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Abstrak :
Investasi di saham bukanlah tanpa risiko. Harga saham selalu mengalami fluktuasi, dapat naik dan dapat turun. Ketidakpastian tersebut tidak dapat diabaikan, karena dapat menyebabkan kerugian jika salah dalam memprediksi arah pergerakan dari harga saham. Prediksi arah pergerakan harga saham yang lebih akurat dapat mengurangi risiko kerugian. Pada penelitian ini, prediksi arah pergerakan harga saham menggunakan faktor yang mempengaruhi arah pergerakan saham itu sendiri, yaitu harga saham sebagai variabel prediktor. Penelitian dilakukan dengan memanfaatkan salah satu metode dalam jaringan syaraf tiruan, yaitu gated recurrent unit dalam membangun model prediksi arah pergerakan harga saham tersebut. Data harga saham yang digunakan pada penelitian ini adalah data harga saham PT. Bank Central Asia Tbk (kode saham: BBCA) dan PT. Pabrik Kertas Tjiwi Kimia Tbk (kode saham: TKIM). Performa model yang digunakan dievaluasi dengan Root Mean Squared Error dan Mean Absolute Error. Pada penelitian ini didapatkan hasil bahwa hyperparameter prediksi harga saham BBCA terbaik diperoleh dengan menggunakan {epoch=500, batch size=32, dan units=24} dan hyperparameter prediksi harga saham TKIM terbaik diperoleh dengan menggunakan {epoch=250, batch size=128, dan unit=24}. Kemudian, dari RMSE dan MAE yang dihasilkan dari kedua saham disimpulkan bahwa model GRU merupakan model yang mampu memprediksi saham dengan baik. ......Investing in stocks is not without risk. The stock price always fluctuates, can go up and can go down. This uncertainty cannot be ignored, because it can cause losses if it is wrong in predicting the direction of movement of the stock price. A more accurate prediction of the direction of stock price movements can reduce the risk of loss. In this study, the prediction of the direction of stock price movements uses factor that influence the direction of stock movement itself, namely the stock price as a predictor variable. The research was conducted by utilizing one of the methods in artificial neural networks, namely the gated recurrent unit in building a predictive model for the direction of the stock price movement. The share price data used in this research is the share price data of PT Bank Central Asia (stock code: BBCA) and PT. Pabrik Kertas Tjiwi Kimia Tbk (stock code: TKIM). The model performance is evaluated by using Root Mean Squared Error and Mean Absolute Error. The results of this study indicate that the best prediction of the direction of BBCA's stock price movement is obtained by using {epoch=500, batch size=32, and units=24} and the best prediction of the direction of TKIM's stock price movement, is obtained by using {epoch=250, batch size=128, and units=24}. Then, from the RMSE and MAE generated from the two stocks, it can be concluded that the GRU model is a model capable of predicting stocks.

Abstrak :
ABSTRAK Penelitian ini bertujuan untuk menguji pengaruh dari ukuran perusahaan, book to market, dan momentum dalam Fama-French Three Factor Model dan Carhart Four Factor Model terhadap imbal hasil saham pada perusahaan yang terdaftar di Bursa Efek Indonesia periode 2007-2016. Variabel dependen yang digunakan dalam penelitian ini adalah imbal hasil portofolio saham. Variabel independen yang digunakan dalam penelitian ini adalah variabel dalam Fama-French Three Factor Model dan Carhart Four Factor Model, yaitu beta, SMB, HML, dan WML. Pengujian hipotesis dalam penelitian ini menggunakan analisis regresi linear berganda menggunakan Fama-French Three Factor Model dan Carhart Four Factor Model. Data yang digunakan dalam penelitian ini merupakan data bulanan. Hasil dari penelitian ini menunjukkan bahwa: 1 Variabel dalam Fama-French Three Factor Model berpengaruh terhadap imbal hasil saham, 2 Variabel dalam Carhart Four Factor Model berpengaruh terhadap imbal hasil saham dengan performa signifikansi variabel HML yang lebih baik, 3 Carhart Four Factor Model mampu menjelaskan variasi imbal hasil saham lebih baik dari Fama-French Three Factor Model dengan nilai koefisien determinasi yang lebih tinggi walaupun perbedaannya tidak signifikan.

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ABSTRACT This study aims to investigate the effect of firm size, book to market, and momentum in Fama French Three Factor Model and Carhart Four Factor Model to the stock return on listed companies during 2007 to 2016. The dependent variable used in this study is portfolio stock returns. The independent variables used in this study are variables on Fama French Three Factor Model and Carhart Four Factor Model that are beta, SMB, HML, and WML. The hypothesis testing was performed using multiple regression analysis of Fama French Three Factor Model and Carhart Four Factor Model. The data used in this study are monthy data. The results indicate that 1 There is an effect of variables on Fama French Three Factor Model to the stock return, 2 There is an effect of variables on Carhart Four Factor Model to the stock return with HML variable performs better in significance, 3 Carhart Four Factor Model provide better explanation to the variation in stocks rates of return than Fama French Three Factor Model with higher coefficient of determination although the difference is not significant.

Abstrak :
Permasalahan optimisasi portofolio merupakan topik penelitian yang cukup banyak dibahas dalam bidang keuangan. Model yang biasa digunakan dalam permasalahan tersebut adalah model mean variance yang berfokus pada expected return dan risiko tanpa mempertimbangkan kendala yang terdapat dalam masalah sebenarnya. Pada skripsi ini digunakan model optimisasi portofolio yang mempertimbangkan kendala seperti kendala kardinal dan kendala kuantitas atau biasa dikenal dengan model Mean Variance Cardinality Constrained Portofolio Optimization MVCCPO. Pada skripsi ini menggunakan metode e-New Local Search based Multiobjective Optimization Algorithm yang menonjolkan metode local search dan non dominated sorting didalamnya. Hasil dari penelitian ini menunjukan bahwa metode e-NSLS cukup baik digunakan dalam permasalahan optimisasi portofolio.

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Portfolio optimization problem is common research topic in finance. The model that usually used of this problem is Markowitz mean variance model focus in expected returns and risks, without conidering constraints in real life. In this thesis used a more realistic portfolio optimization problem, such as cardinality and quantity constraints, which is called Markowitz mean variance cardinality constrained portfolio optimization problem MVCCPO problem. This thesis used an algorithm which is based on a multiobjective local search schema and non dominated sorting. The result of this is simulation is good enough to use e NSLS in portofolio optimization.

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