scholarly journals A Comparison of Equity Valuation Models: Empirical Evidence from a Sample of UK Companies

2019 ◽  
Vol 4 (2) ◽  
pp. 105
Author(s):  
Ja Ryong Kim
Keyword(s):  
Time Series ◽  
Stock Price ◽  
Residual Income ◽  
Main Question ◽  
Book Value ◽  
Pricing Errors ◽  
The Future ◽  
The Uk ◽  
Errors Analysis ◽  
Practical Implications

This paper aims to answer one main question: can the superior models in accounting field be superior in finance field? That is, can models that generate a better approximation to stock price also generate higher returns in the future? To answer this question, I conduct pricing errors analysis and time-series returns analysis. The most important finding is models that approximate stock price better tend to produce higher returns in the future; implying findings in accounting literature have practical implications to analysts and investors. The consistent rankings of models are observed throughout the research: forward earnings multiples perform the best, followed by fundamental valuation models and historical earnings multiples, and book value and sales multiples worst. However, multiples are ranked rather as a group in the UK. Interestingly, residual income models produce similar returns to forward earnings multiples, but the accuracy of their estimates varies depending on their terminal value assumptions.

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.

A Comparative Analysis of Accounting-Based Valuation Models

2016 ◽  
Vol 32 (4) ◽  
pp. 561-575 ◽  
Author(s):  
Kung-Cheng Ho ◽  
Shih-Cheng Lee ◽  
Chien-Ting Lin ◽  
Min-Teh Yu
Keyword(s):  
Stock Prices ◽  
The Other ◽  
Residual Income ◽  
Switching Regression ◽  
Book Value ◽  
Earnings Growth ◽  
Valuation Model ◽  
Pricing Errors ◽  
Residual Income Valuation ◽  
Average Probability

We empirically compare the reliability of the dividend (DIV) model, the residual income valuation (CT, GLS) model, and the abnormal earnings growth (OJ) model. We find that valuation estimates from the OJ model are generally more reliable than those from the other three models, because the residual income valuation model anchored by book value gets off to a poor start when compared with the OJ model led by capitalized next-year earnings. We adopt a 34-year sample covering from 1985 to 2013 to compare the reliability of valuation estimates via their means of absolute pricing errors ( MAPE) and corresponding t statistics. We further use the switching regression of Barrios and Blanco to show that the average probability of OJ valuation estimates is greater in explaining stock prices than the DIV, CT, and GLS models. In addition, our finding that the OJ model yields more reliable estimates is robust to analysts-based and model-based earnings measures.

scholarly journals A Time Series Analysis and Forecasting of Opening Stock Price of McDonalds Crop.

2016 ◽  
Vol 12 (4) ◽  
pp. 6171-6177
Author(s):  
Rawiyah Muneer Alraddadi
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Fast Food ◽  
Stock Price ◽  
Ice Cream ◽  
Series Analysis ◽  
Different Types ◽  
The Future ◽  
Future Condition

McDonalds Corp. is globally famous and is abounded in recent years. It is one of the major chain restaurants that offers a fast food. Basic foods that are served at McDonalds are different types and sizes of burgers, fries, some breakfast, sweets, ice cream and kids meals. McDonalds products have increased loyalty from customers, which has led to the rise of an uneven stock price. So the data is not stationary and makes the role of the analysts ability to forecast the future condition of the organization important. The aim of this paper is to analyze and forecast the opening stock price of McDonalds Corp. over a period time

Leadership for the future

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Keyword(s):  
Content Type ◽  
Knowing That ◽  
The Future ◽  
Pertinent Information ◽  
1960S And 1970S ◽  
The 1960S ◽  
The Uk ◽  
Modern Era ◽  
Practical Implications ◽  
Totalitarian State

Purpose The year 2020 will probably be looked on by the historians of the future as one of the more curious times in the modern era. The year itself, a bit like 1984, had been looked forward to a great deal as it had been the focus for many a discussion and set of predictions of what the future would be like. In the 1960s and 1970s, people read George Orwell’s dystopian novel with wonder and not a little nervous excitement knowing that they would probably live to see the year itself. It was probably almost a disappointment to see that there was no totalitarian state and brutal regime in the UK when the year finally came around, although there were plenty of those to witness elsewhere. Design/methodology/approach This briefing is prepared by an independent writer who adds his/her own impartial comments and places the articles in context. Findings The year 2020 will probably be looked on by the historians of the future as one of the more curious times in the modern era. The year itself, a bit like 1984, had been looked forward to a great deal as it had been the focus for many a discussion and set of predictions of what the future would be like. In the 1960s and 1970s, people read George Orwell’s dystopian novel with wonder and not a little nervous excitement knowing that they would probably live to see the year itself. It was probably almost a disappointment to see that there was no totalitarian state and brutal regime in the UK when the year finally came around, although there were plenty of those to witness elsewhere. Practical implications This paper provides strategic insights and practical thinking that have influenced some of the world’s leading organizations. Originality/value The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

scholarly journals Executives vs. governance: Who has the predictive power? Evidence from narrative tone

2021 ◽  
Author(s):  
Hesham Bassyouny ◽  
Tarek Abdelfattah
Keyword(s):  
Financial Reporting ◽  
Predictive Power ◽  
Annual Reports ◽  
Main Question ◽  
Moderation Effect ◽  
Future Performance ◽  
The Us ◽  
Disclosure Tone ◽  
The Uk ◽  
Practical Implications

AbstractThis study aims to investigate not only Narrative Disclosure Tone predictive power, but also who has this power within companies to predict future performance in the UK context (executive vs. governance). We conduct a computerized textual analysis to measure the tone of UK annual reports narratives. Our results contribute to accounting and financial reporting literature by showing that corporate narrative tone can predict future performance. However, answering our main question about who has this predictive power, we found executives’ reporting tone has the power to predict a company’s future performance but not governance tone. Considering the moderation effect of the 2014 financial reporting guidance, we found this guidance increases corporate narrative tone power in general and executive tone in particular in predicting future performance. Moreover, the current study contributes to financial reporting literature by providing a UK evidence, which operates under the principles-based approach with more flexibility in financial reporting than the US context that follows the rules-based approach. Finally, this study has practical implications for regulators and external users of financial reporting.

PERAMALAN INDEKS HARGA SAHAM MENGGUNAKAN ENSEMBLE EMPIRICAL MODE DECOMPOSITION (EEMD)

2021 ◽  
Vol 10 (2) ◽  
pp. 211-220
Author(s):  
Rosinar Siregar ◽  
Rukun Santoso ◽  
Puspita Kartikasari
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Stock Price ◽  
Price Index ◽  
Time Series Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Series Data ◽  
Polynomial Trend ◽  
Mode Decomposition ◽  
The Future

 Stock price fluctuations make investors tend to hesitate to invest in stock markets because of an uncertain situation in the future. One method that can solve these problems is to use forecasting about the stock prices in the future. Generally, the huge size of data non linear and non stationary, and it is difficult to be interpreted in concrete. This problem can be solved by performing the decomposition process. One of decomposition method in time series data is Ensemble Empirical Mode Decomposition (EEMD). EEMD is process decomposition data into several Intrinsic Mode Function (IMF) and the IMF residue. In this research, this concept applied to data Stock Price Index in Property, Real Estate, and Construction from July 1, 2019 to July 30, 2020 as many as 272 data. Based on the results of data processing, as many as 6 IMF and IMF remaining were used as IMF forecasting and the IMF remaining in the future. The forecast was performed by choosing the best model of each IMF component and IMF remaining, used ARIMA and polynomial trend. Keywords: Time Series Data, Stock Price Index, EEMD, ARIMA, Polynomial Trend.

scholarly journals Prediction of Stock Price using RNN’s LSTM-Based Deep Learning Model

2021 ◽  
Vol 9 (8) ◽  
pp. 2469-2474
Author(s):  
Asmita Pandey
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Stock Market ◽  
Time Series Analysis ◽  
Recurrent Neural Network ◽  
Stock Price ◽  
Listed Companies ◽  
Stock Market Prediction ◽  
The Past ◽  
The Future

Abstract: Stock Market is referred to as a trading platform where trading of listed companies share price is exchanged. It is a place where individuals can buy or sell shares of the publicly listed companies. The prediction of stock market that how it will perform, its movement is one of the challenging tasks to do. Stock market prediction involves determining the future movement of the stock value of a financial exchange. In this paper the prediction of the stock prices using deep learning's LSTM (Long Short-Term Memory) which is the extension of Recurrent Neural Network is done. The previous two years historical dataset from 31/7/2019 to 13/8/2021 is taken for the prediction purpose. The prediction is based on the time series analysis of data, since it can help us to get an idea of the stock price pattern and also it is considered to be the best tool for understanding the pattern of the previously observed values and make the predictions based on it. For a greater accuracy of the predictions, we should consider past happenings or events as the past affects the future. Since for stock market prediction the data will be in time series and LSTM performs well when the information or the data is of the past and the prediction is to be made for the future then we can say that LSTMs are quite capable of doing the prediction for the stock market values. Keywords: Stock Market, prediction, LSTM, Recurrent Neural Network, time series analysis

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.

GLOBAL FINANCIAL CRISIS: THE BUBBLE EFFECT IN INDONESIA

2018 ◽  
pp. 27-33
Author(s):  
Mohammad Benny Alexandri ◽  
Raeny Dwisanti
Keyword(s):  
Financial Crisis ◽  
Stock Price ◽  
Price Index ◽  
Global Financial Crisis ◽  
Price Ratio ◽  
Sharp Decline ◽  
Book Value ◽  
The Us ◽  
Investment Opportunities ◽  
Us Financial Crisis

US and Indonesia stock markets are entering record heights without being offset by economic growthand profitability growth of their traded companies. There are several indicators for the stock marketbubble: (1) Price Ratio (Ear Ratio); (2) Price Ratio / Book (PB Ratio), the latter comparing thenominal price of one share at a market with the book value (the value of company's assets). Thecurrent PB ratio of the composite stock price index being 3.3 means that for each shares the assetvalue of which is 1 IDR, the stock would be worth 3.3 IDR. This is one of the most expensive price in the world today. Based on the above, for Indonesian stock market sharp decline is just a matter of time and waiting. This decline will be much sharper if triggered by the US financial crisis. We can also also see a bubble emerging from increasingly irrational investment attitudes. Currently, in addition to high prices for stocks and bonds, investors have started looking at investment opportunities in digital currencies. This research tries to know the potential of financial crisis and itseffect for the financial market in Indonesia. 

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