Volatility Forecasting Using Financial Statement Information

Sridharan, Suhas A.

doi:10.2308/accr-51025

Cited by 37 publications

(13 citation statements)

References 55 publications

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“…Furthermore, it appears that for each value perspective (price, return and volatility) there is a dominant model used by investors, for instance, investors tend to use the standard RIM for price valuation, DDM and DCF for return estimation, whereas, they use the market model for estimating equity volatility. This is consistent with LeRoy and Porter (1981), who questioned the ability of the accounting models to capture market volatility, but this might challenge the findings of Sridharan (2015), who argues that accounting-based volatility drivers may serve as useful indicators of the variance risk.…”

Section: Analysis and Resultssupporting

confidence: 77%

The relevance of valuation models: insights from Palestine exchange

Harasheh

Amaduzzi

Darwish

2020

IMEFM

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Purpose This paper aims to investigate the relevance of two groups of valuations models as follows: the accounting models based on the residual income (RIM) and the standard market model, on equity price, return and volatility relevance. Design/methodology/approach The models are tested on companies traded on Palestine exchange from 2009 to 2018, using panel regression analysis. Two-price and two-return models derived from RIM to compare with the market model and four volatility models. Findings The standard RIM outperformed other models in equity price modeling. The dividend discount model (DDM) outperformed the rest of the models in terms of return estimation. However, the authors find that the market model can explain equity variance better than RIM and DDM models. Practical implications For investors, market beta does not necessarily capture all relevant factors of value and traditional financial statements are still important in providing relevant information and different models are used for different values perspectives (price, return and volatility). Originality/value Previous studies focus on comparing the price and return relevance of accounting-based models (RIM and cash flow models). Three aspects differentiate this paper and contribute to its originality, namely, the uniqueness of the context, incorporating the market model into the picture along with the accounting-based models and adding Volatility dimensions of relevance.

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Section: Analysis and Resultssupporting

confidence: 77%

The relevance of valuation models: insights from Palestine exchange

Harasheh

Amaduzzi

Darwish

2020

IMEFM

View full text Add to dashboard Cite

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“…The parentheses contain t ‐statistics based on standard errors clustered by firm and year. Columns 1, 2, 3, and 6 show the predictive power of RiskInfo

_{30}

for changes in volatility is highly robust and incremental to standard controls known to explain variation in return volatility: firm size, the log book‐to‐market ratio, and the analyst‐based earnings surprise (e.g., Sridharan [2015]). These results further suggest that the risk information in earnings announcements is not fully captured by the earnings surprise itself.…”

Section: Data and Resultsmentioning

confidence: 99%

Measuring Risk Information

Smith

2021

J of Accounting Research

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We develop a measure of how information events impact investors' expectations of risk. The measure is broadly applicable and simple to implement. We derive it from an option‐pricing model, where investors anticipate an announcement that simultaneously conveys information on the announcer's expected future cash flows and risk profile. We empirically implement the measure using firms' earnings announcements, showing that it closely aligns with our model's predictions and offers strong forecasting power for firms' risk profiles, costs of capital, and future investments. We further highlight pitfalls of using simple changes in option‐implied volatilities to study information gleaned from earnings announcements. Finally, we apply our measure to study disclosure regulation, the efficacy of text‐based proxies, and market‐wide events, which we use to illustrate our measure's uses, and illuminate its potential limitations.

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“… Previous studies in accounting, like Sridharan (), have used firm‐level accounting information to predict market trends and volatility. Our approach relies on firm‐level income statements and balance sheet information to identify economically relevant costs in a particular industry. …”

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confidence: 99%

Relating Product Prices to Long‐Run Marginal Cost: Evidence from Solar Photovoltaic Modules

Reichelstein

Sahoo

2017

Contemporary Accting Res

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A basic tenet of microeconomics is that for a competitive industry in equilibrium the market price of a product will be equal to its marginal cost. This paper develops a model framework and a corresponding empirical inference procedure for estimating long‐run marginal cost in industries where production costs decline over time. In the context of the solar photovoltaic (PV) module industry, we rely primarily on firm‐level financial accounting data to estimate the long‐run marginal cost of PV modules for the years 2008–2013. During those years, the industry experienced both unprecedented price declines and significant expansions of manufacturing capacity. We compare the trajectory of average sales prices with the estimated long‐run marginal costs in order to quantify the extent to which actual price declines were attributable to reductions in production costs. The trajectory of estimated product costs is then extrapolated to forecast an equilibrium trend line for future PV module prices.

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Volatility Forecasting Using Financial Statement Information

Cited by 37 publications

References 55 publications

The relevance of valuation models: insights from Palestine exchange

The relevance of valuation models: insights from Palestine exchange

Measuring Risk Information

Relating Product Prices to Long‐Run Marginal Cost: Evidence from Solar Photovoltaic Modules

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