scholarly journals The Role of Assumptions in Ohlson Model Performance: Lessons for Improving Equity-Value Modeling

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 513
Author(s):  
Olga Fullana ◽  
Mariano González ◽  
David Toscano
Keyword(s):  
Time Series ◽  
Model Performance ◽  
Listed Firms ◽  
Valuation Model ◽  
Residual Income Valuation ◽  
Equity Value ◽  
Short Run ◽  
Time Series Approach ◽  
Linear Information

In this paper, we test whether the short-run econometric conditions for the basic assumptions of the Ohlson valuation model hold, and then we relate these results with the fulfillment of the short-run econometric conditions for this model to be effective. Better future modeling motivated us to analyze to what extent the assumptions involved in this seminal model are not good enough approximations to solve the firm valuation problem, causing poor model performance. The model is based on the well-known dividend discount model and the residual income valuation model, and it adds a linear information model, which is a time series model by nature. Therefore, we adopt the time series approach. In the presence of non-stationary variables, we focus our research on US-listed firms for which more than forty years of data with the required cointegration properties to use error correction models are available. The results show that the clean surplus relation assumption has no impact on model performance, while the unbiased accounting property assumption has an important effect on it. The results also emphasize the uselessness of forcing valuation models to match the value displacement property of dividends.

Implementing Residual Income Valuation With Linear Information Dynamics

1999 ◽  
Vol 74 (1) ◽  
pp. 1-28 ◽  
Author(s):  
James N. Myers
Keyword(s):  
Time Series ◽  
Firm Value ◽  
Residual Income ◽  
Accounting System ◽  
Information Dynamics ◽  
No Arbitrage ◽  
Book Value ◽  
Residual Income Valuation ◽  
Accounting Numbers ◽  
Linear Information

Residual income (RI) valuation is a method of estimating firm value based on expected future accounting numbers. This study documents the necessity of using linear information models (LIMs) of the time series of accounting numbers in valuation. I find that recent studies that make ad hoc modifications to the LIMs contain internal inconsistencies and violate the no arbitrage assumption. I outline a method for modifying the LIMs while preserving internal consistency. I also find that when estimated as a time series, the LIMs of Ohlson (1995), and Feltham and Ohlson (1995) provide value estimates no better than book value alone. By comparing the implied price coefficients to coefficients from a price level regression, I find that the models imply inefficient weightings on the accounting numbers. Furthermore, the median conservatism parameter of Feltham and Ohlson (1995) is significantly negative, contrary to the model's prediction, for even the most conservative firms. To explain these failures, I estimate a LIM from a more carefully modeled accounting system that provides two parameters of conservatism (the income parameter and the book value parameter). However, this model also fails to capture the true stochastic relationship among accounting variables. More complex models tend to provide noisier estimates of firm value than more parsimonious models.

Accruals, Accounting-Based Valuation Models, and the Prediction of Equity Values

2005 ◽  
Vol 20 (4) ◽  
pp. 311-345 ◽  
Author(s):  
Mary E. Barth ◽  
William H. Beaver ◽  
John R. M. Hand ◽  
Wayne R. Landsman
Keyword(s):  
Cash Flow ◽  
Model Parameters ◽  
Prediction Errors ◽  
Equity Values ◽  
Valuation Model ◽  
Equity Value ◽  
Out Of Sample ◽  
Other Information ◽  
Linear Information ◽  
Information Valuation

This study uses out-of-sample equity value estimates to determine whether earnings disaggregation, imposing linear information valuation model (LIM) structure and separate industry estimation of valuation model parameters aid in predicting contemporaneous equity values. We consider three levels of earnings disaggregation: aggregate earnings, cashflow and total accruals and cash flow and four major components of accruals. For pooled estimations, imposing the LIM structure results in significantly smaller prediction errors; for by-industry estimations, it does not. However, by-industry prediction errors are substantially smaller, suggesting that the by-industry estimations are better specified. Mean squared and absolute prediction errors are smallest when earnings are disaggregated into cash flow and major accrual components; median prediction errors are smallest when earnings are disaggregated into cash flow and total accruals. These findings suggest that (1) if concern is with errors in the tails of the equity value prediction error distribution, then earnings should be disaggregated into cash flow and the major accrual components; otherwise earnings should be disaggregated only into cash flow and total accruals; (2) imposing the LIM structure neither increases nor decreases prediction errors, which supports the efficacy of drawing inferences from valuation equations based on residual income models that do not impose the structure implied by the model; (3) valuation of abnormal earnings, accruals, accrual components, equity book value, and other information varies significantly across industries.

The role of international tourism in the Maldives.

2021 ◽  
pp. 132-148
Author(s):  
Rajasundram Sathiendrakumar ◽  
Zameelah Khan Jaffur ◽  
Boopen Seetanah
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Tourism Development ◽  
International Tourism ◽  
Tourism Planning ◽  
Full Effect ◽  
Short Run ◽  
Dynamic Time ◽  
The Impact

Abstract This chapter considers the development of tourism in the Maldives and delves into tourism planning and promotion since the 1970s. It also empirically investigates the impact of tourism on economic growth in the Maldives from 1995 to 2016 in both static and dynamic time-series analysis settings. Both the static and dynamic regression results depicted a positive and quite sizeable relationship between tourism and economic growth. It is noteworthy that the analysis could not confirm any relationship in the short run, suggesting that tourism development has its full effect on the economy with time.

The roles of accounting data in equity valuation: evidence from China

2015 ◽  
Vol 5 (1) ◽  
pp. 69-87 ◽  
Author(s):  
Tiandong Wang ◽  
Tianxi Zhang
Keyword(s):  
Explanatory Power ◽  
Balance Sheet ◽  
Equity Valuation ◽  
Accounting Standard ◽  
Accounting Data ◽  
Content Type ◽  
Valuation Model ◽  
Equity Value ◽  
Chinese Listed Companies

Purpose – The purpose of this paper is to examine the roles of earnings and book value (BV) in equity valuation. Design/methodology/approach – The authors apply model’s explanatory power to analyze the roles of accounting data and test the hypotheses empirically with a sample of Chinese listed companies between 2004 and 2010. Findings – The authors find that impact of accounting data on equity value is also dependent on profitability, but the behavior is non-monotonic. In the intermediate-profitability range, explanatory power of both earnings capitalization model and balance sheet model reach the peak, there are no significant differences between them. In the low-profitability range (small or negative profitability), explanatory power of balance sheet model is larger than earnings capitalization model. In the high-profitability range, explanatory power of balance sheet model is less than earnings capitalization model. Research limitations/implications – The results support that the role of BV is more stable in equity valuation. Moreover, this outcome provides reference for improving existing valuation model and setting accounting standard, and provides some empirical evidence for the practical application of BV in equity valuation. Originality/value – Existing studies treat earnings as main variable of equity valuation, and BV is only added as a supplement. This paper compares roles of accounting earnings and BV in equity valuation, especially investigates the influence of BV in equity valuation, and fills up the deficiency in the related literature.

scholarly journals Forecasting Scrub Typhus Cases in Eight High-Risk Counties in China: Evaluation of Time-Series Model Performance

2022 ◽  
Vol 9 ◽  
Author(s):  
Junyu He ◽  
Xianyu Wei ◽  
Wenwu Yin ◽  
Yong Wang ◽  
Quan Qian ◽  
...  
Keyword(s):  
Public Health ◽  
Time Series ◽  
High Risk ◽  
Scrub Typhus ◽  
Model Performance ◽  
Time Series Modeling ◽  
Exogenous Variables ◽  
Sarima Model ◽  
Risk Areas

Scrub typhus (ST) is expanding its geographical distribution in China and in many regions worldwide raising significant public health concerns. Accurate ST time-series modeling including uncovering the role of environmental determinants is of great importance to guide disease control purposes. This study evaluated the performance of three competing time-series modeling approaches at forecasting ST cases during 2012–2020 in eight high-risk counties in China. We evaluated the performance of a seasonal autoregressive-integrated moving average (SARIMA) model, a SARIMA model with exogenous variables (SARIMAX), and the long–short term memory (LSTM) model to depict temporal variations in ST cases. In our investigation, we considered eight environmental variables known to be associated with ST landscape epidemiology, including the normalized difference vegetation index (NDVI), temperature, precipitation, atmospheric pressure, sunshine duration, relative humidity, wind speed, and multivariate El Niño/Southern Oscillation index (MEI). The first 8-year data and the last year data were used to fit the models and forecast ST cases, respectively. Our results showed that the inclusion of exogenous variables in the SARIMAX model generally outperformed the SARIMA model. Our results also indicate that the role of exogenous variables with various temporal lags varies between counties, suggesting that ST cases are temporally non-stationary. In conclusion, our study demonstrates that the approach to forecast ST cases needed to take into consideration local conditions in that time-series model performance differed between high-risk areas under investigation. Furthermore, the introduction of time-series models, especially LSTM, has enriched the ability of local public health authorities in ST high-risk areas to anticipate and respond to ST outbreaks, such as setting up an early warning system and forecasting ST precisely.

Modelling and forecasting Finnish pine sawlog stumpage prices using alternative time-series methods

2007 ◽  
Vol 37 (1) ◽  
pp. 178-187 ◽  
Author(s):  
Ramses Malaty ◽  
Anne Toppinen ◽  
Jari Viitanen
Keyword(s):  
Time Series ◽  
Structural Changes ◽  
Moving Average ◽  
Real Life ◽  
Autoregressive Moving Average ◽  
Vector Autoregressive ◽  
Structural Time Series ◽  
Short Run ◽  
Time Series Approach ◽  
Cyclical Behaviour

This study analyzes the Nordic pine (Pinus sylvestris L.) sawlog markets in four main regions in Finland by using monthly real stumpage prices over the period January 1995 to June 2005. The special emphasis is on the short-run forecasting of different time-series models up to April 2006. As a benchmark case, we compare the models performance in terms of root mean square forecasting errors (RMSE) of standard autoregressive moving average (ARIMA) and vector autoregressive (VAR) models to those of Harvey's (1989) structural time series model (STSM), which, unlike the standard methods, decomposes the time series into unobservable components, such as deterministic and stochastic trend and seasonal and cyclical behaviour. The results indicate that, in most cases, the STSM together with Kalman filter estimation outperform ARIMA and VAR estimation. With hindsight, stumpage markets experienced a price decrease during July–December 2005 and a turning point up in early 2006 that none of these models were able to accurately predict. Based on these results, it seems to be that, in real-life forecasting situations, it is quite difficult to get precise estimates for the stumpage prices solely using the time-series approach, irrespective of how flexible the models may be with respect to structural changes.

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