A Scalable Algorithm for Sparse Portfolio Selection

The sparse portfolio selection problem is one of the most famous and frequently studied problems in the optimization and financial economics literatures. In a universe of risky assets, the goal is to construct a portfolio with maximal expected return and minimum variance, subject to an upper bound on the number of positions, linear inequalities, and minimum investment constraints. Existing certifiably optimal approaches to this problem have not been shown to converge within a practical amount of time at real-world problem sizes with more than 400 securities. In this paper, we propose a more scalable approach. By imposing a ridge regularization term, we reformulate the problem as a convex binary optimization problem, which is solvable via an efficient outer-approximation procedure. We propose various techniques for improving the performance of the procedure, including a heuristic that supplies high-quality warm-starts, and a second heuristic for generating additional cuts that strengthens the root relaxation. We also study the problem’s continuous relaxation, establish that it is second-order cone representable, and supply a sufficient condition for its tightness. In numerical experiments, we establish that a conjunction of the imposition of ridge regularization and the use of the outer-approximation procedure gives rise to dramatic speedups for sparse portfolio selection problems.

Closing a Bitcoin Trade Optimally under Partial Information: Performance Assessment of a Stochastic Disorder Model

The Bitcoin market exhibits characteristics of a market with pricing bubbles. The price is very volatile, and it inherits the risk of quickly increasing to a peak and decreasing from the peak even faster. In this context, it is vital for investors to close their long positions optimally. In this study, we investigate the performance of the partially observable digital-drift model of Ekström and Lindberg and the corresponding optimal exit strategy on a Bitcoin trade. In order to estimate the unknown intensity of the random drift change time, we refer to Bitcoin halving events, which are considered as pivotal events that push the price up. The out-of-sample performance analysis of the model yields returns values ranging between 9% and 1153%. We conclude that the return of the initiated Bitcoin momentum trades heavily depends on the entry date: the earlier we entered, the higher the expected return at the optimal exit time suggested by the model. Overall, to the extent of our analysis, the model provides a supporting framework for exit decisions, but is by far not the ultimate tool to succeed in every trade.

Optimal portfolio analysis of manufacturing entities in the Indonesia Sharia Stock Index

The purpose of this research is to determine the optimal portfolio for manufacturing entities listed on the Indonesian Sharia Stock Index based on a single index model test. The population of this research is manufacturing entities that have been listed in the Indonesian Sharia Stock Index on the Indonesia Stock Exchange for the Period 2019-2020. This study uses a purposive sampling technique using several criteria. Based on this technique, 31 entities meet the criteria. The results showed that the expected return was 5.65%, and the possible risk was 0.22% for 15 (fifteen) stocks included in the optimal portfolio category.

The Capital Market Response to The Announcement of The 2019-2024 Indonesia Onward Cabinet System

This research aims to examine capital market response to the 2019-2024 Indonesia Onward Cabinet System announced by President Jokowi. This event study research used market estimation model to estimate the expected return with an estimated period of 100 days and window period of seven days. There were 90 companies that are the member of Kompas Index 100 as the sample used in this research. T-test was used to analyze the data. The result shows that the announcement System of Indonesia Onward Cabinet 2019-2024 was responded positively and significantly by capital market. The result supports signaling theory in which the announcement of the 2019-2024 Indonesia Onward Cabinet System gave positive signal (influence) on capital market.

Data Integration Project in Robologee

Robologee is a sub-unit of PT. Bangun Satya Wacana is part of Kompas Gramedia which is focused in Education section for ages 7 to 12 years. Robologee is a diversification of the existing sub-units in PT. Bangun Satya Wacana. Robologee has branches located at Gramedia World so it is expected that it will have an impact on Gramedia traffic. Currently, Robologee is transforming in order to integrate data that will be stored in the cloud by Amazon Web Service.The goal of this project is that data can be accessed by various users and stored in one platform. In the analysis of the digital transformation project, 15 respondents have been determined who are parents as external customers. Based on the indicators used in DMM. It was found that Robologee's current condition is at the Advancing level. Based on the Roadmap this project is implemented for 1 year and consists of four stages. In the Budgeting analysis, Robologee has payback period of 1.7 years with an IRR of 7.512% greater than the expected return of 5% by the company. Then the NVP is in a positive number, so this project is feasible to implement.

ANALISIS PENENTUAN SAHAM PORTOFOLIO OPTIMAL DENGAN MODEL MARKOWITZ PADA PERUSAHAAN SEKTOR PERTAMBANGAN YANG TERDAFTAR DI BURSA EFEK INDONESIA PERIODE JANUARI 2019 – DESEMBER 2020

Dalam kegiatan investasinya, pasar modal menawarkan berbagai pilihan berinvestasi dengan tingkat keuntungan (return) dan tingkat risiko (risk) yang berbeda-beda. Untuk mengurangi risiko dalam investasi tersebut maka investor membentuk sebuah portofolio. Portofolio optimal dengan model Markowitz yang dipilih dari sekian banyak alternatif portofolio efisien dapat memberikan tingkat return yang maksimal sesuai dengan risiko yang berani ditanggung oleh investor. Tujuan penelitian ini untuk mengetahui portofolio saham yang optimal serta proporsi dana masing-masing saham dengan menggunakan model Markowitz. Penelitian dilakukan di BEI pada saham perusahaan sektor pertambangan periode Januari 2019-Desember 2020. Data yang digunakan dalam penelitian adalah data sekunder yang diperoleh dari www.idx.co.id dan www.finance.yahoo.com. Jumlah populasi dalam penelitian ini adalah 47 saham perusahaan sector pertambangan yang terdaftar di Bursa Efek Indonesia periode Januari 2019-Desember 2020 dan jumlah sampel sebanyak 11 saham perusahaan, dengan teknik purposive sampling. Hasil penelitian ini menunjukkan bahwa dari 11 saham perusahaan diperoleh sebanyak 4 saham yang masuk dalam portofolio optimal menggunakan model Markowitz dengan jumlah proporsi dananya yakni PT. Harum Energy Tbk (HRUM) sebesar 12,45%, PT. Timah Tbk (TINS) sebesar 24,42%, PT. Merdeka Copper Gold Tbk (MDKA) sebesar 53,07% dan yang terakhir PT. Indika Energy Tbk (INDY) sebesar 10,05%. Memberikan expected return sebesar 4,70% dan tingkat risiko sebesar 2,83%.

Adjustable Security Proportions in the Fuzzy Portfolio Selection under Guaranteed Return Rates

Based on the concept of high returns as the preference to low returns, this study discusses the adjustable security proportion for excess investment and shortage investment based on the selected guaranteed return rates in a fuzzy environment, in which the return rates for selected securities are characterized by fuzzy variables. We suppose some securities are for excess investment because their return rates are higher than the guaranteed return rates, and the other securities whose return rates are lower than the guaranteed return rates are considered for shortage investment. Then, we solve the proposed expected fuzzy returns by the concept of possibility theory, where fuzzy returns are quantified by possibilistic mean and risks are measured by possibilistic variance, and then we use linear programming model to maximize the expected value of a portfolio’s return under investment risk constraints. Finally, we illustrate two numerical examples to show that the expected return rate under a lower guaranteed return rate is better than a higher guaranteed return rates in different levels of investment risks. In shortage investments, the investment proportion for the selected securities are almost zero under higher investment risks, whereas the portfolio is constructed from those securities in excess investments.

In What Order Should Learners Learn Japanese Vocabulary? A Corpus-based Approach

<p>This thesis attempts to answer the following two main research questions:1) In what order should learners of Japanese as a second language learn words and characters in order to be able to read Japanese? 2) How will the order vary according to the purpose of learning? To answer these questions, a Vocabulary Database for Reading Japanese (VDRJ) and a Character Database of Japanese (CDJ) were first developed from the Balanced Contemporary Corpus of Written Japanese (BCCWJ) 2009 monitor version (NINJAL, 2009) which contains book texts and internet-forum site texts with 33 million running words in total. Word and character rankings for international students, non-academic learners and general written Japanese were included in these databases. These rankings were proven to be valid for their respective purposes as they provided higher text coverage for the target texts than other texts. After analysing the use of vocabulary and characters in Japanese, three groups of domain-specific words, namely common academic words, limited-academic-domain words and literary words were extracted. In order to test the expected efficiency for learning these groups of words, an index entitled Text Covering Efficiency (TCE) in different types of texts was proposed. The TCE represents the expected return per unit of text length from learning a group of words. As such, the TCE score in the target text domain should determine the order in which words in this domain are most efficiently learned. Indeed, the extracted common academic words and limited-academic-domain words showed significantly higher text coverage and TCE scores in academic texts than in other texts. Literary words also provided high text coverage and high TCE scores in literary texts, despite a lower efficiency level than that of academic vocabulary in academic texts. Learning domain-specific words is expected to be much more efficient than learning other words at the intermediate level. At the advanced level or above, learning domain-specific words will be further more efficient in some domains such as the natural sciences. In sum, the TCE has been shown to provide useful information for deciding on the learning order of various groups of words. Other findings based on the analyses using the databases and word lists include the features of some indices for dispersion and adjusted frequency, lexical features of different media and genres, indexicality of the distributions of word origins and parts of speech, and the discrepancy between learning orders of words and Kanji. A Lexical Learning Possibility Index for a Reading Text (LEPIX) was also proposed for the simplification of a text as a vocabulary learning resource.</p>

In What Order Should Learners Learn Japanese Vocabulary? A Corpus-based Approach

<p>This thesis attempts to answer the following two main research questions:1) In what order should learners of Japanese as a second language learn words and characters in order to be able to read Japanese? 2) How will the order vary according to the purpose of learning? To answer these questions, a Vocabulary Database for Reading Japanese (VDRJ) and a Character Database of Japanese (CDJ) were first developed from the Balanced Contemporary Corpus of Written Japanese (BCCWJ) 2009 monitor version (NINJAL, 2009) which contains book texts and internet-forum site texts with 33 million running words in total. Word and character rankings for international students, non-academic learners and general written Japanese were included in these databases. These rankings were proven to be valid for their respective purposes as they provided higher text coverage for the target texts than other texts. After analysing the use of vocabulary and characters in Japanese, three groups of domain-specific words, namely common academic words, limited-academic-domain words and literary words were extracted. In order to test the expected efficiency for learning these groups of words, an index entitled Text Covering Efficiency (TCE) in different types of texts was proposed. The TCE represents the expected return per unit of text length from learning a group of words. As such, the TCE score in the target text domain should determine the order in which words in this domain are most efficiently learned. Indeed, the extracted common academic words and limited-academic-domain words showed significantly higher text coverage and TCE scores in academic texts than in other texts. Literary words also provided high text coverage and high TCE scores in literary texts, despite a lower efficiency level than that of academic vocabulary in academic texts. Learning domain-specific words is expected to be much more efficient than learning other words at the intermediate level. At the advanced level or above, learning domain-specific words will be further more efficient in some domains such as the natural sciences. In sum, the TCE has been shown to provide useful information for deciding on the learning order of various groups of words. Other findings based on the analyses using the databases and word lists include the features of some indices for dispersion and adjusted frequency, lexical features of different media and genres, indexicality of the distributions of word origins and parts of speech, and the discrepancy between learning orders of words and Kanji. A Lexical Learning Possibility Index for a Reading Text (LEPIX) was also proposed for the simplification of a text as a vocabulary learning resource.</p>