scholarly journals Application Of Tactical Style Allocation For Global Equity Portfolios

2012 ◽  
Vol 11 (7) ◽  
pp. 745
Author(s):  
Heng-Hsing Hsieh ◽  
Kathleen Hodnett ◽  
Paul Van Rensburg
Keyword(s):  
Financial Market ◽  
Weighted Least Squares ◽  
Sample Period ◽  
Value Stocks ◽  
Style Rotation ◽  
Out Of Sample ◽  
Study Support ◽  
Equity Portfolios ◽  
Investment Styles ◽  
Specific Timing

Our earlier study suggests that there exists specific timing for the two prominent investment styles, value and momentum. We extend our prior research to test and evaluate a tactical style allocation (TSA) model based on the weighted least squares (WLS) technique for global equities over the out-of-sample period from 1994 through 2008. Two TSA style-based portfolios are constructed in this research, namely, a portfolio with the risk-free proxy (cash component), the global momentum index and the global value index as its constituents, and a portfolio that is comprised of only the global momentum index and the global value index. The optimized portfolios based on the TSA model outperform the MSCI World Index, the global value index and the global momentum index on a risk-adjusted basis over the examination period. The cash component of the style-based portfolio appears to provide necessary protection during financial market crises. The results of our study support the use of the proposed TSA model to perform active style rotation between value stocks and momentum stocks for global equity portfolios.

scholarly journals Improved Dividend Estimation from Intraday Quotes

Entropy ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. 95
Author(s):  
Pontus Söderbäck ◽  
Jörgen Blomvall ◽  
Martin Singull
Keyword(s):  
Least Squares ◽  
Weighted Least Squares ◽  
Statistical Significance ◽  
Ordinary Least Squares ◽  
Options Market ◽  
Market Data ◽  
Out Of Sample ◽  
Intraday Data ◽  
Least Squares Estimates ◽  
Estimation Quality

Liquid financial markets, such as the options market of the S&P 500 index, create vast amounts of data every day, i.e., so-called intraday data. However, this highly granular data is often reduced to single-time when used to estimate financial quantities. This under-utilization of the data may reduce the quality of the estimates. In this paper, we study the impacts on estimation quality when using intraday data to estimate dividends. The methodology is based on earlier linear regression (ordinary least squares) estimates, which have been adapted to intraday data. Further, the method is also generalized in two aspects. First, the dividends are expressed as present values of future dividends rather than dividend yields. Second, to account for heteroscedasticity, the estimation methodology was formulated as a weighted least squares, where the weights are determined from the market data. This method is compared with a traditional method on out-of-sample S&P 500 European options market data. The results show that estimations based on intraday data have, with statistical significance, a higher quality than the corresponding single-times estimates. Additionally, the two generalizations of the methodology are shown to improve the estimation quality further.

Investor Sentiment and Stock Returns

2015 ◽  
pp. 315-328
Author(s):  
Serkan Yılmaz Kandır ◽  
Veli Akel ◽  
Murat Çetin
Keyword(s):  
Stock Returns ◽  
Investor Sentiment ◽  
Consumer Confidence ◽  
Cointegration Test ◽  
Sample Period ◽  
Monthly Data ◽  
Out Of Sample ◽  
Confidence Index ◽  
The Relationship

In this chapter, the authors investigate the relationship between investor sentiment and stock returns in an out of sample market, namely Borsa Istanbul. The authors use the Consumer Confidence Index as an investor sentiment proxy, while utilizing BIST Second National Index as a measure of small capitalized stock returns. The sample period spans from January 2004 to May 2014. By using monthly data, the authors employ cointegration test and error–correction based Granger causality models. The authors' findings suggest that there is a long-term relationship between investor sentiment and stock returns in Borsa Istanbul. Moreover, a unidirectional causal relationship from investor sentiment to stock returns is also found.

scholarly journals Forecasting with ARMA models

2021 ◽  
Vol 10 (1) ◽  
pp. 205-234
Author(s):  
Abdulaleem Isiaka ◽  
Abdulqudus Isiaka ◽  
Abdulqadir Isiaka
Keyword(s):  
Exchange Rate ◽  
Arma Model ◽  
Sample Period ◽  
Correct Sign ◽  
Forecasting Method ◽  
Rolling Window ◽  
The Us ◽  
Out Of Sample ◽  
Exchange Rate Changes ◽  
Changes Over Time

This paper employs the R software in identifying the most suitable ARMA model for forecasting the growth rate of the exchange rate between the US dollar and a unit of the British pound. The data is systematically split into two distinct periods identified as the in-sample period and the out of sample period. The best model selected for the in-sample period is used to make forecasts for the out of sample period. Both traditional and rolling window forecasting methods are employed. This research uses the MSE, MAE, MAPE and correct sign prediction criterion to compare the forecasting performance of the rolling window forecasting method and the traditional forecasting method. The results obtained suggest that the traditional forecasting method performs better judging by MSE, MAE and MAPE. In other words, the traditional forecasting method is more suitable for predicting the magnitude (i.e., size) by which the US /UK exchange rate changes over time. However, the results also suggest that the rolling window forecasting method performs better based on the correct sign prediction criterion. In other words, the rolling window forecasting method is more appropriate for predicting the changes in the sign of the US /UK exchange rate. 

scholarly journals Bitcoin Returns and the Frequency of Daily Abnormal Returns

Ledger ◽  
2021 ◽  
Vol 6 ◽  
Author(s):  
Guglielmo Maria Caporale ◽  
Alex Plastun ◽  
Viktor Oliinyk
Keyword(s):  
Linear Models ◽  
Statistical Tests ◽  
Piecewise Linear ◽  
Weighted Least Squares ◽  
Abnormal Returns ◽  
Future Price ◽  
Consensus Forecasts ◽  
Out Of Sample ◽  
Regression Techniques ◽  
Linear Regressions

This paper investigates the relationship between Bitcoin returns and the frequency of daily abnormal returns over the period from June 2013 to February 2020 using a number of regression techniques and model specifications including standard OLS, weighted least squares (WLS), ARMA and ARMAX models, quantile regressions, Logit and Probit regressions, piecewise linear regressions, and non-linear regressions. Both the in sample and out-of-sample performance of the various models are compared by means of appropriate selection  criteria and statistical tests. These suggest that, on the whole, the piecewise linear models are the best, but in terms of forecasting accuracy they are outperformed by a model that combines the top five to produce “consensus” forecasts. The finding that there exist price patterns that can be exploited to predict future price movements and design profitable trading strategies is of interest both to academics (since it represents evidence against the EMH) and to practitioners (who can use this information for their investment decisions).

Estimating and Forecasting the Term Structure of Korea Markets Using the Nelson-Siegel Model

2004 ◽  
Vol 12 (2) ◽  
pp. 101-126
Author(s):  
Joon Haeng Lee
Keyword(s):  
Term Structure ◽  
Yield Curve ◽  
Principal Component ◽  
Component Model ◽  
Structure Model ◽  
Sample Period ◽  
Term Structure Model ◽  
Long Run ◽  
Out Of Sample ◽  
Principal Component Model

This paper estimates and forecasts yield curve of korea bond market using a three factor term structure model based on the Nelson-Siegel model. The Nelson-Siegel model is in-terpreted as a model of level, slope and curvature and has the flexibility required to match the changing shape of the yield curve. To estimate this model, we use the two-step estima-tion procedure as in Diebold and Li. Estimation results show our model is Quite flexible and gives a very good fit to data. To see the forecasting ability of our model, we compare the RMSEs (root mean square error) of our model to random walk (RW) model and principal component model for out-of sample period as well as in-sample period. we find that our model has better forecasting performances over principal component model but shows slight edge over RW model especially for long run forecasting period. Considering that it is difficult for any model to show better forecasting ability over the RW model in out-of-sample period, results suggest that our model is useful for practitioners to forecast yields curve dynamics.

scholarly journals The Risk-Adjusted Performance of Alternative Investment Funds and UCITS: A Comparative Analysis

2018 ◽  
Vol 10 (7) ◽  
pp. 23 ◽  
Author(s):  
Silvio John Camilleri ◽  
Ritienne Farrugia
Keyword(s):  
The Other ◽  
Sample Period ◽  
Investment Funds ◽  
Regulatory Frameworks ◽  
Out Of Sample ◽  
Overall Performance ◽  
Focus Strategy ◽  
Over Time ◽  
Selection Of ◽  
Alternative Investment

This study evaluates the performance of a selection of Alternative Investment Funds (AIFs), and Undertakings for Collective Investment in Transferable Securities Funds (UCITS) which followed a global geographic focus strategy during the period 2010-2016. These two fund structures are governed by different regulatory frameworks, which have evolved and re-shaped over the years. Various yardsticks are employed to evaluate the risk-adjusted performance of the sampled funds, and Monte-Carlo simulations are used to gauge the possible out-of-sample returns. Most of the sampled funds underperformed the benchmark index in terms of their Sharpe and Treynor ratios. Whilst UCITS registered a better overall performance, AIFs outperformed UCITS towards the end of the sample period. This suggests that investors should not assume that one fund structure is inherently superior to the other, since the relative performance may vary over time.

scholarly journals A box-jenkins sarima analysis of dysentery cases in children aged below five at a provincial hospital in Zimbabwe

2020 ◽  
Vol 3 (7) ◽  
pp. 33-38
Author(s):  
Dr. Smartson. P. Nyon ◽  
Mr. Thabani Nyoni
Keyword(s):  
Unit Root ◽  
Catchment Area ◽  
Time Series Data ◽  
Unit Root Tests ◽  
Series Data ◽  
Policy Recommendation ◽  
Sample Period ◽  
Policy Makers ◽  
Provincial Hospital ◽  
Out Of Sample

This piece of work uses monthly time series data on new dysentery cases at Gweru Provincial Hospital (GPH) from Janaury 2010 to December 2018, to predict dysentery cases over the period January 2019 to December 2020. As confirmed by unit root tests, the series under consideration is basically an I (1) variable. The study applied the Box-Jenkins “catch all” model. Residual analysis of this model indicates that the model is stable and thus suitable for predicting dysentery cases at GPH over the out-of-sample period. The results of the study reveal that dysentery cases will be on the rise at GPH over the out-of-sample period; characterized by seasonal repeats in December each year. The study offers a two-fold policy recommendation in order to help policy makers in the fight against dysentery in children under five years of age within the GPH catchment area.

scholarly journals Size and Operating Profitability Portfolio Returns and Return Premia in Japan—A Tour d’horizon

2021 ◽  
Vol 13 (2) ◽  
pp. 1
Author(s):  
Chikashi Tsuji
Keyword(s):  
Size Effect ◽  
Firm Size ◽  
Equity Market ◽  
Equity Returns ◽  
Sample Period ◽  
Full Sample ◽  
Portfolio Returns ◽  
International Equity ◽  
Equity Portfolios

This study examines the Japanese equity returns and return premia by focusing on firm size- and corporate operating profitability-sorted portfolios over the period from 1990 to 2020. As a result of our explorations, this study derives the following much beneficial findings. (1) The effects of corporate operating profitability and firm size are generally continuously seen in the Japanese equity market. More specifically, (2) the size effect is much stronger in our latter half sub-period; while the operating profitability effect is similarly seen in both our former half and latter half sub-periods. Furthermore, (3) we stress that this study employs the data in US dollars, and calculates several key statistics and measures for not only our full sample period but also many different sub-periods, in which economic and business circumstances are much different. Therefore, for both Japanese and international equity investors, our findings shall be highly useful for enriching and furthering the understanding of returns and return premia of Japanese equity portfolios.

scholarly journals IMPACT OF COVID-19 ON CRYPTOCURRENCIES: EVIDENCE ON INFORMATION TRANSMISSION THROUGH ECONOMIC AND FINANCIAL MARKET SENTIMENTS

2021 ◽  
Vol 10 ◽  
pp. 103-113
Author(s):  
Irfan Haider Shakri ◽  
Jaime Yong ◽  
Erwei Xiang
Keyword(s):  
Financial Market ◽  
Early Phase ◽  
Wavelet Coherence ◽  
Coherence Analysis ◽  
Sample Period ◽  
Market Sentiment ◽  
Economic News ◽  
News Sentiment ◽  
Wavelet Coherence Analysis ◽  
The Relationship

This paper investigates the relationship between the COVID-19 crisis and the two leading cryptocurrencies, Bitcoin and Ethereum, from 31 December 2019 to 18 August 2020. We also use an economic news sentiment index and financial market sentiment index to explore the possible mechanisms through which COVID-19 impacts cryptocurrency. We employ a VAR Granger Causality framework and Wavelet Coherence Analysis and find the cryptocurrency market was impacted in the early phase of the sample period through economic news and financial market sentiments, but this effect diminished after June 2020.  

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