exchange (IDX)


482-487

64. Measuring Model of Islamic Corporate Governance in Islamic Financial Institutions

Sardiyo, Martini Dhasman

488-498

65. Affecting Digital Toursm On Happyness: Preliminary Study In Indonesia-Uzbekistan

Endika Perdana, Alfiatun Sarasati, Astie Darmayantie, Dharma Tintri

Ediraras

489-494

66. The Influence Of Perceived Behavior Control, Entrepreunerial Orientation, Personal Attitude And Managerial Ability On The Entrepreuner Performance

Msme Written Batik In The Village Of Giriloyo Yogyakarta

Bernadette Nanda Puspaningrum

495-504


Patty, Medyawati 1

Modern Technical Analysis Using Relative Strength Index Indicators,

Stochastic Oscillator and MACD In Foreign Exchange Trading

1Yunus Patty, 2Henny Medyawati

1,2 Economics Faculty of Universitas Gunadarma

Jl. Margonda Raya No. 100 Depok 16424, West Java Indonesia [email protected], [email protected]

Abstract

Modern technical analysis with momentum indicators consisting of Relative Strength Index (RSI),

Stochastic Oscillator, and Moving Average Convergence Divergence (MACD) is an analysis that is

often used in foreign exchange trading. This study aims to analyze the buying and selling signals of

foreign exchange generated by momentum indicators, returns obtained in foreign exchange trading

based on momentum indicators, and to analyze whether momentum indicators are the best ones

used in foreign exchange trading based on total returns. The research data was obtained from a

brokerage company, PT. Victory International Futures (VIF) accessed through MetaTrader 5

software. From 19 currency pairs, 7 samples were selected using a purposive sampling technique.

The data analysis technique is performed through the stages of describing historical price data,

determining the buy and sell signals for each indicator, calculating the profit and return obtained

for each indicator, and selecting the best indicator based on the total return obtained. The results

showed that the RSI indicator produced 79 buy signals and 61 sell signals, the Stochastic Oscillator

indicator produced 77 buy signals and 63 sell signals, and the MACD indicator produced 42 buy

signals and 41 sell signals. The total return obtained in foreign exchange trading is the Relative

Strength Index 439.80%, Stochastic Oscillator 435.73%, and MACD 222.44%. Judging from the

total returns, the relative strength index is found to be the best indicator used in foreign exchange

trading.

Keywords: Technical Analysis, Relative Strength Index, Stochastic Oscillator, MACD, Foreign

Exchange

JEL Codes: G15, G11, F31, F65

INTRODUCTION

Foreign currency (forex) is the currency owned by a country or its inhabitants, but the

currency is not issued by the country itself. It is the domestic currency (national currency) that the

country that issued it and is a legal medium of exchange in the country (Judokusumo, 2007). So

currency is said to be foreign exchange in terms of who views the currency. The population of

Indonesia sees the Rupiah as a domestic currency and the Dollar as a foreign currency. United State

will see the dollar as a domestic currency while the Rupiah will be seen as a foreign currency. The

value of one currency will be different from the value of other currencies. This difference in value

is called the exchange rate. Exchange rates fluctuate all the time and this depend on the demand

and supply of each currency. Currencies that have stable demand and low exchange rate

fluctuations are called hard currencies, while soft currencies have high fluctuations and demand is

unstable due to the country's poor economic and political stability (Sholihin, 2010).

Changes in exchange rates that occur at any time, make a foreign exchange as an asset that

can be traded. People will buy foreign currency when the price is low, then sell it when the price

increases. According to Sitanggang and Indrawati (2006), foreign exchange trading began to

develop rapidly in 1973, because of the change in the international monetary system. Changes

occur when most countries in the world change their exchange rate system from a fixed-rate system

to a free-floating rate system which causes a country's currency value to fluctuate.

mailto:[email protected]

Proceeding UG Economic Faculty-International Conference ISSN: 9772654887009 Gunadarma University – Campus J6, Oct 31h 2019

2 Patty, Medyawati

Globally the foreign exchange market has a very large transaction value per day. The average

amount of foreign exchange transactions per day can be seen in the Bank for International

Settlements (2016) data presented in Figure 1. Present the data:

Source: BIS, 2016

Figure 1. Foreign Exchange Average Transaction Value period 2001-2016

Wibowo (2017) states that the conventional way to do conduct foreign exchange

transactions is through the money changer, while the modern way is to simply use foreign

exchange transaction software, through an intermediary broker or an official broker. In Indonesia,

if a stock brokerage company becomes a member of the Indonesia Stock Exchange (IDX), a

foreign exchange brokerage company becomes a member of the Indonesia Commodity and

Derivatives Exchange (BKDI) or the Jakarta Futures Exchange (BBJ). Foreign exchange trading is

included in futures trading, so foreign exchange trading is based on Law Number 32 of 1997. It

states that the development, regulation and daily supervision of Futures Trading activities are

carried out by the Commodity Futures Trading Supervisory Agency (Bappebti).

This study discusses modern technical analysis using the RSI, Stochastic Oscillator, and

MACD indicators in foreign exchange trading. The purpose of this study is to analyze the buy and

sell signals of foreign currencies produced by the RSI, Stochastic Oscillator, and MACD indicators

and analyze the returns obtained based on the RSI, Stochastic Oscillator, and MACD indicators.

Based on the results of this analysis, it can be assumed that the best indicators are those that can be

used in foreign exchange trading when viewed from a total return.

LITERATURE REVIEW

With online foreign exchange trading through futures brokers, currencies are traded in

pairs. This happens because in every foreign exchange transaction there has to be an exchange

between two currencies, also called sales and purchases. The first currency in the pair is called the

base currency, while the second currency is called the cross-currency (Hakim & Rahmad, 2009).

Foreign exchange trading is included as an alternative investment in futures contracts in the

derivatives market. With investment, of course, it is necessary to analyze so that investment

produces profits, namely optimal return. So far, two types of analysis have been used by investors

to predict exchange rate movements. They are fundamental analysis and technical analysis.

Fundamental analysis is an analysis that relies on news that is happening on world markets or that

is currently being circulated in the market (Suharto, 2016). Technical analysis is a method of

evaluating stocks, commodities, or other securities by analyzing statistics generated by past market

activity to predict future price movements. Technical analysis users believe that everything that can

affect prices, both in terms of fundamentals, politics, and other factors, has been psychologically

reflected in price movements that occur in the market. This is due to the law of supply and demand

that forms it (Ong, 2019). A price increase must occur when demand is greater than supply, and

conversely, a price decrease must occur because supply is greater than demand. According to

Rudiyanto (2015), technical analysis is divided into two, namely classical technical analysis and

modern technical analysis. Classical technical analysis is a method of analysis using the method of


Patty, Medyawati 3

dragging lines and recognizing patterns formed from these lines. Modern technical analysis is a

method of analysis that calculates historical data and produces an indicator number, then certain

boundaries or guidelines are made. If the indicator number reaches a certain value, it means it is a

signal to make a purchase or sale. In other words, indicators on modern technical analysis can help

investors with making investment decisions, when to buy or sell foreign exchange. In modern

technical analysis, several types of indicators are often used by investors. Among them are

momentum indicators consisting of Relative Strength Index (RSI) indicators, Stochastic

Oscillators, and Moving Average Convergence Divergence (MACD).

In this study, the calculation of the value of profit or loss and return is calculated. The profit

or loss formula in trading currency pairs according to Sitanggang and Indrawati (2016) is as

follows:

a. Currency pairs EURUSD, GBPUSD, AUDUSD, NZDUSD:

b. Currency pairs USDJPY, USDCHF, USDCAD:

The calculation of return using the formula as seen below:

The RSI indicator was first introduced by J. Welles Wilder in 1978 in his book entitled

New Concept in Technical Trading System and published in the leading magazine Commodities

(Now a Futures Magazine). RSI is in the form of an oscillator that has the lowest and highest level

limits, namely a scale of 0 to 100 (Ong, 2019). The Stochastic Oscillator indicator was discovered

by George C. Lane. The Stochastic Oscillator displays two so-called % K lines and % D lines. Both

of these lines oscillate between 0-100 vertical scale (Ong, 2019). The MACD indicator is an

indicator created by Gerald Appel created in the 1960s by assessing the correlation between two

EMA (Exponential Moving Average), both of which differ in the time period. The oscillator chart

is divided into two parts that do not have the lowest limit or the highest limit by level 0 (zero) line

(Ong, 2019). Eric, Andjelic, and Redzepagic (2009) stated that the MACD indicator had total

profitability of 289.63% and an RVI indicator of 325.85% which was significantly greater than the

total profitability using a buy and hold strategy of only 188.39%.

The following is a description of similar research related to technical analysis in foreign

exchange trading. Alwiyah and Liyanto (2012) conducted technical analysis in online forex trading

and concluded that the use of time series design research design shows instability and

inconsistencies that indicate the existence of other factors that influence profitability beyond

technical analysis. In terms of the number of transactions, technical analysis has an effect of 60%,

and other factors of 40%.

Prabhata (2012) examines the effectiveness of technical analysis in stock trading. The

results showed that the use of Stochastic Oscillators that are statistically significant can produce

capital gains and it is not proven that the use of Stochastic Oscillators can produce abnormal

returns. The use of MACD could produce statistically significant results in capital gains and it is

not proven that the use of MACD could statistically produce abnormal returns. Two Sample T-test

analysis tools showed no evidence of differences in capital gains on the Stochastic Oscillator and

MACD with a probability value of 0.451 (> 0.05). Pramono, Soenhadji, Mariani, and Astuti (2013)

analyzed the optimal stock returns of the LQ 45 banking sector and found that the calculation of

optimal stock returns using the Enhanced System Tester Application with MACD, RSI, SO, and

Buy and Hold indicators shows that the most Buy and Hold method is the right one to obtain

optimal returns. All shares in the study produced positive returns. Based on optimal returns, the


4 Patty, Medyawati

best banks are PT Bank Mandiri Tbk shares, which produce all positive returns, for all methods.

Tilehnouei and Shivaraj (2013) compared the application of the MACD and RSI indicators to

HDFC Bank shares on the Indian Stock Exchange. The results of the study showed that, by using

the performance index, the MACD Performance Index value was 9,796.68 and the RSI

Performance Index value was 9,038.09. Therefore, it can be concluded that the MACD

Performance Index is better than the RSI Performance Index. Pinakin and Manubhai (2015) in their

research comparing Bollinger Band and RSI indicator on stock price movements noted that by

using quantitative descriptive method RSI indicators produces an average return of -12.58% and a

total return of -5.42%. Bollinger Band indicator produces an average return of 43.29% and a total

return of 94.39%. Mahesh, and Anju (2017) examined the implementation of technical analysis on

the stock price movements of steel industry companies and stated that the ranking order from the

best indicators to the worst in the steel industry are MFI, Stochastic Oscillator, RSI, CCI, TSI,

OBV, ROC, ATR, Bollinger Band, AROON, SMA, PPO, EMA, MACD, R%. In this study,

ranking is based on the percentage of success in short-term trading. Anitha, and Padmaja (2017)

examined the application of technical indicators on the stock prices of banking sector companies in

the Indian Stock Exchange and observed that the results of return calculations for MACD, RSI,

Stochastic Oscillator, ADX, and CCI indicators were 14, 81%, 19.06%, 19.11%, 26.40%, and

11.30% respectively. MACD produces 62 buy signals and 62 sell signals, RSI produces 23 buy

signals and 23 sell signals, Stochastic Oscillator produces 47 buy signals and 47 sell signals, ADX

produces 46 buy signals and 46 sell signals, and CCI produces 39 buy signals and 39 signals

selling.

Monika, Yusniar, and Dalimunthe (2017) in their research used the Independent Sample T-

test to analyze the difference between the average price of the MACD indicator and the closing

price group. The results showed that in the MACD indicator price group and the closest closing

price group, the lowest or highest before or after the MACD signal showed a probability of 0.999

(> 0.05). This means that there is no difference between the average price of the MACD indicator

and the average closing price of the closest stock. So the MACD indicator can be said to be

accurate and can be used for investment decision making in the capital market. Tam, and Cuong

(2018) in their research on the effectiveness of investment strategies using technical indicators on

stock price movements on the Vietnam Stock Exchange found that returns obtained using the RSI

indicator were 174%, MACD 58%, and MA 42%.

RESEARCH METHOD

The objective of this study is to investigate the currency pairs traded online through a

brokerage company, PT. Victory International Futures. The reason for this is that the foreign

exchange market has a very large transaction value reaching 5.067 billion USD per day (BIS,

2016). Besides, research on technical analysis of foreign exchange price movements is very

limited. The sample selection is based on the non-probability sampling method, to be precise the

purposive sampling method. The considerations used to select samples in this study are as follows:

(1) Currency pairs are selected are the major currency pairs. This is because major currency pairs

are the most widely traded currency pairs online in the foreign exchange market; (2) The currency

pairs have complete data covering the opening price, the highest price, the lowest price, and the

closing price during the study period 1 May 2018 to 16 May 2019. Based on these criteria a sample

of 7 currency pairs were selected. Secondary data in this study are historical data on currency pair

prices from 1 May 2018 to 16 May 2019 obtained from brokerage firms PT. Victory International

Futures, and data on the value of momentum indicators, the data of which can be accessed through

the trading platform MetaTrader 5. In this study we used descriptive analysis that produces the

value of an independent variable, which is a stand-alone, and is not paired with other variables. The

independent variable in this study is the returns obtained from the use of each of the three

momentum indicators: (1) RSI, (2) Stochastic Oscillator, and (3) MACD. Returns, profits, and

losses are calculated according to the formula in the theoretical framework. The next important

stage is the rules of trade. To maintain the consistency of profit and return calculations, trading

rules must be established in advance for each indicator and applied strictly during the study period.

In this study the trading rules applied are as follows:


Patty, Medyawati 5

1. Buy and sells decisions are made based on buy signals and sell signals from each indicator.

2. When the indicator shows a buy signal or a sell signal, the buy and sell decision is made at time

of the closing price according to the date the indicator showed on the buy signal or sell signal.

3. Considering that foreign exchange trading has a two-way transaction system, the transaction can

be preceded by a decision to buy at the time of the buy signal or a decision to sell at the time of

the sell signal.

4. All transactions that begin with a buying decision will be closed (sold) when the indicator

shows the first sell signal after the buy signal. Buying decisions can be made again on the next

buy signal.

5. All transactions that begin with a sell decision will be closed (bought) when the indicator shows

the first buy signal after the sell signal. A sell decision can be made again on the next sell signal.

6. If a buying or selling decision is made, and the indicator does not show a signal to close the

transaction until the end of the study period, then the transaction is ignored.

7. If the amount of loss has exceeded the initial capital, the transaction is stopped.

Descriptive data analysis was performed using spreadsheet type software and MetaTrader

5. The collection of historical data on the price of currency pairs that had become the study sample

during the period 1 May 2018 to 16 May 2019 was obtained through MetaTrader 5 software that

was connected to a demo account from the brokerage company PT. VIF. The data obtained is, then

entered into spreadsheet software for return calculation. Charts of price movements can be

displayed on the MetaTrader 5 software via the "create a new chart" menu and choose the name of

the currency pair is chosen to display the chart. The graph is displayed in daily time frames and for

a period of one year (1 May 2018 to 16 May 2019). After the graph is displayed, the next step is to

use a momentum indicator consisting of RSI, Stochastic Oscillator, and MACD. The periods used

for each indicator are (a) RSI uses 7 periods; (b) Stochastic Oscillator uses %K 14 periods and %D

uses 3 period SMA of %K; (c) MACD uses EMA 12 and EMA 26 for the MACD line, and EMA 9

from the MACD line for the signal line.

Determining the sell and buy signals from the momentum indicator is done by referring to

the following criteria: (1) If the RSI line from above breaks below the level 70 it will give a bearish

signal (sell signal). Conversely, it is expressed as a bullish signal (buy signal) if the line breaks

above the level 30. (2) It is a buy signal if in the % K oversold zone the line crosses the % D line in

the % K oversold zone. It is a sell signal if in the overbought zone the% K line crosses the% D line.

(3) It is expressed as a buy signal if the MACD line crosses above the signal line below line 0. The

sell signal is obtained when the MACD line crosses below the signal line above line 0. In this

study, it is assumed that all transactions are carried out in 1 lot size. Further, it is assumed that

trading is carried out using the regular account of brokerage company PT. VIF with the following

information (1) Spread: 3 pip, Pip (percentage in point) is the smallest unit in foreign exchange

price movements. For currency pairs, EURUSD, GBPUSD, AUDUSD, NZDUSD, USDCHF,

USDCAD pip is the 4th number behind the comma. For the USDJPY currency pair the pip is the

2nd number behind the comma; (2) Contract size: 1 Lot = USD 100,000; (3) Commission: USD 25

/ lot; (4) The initial capital is considered to be the same as the account opening minimum deposit,

which is USD 10,000.

RESULTS AND DISCUSSION

The results and discussion of the study begin with a descriptive description of the research

data. Descriptive statistics aim to provide a description of the data being analyzed. The results of

the descriptive statistics of this research data can be seen in Table 1.

The EURUSD currency pair during the research period 1 May 2018 to 16 May 2019 had

the highest opening price of EURUSD 1.20776 on 1 May 2018, the lowest opening price of

EURUSD 1,11314 on 26 April 2019, the average opening price was EURUSD 1, 14834, the

highest price during the study period of EURUSD 1,20843 was on 1 May 2018, the lowest price

during the study period of EURUSD 1,11134 was on 26 April 2019, the highest closing price of

EURUSD 1,19921 was on 1 May 2018, the lowest closing price of EURUSD 1,11330 was on 25

April 2019, and the average closing price was EURUSD 1.14801.


6 Patty, Medyawati

The USDJPY currency pair during the May 1 2018 to May 16 2019 research period had the

highest opening price of USDJPY 114,535 on October 4, 2018, the lowest opening price of

USDJPY 107,677 was on January 4, 2019, the average opening price of USDJPY 111,201, the

highest price during the period research valued at USDJPY 114,551 was on October 4, 2018,

lowest prices during the study period valued at USDJPY 104,730 was on January 3, 2019, highest

closing prices valued at USDJPY 114,532 was on October 3, 2018, lowest closing prices valued at

USDJPY 107,640 was on January 3, 2019, and average closing prices worth USDJPY 111,206.

The GBPUSD currency pair during the research period 1 May 2018 to 16 May 2019 had

the highest opening price of GBPUSD 1.37605 on 1 May 2018, the lowest opening price of

GBPUSD 1,24868 on 12 December 2018, the average opening price of GBPUSD 1, 30482, the

highest price during the research period of GBPUSD 1,37729 on 1 May 2018, the lowest price

during the research period of GBPUSD 1.24363 on 3 January 2019, the highest closing price of

GBPUSD 1.36146 on 1 May 2018, the lowest closing price of GBPUSD 1.24884 on December 11,

2018, and the average closing price is GBPUSD 1,30445.

The USDCHF currency pair during the research period 1 May 2018 to 16 May 2019 had

the highest opening price of USDCHF 1.02023 on 24 April 2019, the lowest opening price of

USDCHF 0.95877 on 21 September 2018, the average opening price was USDCHF 0, 99467, the

highest price during the research period of USDCHF 1.02365 on April 26, 2019, the lowest price

during the study period of USDCHF 0.95413 on September 21, 2018, the highest closing price of

USDCHF 1.02049 on April 24, 2019, the lowest closing price of USDCHF 0.95873 on September

21, 2018, and the average closing price is USDCHF 0.99477.

The USDCAD currency pair during the May 1 2018 to May 16 2019 research period had

the highest opening price of USDCAD 1.36391 on December 31, 2018, the lowest opening price of

USDCAD 1.27671 on May 10, 2018, average opening price of USDCAD 1, 31821, the highest

price during the research period of USDCAD 1.36651 on December 31, 2019, the lowest price

during the research period of USDCAD 1.27294 on May 11, 2018, the highest closing price of

USDCAD 1.36604 on December 28, 2018, the lowest closing price of USDCAD 1.27671 on May

10, 2018, and the average closing price is USDCAD 1.31851.

The AUDUSD currency pair during the research period 1 May 2018 to 16 May 2019 had

the highest opening price of AUDUSD 0.76663 on 7 June 2018, the lowest opening price of

AUDUSD 0.69294 on 15 May 2019, the average opening price of AUDUSD 0, 72387, the highest

price during the research period worth AUDUSD 0.76758 on 6 June 2018, the lowest price during

the research period worth AUDUSD 0.67653 on 3 January 2019, the highest closing price of

AUDUSD 0.76667 on 6 June 2018, the lowest closing price of AUDUSD 0.68913 on May 16,

2019, and the average closing price is AUDUSD 0.72363.

Table 1. Descriptive Statistics Pair

Description EURUSD USDJPY GBPUSD USDCHF USDCAD AUDUSD NZDUSD

Highest

opening price

1,20776

114,535

1,37605 1,02023 1,36391 0,76663 0,70413

Lowest

opening price

1,11314

107,677

1,24868 0,95877 1,27671 0,69294 0,64400

Average

opening price 1,14834 111,201 1,30482 0,99467 1,31821 0,72387 0,67627

Highest price 1,20843 114,551 1,37729 1,02365 1,36651 0,76758 0,70613

Lowest price 1,11134 104,730 1,24363 0,95413 1,27294 0,67653 0,64246

Highest

closing price 1,19921 114,532 1,36146 1,02049 1,36604 0,76667 0,70405

Lowest

closing price 1,11330 107,640 1,24884 0,95873 1,27671 0,68913 0,64411

Average

closing price 1,14801 111,206 1,30445 0,99477 1,31851 0,72363 0,67607


Patty, Medyawati 7

The NZDUSD currency pair during the research period 1 May 2018 to 16 May 2019 had

the highest opening price of NZDUSD 0.70413 on 4 May 2018, the lowest opening price of

NZDUSD 0.64400 on 8 October 2018, average opening price of NZDUSD 0, 67627, the highest

price during the study period was NZDUSD 0.70613 on 6 June 2019, the lowest price during the

research period was NZDUSD 0.64246 on 8 October 2018, the highest closing price was NZDUSD

0.70405 on 3 May 2018, the lowest closing price was NZDUSD 0.64411 on October 5, 2018, and

the average closing price is NZDUSD 0.67607.

During the research period, from 1 May 2018 to 16 May 2019, the RSI indicator obtained

140 signals consisting of 79 buy signals and 61 sell signals. On the Stochastic Oscillator indicator,

140 signals are obtained consisting of 77 buy signals and 63 sell signals. In the MACD indicator,

83 signals are obtained consisting of 42 buy signals and 41 sell signals. The RSI and Stochastic

Oscillator indicators produce the most total number of total signals, 140 signals.

The buying and selling decision are made based on the buy and sell signals produced by the

RSI, Stochastic Oscillator and MACD indicators. The total returns obtained from the decision is

439.80% for the RSI indicator, 435.73% for the Stochastic Oscillator indicator, and 222.44% for

the MACD indicator. The RSI indicator is an indicator that produces the highest total. The

Stochastic Oscillator indicator produces the second-highest total and has a difference of 4.07% with

the RSI indicator. The MACD indicator produces the lowest total returns with a difference of

213.29% from the Stochastic Oscillator indicator. So it can be concluded that the RSI indicator is

the best indicator to be used in foreign exchange trading.

The Stochastic Oscillator indicator is an indicator that produces the second -highest total

returns. Although the total returns generated by the RSI indicator is higher than the Stochastic

Oscillator indicator with a difference of 4.07%, the Stochastic Oscillator indicator can still be used

to get returns in foreign exchange trading. This is evident in trading the AUDUSD and NZDUSD

currency pairs. In the AUDUSD currency pair, the Stochastic Oscillator indicator is superior with a

return of 157.82%, and the RSI indicator only produces a return of 145.27%. In the NZDUSD

currency pair, the Stochastic Oscillator indicator is also ahead with a return of 107.73%, and the

RSI indicator produces a loss of 107.12%.

The results of return calculations, the number of buy and sell signals in full can be seen in

Table 2.

Table 2. Return Calculation Results

Indicator Currency Pairs Number of Signals Return

(%) Buy Sell

Relative

Strength Index

(RSI)

EURUSD 14 3 41,18

USDJPY 8 12 167,27

GBPUSD 10 7 -104,82

USDCHF 12 15 189,13

USDCAD 5 10 108,89

AUDUSD 14 7 145,27

NZDUSD 16 7 -107,12

Total 79 61 439,80

Stochastic

Oscillator

EURUSD 14 5 86,07

USDJPY 5 15 0,09

GBPUSD 13 6 -122,95

USDCHF 10 10 159,31

USDCAD 8 11 47,66

AUDUSD 15 9 157,82

NZDUSD 12 7 107,73

Total 77 63 435,73


8 Patty, Medyawati

Indicator Currency Pairs Number of Signals Return

(%) Buy Sell

Moving Average

Convergence

Divergence

(MACD)

EURUSD 9 3 11,07

USDJPY 2 8 122,71

GBPUSD 8 5 -54,35

USDCHF 3 7 176,82

USDCAD 5 8 -19,86

AUDUSD 9 5 -31,69

NZDUSD 6 5 17,73

Total 42 41 222,44

The MACD indicator in this study is the indicator that produces the lowest total return. The

MACD indicator experienced losses in three currency pairs namely GBPUSD, USDCAD, and

AUDUSD. Even so, the MACD indicator has never produced a loss of up to 100%. Unlike the RSI

and Stochastic Oscillator indicators, the MACD indicator generates far fewer signals so that buying

and selling decisions are not as much as the RSI and Stochastic Oscillator indicators.

According to Anitha, and Padmaja (2017), the interpretation of buy and sell signals on

technical indicators and investor discipline will help in generating profits from trading. Even so,

always use stop loss to minimize losses so that the possibility of the profit will be even greater. In

addition, Alwiyah and Liyanto (2012) stated that the use of technical analysis would be better if

using several indicators in decision making and suggested the need for a combination of technical

analysis and fundamental analysis in an effort to increase profits.

Based on the results of the study it appears that to apply risk management is very important

for investors in using the Relative Strength Index, Stochastic Oscillator, and MACD indicators in

foreign exchange trading. Predictions in the form of buy and sell signals based on historical data

generated by indicators are not always true because market conditions cannot be predicted with

certainty. Risk management in the form of limiting the level of loss in order to achieve the

minimum loss must be done using a stop loss. By limiting this level of loss, large losses such as

those experienced in the GBPUSD currency pair can be minimized, and the use of technical

analysis can produce optimal returns.

CONCLUSION

Based on the criteria of buy and sell signals in foreign exchange trading generated by each

momentum indicator, the RSI indicator produces 140 signals consisting of 79 buy signals and 61

sell signals, the Stochastic Oscillator indicator produces 140 signals consisting of 77 buy signals

and 63 sell signals, the MACD indicator generates 83 signals consisting of 42 buy signals and 41

sell signals. RSI and Stochastic Oscillator indicators produce the most number of total signals,

which is 140 signals. The total return obtained in foreign exchange trading is 439.80% for the RSI

indicator, 435.73% for the Stochastic Oscillator indicator, and 222.44% for the MACD indicator.

The RSI indicator is an indicator that produces the highest total returns. The Stochastic Oscillator

indicator produces the second-highest total returns and has a difference of 4.07% with the RSI

indicator. The MACD indicator produces the lowest total returns with a difference of 213.29%

from the Stochastic Oscillator indicator. The analysis based on the total returns generated shows

that the RSI is the best indicator to be used in foreign exchange trading because it produces the

highest number of total returns.

BIBLIOGRAPHY

Alwiyah, & Liyanto. (2012). Analisis teknikal untuk mendapatkan profit dalam forex trading

online. Buletin Studi Ekonomi, 17(2), 221-228.

Anitha, M., & Padmaja, R. (2017). A Study on technical indicators in online share trading and its

impact on profitability using a select stock from banking sector in NSE India– a

comparative approach. IOSR Journal of Business and Management, 19(9), 58-63.


Patty, Medyawati 9

Bank for International Settlement. (2016). Triennial Central Bank Survey of Foreign Exchange and

OTC Derivatives Markets in 2016, available at

https://www.bis.org/publ/rpfx16.htm?m=6%7C381%7C677. Accessed on 20th March 2019

Hakim, R., & Suryo, R. (2009). Panduan menjadi forex trader. Jakarta: Elex Media Komputindo.

Judokusumo, R. S. (2007). Pengantar derivatif dalam moneter Internasional. Jakarta: Grasindo.

Mahesh, Manicka, & Anju M. (2017). A study on implementation of technical trading indicators on

steel industry. Journal of Exclusive Management Science, 6(7), 1-4.

Monika, N.E., Yusniar, M.W & Dalimunthe, R.F. (2017). Analisis teknikal menggunakan indikator

MACD untuk membeli dan menjual dalam perdagangan saham. Proceedings of Seminar

Nasional ASBIS, Politeknik Negeri Banjarmasin, 299-307.

Ong, E. (2019). Technical analysis for mega profit. Jakarta: Gramedia Pustaka Utama.

Pinakin, S. N, & Manubhai, P.T. (2015). A comparative study on technical analysis by bollinger

band and RSI. International Journal in Management and Social Science, 3(6), 234-251.

Prabhata, A. (2012). Efektifitas penggunaan analisis teknikal stochastic oscillator dan moving

average convergence-divergence (MACD) pada perdagangan saham-saham Jakarta Islamic

Index (JII) di Bursa Efek Indonesia. SINERGI Kajian Bisnis dan Manajemen, 13 (1), 1-14.

Pramono, A., Iman. (2013). Analisis teknikal modern menggunakan metode MACD, RSI, SO, dan

Buy and Hold untuk mengetahui return saham optimal pada sektor perbankan LQ 45.

Proceedings of PESAT, 5, E272-E277.

Rudiyanto. (2015). Fit.focus.finish. Jakarta: Elex Media Komputindo.

Sholihin, A. I. (2010). Buku pintar ekonomi syariah. Jakarta: Gramedia Pustaka Utama

Sitanggang, L. M., & Indrawati, Y. (2006). Panduan trading forex. Yogyakarta: Andi.

Suharto, F.T. (2012). Mengungkap rahasia forex. Jakarta: Elex Media Komputindo.

Tam, P.H., & Cuong, N. T. (2018). Effectiveness of investment strategies based on technical

indicators: evidence from Vietnamese stock markets. Journal of Insurance and Financial

Management, 3 (5), 55-68.

Tilehnouei, M. H., & Shivaraj B. (2013). A comparative study of two technical analysis tools:

moving average convergence and divergence V/S relative strength index: A case study of

HDFC Bank ltd listed in National Stock Exchange of India (NSE). International Journal of

Management and Business Research, 3(3), 191-197.

https://www.bis.org/publ/rpfx16.htm?m=6%7C381%7C677


10 Nugraha, Sugiharto

The Effect Of Return On Assets, Return On Equity And Earning Per

Share On Stock Price: An Empirical Study Of Public Firms Listed On

LQ 45 Period 2009-2018

1Kurnia Nugraha , 2Toto Sugiharto 1,2 Economics Faculty of Universitas Gunadarma

Jl. Margonda Raya No. 100 Depok 16424, West Java Indonesia 2Corresponding Author:

[email protected]

Abstract

The study aims to analyze the effect of return on assets, return on equity and earnings per share on

stock prices both simultaneously and partially and to identify the most dominant variable in

affecting the stock prices. Secondary data including of return on assets, return on equity, earnings

per share and the closing price of 10 selected LQ45 firms in the periods of 2009-2018 was used.

Multiple linear regression analysis was performed to test the hypotheses. Results of the study

showed that return on assets, return on equity and earnings per share simultaneously affect the

stock prices. Partially only return on equity and earnings per share which affect the stock price. The

most dominant variable is earnings per share.

Keywords: stock price, return on asset, return on equity, earning

JEL Codes: F65, F14, M41

INTRODUCTION

Economic growth in Indonesia continues to increase every year. According to the

Indonesia Statistics, Indonesia's economic growth in 2018 increase 5.17% higher than the

achievements in 2017 amounted to 5.07%. One of the factors that determines Indonesia's economic

growth is the company. The company is one of the supporting factors in Indonesia's economic

growth. The purpose of the company is to achieve what has been planned by the company. But not

only to achieve these goals, but for the survival of the company.

The increasing development of the business world today is encouraging a company to

compete with each other and always create new products and innovations to maintain the

company's survival. This is very important for the company if you want to keep the company. The

more prosperous a company is, the more sources of funds that can be received by the company

either from profits earned or from investors by purchasing securities through the capital market.

The capital market has an important role, this is because the capital market performs an

economic function to provide facilities that bring together two interests, namely those who have

excess funds (investors) with those who need funds (issuer). The capital market is a funding facility

that can be utilized by companies to get funds from the public investors (investors). Through the

capital market, companies can raise funds from investors which are then used to meet their funding

needs. The capital market is one source of economic progress because it can be a source and

alternative for companies besides banks to obtain capital at a relatively low cost and also a place

for short-term and long-term investment. The better the capital market conditions in a country

shows the better business conditions in the country concerned. The capital market is a place for

people to invest. Through the capital market, investors can invest in several companies through the

purchase of securities offered in the capital market (Hermuningsih, 2012). One of the financial

instruments traded on the capital market is share or stock.

According to Fahmi (2012) share is defined as proof of ownership or capital ownership of

a company; the paper is clearly listed in nominal value, the name of the company and followed by

the rights and obligations described to each holder; availability that is ready for sale.

Through the shares investors can place the funds they have in order to obtain the expected

profit either from dividends or from capital gains. Before investing, investors must have a number

of information relating to the dynamics of stock prices in order to determine the appropriate

Proceeding UG Economic Faculty-International Conference Gunadarma University – Campus J6, Oct 31h 2019 ISSN: 9772654887009

Nugraha, Sugiharto 11

company stock to be selected. Investors must also do an analysis in advance about the financial

performance of the company concerned to avoid losses caused by the company's poor financial

condition. The company's financial performance will affect stock prices by showing an increase or

decrease in stock prices. The better the performance of a company, the greater its influence on

rising stock prices. Vice versa, the more a company's performance decreases, it is likely that its

share price will also fall. The high stock price is a good signal for investors, because it shows a

good company performance. These conditions will encourage investors to invest their funds in the

form of securities or shares. Stock demand will increase stock prices, high profitability is a positive

signal for investors and the value of the company will increase.

Basically, stock prices are formed from interactions between sellers and buyers who will

move according to the strength of demand and supply that occur on shares on the exchange. The

stock price is an indicator of a company's success, if the price of a company's shares always

experiences an increase, then investors consider that the company is successful in managing its

business. Investor trust is very beneficial for the company, because the more investors who trust the

higher the desire to invest in the company. More and more demand for shares can increase the

company's stock price. Conversely, if the share price decreases continuously it can reduce the value

of the company in the eyes of investors (Priantinah, 2013).

Stock prices in the capital market always change from time to time, changes in stock prices

are influenced by many things. According to Arifin (2001) factors that influence stock prices are

First, non-financial factors such as the movement of stock price trends, which are used by investors

to make decisions to buy or sell shares. Second, financial factors in the form of information in

financial statements, such as profitability and profitability. The financial information used to

measure the company's performance will be used as a reference of the value of shares in the eyes of

investors. Third, external factors which are things that occur outside the company such as an

increase in interest rates resulting in market uncertainty, inflation and deflation that results in

uncertainty of people's purchasing power, the state of a country's security, government policies and

socio-political conditions. The state of the company will be a measure of how much risk will be

borne by investors and how much profit can be obtained by investors.

LQ45 Company is a company that has financial conditions, high growth prospects and

transaction value as well as with high liquidity and market capitalization making the company

recommended for investors and prospective investors to be able to invest as much shares as

possible in the company. With good financial condition, investors will be interested in buying the

company's shares so that it will raise the stock price. To ascertain whether the company's condition

in a good or bad position can be assessed first.

According to Bodie et al. (2014) stock valuation is the process of using information about

the current and future profits of a company to find and predict the fair value of a stock. There are

two approaches in stock price analysis that are used to provide an assessment of stock prices,

namely technical and fundamental analysis by looking at the company's financial performance.

Based on previous research the company's financial performance most commonly used for analysis

is its profitability ratio. The profitability ratios discussed in this study whose effect on stock price

which will be analyzed prices are return on assets, return on equity and earning per share.

Return on assets (ROA) is the ratio used to measure the net profit gained from the use of

assets. In other words, the higher this ratio, the better the productivity of assets in obtaining net

profits. This will further increase the attractiveness of the company to investors. This increase in

attractiveness will then make the company more attractive to investors which will affect the stock

price to rise. Research conducted by Alipudin and Oktaviani (2016) found that ROA affects stock

prices. Meanwhile, according to Egam et al. (2017) in his study stated that ROA has no effect on

stock prices.

Return on equity (ROE) is used to measure the amount of return on investment of

shareholders. This ratio shows how well management utilizes the investment of shareholders. ROE

is the ratio between income after tax with own capital. An increase in ROE is usually followed by

an increase in a company's stock price. The greater the ROE the greater the stock price because the

amount of ROE gives an indication that the returns to be received by investors will be high so that

investors will be interested in buying these shares and this causes the stock market prices to tend to



rise (Harahap, 2007). According to Nugraha and Sudaryanto (2016) ROE affects stock prices.

Whereas Tyas and Saputra (2016) in their research stated that ROE had no effect on stock prices.

Earnings per Share (EPS) is a market ratio used to measure how much market recognition a

company has by comparing net income with the number of shares outstanding on the market. The

rising EPS indicates that the company has succeeded in increasing investor prosperity by dividend

distribution. This can increase investor demand for shares which in turn will also increase the

company's stock price (Eduardus, 2010). According to Hidayat and Topowijono (2018) EPS affects

stock prices. Meanwhile, according to Rahmadewi and Abundanti (2018) in their research.

RESEARCH METHOD

Data Source

The data used are secondary data in the form of return on assets, return on equity and earnings per

share and the closing price of the LQ45 company's year-end closing period 2009-2018 which was

published by the Indonesia Stock Exchange in the form of a summary of financial performance.

Sample and Population

The population used in this study was LQ45 companies from 2009-2018, and 10 companies were

sampled as research samples. The sampling technique used is the conditional sample sample

technique, with the conditions specified in table 1.

Dependent Variable

The dependent variable in this study is the stock price as seen from the closing price (closing price)

at the end of the annual financial statement period in the sample company. Closing Price is the

price that occurs in a stock due to demand and supply in the market, which is determined before

closing on the stock every day, then the annual closing price is the average price that occurs in a

stock in a certain year (Sugiyono, 2010).

Independent Variable

The independent variable is a variable whose situation is not influenced by other variables. Instead,

this variable can be used to examine its effect on other variables. The independent variables in this

study are return on assets, return on equity and earnings per share.

Table1. Research Samples Selection Procedures No. Criteria Number of Firm

1. Companies are listed in the LQ45 index during the observation period, from

2014 to 2018 16

2. The company periodically issues financial statements and has complete data

during the observation period 16

3. The company does not split the par value of shares (stock split) (6)

4. The number of LQ45 companies that became the research sample 10

Total 10

a. Return on Assets Return on assets (ROA) is a financial ratio used to assess the financial condition of a company by

using a certain scale or a tool to assess whether all assets owned by the company have been used as

much as possible to get a profit (Porman, 2007).

ROA serves to measure the effectiveness of the company in generating profits by utilizing the

assets it has. The greater the ROA owned by a company, the more efficient the user of assets so

that it will increase profits. Large profits will attract investors because the company has a higher

rate of return. The ROA calculation formula is as follows.



b. Return on Equity Return on Equity (ROE) is a ratio to measure the level of profitability of a company, which is

measuring the company's ability to generate profits. This ratio measures the efficiency of a

company in generating profits by using existing equity (equity). The higher the ROE value, the

better the meal (Naibaho, 2011). ROE can be calculated using the following equation.

c. Earning per Share Earnings per Share (EPS) is a ratio that shows the share of profit for each share. Increase or

decrease in EPS from year to year is an important measure to determine whether or not the work

carried out by its shareholder companies (Darmaji and Fakhruddin, 2012). A high EPS indicates

that the company can provide a level of profit to shareholders, whereas a lower EPS provides a low

level of profit to shareholders. EPS can be calculated using the following equation.

Classical Assumption Test

The classic assumption test is a preliminary test or a requirement that must first be met before

carrying out an analysis relating to testing a hypothesis (Sugiyono, 2013). A good regression model

must have a data distribution that is normal or close to normal and free from the classic assumption

test so that the tested data get unbiased and efficient results so the results of the data used can be

said to be feasible for analysis. The classical assumption test consists of normality test,

multicollinearity test, autocorrelation test, and heteroscedasticity test.

Multiple Linear Regression Analysis

The model used in this research is multiple linear regression analysis to see the relationship

between the independent variables and the dependent variable. Multiple linear regression analysis

is a linear relationship between two or more independent variables with the dependent variable.

This analysis is to determine the direction of the relationship between the independent variables

with the dependent variable whether each independent variable has a positive or negative

relationship. The regression model used to examine the effect of the independent variables with the

dependent variable in this study is as follows.

Stock Price = α + β1ROA + β2ROE+ β3EPS + ε

where α : constant; β: coefficient of regression; ROA: return on assets; ROE: return on equity;

EPS: earning per share; ε : residuals.

RESEARCH RESULTS

Descriptive Statistics

Based on the results of the descriptive analysis in table 2, it can be seen the maximum,

minimum, mean, standard deviation and coefficients of variation values of research variables.



Table 2. Descriptive Analysis of Research Variables

Variables Minimum Maximum Mean Standard

Deviation

Coefficient of

Variation (%)

Return on Assets (%) 1.09 71.51 11,57 12.17 105.19

Return on Equity (%) 4.50 142.90 27,48 32.19 117.14

Earning per Share

(IDR) 65 4,050 819,70 865.12 105.54

Stock Price (IDR) 515 83,800 16,250.63 18,116.90 111.48

Source: Secondary data processed

Research variables are greatly varied. This is indicated by their coefficient of variations

which are greater than 100 percent. Return on assets varies from 1.09 to 71.51 percent, return on

equity varies from 4.5o to 142.90 percent, earning per share varies from 65 to 4,050 IDR, and stock

price varies from 515 to 83,800 IDR.

Results of Classical Assumption Tests

This test include normality, multicollinearity, autocorrelation, and heteroscedasticity tests.

Results of these tests are summarized in the following table.

Table 3. Summary of Classical Assumption Tests Tests Results

Normality Normally distributed

Multicollinearity No multicollinearity

Autocorrelation No autocorrelation

Heteroscedasticity No heteroscedasticity

Multiple Linear Regression Analysis

Results of the multiple linear regression analysis is depicted in the following table.

Table 4. Summary of Multiple Linear Regression Analysis Results

Variables Regression Coefficients

t-value Significance Unstandardized Standardized

Constant 1634.750 1.897 0.000

ROA -75.356 -.051 -.649 0.518

ROE 189.469 .308 3.949 0.000

EPS 18.074 .874 19.165 0.000

Dependent variable: Stock Price

F = 133,392 (p



regression coefficients (i.e., 189.47), every percent of return on equity increase will result in

increase in stock price by approximately 189.47 IDR. This finding is in line with Nugraha and

Sudaryanto (2016) who state that return on equity positively contributes to stock price where firms

with better financial performance represented by higher return on equity tend to have higher stock

price. Investors, according to this finding, take return on equity into account in making investment

decision. On the other hand, it is different from Tyas and Saputra (2016), Faradillah (2017) and

Rahmadewi and Abundanti (2018). They found that return on equity has no significant effect on

stock price. Accordingly, return on equity was not considered by investors in making investment

decisions.

Earnings per share positively affects stock price in a lower magnitude as to compare with

return on equity. One percent increases of earnings per share is able to increase stock price by

approximately 18.07 IDR. Research findings of Alipudin and Oktaviani (2016), Egam et al. (2017)

and Hidayat and Topowijono (2018) which found that earnings per share positively affects stock

price in accordance with this study finding. This indicates that earnings per share is of importance

to investors in making investment decisions. However, it is not in line with Rahmadewi and

Abundanti (2018) whose findings indicate that stock price is independent from earnings per share.

Nugraha and Sudaryanto (2016), Alipudin and Oktaviani (2016) and Egam et al. (2017)

recognized through their study that return on assets has no significant contribution towards stock

price. Similar with their findings, the present study found that return on assets has insignificant

effect on stock price. However, it is different from findings of Vireyto and Sulasmiyati’s (2017)

study which indicates that return on asset significantly contributes to stock price. Different from

those findings of Nugraha and Sudaryanto (2016), Alipudin and Oktaviani (2016) and Egam et al.

(2017) as well as the present study, return on assets increase stock price i.e. the higher the value of

return on assets—better financial performance of a firm—the higher the price of stock of the

related firm.

Earnings per share is identified as the most dominant variable in affecting stock price of

firms listed on the LQ45 indicating that investors prefer use this variable as the primary aspect that

should be taken into account in making investment decisions.

Similarities, particularly differences in findings of these studies could be caused by a

number of reasons. One of which is periods of time the study is conducted. This is related closely

with economic conditions which generally represented by macroeconomic indicators such as rates

of inflation, exchanges rates, interests rates and economic development. Different industries will

result in different findings or research results. Research objects of this study consist of 10 firms in

various industries which is similar with study of Egam et al. (2017). Study of Alipudin and

Oktaviani (2016) was on manufacturing industries sub-sector cements, Nugraha and Sudaryanto

(2016) was on chemicals sub-sector of manufacturing industries. Hidayat and Vireyto and

Sulasmiyati (2017) were on banking sectors of financial industries. Meanwhile, Rahmadewi and

Abundanti (2018) and Fadillah (2017) were, respectively various industries within the Indonesia

Stock Exchange and constructions. It is believed that every industries have different characteristics

and, accordingly response towards economic conditions and changes.

CONCLUSION AND RECOMMENDATIONS

Return on assets, return on equity and earnings per share are found to have significant

effect on stock price of LQ45 firms in simultaneous way. Return on equity and earnings per share,

in the mean times, are variable that have partial effect on stock price. Effects of both variables are

to increase stock price. Firms with higher return on equity and earnings per share will have higher

stock price. Earnings per share is identified as the most important variable in determining stock

price.

It is important to recommend that firms’ managers to take return on assets, return on equity

and earnings per share into account carefully because they significantly contribute to the price of



stock of their firms. Special attention, however, should be paid towards return on equity and

earnings per share since they have partial effects on stock price.

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