Stationarity and Cointegration Tests of the Ohlson Model

2000 ◽  
Vol 15 (2) ◽  
pp. 141-160 ◽  
Author(s):  
Daqing D. Qi ◽  
Y. Woody Wu ◽  
Bing Xiang
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Empirical Studies ◽  
Market Value ◽  
Residual Income ◽  
Series Data ◽  
Book Value ◽  
Cointegration Tests ◽  
Significant Difference ◽  
Out Of Sample

This paper investigates the time-series properties of the Ohlson (1995) model and examines their implications for empirical studies that use time-series data but do not explicitly account for such properties. Based on a sample of 95 firms with complete data from 1958 to 1994, we show that the null hypothesis that market value and book value are nonstationary cannot be rejected for most of the sample firms. More importantly, book value and residual income do not cointegrate with market value for 80 percent of the sample firms. We demonstrate the importance and relevance of the time-series properties of the model to OLS regressions by showing that the OLS out-of-sample forecasts of market value are significantly more accurate and less biased for the cointegrated firms than for the non-cointegrated firms. We also explore methods to improve the specification of OLS regressions based on the Ohlson (1995) model and suggest that scaling the variables with lagged market value can significantly alleviate the problem with nonstationarity of the unsealed time-series data. While the generality of our results is limited by the survivorship bias of our sample, we believe that our paper has some important implications for studies motivated by the Ohlson (1995) model. First, because market value and book value are nonstationary and book value and residual income do not cointegrate with market value for most firms, the other information variable has to be nonstationary so that a linear combination of the independent variables can cointegrate with market value. Second, direct tests of the Ohlson (1995) model through OLS regressions using time-series data are questionable because they are likely to be misspecified. This may partially explain the underestimation of market value widely documented by previous studies and the significant difference between parameters predicted by the Ohlson (1995) model and estimated from OLS regressions. Third, our results also suggest that scaling the data with lagged market value can mitigate the problems with nonstationarity. For studies using unsealed time-series data, a cointegration test should be conducted first and a sensitivity analysis based on the cointegrated sub-sample should be performed to examine whether the results based on the full sample are robust.

What Matters from the Past: Market Value, Book Value, or Earnings? Earnings Valuation and Sufficient Statistics for Prior Information

1992 ◽  
Vol 7 (4) ◽  
pp. 423-464 ◽  
Author(s):  
Ram T.S. Ramakrishnan ◽  
Jacob K. Thomas
Keyword(s):  
Stock Prices ◽  
Time Series Data ◽  
Explanatory Power ◽  
Weighted Average ◽  
Market Value ◽  
Series Data ◽  
Cross Sectional ◽  
Sufficient Statistics ◽  
Book Value ◽  
Value Model

A number of recent analytical and empirical papers seek to identify the variables that best explain stock prices. We derive the relation between prices and earnings for three one-parameter “excess earnings” evolution processes that describe three different ways in which current period shocks to earnings persist in future. In each case, we show that price is a weighted average of capitalized current earnings and a sufficient statistic for all past information. The sufficient statistics are three amounts from last year: book value, market value, and capitalized earnings. Using time-series data over the 1969-1988 period for a sample of 511 firms, we estimate firm-specific excess earnings regressions and price regressions for the three cases. Although the book value model provides the best fit for a majority of firms for the excess earnings regressions, the market value model is far superior for the price regressions. We argue that this latter result is due to prior period price (included in the market value model alone), reflecting other information that is not explicitly modeled here. Despite this bias in favor of the market value model for the price regressions, we find a positive cross-sectional association between the relative explanatory power of the three models in the excess earnings regressions and the corresponding relative explanatory power in the price regressions. That is, if a firm's excess earnings series is best described by a particular model, its price series is also likely to be best described by the valuation relation derived from the same model.

scholarly journals WHICH VARIABLES PREDICT INDONESIA'S INFLATION?

2019 ◽  
Vol 22 (1) ◽  
pp. 87-102 ◽  
Author(s):  
Susan Sunila Sharma
Keyword(s):  
Time Series ◽  
Comparative Analysis ◽  
Inflation Rate ◽  
Time Series Data ◽  
Predictor Variables ◽  
Series Data ◽  
Macroeconomic Variables ◽  
Monthly Time Series ◽  
Out Of Sample ◽  
Exhaustive List

We use an exhaustive list of Indonesia’s macroeconomic variables in a comparative analysis to determine which predictor variables are most important in forecasting Indonesia’s inflation rate. We use monthly time-series data for 30 macroeconomic variables. Using both in-sample and out-of-sample predictability evaluations, we report consistent evidence of inflation rate predictability using 11 out of 30 macroeconomic variables.

scholarly journals The Influencing Factors on Unemployment Level - The Case of Albania

2017 ◽  
Vol 3 (3) ◽  
pp. 103
Author(s):  
Eleni Vangjeli ◽  
Jorida Agolli
Keyword(s):  
Time Series ◽  
Fast Food ◽  
Time Series Data ◽  
Empirical Studies ◽  
Mutual Effect ◽  
Series Data ◽  
Total Employment ◽  
Employment Level ◽  
Domestic Investments ◽  
The Impact

Research background: The empirical studies in labor market indicated that there are many factors that affect unemployment. These studies have analyzed these factors and concluded that exist a mutual relationship between them and unemployment. The relation between employment and FDI were studied by Craigwell (2006) and Karlsson et al. (2009). The effects of minimal wage on employment were studied by Katz and Kruger (1992) and Card (1992a) as well as Stephen Machin and Alan Manning (1994). Card, D. and Krueger, B. (1994) analyzed the effects of minimum wage raise, on fast-food restaurants in New Jersey and Pennsylvania. On the other hand, Neumark and Wascher (2000) in their findings explained that raising the minimal wage by 10% reduced the teenager employment rate with 1-2% and brought the reduction of total employment by 1.5-2%. Meanwhile, Grossberg and Sicilian (2004), found mixed results in their estimations of the minimal wage effects on employment duration period. Krugman, P(2015) one of the economy nobelist defends the theory of raising the minimal wage as a condition for improving the wellbeing. W. Phillips, (1958) studied a negative inverse relation between unemployment and inflation. Barro (1995), De Gregorio (1994), Bruno (1994) concluded that low inflation is accompanied by economic growth and higher employment level. Purpose of the article: The main aim of this article is to study and analyse factors affecting unemployment levels, because the unemployment is a critical problem in our country. We have analyzed the mutual effect of selected factors on unemployment level. The selected factors are FDI, domestic investments, inflation and minimal wage. Methodology/methods: To calculate the impact of this factors on the unemploymentlevel was used time series data for the period 1995 – 2013. Relying on time series data was made regression analysis using SPPS-21 program. Findings: Based on the testing results, we conclude that FDI, domestic investments and inflation affect negatively the unemployment level and this effect is statistically important, whereas the minimal wage has a low positive effect but such effect is not important.

scholarly journals Magnetospheric chaos and dynamical complexity response during storm time disturbance

2020 ◽  
Author(s):  
Irewola Aaron Oludehinwa ◽  
Olasunkanmi Isaac Olusola ◽  
Olawale Segun Bolaji ◽  
Olumide Olayinka Odeyemi ◽  
Abdullahi Ndzi Njah
Keyword(s):  
Time Series ◽  
Solar Wind ◽  
Geomagnetic Storm ◽  
Time Series Data ◽  
Geomagnetic Storms ◽  
Approximate Entropy ◽  
Series Data ◽  
Dynamical Complexity ◽  
Significant Difference ◽  
Dynamical Features

Abstract. In this study, we examine the magnetospheric chaos and dynamical complexity response in the disturbance storm time (Dst) and solar wind electric field (VBs) during different categories of geomagnetic storm (minor, moderate and major geomagnetic storm). The time series data of the Dst and VBs are analyzed for the period of nine years using nonlinear dynamics tools (Maximal Lyapunov Exponent, MLE, Approximate Entropy, ApEn and Delay Vector Variance, DVV). We found a significant trend between each nonlinear parameter and the categories of geomagnetic storm. The MLE and ApEn values of the Dst indicate that chaotic and dynamical complexity response are high during minor geomagnetic storms, reduce at moderate geomagnetic storms and declined further during major geomagnetic storms. However, the MLE and ApEn values obtained in VBs indicate that chaotic and dynamical complexity response are high with no significant difference between the periods that are associate with minor, moderate and major geomagnetic storms. The test for nonlinearity in the Dst time series during major geomagnetic storm reveals the strongest nonlinearity features. Based on these findings, the dynamical features obtained in the VBs as input and Dst as output of the magnetospheric system suggest that the magnetospheric dynamics is nonlinear and the solar wind dynamics is consistently stochastic in nature.

scholarly journals MODELING TIME SERIES DATA OF REAL SYSTEMS

2007 ◽  
Vol 18 (02) ◽  
pp. 235-252 ◽  
Author(s):  
DILIP P. AHALPARA ◽  
JITENDRA C. PARIKH
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Financial Time Series ◽  
Prediction Method ◽  
Series Data ◽  
Lorenz Equations ◽  
Linear Dynamics ◽  
Financial Time ◽  
Out Of Sample ◽  
Prediction Capability

Dynamics of complex systems is studied by first considering a chaotic time series generated by Lorenz equations and adding noise to it. The trend (smooth behavior) is separated from fluctuations at different scales using wavelet analysis and a prediction method proposed by Lorenz is applied to make out of sample predictions at different regions of the time series. The prediction capability of this method is studied by considering several improvements over this method. We then apply this approach to a real financial time series. The smooth time series is modeled using techniques of non linear dynamics. Our results for predictions suggest that the modified Lorenz method gives better predictions compared to those from the original Lorenz method. Fluctuations are analyzed using probabilistic considerations.

scholarly journals Generating Accurate Personalized Predictions of Future Behavior: A Smoking Exemplar

2019 ◽  
Author(s):  
Aaron Jason Fisher ◽  
Peter D. Soyster
Keyword(s):  
Time Series ◽  
Prediction Models ◽  
Time Series Data ◽  
Area Under The Curve ◽  
Time Of Day ◽  
Series Data ◽  
Future Behavior ◽  
Out Of Sample ◽  
Time Stamps ◽  
Time Lagged

The present study sought to apply statistical classification methods to idiographic time series data in order to make accurate future predictions of behavior. We recruited 70 individuals who presented as regular smokers; 52 completed experience sampling method (ESM) data collection and provided sufficient time series data. Time stamps from ESM surveys were used to calculate the time of day, day of the week, and continuous time—where the last datum was, in turn, used to calculate 12-hr and 24-hr cycles. Each individual’s time series was split into sequential training and testing sections, so that trained models could be tested on future observations. Prediction models were trained on the first 75% of the individual’s data and tested on the last 25%. Predictions of future behavior were made on a person by person basis. Two prediction algorithms were employed, elastic net regularization and naïve Bayes classification. Sample-wide area under the curve was nearly 80%, with some models demonstrating perfect prediction accuracies. Sensitivity and specificity were between 0.78 and 0.81 across the two approaches. Importantly, prediction models were based on a lagged data structure. Thus, in addition to supporting the prediction accuracy of our models with out-of-sample tests in time-forward data, the models themselves were time-lagged, such that each prediction was for the subsequent measurement. Such a system could be the basis for mobile, just-in-time interventions for substance use, as models that accurately predict future behavior could ostensibly be used for delivering personalized interventions at empirically-indicated moments of need.

scholarly journals Perbandingan Kontribusi Bruto Asuransi Syariah Sebelum dan Sesudah Pandemi Covid-19

2021 ◽  
Vol 2 (2) ◽  
pp. 142
Author(s):  
Arin Ramadhiani Soleha ◽  
Iza Hanifuddin
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Insurance Industry ◽  
Series Data ◽  
Islamic Finance ◽  
Mutual Help ◽  
Paired Samples ◽  
Significant Difference ◽  
Before And After ◽  
Islamic Insurance

AbstractIslamic insurance has a big role in the Islamic finance sector with the principle of mutual help. Gross contribution is one of the funds that can be utilized for insurance participants and companies. Covid19, which has an impact on the economic sector, makes understanding the growth of sharia insurance before and after the pandemic in terms of gross contribution very important. This study aims to further review the gross contribution from March-December to find out whether there is a significant difference from the gross contribution of sharia insurance before and after the Covid-19 pandemic. The study was conducted using a comparative quantitative approach with two paired samples. The research sample uses time series data, namely the gross contribution of sharia insurance in 2019 and 2020 for the period from March to December. The results of this study found that the comparison of gross contribution to the Islamic insurance industry seen before the 2019 pandemic and after the 2020 pandemic which was taken from March to December was normally distributed. This means that the development of sharia insurance when viewed before the 2019 pandemic and after the 2020 pandemic according to the gross contributions from sharia insurance participants did not experience a significant difference and will certainly increase.AbstrakAsuransi syariah memiliki peran besar pada sektor keuangan syariah dengan prinsip saling tolongmenolong. Kontribusi bruto merupakan salah satu dana yang dapat dimanfaatkan bagi peserta asuransi maupun perusahaan. Covid-19 yang berdampak pada sektor perekonomian, menjadikan pemahaman mengenai pertumbuhan asuransi syariah sebelum dan sesudah pandemi ditinjau dari kontribusi bruto sangat penting. Penelitian ini bertujuan untuk meninjau lebih lanjut kontribusi bruto dari Maret-Desember untuk mengetahui apakah ada perbedaan yang signifikan dari kontribusi bruto asuransi syariah sebelum dan sesudah pandemi Covid-19. Penelitian dilakukan dengan pendekatan kuantitatif komparatif dengan dua sampel berpasangan. Sampel penelitian menggunakan data time series yaitu kontribusi bruto asuransi syariah tahun 2019 dan 2020 periode bulan Maret hingga Desember. Hasil penelitian ini menemukan bahwa perbandingan kontribusi bruto pada industri asuransi syariah dilihat saat sebelum pandemi tahun 2019 dan sesudah pandemi 2020 yang diambil pada periode Maret hingga Desember berdistribusi normal. Hal tersebut berarti bahwa perkembangan asuransi syariah jika ditinjau pada saat sebelum pandemi tahun 2019 dan sesudah pandemi tahun 2020 menurut kontribusi bruto yang berasal dari para peserta asuransi syariah tidak mengalami perbedaan yang signifikan dan dapat dipastikan akan mengalami peningkatan.

scholarly journals REGIME CHANGE AND TREND PREDICTION FOR BITCOIN TIME SERIES DATA

2017 ◽  
Vol 5 ◽  
pp. 384-388 ◽  
Author(s):  
Osamu Kodama ◽  
Lukáš Pichl ◽  
Taisei Kaizoji
Keyword(s):  
Time Series ◽  
Regime Change ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Series Data ◽  
Strong Correlations ◽  
Markov Switching Model ◽  
Trend Prediction ◽  
Time Series Dataset ◽  
Out Of Sample

Bitcoin time series dataset recording individual transactions denominated in Euro at the COINBASE market between April 23, 2015 and August 15, 2016 is analyzed. Markov switching model is applied to classify the regions of varying volatility represented by three hidden state regimes using univariate autoregressive model and dependent mixture model. Causality extraction and price prediction of daily BTCEUR exchange rates is performed by means of a recurrent neural network using the standard Elman model. Strong correlations is found between the normalized mean squared error of the Elman network (out-of-sample 5-day-ahead prediction) and the realized volatility (sum of minute returns squared throughout the trading day). The present approach is calibrated using simulated regime change in standard econometric models. Our results clearly demonstrate the applicability of recurrent neural networks to causality extraction even in the case of highly volatile cryptocurrency exchange rate time series data.

scholarly journals Magnetospheric chaos and dynamical complexity response during storm time disturbance

2021 ◽  
Vol 28 (2) ◽  
pp. 257-270
Author(s):  
Irewola Aaron Oludehinwa ◽  
Olasunkanmi Isaac Olusola ◽  
Olawale Segun Bolaji ◽  
Olumide Olayinka Odeyemi ◽  
Abdullahi Ndzi Njah
Keyword(s):  
Time Series ◽  
Solar Wind ◽  
Geomagnetic Storm ◽  
Time Series Data ◽  
Geomagnetic Storms ◽  
Approximate Entropy ◽  
Series Data ◽  
Dynamical Complexity ◽  
Significant Difference ◽  
Non Linear

Abstract. In this study, we examine the magnetospheric chaos and dynamical complexity response to the disturbance storm time (Dst) and solar wind electric field (VBs) during different categories of geomagnetic storm (minor, moderate and major geomagnetic storm). The time series data of the Dst and VBs are analysed for a period of 9 years using non-linear dynamics tools (maximal Lyapunov exponent, MLE; approximate entropy, ApEn; and delay vector variance, DVV). We found a significant trend between each non-linear parameter and the categories of geomagnetic storm. The MLE and ApEn values of the Dst indicate that chaotic and dynamical complexity responses are high during minor geomagnetic storms, reduce at moderate geomagnetic storms and decline further during major geomagnetic storms. However, the MLE and ApEn values obtained from VBs indicate that chaotic and dynamical complexity responses are high with no significant difference between the periods that are associated with minor, moderate and major geomagnetic storms. The test for non-linearity in the Dst time series during major geomagnetic storm reveals the strongest non-linearity features. Based on these findings, the dynamical features obtained in the VBs as input and Dst as output of the magnetospheric system suggest that the magnetospheric dynamics are non-linear, and the solar wind dynamics are consistently stochastic in nature.

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