Forecasting incentive to self-select to forecast, and their

Forecasting is certain assumptions supported the management’s expertise, knowledge, and judgment. These estimates are projected into the coming months or years.

Since any error within the assumptions can end in an identical or exaggerated error in forecasting, the technique of sensitivity analysis is employed that assigns a spread of values to the unsure factors Relying mainly on information from the past and present and analysis of trends. “The forecasting is examines the links between management forecast accuracy and earnings management, inside information, and motives for issuing a forecast. Our results provide empirical evidence of a link between managers’ private information and their forecast accuracy. In addition, we find evidence supporting the links between forecast accuracy and both self-selection to forecast and discretionary accruals. However, the evidence varies across type of forecast (good or bad news) and whether the forecast is an initial forecast or the continuance of previous forecasting behavior. Overall, managers’ forecast accuracy is linked to a combination of their inside information regarding performance, their incentive to self-select to forecast, and their ability to manage earnings.” (Guo, 2012). Forecasting methods fall into two major categories: Quantitative can be applied when two conditions are satisfied numerical information about the past is available, and it is reasonable to assume that some aspects of the past patterns will continue into the future.

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And qualitative methods are used when one or both of the above conditions does not hold. They are also used to adjust quantitative forecasts, taking account of information that was not able to be incorporated into the formal statistical model. These are not purely guesswork there are well-developed structured approaches to obtaining good judgmental forecasts. “This study attempts to determine the degree of improvement in accuracy of each forecasting method tested when seasonally adjusted data is employed. This study also seeks to identify the most accurate forecasting method of the forecasting methods used in this study: naïve, moving average, simple exponential smoothing, and linear regression.

Accuracy is measured using Mean Squared Error, Mean Absolute Percentage Error, and Mean Percentage Error. Outperforms the other five methods when raw data is used, while Moving Average method, when used with seasonally adjusted data, is the most accurate forecasting technique. Seasonally adjusted data is found to greatly improve forecasting accuracy in most of the methods. The findings of this study indicate that seasonally adjusted data is more effective in forecasting customer counts in the university foodservice operations than raw data.” (Ryu and Sanchez, 2008). “Combining forecasts within individual methods and across different methods can reduce forecast errors by as much as 50%.

Forecasts errors from currently used methods can be reduced by increasing their compliance with the principles of conservatism (Golden Rule of Forecasting) and simplicity. Clients and other interested parties can use the checklists to determine whether forecasts were derived using evidence-based procedures and can, therefore, be trusted for making decisions. Scientists can use the checklists to devise tests of the predictive validity of their findings.” (Armstrong and Green, 2018).” Inaccurate forecasts are the single most common problem that every company faces.

Nowadays due to the rise of the technology there are many events or areas that can be predicted such as seasonality, average relationships, and average cyclical patterns, emerging technological trends and their influence and many other factors. But on the other hand because future is something unknown there are always situations that are very difficult to predict such as competitive actions or reactions, sales of new products, changes in trends, and changes in relationships or attitudes”. (Makridakis and Wheelwright, 1989). “When carrying out market demand forecasts, one often confronts with the problem of the inappropriate selection of a forecast method. It should be noted that in every actual forecast situation methods have their advantages and disadvantages, hence, it is important to define and analyses forecast method selection criteria”. (Pilinkiene, 2008). “All business enterprises are characterized by risk and have to work within the ups and downs of the industry. The risk depends on the future happenings and forecasting provides help to overcome the problem of uncertainties.”