Extracting Forward-Looking Information from Security Prices: A New Approach

2008 ◽  
Vol 83 (4) ◽  
pp. 1101-1124 ◽  
Author(s):  
Dan Weiss ◽  
Prasad A. Naik ◽  
Chih-Ling Tsai
Keyword(s):  
Time Series ◽  
Stock Returns ◽  
Time Series Models ◽  
Security Prices ◽  
Firm Level ◽  
Analyst Following ◽  
New Approach ◽  
Future Earnings ◽  
Market Participants ◽  
Forward Looking

ABSTRACT: This paper proposes a new index to extract forward-looking information from security prices and infer market participants’ expectations of future earnings. The index, called market-adapted earnings (MAE), utilizes stock returns and fundamental accounting signals to estimate market expectations of future earnings at the firm level. MAE outperforms time-series models (e.g., random-walk) in predicting future earnings. Results demonstrate the usefulness of MAE for firms that have no analyst following.

The impact of industry concentration on the market’s ability to anticipate future earnings

2016 ◽  
Vol 24 (4) ◽  
pp. 443-475 ◽  
Author(s):  
In-Mu Haw ◽  
Bingbing Hu ◽  
Jay Junghun Lee ◽  
Woody Wu
Keyword(s):  
Stock Returns ◽  
Market Power ◽  
Information Transfer ◽  
Positive Impact ◽  
Product Market ◽  
Industry Concentration ◽  
Firm Level ◽  
Content Type ◽  
Future Earnings ◽  
The Impact

Purpose The existing literature has established the importance of industry concentration in explaining firm performance and information environments. However, little is known about whether and how industry concentration affects investors’ ability to anticipate future earnings. This paper aims to investigate this query by identifying and testing two channels, product market power and intra-industry information transfer, through which industry concentration affects the informativeness of stock returns about future earnings. Design/methodology/approach The paper measures the informativeness of stock returns about future earnings by the future earnings response coefficient (FERC)). This study estimates the FERC using a firm-level sample from 38 economies. Findings The authors find that industry concentration significantly enhances investors’ ability to predict future earnings. Further tests show that both product market power and intra-industry information transfer contribute to explaining the positive association between industry concentration and the FERC, with the former playing a more salient role. Finally, the authors show that a country’s effective competition law attenuates the positive impact of industry concentration on the FERC by weakening the economic impact of the two underlying channels. Originality/value This study contributes to the growing literature on the price-leading-earnings relation, industry concentration and international corporate governance.

scholarly journals Lévy copulae for financial returns

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
Ostap Okhrin
Keyword(s):  
Time Series ◽  
Stock Returns ◽  
Lévy Processes ◽  
Levy Processes ◽  
Time Series Models ◽  
Financial Returns ◽  
Levy Copula

AbstractThe paper uses Lévy processes and bivariate Lévy copulae in order to model the behavior of intraday log-returns. Based on assumptions about the form of marginal tail integrals and a Clayton Lévy copula, the model allows for capturing intraday cross-dependency. The model is applied to VaR of the portfolios constructed on stock returns as well as on cryptocurrencies. The proposed method shows fair performance compared to classical time series models.

Discussion—Innovation, Future Earnings, and Market Efficiency

2005 ◽  
Vol 20 (4) ◽  
pp. 419-422 ◽  
Author(s):  
Chandra Seethamraju
Keyword(s):  
Stock Returns ◽  
Stock Prices ◽  
Citation Impact ◽  
Earnings Forecasts ◽  
Patent Citation ◽  
Time Lags ◽  
Future Earnings ◽  
Market Participants ◽  
Technological Advances ◽  
Abnormal Stock Returns

This study considers patent citation impact as a proxy for a leading indicator of technology firms' innovation capabilities. The author examines whether patent citation impact is associated with future earnings and whether this association is appropriately reflected in stock prices and analysts' earnings forecasts of patent-rich companies. The author reports results which indicate that change of patent citation impact is positively associated with future earnings up to five years in the future, particularly in the computer, electronics, and medical equipment industries. These are industries with relatively short time lags between technological advances and profit realization. The author also reports that investors and analysts do not seem to fully incorporate the implication of enhanced innovation capabilities for future earnings into stock prices and earnings forecasts. Based on this information, the paper develops a trading strategy that generates future abnormal stock returns. In my view, this paper asks an important question. If a researcher could come up with an appropriate leading measure of innovation, then examining the reliability of that measure through its association with future benefits, and whether the implications of the measure are understood by market participants, is an interesting exercise. In my discussion, I will focus on (a) some of the issues with the patent citation index (the measure of innovation capabilities), (b) problems with the databases used to construct this measure which suggest that the results should be interpreted with caution, and (c) some additional comments on the mispricing and portfolio tests.

The Reversal of Abnormal Accruals and the Market Valuation of Earnings Surprises

2001 ◽  
Vol 76 (3) ◽  
pp. 375-404 ◽  
Author(s):  
Mark L. DeFond ◽  
Chul W. Park
Keyword(s):  
Stock Returns ◽  
Earnings Forecasts ◽  
Market Valuation ◽  
Working Capital ◽  
Future Earnings ◽  
Market Participants ◽  
Announcement Date ◽  
Earnings Surprises ◽  
Abnormal Accruals ◽  
The Difference

If the market anticipates the reversing nature of abnormal working capital accruals, then the reported magnitude of earnings surprises that contain abnormal accruals will differ from the underlying magnitude that is priced by the market. We expect the market's perception of this difference to affect the ERCs associated with earnings surprises that contain abnormal accruals. We test our predictions using an abnormal accruals measure that captures the difference between reported working capital and a proxy for the market's expectations of the level of working capital required to support current sales levels. Consistent with our hypotheses, we find higher ERCs when abnormal accruals suppress the magnitude of earnings surprises, and lower ERCs when abnormal accruals exaggerate the magnitude of earnings surprises. We also find results consistent with analysts predictably considering the reversing implications of abnormal accruals in revising future earnings forecasts. These findings are consistent with market participants anticipating the reversing implications of abnormal accruals. However, analysis of subsequent stock returns provides evidence that market participants do not fully impound the pricing implications of abnormal accruals at the earnings announcement date.

Time-Series Models in Marketing: Some Recent Developments

Marketing ZFP ◽  
2010 ◽  
Vol 32 (JRM 1) ◽  
pp. 24-29
Author(s):  
Marnik G. Dekimpe ◽  
Dominique M. Hanssens
Keyword(s):  
Time Series ◽  
Time Series Models ◽  
Recent Developments

A COMPARATIVE STUDY BETWEEN UNIVARIATE AND BIVARIATE TIME SERIES MODELS FOR CRUDE PALM OIL INDUSTRY IN PENINSULAR MALAYSIA

2020 ◽  
Vol 5 (1) ◽  
pp. 374
Author(s):  
Pauline Jin Wee Mah ◽  
Nur Nadhirah Nanyan
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Peninsular Malaysia ◽  
Time Series Models ◽  
Crude Palm Oil ◽  
Univariate Time Series ◽  
Import And Export ◽  
Bivariate Time Series

The main purpose of this study is to compare the performances of univariate and bivariate models on four time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive autoregressive (ARAR) algorithm were used for modelling and forecasting purposes. Subsequently, the dependence between any two of the four variables were checked using the residuals’ sample cross correlation functions before modelling the bivariate time series. In order to model the bivariate time series and make prediction, the transfer function models were used. The forecast accuracy criteria used to evaluate the performances of the models were the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results of the univariate time series showed that the best model for predicting the production was ARIMA  while the ARAR algorithm were the best forecast models for predicting both the import and export of crude palm oil. However, ARIMA  appeared to be the best forecast model for price based on the MAE and MAPE values while ARFIMA  emerged the best model based on the RMSE value.  When considering bivariate time series models, the production was dependent on import while the export was dependent on either price or import. The results showed that the bivariate models had better performance compared to the univariate models for production and export of crude palm oil based on the forecast accuracy criteria used.

Sign in / Sign up

Export Citation Format

Share Document