Trends in statistically based quarterly cash-flow prediction models

2014 ◽  
Vol 38 (2) ◽  
pp. 145-151 ◽  
Author(s):  
Kenneth S. Lorek
Keyword(s):  
Cash Flow ◽  
Prediction Models ◽  
Flow Prediction

scholarly journals Predicting Cash Flow From Operations: Evidence On The Comparative Abilities For A Continuum Of Measures

2011 ◽  
Vol 17 (3) ◽  
Author(s):  
Charles E. Jordan ◽  
Marilyn A. Waldron
Keyword(s):  
Conceptual Framework ◽  
Cash Flow ◽  
Prediction Models ◽  
Petroleum Industry ◽  
Cash Flows ◽  
Predictor Variables ◽  
Accrual Accounting ◽  
Operating Cash Flows ◽  
Flow Prediction ◽  
Cash Basis

Prior studies have attempted to confirm or reject the FASB's assertion in its Conceptual Framework that accrual accounting measures provide better information for predicting cash flows than do cash basis measures.  However, their results proved largely inconclusive and contradictory.  The current study identifies research constructs that may be driven these inconsistent findings and makes adjustments to mitigate their effects.  Univariate cash flow prediction models are developed for companies in the petroleum industry using a continuum of predictor variables.  In predicting operating cash flows, one variable, net earnings plus depreciation and amortization, consistently achieves superior results.

Multi-Step-Ahead Quarterly Cash-Flow Prediction Models

2011 ◽  
Vol 25 (1) ◽  
pp. 71-86 ◽  
Author(s):  
Kenneth S. Lorek ◽  
G. Lee Willinger
Keyword(s):  
Time Series ◽  
Cash Flow ◽  
Prediction Models ◽  
Financial Analysis ◽  
Moving Average ◽  
Arima Model ◽  
Cash Flows ◽  
Quarterly Earnings ◽  
Flow Prediction ◽  
Accounting Accruals

SYNOPSIS: We provide new empirical evidence supportive of the Brown-Rozeff ARIMA model as a candidate univariate statistically based expectation model for multi-period-ahead projections of quarterly cash flows. It provides 1- through 20-step-ahead projections of quarterly cash flows that are significantly more accurate than those generated by the premier multivariate quarterly time-series, disaggregated-accrual regression model popularized by Lorek and Willinger (1996). We also find that both quarterly earnings and quarterly cash flow from operations are modeled by the same Brown-Rozeff ARIMA structure, although the autoregressive and seasonal moving-average parameters of the quarterly earnings model are significantly larger than those of the cash-flow prediction model. This finding is consistent with Beaver (1970) and Dechow and Dichev (2002), among others, who argue that accounting accruals induce incremental amounts of serial correlation in the quarterly earnings time series vis-a`-vis the time series of quarterly cash flows. Such findings may be of interest to analysts who wish to derive multi-step-ahead cash-flow predictions, and accounting researchers attempting to adopt a statistical proxy for the market’s expectation of quarterly cash flows. Finally, we propose a forecasting schema by which statistically based cash-flow forecasts are adjusted upwards or downwards via qualitative assessments regarding the economy, industry, and firm by analysts employing fundamental financial analysis.

scholarly journals Multidimensional Scaling-Based Data Dimension Reduction Method for Application in Short-Term Traffic Flow Prediction for Urban Road Network

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Yi Zhao ◽  
Satish V. Ukkusuri ◽  
Jian Lu
Keyword(s):  
Dimension Reduction ◽  
Traffic Flow ◽  
Reduction Method ◽  
Prediction Models ◽  
Urban Traffic ◽  
Dimension Reduction Method ◽  
Short Term ◽  
Urban Road ◽  
Traffic Flow Prediction ◽  
Flow Prediction

This study develops a multidimensional scaling- (MDS-) based data dimension reduction method. The method is applied to short-term traffic flow prediction in urban road networks. The data dimension reduction method can be divided into three steps. The first is data selection based on qualitative analysis, the second is data grouping using the MDS method, and the last is data dimension reduction based on a correlation coefficient. Backpropagation neural network (BPNN) and multiple linear regression (MLR) models are employed in four kinds of urban traffic environments to test whether the proposed method improves the prediction accuracy of traffic flow. The results show that prediction models using traffic data after dimension reduction outperform the same prediction models using other datasets. The proposed method provides an alternative to existing models for urban traffic prediction.

scholarly journals Spatiotemporal Traffic Flow Prediction with KNN and LSTM

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Xianglong Luo ◽  
Danyang Li ◽  
Yu Yang ◽  
Shengrui Zhang
Keyword(s):  
Support Vector Regression ◽  
Traffic Flow ◽  
Prediction Models ◽  
Short Term Memory ◽  
Transportation Systems ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Proposed Model

The traffic flow prediction is becoming increasingly crucial in Intelligent Transportation Systems. Accurate prediction result is the precondition of traffic guidance, management, and control. To improve the prediction accuracy, a spatiotemporal traffic flow prediction method is proposed combined with k-nearest neighbor (KNN) and long short-term memory network (LSTM), which is called KNN-LSTM model in this paper. KNN is used to select mostly related neighboring stations with the test station and capture spatial features of traffic flow. LSTM is utilized to mine temporal variability of traffic flow, and a two-layer LSTM network is applied to predict traffic flow respectively in selected stations. The final prediction results are obtained by result-level fusion with rank-exponent weighting method. The prediction performance is evaluated with real-time traffic flow data provided by the Transportation Research Data Lab (TDRL) at the University of Minnesota Duluth (UMD) Data Center. Experimental results indicate that the proposed model can achieve a better performance compared with well-known prediction models including autoregressive integrated moving average (ARIMA), support vector regression (SVR), wavelet neural network (WNN), deep belief networks combined with support vector regression (DBN-SVR), and LSTM models, and the proposed model can achieve on average 12.59% accuracy improvement.

scholarly journals Usefulness of fair valuation of biological assets for cash flow prediction

2017 ◽  
Vol 47 (2) ◽  
pp. 157-180 ◽  
Author(s):  
Josep Maria Argilés-Bosch ◽  
Meritxell Miarons ◽  
Josep Garcia-Blandon ◽  
Carmen Benavente ◽  
Diego Ravenda
Keyword(s):  
Cash Flow ◽  
Flow Prediction ◽  
Fair Valuation
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