A semiparametric stochastic input distance frontier model with application to the Indonesian banking industry

This paper proposes a semiparametric smooth-varying coefficient input distance frontier model with multiple outputs and multiple inputs, panel data, and determinants of technical inefficiency for the Indonesian banking industry during the period 2000 to 2015. The technology parameters are unknown functions of a set of environmental factors that shift the input distance frontier non-neutrally. The computationally simple constraint weighted bootstrapping method is employed to impose the regularity constraints on the distance function. As a by-product, total factor productivity (TFP) growth is estimated and decomposed into technical change, scale component, and efficiency change. The distance elasticities, marginal effects of the environmental factors on the distance elasticities, temporal behavior of technical efficiency, and also TFP growth and its components are investigated.

     

Impact of R&D expenditures,rainfall and temperature variations in agricultural productivity: empirical evidence from Bangladesh

ABSTRACT

This study aims to investigate dynamic relationships between research and development (R&D) expenditure, climate change (measured by annual rainfall and temperature variations), human capital (proxied by literacy) and total factor productivity (TFP) growth in Bangladesh agriculture. Pesaran’s Pooled Mean Group (PMG) estimator is used to a unique panel data of 17 regions of Bangladesh covering a 61-year period (1948–2008). In addition, the panel vector autoregression (PVAR) model is also applied to trace the responsiveness of TFP from a shock to R&D, extension services, and literacy rate. Results reveal that R&D has an insignificant impact on TFP in the short-run, while it has a significant positive impact in the long-run. The contributions of climate variables (i.e., rainfall and temperature variations) are highly significant and negative in the long run. The literacy rate is found to have a significant positive impact on TFP as expected. These results suggest that agricultural R&D investment and human capital could play an important role to ameliorate the adverse effects of climate change in the agricultural sector of Bangladesh.     

Cross‐market correlations and transmission of information

We investigate characteristics of cross‐market correlations using daily data from U.S. stock, bond, money, and currency futures markets using a new multivariate GARCH model that permits direct hypothesis testing on conditional correlations. We find evidence that arrival of information in a market affects subsequent cross‐market conditional correlations in the sample period following the stock market crash of 1987, but there is little evidence of such a relationship in the precrash period. In the postcrash period, we also find evidence that the prime rate of interest affects daily correlations between futures returns. Furthermore, we find that conditional correlations between currency futures and other markets decline steeply a few months before the crash and revert to normal dynamics after the crash. © 2002 Wiley Periodicals, Inc. Jrl Fut Mark 22:1059–1082, 2002     

Forecasting profitability and earnings: a study of the UK market (1982–2000)

An approach recently developed by Fama and French (2000 Fama, EF and French, KR. 2000. Forecasting profitability and earnings. The Journal of Business, 73: 161–75.  ) is applied to the study of whether UK company profitability is mean-reverting. A sample of roughly 987 firms per year for a period from 1982–2000 is used, drawn from Datastream. In a simple partial adjustment model convergence towards the mean at a rate of about 25% per year is found. The results are very similar in direction to those of Fama and French (2000 Fama, EF and French, KR. 2000. Forecasting profitability and earnings. The Journal of Business, 73: 161–75.  ) but the results do not display significant non-linearities. The change in profitability appears to be more strongly influenced by dividends in the UK.     

Can machine learning approaches predict corporate bankruptcy? Evidence from a qualitative experimental design

Bankruptcy prediction has received a growing interest in corporate finance and risk management recently. Although numerous studies in the literature have dealt with various statistical and artificial intelligence classifiers, their performance in credit risk forecasting needs to be further scrutinized compared to other methods. In the spirit of Chen, Härdle and Moro (2011, Quantitative Finance), we design an empirical study to assess the effectiveness of various machine learning topologies trained with big data approaches and qualitative, rather than quantitative, information as input variables. The experimental results from a ten-fold cross-validation methodology demonstrate that a generalized regression neural topology yields an accuracy measurement of 99.96%, a sensitivity measure of 99.91% and specificity of 100%. Indeed, this specific model outperformed multi-layer back-propagation networks, probabilistic neural networks, radial basis functions and regression trees, as well as other advanced classifiers. The utilization of advanced nonlinear classifiers based on big data methodologies and machine learning training generates outperforming results compared to traditional methods for bankruptcy forecasting and risk measurement.     

The effects of venture capital syndicate diversity on earnings management and performance of IPOs in the US and UK: An institutional perspective

This study examines the extent to which principal–principal agency conflicts within venture capital (VC) syndicates lead to additional principal–agent conflicts in IPO firms in two institutional contexts. Using a matched sample of 274 VC-backed IPOs in the US and the UK, it shows that the diversity of a VC syndicate increases pre-IPO discretionary current accruals, used as a proxy for earnings management, but the impact of such diversity is higher in the US. There is also evidence of higher underpricing and lower aftermarket performance in firms with higher earnings management and VC diversity, and these negative performance effects are also higher in the US. Our findings indicate that local and informal institutions have a significant effect on multiple agency conflicts in IPO firms and performance outcomes.     

A predictive system integrating intrinsic mode functions,artificial neural networks,and genetic algorithms for forecasting S&P500 intra‐day data

There is an abundant literature on the design of intelligent systems to forecast stock market indices. In general, the existing stock market price forecasting approaches can achieve good results. The goal of our study is to develop an effective intelligent predictive system to improve the forecasting accuracy. Therefore, our proposed predictive system integrates adaptive filtering, artificial neural networks (ANNs), and evolutionary optimization. Specifically, it is based on the empirical mode decomposition (EMD), which is a useful adaptive signal‐processing technique, and ANNs, which are powerful adaptive intelligent systems suitable for noisy data learning and prediction, such as stock market intra‐day data. Our system hybridizes intrinsic mode functions (IMFs) obtained from EMD and ANNs optimized by genetic algorithms (GAs) for the analysis and forecasting of S&P500 intra‐day price data. For comparison purposes, the performance of the EMD‐GA‐ANN presented is compared with that of a GA‐ANN trained with a wavelet transform's (WT's) resulting approximation and details coefficients, and a GA‐general regression neural network (GRNN) trained with price historical data. The mean absolute deviation, mean absolute error, and root‐mean‐squared errors show evidence of the superiority of EMD‐GA‐ANN over WT‐GA‐ANN and GA‐GRNN. In addition, it outperformed existing predictive systems tested on the same data set. Furthermore, our hybrid predictive system is relatively easy to implement and not highly time‐consuming to run. Furthermore, it was found that the Daubechies wavelet showed quite a higher prediction accuracy than the Haar wavelet. Moreover, prediction errors decrease with the level of decomposition.