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This study aims to investigate the motivation of financial analysts issuing forecasts on weekends and the impact of such behavior on forecast accuracy and analysts’ careers.

Logistic regression and ordinary least squares models with Huber–White standard errors were used in this study.

This paper first documented the emerging trends of the weekend forecasts after 2000. Longitudinal data from 2002 to 2011 validated that analysts’ conscientious timing of information release in line with their workload and confidence level gives more accurate forecasts. Further, given the same accuracy, analysts exhibiting diffident behaviors (analysts who are predicted to work on weekdays but in fact work on weekends) are not fired or demoted by brokerage houses, but those exhibiting inactive behaviors (analysts who are predicted to work on weekends but did not do so) are more likely to be dismissed or demoted by brokerage houses, indicating that brokerage houses are aware of the negative effect of both behaviors, but treat them differently.

Weekend versus weekday proxies for an analyst’s timing of information release consider only one of many timing options. Other timing proxies, the nature and the composition of the information release of analysts are not examined in this study.

For practitioners, the results indicate that depending on the alignment, capital market can predict analysts’ future forecast accuracy, and hence, respond accordingly. For example, in addition to analyst forecast level or change, investors could pay attention to when the information is released to the market and possible reasons behind the choice of timing. Investors can thus better assess the forecast accuracy of one specific forecast and respond with the right action. Furthermore, analysts can better project their own forecast accuracy and career potential by assessing to what extent their forecasts are released conscientiously.

This study examines analysts’ forecast behavior, but generate some insights on linking the analysts and investors in the capital market.

This study is the author’s original work.

Several research papers related to electricity price forecasting have been reported in the leading journals in last 20 years. The purpose of this paper is to present a comprehensive survey and comparison of these techniques.

The present article provides an overview of the statistical short‐term price forecasting (STPF) models. The basic theory of these models, their further classification and their suitability to STPF has been discussed. Quantitative evaluation of the performance of these models in the framework of accuracy achieved and computation time taken has been performed. Some important observations of the literature survey and key issues regarding STPF methodologies are analyzed.

It has been observed that price forecasting accuracy of the reported models in day‐ahead markets is better as compared to that in real time markets. From a comparative analysis perspective, there is no hard evidence of out‐performance of one model over all other models on a consistent basis for a very long period. In some of the studies, linear models like dynamic regression and transfer function have shown superior performance as compared to non‐linear models like artificial neural networks (ANNs). On the other hand, recent variations in ANNs by employing wavelet transformation, fuzzy logic and genetic algorithm have shown considerable improvement in forecasting accuracy. However more complex models need further comparative analysis.

This paper is intended to supplement the recent survey papers, in which the researchers have restricted the scope to a bibliographical survey. Whereas, in this work, after providing detailed classification and chronological evolution of the STPF techniques, a comparative summary of various price‐forecasting techniques, across different electricity markets, is presented.

Study conditions of empirical publications on time series modeling and forecasting of electricity prices vary widely, making it difficult to generalize results. The key purpose of the present study is to offer a comparison of different model types and modeling conditions regarding their forecasting performance.

The authors analyze the forecasting performance of AR (autoregressive), MA (moving average), ARMA (autoregressive moving average) and GARCH (generalized autoregressive moving average) models with and without the explanatory variables, that is, power consumption and power generation from wind and solar. Additionally, the authors vary the detailed model specifications (choice of lag-terms) and transformations (using differenced time series or log-prices) of data and, thereby, obtain individual results from various perspectives. All analyses are conducted on rolling calibrating and testing time horizons between 2010 and 2014 on the German/Austrian electricity spot market.

The main result is that the best forecasts are generated by ARMAX models after spike preprocessing and differencing the data.

The present study extends the existing literature on electricity price forecasting by conducting a comprehensive analysis of the forecasting performance of different time series models under varying market conditions. The results of this study, in general, support the decision-making of electricity spot price modelers or forecasting tools regarding the choice of data transformation, segmentation and the specific model selection.

This study aims to predict hotel demand through text analysis by investigating keyword series to increase demand predictions’ precision. To do so, this paper presents a framework for modeling hotel demand that incorporates machine learning techniques.

The empirical forecasting is conducted by introducing a segmented machine learning approach of leveraging hierarchical clustering tied to machine learning and deep learning techniques. These features allow the model to yield more precise estimates. This study evaluates an extensive range of social media–derived words with the most significant probability of gradually establishing an understanding of an optimal outcome. Analyzes were performed on a major hotel chain in an urban market setting within the USA.

The findings indicate that while traditional methods, being the naïve approach and ARIMA models, struggled with forecasting accuracy, segmented boosting methods (XGBoost) leveraging social media predict hotel occupancy with greater precision for all examined time horizons. Additionally, the segmented learning approach improved the forecasts’ stability and robustness while mitigating common overfitting issues within a highly dimensional data set.

Incorporating social media into a segmented learning framework can augment the current generation of forecasting methods’ accuracy. Moreover, the segmented learning approach mitigates the negative effects of market shifts (e.g. COVID-19) that can reduce in-production forecasts’ life-cycles. The ability to be more robust to market deviations will allow hospitality firms to minimize development time.

The results are expected to generate insights by providing revenue managers with an instrument for predicting demand.

The purpose of this paper is to analyze the relationship between new product diffusion and consumer internet search patterns using big data and to investigate whether such data can be used in forecasting new product diffusion.

This research proposes a new product diffusion model based on the Bass diffusion model by incorporating consumer internet search behavior. Actual data from search engine queries and new vehicle sales for each vehicle class and region are used to estimate the proposed model. Statistical analyses are used to interpret the estimated results, and the prediction performance of the proposed method is compared with other methods to validate the usefulness of data for internet search engine queries in forecasting new product diffusion.

The estimated coefficients of the proposed model provide a clear interpretation of the relationship between new product diffusion and internet search volume. In 83.62 percent of 218 cases, analyzing the internet search pattern data are significant to explain new product diffusion and that internet search volume helps to predict new product diffusion. Therefore, marketing that seeks to increase internet search volume could positively affect vehicle sales. In addition, the demand forecasting performance of the proposed diffusion model is superior to those of other models for both long-term and short-term predictions.

As search queries have only been available since 2004, comparisons with data from earlier years are not possible. The proposed model can be extended using other big data from additional sources.

This research directly demonstrates the relationship between new product diffusion and consumer internet search pattern and investigates whether internet search queries can be used to forecast new product diffusion by product type and region. Based on the estimated results, increasing internet search volume could positively affect vehicle sales across product types and regions. Because the proposed model had the best prediction power compared with the other considered models for all cases with large margins, it can be successfully utilized in forecasting demand for new products.

The purpose of this paper is to examine whether analysts’ cash flow forecasts improve the profitability of their stock recommendations and whether the positive effect of cash flow forecasts on analysts’ stock recommendation performance varies with firms’ earnings quality.

To test the authors’ predictions, they identify a sample of 161,673 stock recommendations with contemporaneous earnings forecasts and/or cash flow forecasts and regress market-adjusted stock returns on a binary variable that proxies for the issuance of cash flow forecasts while controlling for contemporaneous earnings forecast accuracy, earnings quality, analysts’ forecast experience and capability and certain firm characteristics. The authors’ test results are robust to alternative measures of recommendation profitability, earnings quality and the use of recommendation revisions instead of recommendation levels.

The authors find that when analysts issue cash flow forecasts concurrently with earnings forecasts, their stock recommendations lead to higher profitability than when they only issue earnings forecasts, after controlling for analysts’ forecast capability. Moreover, the authors document that the contemporaneous positive relationship between cash flow forecasts and recommendations profitability is stronger for firms with low earnings quality than for firms with high earnings quality. The findings suggest that cash flow forecasts issued by analysts in response to market demand likely play a more important role in firm valuation than cash flow forecasts issued by analysts mainly because of supply-side considerations.

Future research could build on these findings to conduct further investigation on the alternative incentives for analysts’ forecasts of sales growth and long-term growth rates.

These findings may also help investors to better assess the quality of analysts’ research outputs and to identify superior stock recommendations.

This study provides insight into the role of cash flow forecasts in firm valuation and adds fresh evidence to the debate on the usefulness of cash flow forecasts. It extends the stream of research on the characteristics of analyst forecasts and increases our knowledge about the role of analysts in the financial market.

Forecasting.

The case is intended for Postgraduate level management students.

The purpose of this case study is to explain various forecasting techniques, their applicability and the importance of forecasting to the students. This case also explains the management situations where the application of one technique may not be sufficient, thereby explaining the importance of simultaneous usage of qualitative and quantitative techniques for making crucial decisions. The case is focused on the district of Etah of the state of Uttar Pradesh in India. The real-life situation of elections in this district of an Indian state has been taken to explain the critical nature of forecasting accuracy in a management situation where the manager has only one chance to execute his project. Discussion in this case is limited to explaining various techniques available for forecasting and their applications and does not provide a solution to a management problem.

The students are expected to understand various forecasting methods and the managerial situations where these can be applied. The case also explains situations where it becomes extremely important to have fairly accurate estimates of future requirements and the application of one technique may not be sufficient, thereby explaining the importance of simultaneous usage of qualitative and quantitative techniques for making crucial decisions.

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CSS 9: Operations and Logistics.