Hasil Pencarian  ::  Simpan CSV :: Kembali

Abstrak :
Guna mempersiapkan kebutuhan yang terencana dan tidak terencana di masa depan, perlu adanya investasi sejak dini. Dalam berinvestasi, seorang investor dihadapkan pada permasalahan dalam menentukan jumlah aset yang optimal dan proporsi modal pada masing-masing aset dalam menyusun portofolio investasinya. Masalah ini adalah masalah pengoptimalan portofolio. Dalam menyusun portofolio perlu dilakukan diversifikasi yaitu menggabungkan aset dengan karakteristik yang berbeda untuk mengurangi risiko investasi. Clustering dapat digunakan sebagai strategi diversifikasi. Tujuan dari penelitian ini adalah untuk mengetahui strategi diversifikasi aset dalam portofolio dengan metode clustering Density Based Spatial Clustering of Applications with Noise (DBSCAN) dan memilih aset serta menentukan proporsi modal yang optimal pada setiap portofolio aset penyusun portofolio dengan Multi- objektif algoritma metaheurysitic Co-variance. Berbasis Artificial Bee Colony (M-CABC). DBSCAN adalah algoritma clustering berbasis kepadatan cluster yang dirancang untuk membentuk cluster dan menemukan noise dalam data. Algoritma M-CABC merupakan pengembangan dari algoritma Artificial Bee Colony (ABC) dengan menambahkan konsep statistic covariance untuk mempercepat konvergensi. Aset yang digunakan dalam penelitian ini adalah saham. Kami menggunakan lima data portfolio saham dengan persentase saham yang memiliki mean return negatif untuk setiap data yang berbeda. Implementasi dilakukan dalam tiga kasus metode yang berbeda: optimalisasi portofolio saham tanpa DBSCAN, optimalisasi portofolio saham dengan DBSCAN tanpa noise, dan optimalisasi portofolio saham dengan DBSCAN dengan noise. Hasilnya adalah besarnya persentase saham yang memiliki mean return pada data negatif berpengaruh terhadap pemilihan metode yang digunakan untuk memperoleh portofolio dengan risiko terkecil. ......In order to prepare for planned and unplanned needs in the future, it is necessary to invest from an early age. In investing, an investor is faced with problems in determining the optimal amount of assets and the proportion of capital in each asset in compiling his investment portfolio. This issue is a portfolio optimization problem. In compiling a portfolio, it is necessary to diversify, namely combining assets with different characteristics to reduce investment risk. Clustering can be used as a diversification strategy. The purpose of this study is to determine the diversification strategy of assets in portfolios with the Density Based Spatial Clustering of Applications with Noise (DBSCAN) clustering method and to select assets and determine the optimal proportion of capital in each portfolio compiler portfolio assets with the Multi-objective Co-variance metaheurysitic algorithm. . Based on Artificial Bee Colony (M-CABC). DBSCAN is a cluster density based clustering algorithm designed to form clusters and find noise in data. The M-CABC algorithm is a development of the Artificial Bee Colony (ABC) algorithm by adding the concept of statistical covariance to accelerate convergence. The assets used in this study are stocks. We use five stock portfolio data with the percentage of stocks that have a negative mean return for each of the different data. The implementation is carried out in three cases with different methods: optimization of stock portfolios without DBSCAN, optimizing stock portfolios with DBSCAN without noise, and optimizing stock portfolios with DBSCAN with noise. The result is the large percentage of stocks that have a mean return on negative data that affects the choice of the method used to obtain the portfolio with the smallest risk.

Abstrak :
Pembentukan portofolio investasi merupakan salah satu bagian penting bagi investor untuk mengantisipasi kerugian. Untuk mendapatkan hasil investasi yang optimal, maka perlu untuk mencari portofolio yang optimal. Optimisasi portofolio mean-variance dilakukan dengan meminimumkan risiko portofolio yang diukur dari variansi portofolio dan kendala ekspektasi return portofolio sudah ditentukan. Optimisasi portofolio mean-variance dikategorikan sebagai masalah kontrol optimal stokastik, karena merupakan optimisasi dari suatu sistem dinamis. Untuk menyelesaikan masalah optimisasi portofolio mean-variance digunakan teori dualitas Lagrange dan persamaan Hamilton-Jacobi-Bellman. Solusi penyelesaian masalah yang diperoleh adalah formulasi proporsi investasi dalam portofolio yang memberikan portofolio optimal. Formula proporsi yang diperoleh merupakan fungsi dari waktu. Menggunakan data dari harga saham, diperoleh estimasi parameter dalam formula proporsi yang optimal. Dari hasil penghitungan formula, diperoleh bahwa proporsi portofolio dapat berubah seiring berjalannya waktu.

---

The establishment of an investment portfolio is an important part for investors to anticipate losses. It is necessary to find the optimal portfolio to get the optimal investment result. The optimization of the mean variance portfolio is built by minimizing the portfolio risk measured by the portfolio variance and the specified expectation portfolio return becomes the constraint. The mean variance portfolio optimization is categorized as a stochastic optimal control problem, since it is an optimization of a dynamic system. The Lagrange duality and the Hamilton Jacobi Bellman equation are used to solve the mean variance portfolio optimization problem. The solution obtained is the formulation of the proportion of investment in the portfolio that provides an optimal portfolio. The proportion formula is a function of time. Using data from the stock price, parameter estimation in optimal proportion formula are obtained. The results of the calculation are portfolio proportions that may change over time.

Abstrak :
This book concentrates on discrete derivative pricing models, culminating in a careful and complete derivation of the Black-Scholes option pricing formulas as a limiting case of the Cox-Ross-Rubinstein discrete model. In this edition the material on probability has been condensed into fewer chapters, and the material on the capital asset pricing model has been removed. The mathematics is not watered down, but it is appropriate for the intended audience. Previous knowledge of measure theory is not needed and only a small amount of linear algebra is required. All necessary probability theory is developed throughout the book on a "need-to-know" basis. No background in finance is required, since the book contains a chapter on options.

Abstrak :
The science of algorithmic trading and portfolio management, with its emphasis on algorithmic trading processes and current trading models, sits apart from others of its kind. Robert Kissell, the first author to discuss algorithmic trading across the various asset classes, provides key insights into ways to develop, test, and build trading algorithms. This valuable book summarizes market structure, the formation of prices, and how different participants interact with one another, including bluffing, speculating, and gambling. Portfolio management topics, including quant factors and black box models, are discussed, and an accompanying website includes examples, data sets supplementing exercises in the book, and large projects