Factor Momentum Everywhere

2019 ◽  
Keyword(s):  
Time Series ◽  
Sharpe Ratio ◽  
Individual Factors ◽  
Investment Strategies ◽  
Large Collection ◽  
Stock Performance ◽  
Timing Strategies ◽  
Return Factors

In this article, the authors document robust momentum behavior in a large collection of 65 widely studied characteristic-based equity factors around the globe. They show that, in general, individual factors can be reliably timed based on their own recent performance. A time-series factor momentum portfolio that combines timing strategies of all factors earns an annual Sharpe ratio of 0.84. Factor momentum adds significant incremental performance to investment strategies that employ traditional momentum, industry momentum, value, and other commonly studied factors. The results demonstrate that the momentum phenomenon is driven in large part by persistence in common return factors and not solely by persistence in idiosyncratic stock performance.

Dissecting Investment Strategies in the Cross Section and Time Series

2015 ◽  
Author(s):  
Jamil Baz ◽  
Nicolas M Granger ◽  
Campbell R. Harvey ◽  
Nicolas Le Roux ◽  
Sandy Rattray
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Investment Strategies ◽  
The Cross

scholarly journals Zakah Rate In Islamic Stock Performance Models: Evidence From Indonesia

2020 ◽  
Vol 13 (1) ◽  
pp. 107
Author(s):  
Mohammad Farhan Qudratullah
Keyword(s):  
Interest Rates ◽  
Capital Market ◽  
Sharpe Ratio ◽  
Islamic Finance ◽  
Performance Models ◽  
Stock Performance ◽  
Very High

<p>There are three models commonly used to measure the performance of Islamicstocks, named Treynor Ratio, Sharpe Ratio, and Jansen Index. One component of the three models is risk-free returns which are usually approached with interest rates, whereas interest rates are prohibited in the concept of Islamic finance. This paper will approach a risk-free return with zakat-rate on the Islamic capital market in Indonesia from January 2011 - July 2018, then compare it with a model that uses interest rates. The results obtained by the model with interest rates and zakah-rate in this third model have very high suitability values, so that zakah-rate can be used as an alternative substitute for interest rates in measuring the Islamic stock performance. Beside not contradicting the concept of Islamic economics, calculation of models with zakah-rate is simpler than models with interest rates.</p>

scholarly journals Perbandingan Kinerja Saham Syariah di Bursa Efek Indonesia dan Bursa Malaysia

2019 ◽  
Vol 10 (2) ◽  
pp. 82
Author(s):  
Hisbullah Basri ◽  
Veny Mayasari
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Cross Section ◽  
Stock Exchange ◽  
Secondary Data ◽  
Risk Difference ◽  
T Test ◽  
Stock Performance ◽  
Book Value ◽  
Purposive Sampling

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>ABSTRACT</span></p><p><span>This research aims to determine the difference in Sharia stock performance in the Indonesia Stock Exchange and Bursa Malaysia. The population in the study is all sharia stocks listed on the Indonesia Stock Exchange and Bursa Malaysia amounting to 1,280 Sharia shares. The retrieval technique uses purposive sampling where the samples are taken with certain criteria. Meanwhile, the research sample amounted to 60 Sharia shares in which 30 sharia stocks are found on the Indonesia Stock Exchange and 30 Sharia shares in Bursa Malaysia. The research uses four indicators to use the stock performance of Return shares, Price Earning Ratio (PER), Price to Book Value (PBV), and Earning per Share (EPS). The data analysis method used in this study is quantitative analysis. The data used is secondary data, time series, and cross-section. The data analysis technique used is by conducting independent sample T-Test. The results showed that there was no difference in the performance of sharia stocks on the Indonesia Stock Exchange and Bursa Malaysia given price to book Value (PBV) and Price Earning Ratio (PER) with significance values of 0.308 and 0.264 respectively. As for the performance of shares with indicators return and EPS obtained the result that there is a difference in the performance of sharia stocks on the Indonesian stock exchange with significance values of 0.006 and 0.000 respectively. This difference is more due to the risk difference in Indonesia and Malaysia.</span></p><p><span>Key words </span><span>: Stock Performance, Return, EPS, PBV, PER</span><span>ABSTRAK</span></p><p><span>Penelitian ini bertujuan untuk mengetahui perbedaan kinerja saham syariah di Bursa Efek Indonesia dan Bursa Malaysia. Populasi pada penelitian adalah seluruh saham syariah yang terdaftar di Bursa Efek Indonesia dan Bursa Malaysia yang berjumlah 1.280 saham syariah. Teknik pengambilan menggunakan purposive sampling dimana sampel diambil dengan kriteria tertentu. Sedangkan sampel penelitian ini berjumlah 60 saham syariah dimana 30 saham syariah yang terdapat di Bursa Efek Indonesia dan 30 saham syariah yang terdapat di Bursa Malaysia. Penelitian ini menggunakan empat indikator untuk menggukur kinerja saham yaitu Return Saham, Price Earning Ratio (PER), Price to Book Value (PBV), dan Earning per Share (EPS). Metode Analisis data yang digunakan dalam penelitian ini adalah analisis kuantitatif. Data yang digunakan adalah data sekunder, time series dan cross section. Teknik analisis data yang digunakan yaitu dengan melakukan independent sampel t-test. Hasil penelitian menunjukkan bahwa tidak terdapat perbedaan kinerja saham syariah di Bursa Efek Indonesia dan Bursa Malaysia di lihat dari price to book Value (PBV) dan Price Earning Ratio (PER) dengan nilai signifikansi masing-masing sebesar 0,308 dan 0,264. Sedangkan untuk kinerja saham dengan indikator return dan EPS didapat hasil bahwa terdapat perbedaan kinerja saham syariah di Bursa Efek Indonesia dengan nilai signifikansi masing-masing sebesar 0,006 dan 0,000.</span></p><p><span>Kata kunci</span><span>: Kinerja Saham, Return, EPS, PBV, PER</span></p></div></div></div>

scholarly journals Measuring Islamic Stock Performance in Indonesia with A Modified Sharpe Ratio

2021 ◽  
Vol 10 (2) ◽  
pp. 155
Author(s):  
Mohammad Farhan Qudratullah
Keyword(s):  
Standard Deviation ◽  
Asset Pricing ◽  
Interest Rates ◽  
Value At Risk ◽  
Sharpe Ratio ◽  
Islamic Finance ◽  
Pricing Model ◽  
Stock Performance ◽  
Asset Pricing Model ◽  
The Interest Rate

Since the late 1960s, one of the stock performance analysis tools commonly used is Sharpe Ratio. The Sharpe Ratio consists of three components, namely stock return, risk-free returns, and stock risk. Many studies approach risk-free returns with interest rates, including when measuring the performance of Islamic stocks, while interest rates are prohibited in the concept of Islamic finance. Moreover, the stock risk is measured by a standard deviation which assumes returns are normally distributed, while many stock returns are non-normally distributed. This paper intends to measure the performance of Islamic stocks listed on the Indonesian Stock Exchange (IDX) for the period of January 2011 to July 2018 using a modified Sharpe Ratio. The ratio is modified by replacing the interest rate with four approaches: eliminating the interest rate, changing with zakah rates, changing with inflation, changing with the nominal gross domestic product, and replacing the risk measurement from Standard Deviation to Value at Risk (VaR). The findings provide almost the same results as the original measurement and thus, show very high suitability for using these models in other circumstances. Therefore, on the concept of Islamic finance, risk-free returns can be measured using these four approaches, especially inflation and GDP. This study also recommends inflation and GDP to measure risk-free returns in the Sharia's Compliant Asset Pricing Model (SCAPM) or Islamic Capital Asset Pricing Model (ICAPM).====================================================================================================ABSTRAK – Pengukuran Kinerja Saham Syariah di Indonesia menggunakan Sharpe Ratio Modifikasi. Sejak akhir 1960-an, salah satu alat mengukur kinerja saham yang biasa digunakan adalah Sharpe Ratio. Model Sharpe Ratio terdiri atas tiga komponen, yaitu return saham, return bebas risiko, dan risiko saham. Return bebas risiko diukur mengunakan variabel suku bunga yang digolongkan riba dan dilarang dalam konsep keuangan islam. Sedangkan risiko saham diukur dengan standar deviasi yang mengasumsikan data berdistribusi normal. Paper ini bertujuan untuk mengukur kinerja saham syariah yang terdaftar pada Bursa Efek Indonesia (BEI) untuk periode Januari 2011 sampai Juli 2018 dengan menggunakan Sharpe Ratio modifikasi. Kajian akan memodifikasi model Sharpe Ratio dengan mencari variabel alternatif penganti suku bunga dengan empat pendekatan, yaitu: menghilangkan variabel suku bunga tersebut, mengganti dengan zakat rate, mengganti dengan inflasi, dan mengganti dengan produk domestik bruto, serta mengganti standar deviasi dengan Value at Risk (VaR) sebagai pengukur risiko saham yang selanjutnya diimplementasikan pada pasar modal syariah di Indonesia periode Januari 2011 - Juli 2018. Hasil kajian menunjukkan kesesuaian yang sangat tinggi untuk hasil pengukuran kelima model tersebut. Dilihat dari kedekatan hasil pengukuran kinerja, kelima model tersebut dapat dikelompokkan menjadi dua, yaitu model dengan tingkat suku bunga, inflasi, dan PDB sebagai kelompok pertama, sedangkan model tanpa suku bunga dan tingkat zakat sebagai kelompok kedua 

scholarly journals Prediction of Stock Performance Using Deep Neural Networks

2020 ◽  
Vol 10 (22) ◽  
pp. 8142
Author(s):  
Yanlei Gu ◽  
Takuya Shibukawa ◽  
Yohei Kondo ◽  
Shintaro Nagao ◽  
Shunsuke Kamijo
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Performance Prediction ◽  
Deep Neural Network ◽  
Series Data ◽  
Trading System ◽  
Stock Performance ◽  
Trading Systems ◽  
Stock Traders ◽  
Proven Method

Stock performance prediction is one of the most challenging issues in time series data analysis. Machine learning models have been widely used to predict financial time series during the past decades. Even though automatic trading systems that use Artificial Intelligence (AI) have become a commonplace topic, there are few examples that successfully leverage the proven method invented by human stock traders to build automatic trading systems. This study proposes to build an automatic trading system by integrating AI and the proven method invented by human stock traders. In this study, firstly, the knowledge and experience of the successful stock traders are extracted from their related publications. After that, a Long Short-Term Memory-based deep neural network is developed to use the human stock traders’ knowledge in the automatic trading system. In this study, four different strategies are developed for the stock performance prediction and feature selection is performed to achieve the best performance in the classification of good performance stocks. Finally, the proposed deep neural network is trained and evaluated based on the historic data of the Japanese stock market. Experimental results indicate that the proposed ranking-based stock classification considering historical volatility strategy has the best performance in the developed four strategies. This method can achieve about a 20% earning rate per year over the basis of all stocks and has a lower risk than the basis. Comparison experiments also show that the proposed method outperforms conventional methods.

scholarly journals What Coins Lead in the Cryptocurrency Market: Using Copula and Neural Networks Models

2019 ◽  
Vol 12 (3) ◽  
pp. 132 ◽  
Author(s):  
Steve Hyun ◽  
Jimin Lee ◽  
Jong-Min Kim ◽  
Chulhee Jun
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Market Timing ◽  
The Other ◽  
Gaussian Copula ◽  
Serial Dependence ◽  
Directional Flow ◽  
Wide Range ◽  
Autoregression Model ◽  
Timing Strategies

Exploring dependence structures between financial time series has been important within a wide range of applications. The main aim of this paper is to examine dependence relationships among five well-known cryptocurrencies—Bitcoin, Ethereum, Litecoin, Ripple, and Stella—by a copula directional dependence (CDD). By employing a neural network autoregression model to avoid the serial dependence in each individual cryptocurrency, we generate residuals of the fitted models with time series of daily log-returns in percentage of the five cryptocurrencies and then we apply a Gaussian copula marginal beta regression model to the residuals to explore the CDD. The results show that the CDD from Bitcoin to Litecoin is highest among all ordered directional dependencies and the CDDs from Ethereum to the other four cryptocurrencies are relatively higher than the CDDs to Ethereum from those cryptocurrencies. This finding implies that the return shocks of Bitcoin have the most effect on Litecoin and the return shocks of Ethereum relatively influence the shocks on the other four cryptocurrencies instead of being affected by them. This allows investors to build the market-timing strategies by observing the directional flow of return shocks among cryptocurrencies.

scholarly journals Application of GARCH-Copula Model in Portfolio Optimization

2015 ◽  
Vol 6 (2) ◽  
pp. 7-20 ◽  
Author(s):  
Aleš Kresta
Keyword(s):  
Time Series ◽  
Portfolio Optimization ◽  
Financial Time Series ◽  
Sharpe Ratio ◽  
Copula Model ◽  
Future Evolution ◽  
The Future ◽  
Future Prediction ◽  
Portfolio Optimization Problem ◽  
Maximum Drawdown

Although the cornerstone of modern portfolio theory was set by Markowitz in 1952, the portfolio optimization problem is a never-ending research topic for both academics and practitioners. In this problem the future prediction of time series evolution plays an important role. However, it is rarely addressed in research. In the paper we analyze the applicability of the GARCH-copula model. To be more concrete we assume the investor maximizing Sharpe ratio while the future evolution of the time series is simulated by means of the AR(1)-GARCH(1,1) model using the copula modelling approach. The bootstrapping technique is applied as a benchmark. From the empirical results we found out that the GARCH-copula model provides better forecasts of future financial time series evolution than the bootstrapping method. Assuming the investor is maximizing the Sharpe ratio, both the final wealth increases and maximum drawdown decreases when we apply the GARCH-copula model compared to the application of bootstrapping technique.

scholarly journals Time Series Clustering for Robust Mean-Variance Portfolio Selection: Comparison of Several Dissimilarity Measures

2021 ◽  
Vol 2123 (1) ◽  
pp. 012021
Author(s):  
La Gubu ◽  
Dedi Rosadi ◽  
Abdurakhman
Keyword(s):  
Time Series ◽  
Portfolio Selection ◽  
Stock Exchange ◽  
Sharpe Ratio ◽  
Minimum Covariance Determinant ◽  
Time Series Clustering ◽  
Dissimilarity Measures ◽  
Partitioning Around Medoids ◽  
Robust Portfolio ◽  
Mean Variance Portfolio

Abstract This paper shows how to create a robust portfolio selection with time series clustering by using some dissimilarity measure. Based on such dissimilarity measures, stocks are initially sorted into multiple clusters using the Partitioning Around Medoids (PAM) time series clustering approach. Following clustering, a portfolio is constructed by selecting one stock from each cluster. Stocks having the greatest Sharpe ratio are selected from each cluster. The optimum portfolio is then constructed using the robust Fast Minimum Covariance Determinant (FMCD) and robust S MV portfolio model. When there are a big number of stocks accessible for the portfolio formation process, we can use this approach to quickly generate the optimum portfolio. This approach is also resistant to the presence of any outliers in the data. The Sharpe ratio was used to evaluate the performance of the portfolios that were created. The daily closing price of stocks listed on the Indonesia Stock Exchange, which are included in the LQ-45 indexed from August 2017 to July 2018, was utilized as a case study. Empirical study revealed that portfolios constructed using PAM time series clustering with autocorrelation dissimilarity and a robust FMCD MV portfolio model outperformed portfolios created using other approaches.

scholarly journals Time-Varying Sharpe Ratios and Market Timing

2011 ◽  
Vol 01 (03) ◽  
pp. 465-493 ◽  
Author(s):  
Yi Tang ◽  
Robert F. Whitelaw
Keyword(s):  
Time Variation ◽  
Market Timing ◽  
Sharpe Ratio ◽  
Equity Returns ◽  
Conditional Mean ◽  
Financial Variables ◽  
Out Of Sample ◽  
The Business Cycle ◽  
Sharpe Ratios ◽  
Timing Strategies

This paper documents predictable time-variation in stock market Sharpe ratios. Predetermined financial variables are used to estimate both the conditional mean and volatility of equity returns, and these moments are combined to estimate the conditional Sharpe ratio, or the Sharpe ratio is estimated directly as a linear function of these same variables. In sample, estimated conditional Sharpe ratios show substantial time-variation that coincides with the phases of the business cycle. Generally, Sharpe ratios are low at the peak of the cycle and high at the trough. In an out-of-sample analysis, using 10-year rolling regressions, relatively naive market-timing strategies that exploit this predictability can identify periods with Sharpe ratios more than 45% larger than the full sample value. In spite of the well-known predictability of volatility and the more controversial forecastability of returns, it is the latter factor that accounts primarily for both the in-sample and out-of-sample results.

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