Asset Return Statistics

2021 ◽  
pp. 13-60
Author(s):  
Carlo Requião da Cunha
Keyword(s):  
Asset Return

ANALISIS KINERJA KEUANGAN PERUSAHAAN PROPERTI YANG TERDAFTAR DI BEI SEBELUM DAN SESUDAH KEBIJAKAN LOAN TO VALUE

2019 ◽  
Vol 4 (1) ◽  
pp. 79-90
Author(s):  
Ghaniy Ridha Prima ◽  
Hermanto Siregar ◽  
Ferry Syarifuddin
Keyword(s):  
Real Estate ◽  
Financial Performance ◽  
Stock Exchange ◽  
Sample Selection ◽  
Test Analysis ◽  
Asset Return ◽  
Return On Equity ◽  
Analysis Technique ◽  
Significant Difference ◽  
Before And After

The purpose of this study is to provide empirical evidence of the effects of the Loan to Value (LTV) policy on the financial performance of property and real estate companies listed on the Indonesia Stock Exchange (IDX). The sample selection uses a purposive sampling method of 42 property and real estate companies that meet the criteria. The research period is divided into 2 namely before the Loan to Value policy (2013-2014) and after the Loan to Value policy (2016-2017) with the Paired Sample t Test analysis technique. The test results show if the current ratio, Return on Asset, Return on Equity and Debt to Asset have significant differences between before and after the LTV policy is applied. While the fast ratio, cash ratio, net profit margin and Debt to Equity did not show a significant difference. Keywords: Financial Performance, Loan to Value, Property and Real Estate, Profitability Ratio, Liquidity Ratio, Solvability Ratio.

Pengaruh Return On Asset, Return On Equity Dan Earning Per Share Terhadap Harga Saham Pada Perusahaan Property Dan Real Estate Yang Terdaftar Di Bursa Efek Indonesia

2018 ◽  
Vol 9 (2) ◽  
pp. 133-147
Author(s):  
Winda Lestari
Keyword(s):  
Real Estate ◽  
Asset Return ◽  
Return On Equity ◽  
Purposive Sampling

Penelitian ini bertujuan untuk mengetahui pengaruh return on asset, return on equity dan earning per share terhadap harga saham pada perusahaan Property dan Real Estate yang terdaftar di Bursa Efek Indonesia. Populasi yang digunakan adalah 48 perusahaan Property dan Real Estate periode 2015-2016. Sampelnya adalah 35 perusahaan Property dan Real Estate dengan menggunakan teknik purposive sampling, metode analisis yang digunakan adalah regresi linier berganda. Hasil koefisien korelasi secara simultan menunjukkan bahwa terdapat hubungan yang lemah antara return on asset, return on equity dan earning per share terhadap harga saham. Berdasarkan uji F bahwa return on asset, return on equity dan earning per share secara simultan mempunyai pengaruh yang signifikan terhadap harga saham pada perusahaan Property dan Real Estate yang terdaftar di Bursa Efek Indonesia karena Fhitung > Ftabel. Berdasarkan uji t bahwa earning per share berpengaruh yang dominan terhadap pertumbuhan harga saham pada perusahaan Property dan Real Estate yang terdaftar di Bursa Efek Indonesia, karena mempunyai thitung dan r parsial paling besar dibanding return on asset dan return on equity.

Expansion methods applied to asset return distributions

2007 ◽  
Vol 10 (2) ◽  
pp. 3-24 ◽  
Author(s):  
Kohei Marumo ◽  
Rodney Wolff
Keyword(s):  
Asset Return ◽  
Return Distributions

scholarly journals A Comparative Analysis on Probability of Volatility Clusters on Cryptocurrencies, and FOREX Currencies

2021 ◽  
Vol 14 (7) ◽  
pp. 308
Author(s):  
Usha Rekha Chinthapalli
Keyword(s):  
Investment Decision ◽  
Theoretical Work ◽  
Financial Asset ◽  
Dynamic Features ◽  
Asset Return ◽  
Financial Variables ◽  
Ann Models ◽  
Hidden Layer ◽  
Association Structure ◽  
The Impact

In recent years, the attention of investors, practitioners and academics has grown in cryptocurrency. Initially, the cryptocurrency was designed as a viable digital currency implementation, and subsequently, numerous derivatives were produced in a range of sectors, including nonmonetary activities, financial transactions, and even capital management. The high volatility of exchange rates is one of the main features of cryptocurrencies. The article presents an interesting way to estimate the probability of cryptocurrency volatility clusters. In this regard, the paper explores exponential hybrid methodologies GARCH (or EGARCH) and through its portrayal as a financial asset, ANN models will provide analytical insight into bitcoin. Meanwhile, more scalable modelling is needed to fit financial variable characteristics such as ANN models because of the dynamic, nonlinear association structure between financial variables. For financial forecasting, BP is contained in the most popular methods of neural network training. The backpropagation method is employed to train the two models to determine which one performs the best in terms of predicting. This architecture consists of one hidden layer and one input layer with N neurons. Recent theoretical work on crypto-asset return behavior and risk management is supported by this research. In comparison with other traditional asset classes, these results give appropriate data on the behavior, allowing them to adopt the suitable investment decision. The study conclusions are based on a comparison between the dynamic features of cryptocurrencies and FOREX Currency’s traditional mass financial asset. Thus, the result illustrates how well the probability clusters show the impact on cryptocurrency and currencies. This research covers the sample period between August 2017 and August 2020, as cryptocurrency became popular around that period. The following methodology was implemented and simulated using Eviews and SPSS software. The performance evaluation of the cryptocurrencies is compared with FOREX currencies for better comparative study respectively.

scholarly journals Multi-Period Portfolio Optimization with Investor Views under Regime Switching

2020 ◽  
Vol 14 (1) ◽  
pp. 3
Author(s):  
Razvan Oprisor ◽  
Roy Kwon
Keyword(s):  
Regime Switching ◽  
Portfolio Allocation ◽  
Ease Of Use ◽  
Future Research ◽  
Expected Return ◽  
Asset Return ◽  
Market Portfolio ◽  
New Information ◽  
Trading Model ◽  
Portfolio Managers

We propose a novel multi-period trading model that allows portfolio managers to perform optimal portfolio allocation while incorporating their interpretable investment views. This model’s significant advantage is its intuitive and reactive design that incorporates the latest asset return regimes to quantitatively solve managers’ question: how certain should one be that a given investment view is occurring? First, we describe a framework for multi-period portfolio allocation formulated as a convex optimization problem that trades off expected return, risk and transaction costs. Using a framework borrowed from model predictive control introduced by Boyd et al., we employ optimization to plan a sequence of trades using forecasts of future quantities, only the first set being executed. Multi-period trading lends itself to dynamic readjustment of the portfolio when gaining new information. Second, we use the Black-Litterman model to combine investment views specified in a simple linear combination based format with the market portfolio. A data-driven method to adjust the confidence in the manager’s views by comparing them to dynamically updated regime-switching forecasts is proposed. Our contribution is to incorporate both multi-period trading and interpretable investment views into one framework and offer a novel method of using regime-switching to determine each view’s confidence. This method replaces portfolio managers’ need to provide estimated confidence levels for their views, substituting them with a dynamic quantitative approach. The framework is reactive, tractable and tested on 15 years of daily historical data. In a numerical example, this method’s benefits are found to deliver higher excess returns for the same degree of risk in both the case when an investment view proves to be correct, but, more notably, also the case when a view proves to be incorrect. To facilitate ease of use and future research, we also developed an open-source software library that replicates our results.

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