scholarly journals Portfolio Selection and Optimization through Neural Networks and Markowitz Model: A Case of Pakistan Stock Exchange Listed Companies

2019 ◽  
Vol 5 (1) ◽  
pp. 183-196
Author(s):  
Javed Iqbal ◽  
Moeed Ahmad Sandhu ◽  
Shaheera Amin ◽  
Aliya Manzoor
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Empirical Data ◽  
Stock Exchange ◽  
Expected Returns ◽  
Information Ratio ◽  
Portfolio Returns ◽  
Risk Free Rate ◽  
Mean Variance ◽  
Free Rate

This paper used artificial neural networks (ANNs) time series predictor for approximating returns of Pakistan Stock Exchange (PSX) listed 100 companies. These projected returns are then substituted into expected returns in the Markowitz’s Mean Variance (MV) portfolio Model. For comparison empirical data used is closing prices of PSX listed stocks, Karachi Inter Bank Offer Rates (KIBOR) as risk free rate and KSE-all share index as benchmark. The Portfolio returns are compared for two datasets by employing various constraints like budget, transaction costs, and turnover constraints. The value of portfolios is measured through Sharpe ratio and Information ratio. Both Sharpe and Information ratios support use of ANNs as return predictor and optimisation tool over simple MV model implemented for empirical data as well as predicted data. ANNs framework performed better in both Long and Short positions and its portfolio returns are significantly higher as compared with MV.

scholarly journals CONNECTING THEORY AND EMPIRICS FOR ANIMAL SPIRITS, RETURNS AND INTEREST RATES: A CLARIFICATION OF “RISK-FREE RATES AND ANIMAL SPIRITS IN FINANCIAL MARKETS”

2017 ◽  
Vol 12 (01) ◽  
pp. 1750002 ◽  
Author(s):  
JUKKA ILOMÄKI
Keyword(s):  
Financial Markets ◽  
Interest Rates ◽  
Expected Returns ◽  
Animal Spirits ◽  
Empirical Results ◽  
Upper Limit ◽  
Risk Free Rate ◽  
Free Rate

I clarify and combine the results of Ilomäki (2016a) and Ilomäki (2016b) and find several interesting conclusions. First, the effect of the animal spirits component to the expected returns of investors depends on the risk-free rate. Second, there must be an upper limit for the risk-free rate, where the component that reduces the expected returns of informed investors in Ilomäki (2016a) disappears. Third, the empirical results of Ilomäki (2016b) indicates that the break-even level is as low as 3%.

scholarly journals ANALISIS RASIONALITAS INVSETOR DALAM PEMILIHAN DAN PENENTUAN PORTOFOLIO OPTIMAL PADA SAHAM-SAHAM JAKARTA ISLAMIC INDEX

2016 ◽  
Vol 2 (2) ◽  
pp. 1
Author(s):  
Raden Arfan Rifqiawan
Keyword(s):  
Central Bank ◽  
Empirical Evidence ◽  
Lower Risk ◽  
Stock Exchange ◽  
Trade Volume ◽  
Index Model ◽  
Single Index Model ◽  
Risk Free Rate ◽  
Asset Data ◽  
Free Rate

<p>The purpose of this study is to determine that  wether investor rationality exist in undergoing the stock choice in  Jakarta Islamic Index at Indonesia Stock Exchange. Population to be chosen in the study is 44 firms listed on JII. However, the sample included are only 19 firms that present 30 times consecutively of simultan monitoring on JII. From 19 firms  after analyzed with single index model  found 11 has firms has  ERB &gt; Ci*, that mean if investor invests  in 11 stocks will  get return higher with lower risk in comparison with investment in risk free  asset. Data to be used in the study is the secondary one, which is collected from Indonesia Stock Exchange Monthly Statistic and risk free rate report from Central Bank of Indonesia.Result to be obtained from the study demonstrates on empirical evidence of investor rationally in choosing the stock on JII. The value is  showed averagely stocks trade  volume that has  ERB &gt; Ci* higher is compared averagely stocks trade  volume that has  ERB&lt; Ci*.</p>

scholarly journals Robust optimization of time series momentum portfolios

2021 ◽  
Vol 19 (1) ◽  
pp. 52-69
Author(s):  
Jeremy Fague ◽  
Caio Almeida
Keyword(s):  
Time Series ◽  
Robust Optimization ◽  
Transaction Costs ◽  
Historical Data ◽  
Expected Returns ◽  
Trading Costs ◽  
Portfolio Composition ◽  
Mean Variance

Mean-Variance Optimization (MVO) is well-known to be extremely sensitive to slight differences in the expected returns and covariances: if these measures change day to day, MVO can specify very different portfolios. Making wholesale changes in portfolio composition can cause the incremental gains to be negated by trading costs. We present a method for regularizing portfolio turnover by using the ℓ1 penalty, with the amount of penalization informed by recent historical data. We find that this method dramatically reduces turnover, while preserving the efficiency of mean-variance optimization in terms of risk-adjusted return. Factoring in reasonable estimates of transaction costs, the turnover-regularized MVO portfolio substantially outperforms a leverage-constrained MVO approach, in terms of risk-adjusted return.

scholarly journals Development of the Risk Accumulation Method for Calculating the Capitalization Rate

2021 ◽  
pp. 7-14
Author(s):  
Nikolai Yu. Trifonov
Keyword(s):  
Risk Adjustment ◽  
Risk Premium ◽  
Stock Exchange ◽  
Country Risk ◽  
Monetary Unit ◽  
Capitalization Rate ◽  
Country Risk Premium ◽  
Risk Free Rate ◽  
The Relationship ◽  
Free Rate

Risk build-up method is the most used for calculating the capitalization rates. With the help of the literature analysis, the origin of this method is considered. The method was based on the relationship between risk and profitability of a stock in exchange trading, proven statistically. Later, when formulating the build-up method, this idea was transferred without any justification to the valuation of enterprises that do not list their securities on stock exchange. In other words, the formulas traditionally used in the application of the build-up method are empirical in nature and not precise.It is more accurate to write them down by analogy with Irwin Fisher's equation of returns. Based on the principle of dependence, one of the main ones for the valuation procedure, the essence of which is that the value of the valuation subject depends on its economic location, a set of four independent risks is given for use in the build-up method in general case: risk-free rate, country risk premium, branch risk premium, and subject risk adjustment. It is noted that the numerical value of these parameters used in the method fundamentally depends on the monetary unit used in the calculation (the valuation currency). Recommendations are given on finding a risk-free rate for various currencies, on calculating country risk premium, branch risk premium, and subject risk adjustment. The article is intended for academics, lecturers, and practitioners in such areas as corporate finance, business microeconomics, valuation, and investment analysis.

scholarly journals Ambiguity Aversion and the Variance Premium

2019 ◽  
Vol 09 (02) ◽  
pp. 1950003 ◽  
Author(s):  
Jianjun Miao ◽  
Bin Wei ◽  
Hao Zhou
Keyword(s):  
Risk Aversion ◽  
Ambiguity Aversion ◽  
Equity Premium ◽  
Return Variance ◽  
Quantitative Performance ◽  
The Mean ◽  
The Difference ◽  
Risk Free Rate ◽  
Mean Variance ◽  
Free Rate

This paper offers an ambiguity-based interpretation of the variance premium — the difference between risk-neutral and objective expectations of market return variance — as a compounding effect of both belief distortion and variance differential regarding the uncertain economic regimes. Our calibrated model can match the variance premium, the equity premium, and the risk-free rate in the data. We find that about 97% of the mean–variance premium can be attributed to ambiguity aversion. A three-way separation among ambiguity aversion, risk aversion, and intertemporal substitution, permitted by the smooth ambiguity preferences, plays a key role in our model’s quantitative performance.

RISK-FREE RATES AND ANIMAL SPIRITS IN FINANCIAL MARKETS

2016 ◽  
Vol 11 (03) ◽  
pp. 1650011 ◽  
Author(s):  
JUKKA ILOMÄKI
Keyword(s):  
Financial Markets ◽  
Expected Returns ◽  
Risk Averse ◽  
Animal Spirits ◽  
Local Risk ◽  
Global Stock Markets ◽  
Animal Spirit ◽  
Risk Free Rate ◽  
Global Stock ◽  
Free Rate

We show analytically that animal spirit excess profits for uninformed investors fall (increase) when the risk-free rate rises (falls). In the theoretical analysis, we examine the expected returns of risk-averse, short-lived investors. In addition, we find empirically that the local risk-free rates explain 14% of the changes in the animal spirit excess profits in the global stock markets for the last 29 years when the animal spirits is characterized as a product of the trend-chasing rule.

scholarly journals Backpropagation Neural Networks Implementation for JKSE Forecasting

2019 ◽  
Vol 8 (4) ◽  
pp. 9902-9905
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Time Series Analysis ◽  
Stock Exchange ◽  
Promising Result ◽  
Composite Index ◽  
Experimental Result ◽  
Market Change ◽  
Series Analysis ◽  
Backpropagation Neural Networks

Neural networks is a type of soft computing methods that widely has been used and implemented in many fields, including time series analysis. One of the goals of time series analysis is to predict future data value.In this study, we try to implement another approach using the backpropagation neural networks method to forecast the Jakarta Stock Exchange (JKSE) composite index data, which is one of the stock market change indicators in Indonesia.The study then is continued by calculating the accuracy and robustness levels of Backpropagation NN in forecasting JKSE data. The experimental result on the case taken shows an encouraging and promising result.

AN ANALYTICAL FRAMEWORK FOR EXPLAINING RELATIVE PERFORMANCE OF CAPM BETA AND DOWNSIDE BETA

2009 ◽  
Vol 12 (03) ◽  
pp. 341-358 ◽  
Author(s):  
DON U. A. GALAGEDERA
Keyword(s):  
Risk Measures ◽  
Systematic Risk ◽  
Analytical Framework ◽  
Portfolio Returns ◽  
Market Portfolio ◽  
Empirical Performance ◽  
Capm Beta ◽  
Using Data ◽  
Risk Free Rate ◽  
Mean Variance

Even though investors' view of risk is generally regarded as related to the downside of the return distribution the CAPM beta is still a widely used measure of systematic risk. A number of studies compare the empirical performance of CAPM beta and downside beta in explaining the variation in portfolio returns and report mixed results. This paper provides a basis for explaining such mixed results. Using data generating processes in the mean-variance and mean-lower partial moment frameworks, analytical relationships between the CAPM beta and downside beta are derived. The derived relationships reveal that the association between the two systematic risk measures is to a great extent dependent on the volatility of the market portfolio returns and the deviation of the target rate from the risk-free rate. How the relationships derived here may be used in practice is demonstrated using empirical data.

Factors That Fit the Time Series and Cross-Section of Stock Returns

2020 ◽  
Vol 33 (5) ◽  
pp. 2274-2325 ◽  
Author(s):  
Martin Lettau ◽  
Markus Pelger
Keyword(s):  
Time Series ◽  
Stock Returns ◽  
Cross Section ◽  
Principal Component ◽  
Expected Returns ◽  
Portfolio Returns ◽  
Pricing Errors ◽  
Out Of Sample ◽  
Pricing Error ◽  
Sharpe Ratios

Abstract We propose a new method for estimating latent asset pricing factors that fit the time series and cross-section of expected returns. Our estimator generalizes principal component analysis (PCA) by including a penalty on the pricing error in expected returns. Our approach finds weak factors with high Sharpe ratios that PCA cannot detect. We discover five factors with economic meaning that explain well the cross-section and time series of characteristic-sorted portfolio returns. The out-of-sample maximum Sharpe ratio of our factors is twice as large as with PCA with substantially smaller pricing errors. Our factors imply that a significant amount of characteristic information is redundant. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

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