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"Pemahaman yang kurang dari para investor dapat menyebabkan kerugian investasi yang signifikan karena mereka hanya menggunakan spekulasi untuk menentukan strategi investasinya. Oleh karena itu, diperlukan analisis saintifik dan statistik untuk menghindari spekulasi saat berinvestasi. Jika investor dapat menghindari spekulasi dengan baik, maka investor tersebut dapat membangun portofolio yang optimal. Namun, menghindari spekulasi merupakan hal yang tidak mudah. Tujuan dari penelitian ini adalah menjelaskan stock market filtering model untuk membantu investor dalam membentuk portofolio yang optimal. Stock market filtering model ini akan mengurangi jumlah perusahaan yang akan dikombinasikan ke dalam portofolio terakhir atau portofolio yang paling optimal. Stock market filtering model ini mengimplementasikan teori graf dengan minimum spanning tree yang ditentukan menggunakan algoritma Kruskal. Data yang digunakan adalah data harga closing harian dari setiap perusahaan yang akan diteliti. Data harga closing tersebut akan diolah sampai menemukan matriks dan graf jarak antara perusahaan. Graf jarak ini akan dimasukkan ke dalam algoritma Kruskal untuk mencari minimum spanning tree dari graf tersebut. Setelah itu, akan dipilih simpul yang hanya memiliki satu sisi (edges) sebagai perusahaan yang akan dimasukkan ke dalam portofolio. Analisis performa portofolio akan menggunakan modern portfolio theory dengan komponen utama yang dilihat adalah Sharpe ratio dan RAP versi Modigliani. Performa dari portofolio dengan filtrasi dibandingkan dengan portofolio tanpa filtrasi untuk melihat seberapa besar penurunan atau kenaikan performa yang terjadi setelah proses filtrasi. Model filtrasi berhasil memfiltrasi 42,39% perusahaan dengan penurunan performa hanya 5,2042%.

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A lack of understanding among investors can lead to significant investment losses because they are merely using speculation to determine their investment strategies. Therefore, scientific and statistical analysis is necessary to avoid speculation when investing. If investors can successfully avoid speculation, then they can build an optimal portfolio. However, avoiding speculation is certainly not an easy task. The aim of this thesis is to give an explenation of stock market filtering model to assist investors in building an optimal portfolio. This stock market filtering model reduces the number of companies to be combined into the best optimal portfolio. This model is implementing graph theory to construct a minimum spanning tree using Kruskal's algorithm. The data used is the daily closing price data from each company that's included in the scope of research. These closing prices will be processed until a matrix and a graph containing distance values between companies are obtained. The graph will be used as the input of the Kruskal's algorithm to find the minimum spanning tree of the graph. Afterward, nodes that only have one edge will be selected as the companies to be included in the portfolio. The performance analysis of the portfolio uses modern portfolio theory, with the primary components examined being the Sharpe ratio and Modigliani's RAP. The performance of the portfolio with filtering is compared to the portfolio without filtering to see how significant the increase or decrease of performance because of the effect of the filtering process. The filtering model successfully decrease 42.39% of companies with only 5.2042% decrease in performance."

"Penelitian ini fokus pada pengukuran interkoneksi pasar saham pada negara-negara yang tergolong dalam wilayah Systemically Important Regions (SIRs). Dengan menggunakan variabel makro, negara-negara yang diidentifikasi sebagai SIRs memiliki connected lines lebih dari 4 lines di tiap tahunnya. Sedangkan besarnya nilai interkoneksi pada pasar saham SIRs menjadi bias karena adanya pengaruh dari faktor dinamika global. Oleh karena itu, penulis melakukan estimasi ulang nilai returns tiap pasar saham SIRs menggunakan simple market model. Untuk melihat efek dari faktor dinamika global, penelitian ini membandingkan hasil interkoneksi pasar saham sebelum dan setelah proses filtering menggunakan model Vector Autoregression yang diinterpretasi melalui hasil Generalized Forecast Error Variance Decompositions (GFEVD). Terdapat perbedaan signifikan pada hasil interkoneksi pasar saham setelah proses filtering. Interkoneksi tergolong tinggi sebelum filtering, dengan pasar saham Amerika Serikat, Swedia, dan Jerman sebagai poros. Setelah filtering, poros dari interkoneksi menyebar ke beberapa negara Eropa lainnya. Perbedaan hasil ini direpresentasikan pada nilai GFEVD yang turun secara signifikan antara sebelum dan setelah proses filtering.

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This research focuses on the measure of stock market interconnectedness in Systemically Important Regions or SIRs. By using macro variables, countries identified as SIRs have the average 4 connected lines in each year on the research period.  Thus, the author is reestimate the returns of each stock markets of SIRs using the simple market model. To see the effects from common global factors, this research compares the interconnectedness level of stock markets before and after filtering process using the Vector Autoregression Model (VAR Model). The author found significant changes of the stock market interconnectedness after the filtering process. The interconnectedness is considered high before filtering, with the US, Sweden, and German stock markets as the pivot. After filtering, the pivot of interconnectedness spreads to other Europe stock markets. The results are represented on the GFEVD value which decreased significantly between the before and after filtering process."

"The stock markets are becoming an essential and inseparable part of the economies in many countries, including Indonesia. The fact that stock markets indices become one of the indicators to determine the healthiness of country economics showing the importance of the stock market in a country. Whenever the stock market experiences a substantial decline, there is reason to fear that a recession may come. Thus, policymakers and the government have to be aware of this matter. Macroeconomics plays an essential role in economies activities as well as brings an effect to stock market performance. GDP as an indicator of economic growth, inflation that is limiting consumption, interest rate, and exchange rate are selected macroeconomic variables that affect the stock market performance. Using multiple regression analysis, it is known that GDP and inflation contributing to the rise of stock market value, albeit the effect of inflation is not significant. Contrary, interest rate and exchange rate bring a negative impact on the stock market performance, primarily interest rate, which has a significant effect."

"Penelitian ini bertujuan untuk mengetahui pengaruh aktivitas perdagangan saham investor asing terhadap performa pasar modal Indonesia sebelum dan saat periode krisis pandemi COVID-19. Aktivitas perdagangan investor asing diproksikan oleh volume pembelian, nilai pembelian, frekuensi pembelian, volume penjualan, nilai penjualan, dan frekuensi penjualan harian. Lalu, proksi yang digunakan untuk performa pasar modal Indonesia adalah return, likuiditas dan volatilitas harian IHSG. Data perdagangan saham diproses dengan menggunakan metode regresi OLS dan prosedur Newey-West HAC. Penelitian menemukan bahwa aktivitas perdagangan saham investor asing memiliki pengaruh dua arah terhadap return dan likuiditas pada periode normal dan pandemi, dimana pengaruh pada periode pandemi lebih besar dari pada pengaruh pada periode normal. Lalu, aktivitas perdagangan saham investor asing mempengaruhi volatilitas pada periode normal dan meningkatkannya pada periode krisis

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The COVID-19 pandemic had caused a decline in the Indonesian stock market performance. This paper analyzes how much of the decline was due to foreign investor trading activity and how much its impact changed from before the crisis period. The stock market’s performance is proxied by return, liquidity, and volatility. While foreign investor trading activity is proxied by buying and selling volume, value and frequency. Trading data for each period are processed through OLS regression with Newey-West's HAC procedure. The results show that foreign investor trading activity has bi-directional impact on return and liquidity, where the impact on both are greater during the crisis period. Additionally, foreign investor trading activity impacts volatility significantly in the non-crisis period, while an increase in trading activity increases volatility in the crisis period"

"Pada akhir tahun 2008, perekonomian dunia mengalami krisis yang berimbas pada pembekuan beberapa sekuritas terkait kredit perumahan di AS. Krisis ini menyebabkan gejolak di pasar finansial dan akhirnya merambat keseluruh dunia termasuk Indonesia. Krisis finansial ini menyebabkan perilaku irasional investor salah satunya yaitu perilaku herding.Penelitian ini membahas mengenai pendeteksian perilaku herding yang terjadi pada saham kompas 100 tahun 2008-2012. Penelitian ini bertujuan untuk mendeteksi adanya perilaku herding pada saham kompas 100 periode 2008-2012. Variabel dependen dalam penelitian ini adalah Cross-Sectional Absolute Deviation (CSAD). Variabel bebas dalam penelitian ini adalah return pasar. Sampel dari penelitian ini bahwa saham perusahaan yang terdaftar dalam indeks Kompas 100 periode 2008-2012. Hasil penelitian ini, mengatakan bahwa di Indonesia secara keseluruhan tidak ditemukan perilaku herding selama periode 2008-2012. Hal ini dapat di lihat dari hasil regresi γ2 yaitu positif signifikan. Namun pada tahun 2010, menemukan adanya perilaku herding.

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At the end of 2008, the world economy experienced a crisis which impact on freezing some mortgage-related securities in the United States. This crisis led to turmoil in the financial markets and eventually spread throughout the world, including Indonesia. The financial crisis led to irrational behavior of investors one of which is herding behavior. This study discusses about the detection of herding behavior that occurs on the Kompas 100 index from 2008-2012. This study aimed to detect the presence of herding behavior on Kompas 100 index from 2008-2012. The dependent variable in this study is Cross-Sectional Absolute Deviation (CSAD).

The independent variable in this study is the market return. Samples from this study that the shares of companies listed on the Kompas 100 index from 2008-2012 period. The results of this study, said that in Indonesia as a whole is not found herding behavior during the period 2008-2012. It can be seen from the regression results γ2 is significantly positive. But in 2010, found the herding behavior"

""Want to start investing your money wisely but not sure how? Get to grips with the UK investment and financial markets with this practical and easy guide that tells you everything you need to know about the stock market. In today's economy it is clear to see how everyday politics and global events can have a lasting impact on your pension, savings and investments which can make you feel powerless to safeguard your future. Now more than ever, it is vital to understand how the markets work and what you can do to maintain financial security. This book gives concise and practical information that helps you understand how the stock markets work, breaking down what may seem like complex jargon to simple explanations. Fully updated for this seventh edition, How the Stock Markets Works aims to provide you with the tools needed to understand investing in the light of major events such as the UK's exit from the EU. Chapters cover the basics such as explanations of shares, bonds and gilts and range to where to find help and advice when needed. With guidance on how to be a responsible shareholder and information on the taxation regime, this established guide will help you take control of your finances"--"

"Penelitian ini bertujuan untuk menganalisis pembentukan portofolio dengan menggunakan model Fama-French Lima Faktor dengan modifikasi pada variabel profitabilitas. Pembentukan portofolio yang berbeda akan dilakukan untuk tiga macam variabel, yaitu berdasarkan laba operasional tahunan per total ekuitas RMW, laba operasional bulanan per total ekuitas ROE, dan laba operasional tahunan per total aset ROA. Portofolio jenis pertama ditujukan untuk variabel RMW, portofolio jenis kedua untuk variabel ROE, dan jenis ketiga untuk variabel ROA. Model penelitian yang digunakan dalam penelitian ini berbasiskan model Fama-French Lima Faktor 2015. Hasil penelitian ini menunjukkan pembentukan portofolio untuk variabel RMW memiliki pengaruh yang paling tinggi terhadap return saham. Hasil ini sesuai dengan hasil penelitian Fama-French 2015.

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This study aims to analyze the portfolio formations using Fama French five factors model with modification on profitability variable. Different portfolio formations are performed for three kinds of profitability variables, which are annual operating profit per total equity RMW, monthly operating profit per total equity ROE, and annual operating profit per total assets ROA. The first portfolio formation type is for RMW variable, the second portfolio formation type is for ROE variable, and the third portfolio type is for ROA variable. The method used in this study is based on the Fama French Five Factor Model 2015. The result shows portfolio formation for RMW variable has the highest impact on stock return. This result is consistent with the results of the Fama French 2015. "

"ABSTRAKPenelitian ini mengembangkan permodelan gelembung harga saham rasional dan irasional menggunakan model state-space dengan markov switching. Gelembung harga saham merupakan unobserved variable yang dapat ditentukan dengan pendekatan teori ekspektasi rasional dimana dalam penelitian ini gelembung harga saham ditentukan oleh informasi ekstraneus yang terdiri dari faktor eksternal dow jones Index dan Hangseng Index dan faktor internal faktor politik dan keamanan . Untuk menganalisis unobserved variable tersebut digunakan model state-space yaitu Kalman Filter. Selain itu gelembung harga saham dapat megalami perubahan struktural yang juga bersifat unobserved yaitu adanya gelembung harga saham irasional. Dikarenakan gelembung irasional juga bersifat unobserved maka digunakan model markov switching.Untuk melihat bagaimana model state space dengan markow switching bekerja, penelitian ini menerapkan model tersebut pada data harga saham Indonesia periode Januari 1989 hingga Desember 2013. Hasil estimasi model menunjukan bahwa DJIA berpengaruh positif signifikan terhadap gelembung harga saham di Indonesia, sedangkan HSI tidak berpengaruh signifikan. Dari faktor internal, faktor politik berpengaruh negatif signifikan terhadap gelembung harga saham di Indonesia, sedangkan faktor keamanan tidak berpengaruh signifikan. Dengan permodelan ini, gelembung harga saham rasional dan irasional dapat di identifikasi di pasar modal Indonesia namun gelembung harga saham tidak terjadi setiap tahun selama periode penelitian.

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ABSTRACTThis paper develops a model of rational and irrational stock price bubbles using State Space Model with Markov Switching. Stock price bubbles are the unobserved variables that can be determined with rational expectations theory approach, which in this study is determined by stock price bubbles extraneous information consisting of external factors Dow Jones Index and the Hang Seng Index and internal factors political and security factors . A state space model, Kalman filter, is used to analyze this unobserved variables. Stock price bubbles may undergo structural changes that are also unobserved, namely the irrational stock price bubble. Thus, due to this unobserved nature, the Markov switching model is used to analyze this unobserved structural changes. To see how the model state space with Markov switching works, this study applies the model to the Indonesian stock price data from the period of January 1989 to December 2013. The result of the model estimation shows that the external factor, DJIA has significant positive effects, whereas HSI has no significant effect on the stock price bubble in Indonesia. The result from internal factors shows that a political factor has significant negative effect, whereas the safety factor has no significant effect on the stock price bubble in Indonesia. Furthermore, this model could help identify the rational and irrational stock price bubble in the Indonesian stock market, nonetheless, this stock price bubbles do not occur every year during the study period."

"Penelitian ini bertujuan untuk menganalisis hubungan antara volatilitas pasar saham, Foreign Portfolio Investment, suku bunga, nilai tukar, dan Indeks Produksi Industri pada saham syariah sektor energi di Indonesia tahun 2011-2023. Penelitian ini menggunakan data dengan frekuensi bulanan sejak tahun 2011. Penelitian ini menggunakan Autoregressive Distributed Lag (ARDL) dengan melibatkan fenomena structural breaks yang terjadi pada September 2013 dan fenomena tersebut digunakan sebagai variabel dummy. Hasil penelitian menunjukkan adanya kointegrasi atau hubungan jangka panjang pada setiap variabel yang diuji sebagai variabel dependen. Hasil uji jangka panjang menunjukkan bahwa volatilitas pada saham syariah sektor energi dipengaruhi secara positif oleh aktivitas Foreign Portfolio Investment. Selain itu, nilai tukar secara positif mempengaruhi Indeks Produksi Industri. Sedangkan dalam jangka pendek, aktivitas Foreign Portfolio Investment dipengaruhi secara negatif oleh nilai Foreign Portfolio Investment periode sebelumnya. Di sisi lain, volatilitas pasar saham secara positif mempengaruhi Foreign Portfolio Investment. Selain itu, Indeks Produksi Industri dipengaruhi secara positif oleh nilai Indeks Produksi Industri selama 3 bulan sebelumnya, nilai tukar, aktivitas Foreign Portfolio Investment, serta volatilitas pasar saham hingga 1 bulan sebelumnya.

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This research aims to analyze the relationship between stock market volatility, Foreign Portfolio Investment, interest rates, exchange rates, and the Industrial Production Index on sharia stocks in the energy sector in Indonesia in 2011-2023. This research uses data with a monthly frequency since 2011 as the initial year when the Indeks Saham Syariah Indonesia (ISSI) was launched. This research uses Autoregressive Distributed Lag (ARDL) involving the structural breaks phenomenon that occurred in September 2013 and this phenomenon is used as a dummy variable. The research results show that there is cointegration or long-term relationship in each variable tested as the dependent variable. Long-term test results show that volatility in sharia stock in the energy sector is positively influenced by Foreign Portfolio Investment activity. In addition, exchange rate positively influences the Industrial Production Index. Meanwhile, in the short term, Foreign Portfolio Investment activity is negatively influenced by the value of Foreign Portfolio Investment in the previous period. On the other hand, stock market volatility positively affects Foreign Portfolio Investment. In addition, the Industrial Production Index is positively influenced by the value of the Industrial Production Index for the previous 3 months, exchange rates, Foreign Portfolio Investment activity, and stock market volatility for the previous 1 month."

"Saat ini terdapat hampir 400 emiten saham syariah yang terdaftar di Bursa Efek Indonesia. Banyaknya jumlah tersebut menyulitkan para investor untuk memilih saham terbaik yang akan dijadikan portfolio. Oleh karena itu, diperlukan analisis clustering untuk mendapatkan kelompok saham yang memiliki nilai imbal hasil yang diharapkan. Sejauh ini, aktivitas yang penting dalam aktivitas clustering adalah penentuan tingkat perbedaan antar objek dalam suatu dataset. Dalam studi kasus penelitian ini, objek yang dimaksud adalah data deret waktu terkait harga saham untuk emiten-emiten saham syariah. Metode yang umum digunakan dalam mengukur dissimilarity measure tersebut adalah Euclidean distance dan dynamic time warping (DTW). Euclidean distance merupakan metode konvensional yang masih sering digunakan meskipun telah berkembang metode yang lebih kompleks, DTW. Penelitian ini bertujuan untuk membandingkan karakteristik kedua metode tersebut dalam studi kasus data deret waktu tentang harga saham. Selain itu, penelitian ini juga ingin mengetahui efektivitas variabel candle dalam memprediksi harga saham. Mulanya, dilakukan pembersihan terhadap dataset sebelum diaplikasikan metode dissimilarity measure. Selanjutnya, hasil tersebut menjadi acuan dalam analisis hierarchical clustering dan dipilih kelompok terbaik. Selain itu, dilakukan pula prediksi harga saham menggunakan kombinasi beberapa variabel bebas yang terdiri atas OHLC, technical, dan candle. Hasil dari penelitian ini menunjukkan bahwa Euclidean distance lebih efektif dibandingkan DTW dalam memberikan rekomendasi portfolio pilihan. Selain itu, tambahan variabel candle dalam kombinasi variabel OHLC dan technical mampu meminimalkan error dengan baik.

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Currently, there are nearly 400 sharia stocks listed on the Indonesia Stock Exchange. A large number of these makes it difficult for investors to choose the best stocks that will be used as a portfolio. Therefore, clustering analysis is needed to get a group of stocks that have the expected return value. So far, an important activity in clustering activities is to determine the dissimilarity among datasets. In this research, it used time-series data related to stock prices for sharia stocks. The methods commonly used in measuring the dissimilarity measure are Euclidean distance and dynamic time warping (DTW). Euclidean distance is a conventional method that is still frequently used rather than a more complex method, DTW. This study aims to compare the characteristics of those two methods using the time series dataset. Besides, this study also wants to determine the effectiveness of the candle variable in predicting stock prices. At first, the missing value in the dataset was omitted before the dissimilarity measure method was applied. Furthermore, these results become a reference in hierarchical clustering analysis and the best group is selected. Also, stock price predictions are carried out using the combination of several independent variables consisting of OHLC, technical, and candles. The results indicate that Euclidean distance is more effective than DTW in providing a selected portfolio. In another hand, the additional candle variable combined with OHLC and technical variables can minimize errors properly."