The Time-Series Relations among Expected Return, Risk, and Book-to-Market

CFA Digest ◽  
2000 ◽  
Vol 30 (3) ◽  
pp. 72-74
Author(s):  
Stephen M. Horan
Keyword(s):  
Time Series ◽  
Expected Return

scholarly journals Commodity Indices Risk and Return Analysis Against Libor Benchmark

2018 ◽  
Vol 12 (3-4) ◽  
pp. 55-66
Author(s):  
Subhakara Valluri
Keyword(s):  
Time Series ◽  
Asset Allocation ◽  
Time Series Data ◽  
Geometric Mean ◽  
Precious Metals ◽  
Jel Classification ◽  
Series Data ◽  
Expected Return ◽  
Risk And Return ◽  
Allocation Strategies

This study analyze the risk and return characteristics of commodity index investments against the LIBOR benchmark. Commodity-based asset allocation strategies can be optimized by benchmarking the risk and return characteristics of commodity indices with LIBOR index rate. In this study, we have considered agriculture, energy, and precious metals commodity indices and LIBOR index to determine the risk and return characteristics using estimation techniques in terms of expected return, standard deviation, and geometric mean. We analyzed the publicly available daily market data from 10/9/2001 to 12/30/2016 for benchmarking commodity indices against LIBOR. S&P GSCI Agriculture Index (SGK), S&P GSCI Energy Index (SGJ), and S&P GSCI Precious Metals Index (SGP) are taken to represent each category of widely traded commodities in the regression analysis. Our study uses time series data based on daily prices. Alternative forecasting methodologies for time series analysis are used to cross-check the results. The forecasting techniques used are Holt-Winters Exponential Smoothing and ARIMA. This methodology predicts forecasts using smoothening parameters. The empirical research has shown that the risk of each of the commodity index that represents agriculture, energy, and precious metals sector is smaller compared to its return, whereas LIBOR based interest rate benchmark shows higher risk compared to its return in recession, non-recession and overall periods. JEL Classification: C43, G13, G15

scholarly journals Research on Expected Return Based on Time Series Model: Taking Kweichow Moutai Co., Ltd. as an Example

2020 ◽  
Vol 9 (2) ◽  
pp. 52
Author(s):  
Wennuan Fang
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Cash Flow ◽  
Error Rate ◽  
Linear Regression Model ◽  
Free Cash Flow ◽  
Evaluation Index ◽  
Expected Return ◽  
Enterprise Value ◽  
Value Evaluation

<p>With the stable development of China's economy, the economic activities among enterprises are more diversified, and the enterprise value evaluation index system is more perfect. As an important parameter in the enterprise value evaluation index, the expected income can be used to measure the profit quality of the enterprise. In order to explore the expected return of enterprises, this paper selects free cash flow as the specific index, and takes Kweichow moutai Co., Ltd. as an example, analyzes the earnings trend of enterprises through the method of time series. Time series prediction models are constructed to provide the basis for enterprise value evaluation. At the same time, by fitting single linear regression model and Autoregressive Integrated Moving Average model, the free cash flow is predicted, and finally the ARIMA (1,2,2) model is obtained. The results show that the single linear regression model has a higher error rate, while ARIMA (1,2,2) has a better fitting degree and a lower error rate. It can be used for the results of expected earnings of enterprises and provides a reference for enterprise value evaluation.</p>

scholarly journals Evaluating Firm-Level Expected-Return Proxies: Implications for Estimating Treatment Effects

2020 ◽  
Author(s):  
Charles M C Lee ◽  
Eric C So ◽  
Charles C Y Wang
Keyword(s):  
Time Series ◽  
Measurement Error ◽  
Cross Section ◽  
Treatment Effects ◽  
Relative Performance ◽  
Expected Return ◽  
Firm Level ◽  
Ex Ante ◽  
The Cross ◽  
New Evidence

Abstract We introduce a parsimonious framework for choosing among alternative expected-return proxies (ERPs) when estimating treatment effects. By comparing ERPs’ measurement error variances in the cross-section and in the time series, we provide new evidence on the relative performance of firm-level ERPs nominated by recent studies. Generally, “implied-costs-of-capital” metrics perform best in the time series, whereas “characteristic-based” proxies perform best in the cross-section. Factor-based ERPs, even the latest renditions, perform poorly. We revisit four prior studies that use ex ante ERPs and illustrate how this framework can potentially alter either the sign or the magnitude of prior inferences.

The LDE-Type Flare Occurrence (1969-1993)

1994 ◽  
Vol 144 ◽  
pp. 279-282
Author(s):  
A. Antalová
Keyword(s):  
Time Series ◽  
Fourier Analysis ◽  
Sunspot Cycle ◽  
List Type ◽  
Type Number ◽  
Solar Cycles ◽  
Type Iv ◽  
Long Duration ◽  
Cycle Maximum ◽  
Flare Index

AbstractThe occurrence of LDE-type flares in the last three cycles has been investigated. The Fourier analysis spectrum was calculated for the time series of the LDE-type flare occurrence during the 20-th, the 21-st and the rising part of the 22-nd cycle. LDE-type flares (Long Duration Events in SXR) are associated with the interplanetary protons (SEP and STIP as well), energized coronal archs and radio type IV emission. Generally, in all the cycles considered, LDE-type flares mainly originated during a 6-year interval of the respective cycle (2 years before and 4 years after the sunspot cycle maximum). The following significant periodicities were found:• in the 20-th cycle: 1.4, 2.1, 2.9, 4.0, 10.7 and 54.2 of month,• in the 21-st cycle: 1.2, 1.6, 2.8, 4.9, 7.8 and 44.5 of month,• in the 22-nd cycle, till March 1992: 1.4, 1.8, 2.4, 7.2, 8.7, 11.8 and 29.1 of month,• in all interval (1969-1992):a)the longer periodicities: 232.1, 121.1 (the dominant at 10.1 of year), 80.7, 61.9 and 25.6 of month,b)the shorter periodicities: 4.7, 5.0, 6.8, 7.9, 9.1, 15.8 and 20.4 of month.Fourier analysis of the LDE-type flare index (FI) yields significant peaks at 2.3 - 2.9 months and 4.2 - 4.9 months. These short periodicities correspond remarkably in the all three last solar cycles. The larger periodicities are different in respective cycles.

Filter Design for the Seasonal Adjustment of a Time Series

1974 ◽  
Vol 3 (12) ◽  
pp. 1171-1186
Author(s):  
Edward Melnick ◽  
John Moussourakis
Keyword(s):  
Time Series ◽  
Filter Design ◽  
Seasonal Adjustment
Sign in / Sign up

Export Citation Format

Share Document