scholarly journals FINANCIAL VARIABLES AND THE OUT-OF-SAMPLE FORECASTABILITY OF THE GROWTH RATE OF INDIAN INDUSTRIAL PRODUCTION

2014 ◽  
Vol 19 (Supplement_1) ◽  
pp. S83-S99 ◽  
Author(s):  
Rangan Gupta ◽  
Yuxiang Ye ◽  
Christopher M. Sako
Keyword(s):  
Growth Rate ◽  
Industrial Production ◽  
Predictive Ability ◽  
Share Price ◽  
Real Effective Exchange Rate ◽  
Dividend Yield ◽  
Term Spread ◽  
Financial Variables ◽  
Lending Rate ◽  
Out Of Sample

In this paper, we consider the forecasting power, both in- and out-of-sample, of 11 financial variables with respect to the growth rate of Indian industrial production over the monthly out-ofsample period of 2005:4–2011:4, using an in-sample of 1994:1–2005:3. The financial variables used are: M0, M1, M2, M3, lending rate, 3-month Treasury bill rate, term spread, real effective exchange rate, real stock prices, dividend yield and non-food credit growth. We observe that that, at times, in-sample and out-of-sample predictive ability of the financial variables tend to coincide. We find relatively strong evidence of out-of-sample predictability for at least one of the horizons for M0, M1, M2, M3, the lending rate and real share price growth rate. The term-spread and dividend yield are added to the list when weaker versions of the out-of-sample test statistics are considered as well. Given that we consider a large number of financial variables, when we checked the significant results by accounting for data mining across the 11 financial variables, majority of these results ceases to be significant, with only M0, M1 and M2 retaining some of its predictive ability.

scholarly journals Forecasting Capacity of ARIMA Models; A Study on Croatian Industrial Production and its Sub-sectors

2017 ◽  
Vol 20 (1) ◽  
pp. 81-99 ◽  
Author(s):  
Daniel Tomić ◽  
Saša Stjepanović
Keyword(s):  
Industrial Production ◽  
Moving Average ◽  
Predictive Ability ◽  
Predictive Performance ◽  
Arima Models ◽  
Important Indicator ◽  
Out Of Sample ◽  
Short And Long Term ◽  
Data Evolution

Abstract As one of the most important indicator for monitoring the production in industry as well as for directing investment decisions, industrial production plays important role within growth perspectives. Not only does the composition and/or fluctuation of the goods produced indicate the course of economic activity but it also reflects the changes in cyclical development of the economy thereby providing opportunity to macro-manage with early signs of (short-term) turning-points and (long-term) trend variations. In this paper, we compare univariate autoregressive integrated moving average (ARIMA) models of the Croatian industrial production and its subsectors in order to evaluate their forecasting features within short and long-term data evolution. The aim of this study is not to forecast industrial production but to analyze the out-of-sample predictive performance of ARIMA models on aggregated and disaggregated level inside different forecasting horizons. Our results suggest that ARIMA models do perform very well over the whole rage of the prediction horizons. It is mainly because univariate models often improve the predictive ability of their single component over the short horizons. In that manner ARIMA modelling could be used at least as a benchmark for more complex forecasting methods in predicting the movements of industrial production in Croatia.

scholarly journals A Set of State–Space Models at a High Disaggregation Level to Forecast Italian Industrial Production

J ◽  
2019 ◽  
Vol 2 (4) ◽  
pp. 508-560
Author(s):  
Riccardo Corradini
Keyword(s):  
State Space ◽  
Industrial Production ◽  
Forecast Error ◽  
Moving Average ◽  
Predictive Ability ◽  
State Space Models ◽  
Vector Autoregressive ◽  
Autoregressive Moving Average Model ◽  
New Strategy ◽  
Moving Average Model

Normally, econometric models that forecast the Italian Industrial Production Index do not exploit information already available at time t + 1 for their own main industry groupings. The new strategy proposed here uses state–space models and aggregates the estimates to obtain improved results. The performance of disaggregated models is compared at the same time with a popular benchmark model, a univariate model tailored on the whole index, with persistent not formally registered holidays, a vector autoregressive moving average model exploiting all information published on the web for main industry groupings. Tests for superior predictive ability confirm the supremacy of the aggregated forecasts over three steps horizon using absolute forecast error and quadratic forecast error as a loss function. The datasets are available online.

scholarly journals Determinants of Share Price of Nepalese Commercial Banks

2016 ◽  
pp. 187-198 ◽  
Author(s):  
Yuga Raj Bhattarai
Keyword(s):  
Commercial Banks ◽  
Stock Exchange ◽  
Positive Association ◽  
Share Price ◽  
Annual Reports ◽  
Inverse Association ◽  
Dividend Yield ◽  
Significant Positive Association ◽  
Price Earnings Ratio ◽  
Economic Journal

This study examines the determinants of share price of commercial banks listed on the Nepal Stock Exchange Limited over the period of 2006 to 2014. Data were sourced from the annual reports of the sampled banks and analyzed using regression model. The results revealed that earning per share and price- earnings ratios have the significant positive association with share price while dividend yield showed the significant inverse association with share price. The major conclusion of the study is that dividend yield, earning per share and price-earnings ratio are the most influencing factors in determining share price in Nepalese commercial banks. Economic Journal of Development Issues Vol. 17 & 18 No. 1-2 (2014) Combined Issue,Page: 187-198

scholarly journals Business Distress Prediction Using Bayesian Logistic Model for Indian Firms

Risks ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 113 ◽  
Author(s):  
Arvind Shrivastava ◽  
Kuldeep Kumar ◽  
Nitin Kumar
Keyword(s):  
Bayesian Modeling ◽  
Logistic Model ◽  
Predictive Ability ◽  
Emerging Economy ◽  
Predictive Capability ◽  
Corporate Sector ◽  
Distress Prediction ◽  
Out Of Sample ◽  
Distressed Firms ◽  
Corporate Distress

The objective of the study is to perform corporate distress prediction for an emerging economy, such as India, where bankruptcy details of firms are not available. Exhaustive panel dataset extracted from Capital IQ has been employed for the purpose. Foremost, the study contributes by devising novel framework to capture incipient signs of distress for Indian firms by employing a combination of firm specific parameters. The strategy not only enables enlarging the sample of distressed firms but also enables to obtain robust results. The analysis applies both standard Logistic and Bayesian modeling to predict distressed firms in Indian corporate sector. Thereby, a comparison of predictive ability of the two approaches has been carried out. Both in-sample and out of sample evaluation reveal a consistently better predictive capability employing Bayesian methodology. The study provides useful structure to indicate the early signals of failure in Indian corporate sector that is otherwise limited in literature.

scholarly journals Predicting Stock Return of UAE Listed Companies Using Financial Ratios

2019 ◽  
Vol 8 (2) ◽  
pp. 214
Author(s):  
Arindam Banerjee
Keyword(s):  
Stock Returns ◽  
Stock Return ◽  
Share Price ◽  
Financial Ratios ◽  
P Value ◽  
Dividend Yield ◽  
Value Analysis ◽  
Return On Equity ◽  
Equity Ratio ◽  
The Impact

Over the past few decades, numerous research across the globe has been conducted to examine the impact of firm performance on its stock return. The findings of these studies have been varied. In spite of the long standing research in this area, several attempt towards exploring this relationship has led to limited success owing largely to the existence of volatility across different stock markets. The variance in the volatility in these markets make it extremely difficult to obtain a uniform measure. A volatile stock market makes it difficult for the accounting and financial variables to accurately predict the stock returns (Feris & Erin, 2018).  The primary aim of this paper is aimed to investigate whether financial ratios can be used as a predictor of stock returns in the context of United Arab Emirates (UAE). The sample of the study includes thirty companies from the Dubai Financial Market (DFM) and Abu Dhabi stock exchange (ADX). Data is collected for the period of 2017. This research comprises of five independent variables namely, Earning Per Share ratio (EPS), Price Earning ratio (PE), Return on Equity ratio (ROE), Dividend Yield ratio (DY) and Debt Equity ratio (DE) and stock return is taken as the dependent variable. The study examines which among the given ratios can better predict stock returns both in the short run and the long run. The analysis is based on the regression analysis and correlation matrix. The results of correlation test revealed less multicollinearity between the variables and the regression results showed that Dividend Yield and the Return on Equity are statistically significant to predict the stock returns. However, Earning Per Share, Price Earning and Debt Equity could not predict the stock returns and thus can be safely considered as statistically insignificant. The t-stats test and p-value analysis were key indicators for arriving at the conclusion. The study can significantly benefit investors who can examine closely the dividend yield and return on equity while selecting an optimal portfolio. 

scholarly journals Portfolio Investment Flows, GDP, and Investment in Brazil

2016 ◽  
Vol 8 (12) ◽  
pp. 1
Author(s):  
Roberto Meurer
Keyword(s):  
Exchange Rate ◽  
Correlation Coefficients ◽  
Strong Relationship ◽  
Production Costs ◽  
Real Effective Exchange Rate ◽  
Portfolio Investment ◽  
The Real ◽  
Financial Variables ◽  
Effective Exchange ◽  
Effective Exchange Rate

Foreign portfolio investment (FPI) flows have grown substantially in recent decades, following changes in the international financial system. In Brazil, FPI represented 66% of foreign direct investment between 1995 and 2009, which makes it meaningful to analyze these flows. In this paper, the relationships between FPI flows to Brazil, GDP, investment, and financial variables from 1995 to 2009 are analyzed, employing quarterly data and applying descriptive statistics, correlation coefficients, and Granger causality tests. Results show a positive relationship between flows, GDP, and investment. Relationships between flows and financial variables show a strong relationship between FPI and the real effective exchange rate, which could be one of the channels through which the flows are related to real variables by means of changes in relative domestic and foreign production costs. Expectations about future behavior of the economy seem to be an important explanation for the relationship between flows and the real variables. Because FPI is volatile and this volatility relates to real variables through the real effective exchange rate and the interest rate, there is a case to be made for the implementation of capital controls.

Purges and Production: Soviet Economic Growth, 1928–1940

1975 ◽  
Vol 35 (3) ◽  
pp. 567-590 ◽  
Author(s):  
Barbaba G. Katz
Keyword(s):  
Growth Rate ◽  
Industrial Production ◽  
Alternative Hypothesis ◽  
Second World War ◽  
Constant Elasticity ◽  
Constant Elasticity Of Substitution ◽  
Sequence Of Events ◽  
Massive Scale ◽  
The Impact ◽  
Second World

Did preparations for the Second World War account for the precipitous drop in the growth rate of Soviet industrial production from 10–12 percent per annum in the period 1928–1937 to only 2–3 percent per annum in the period 1937–1940? According to some who study the Soviet economy the answer is “yes.” This view has been succinctly expressed by Stanley Cohn: “After 1937, the rising spectre of Hitler forced the Soviet leadership to shift resources into armaments on a massive scale. As a result, the growth rate fell drastically to 3.6 percent per year between 1937 and 1940.” Such a sequence of events, however, has never been empirically demonstrated. The purpose of this paper is to investigate formally the validity of this explanation, via aggregate production functions, particularly of the CES (constant elasticity of substitution) variety, as well as to explore an alternative hypothesis, espoused, among others, by Naum Jasny, Alec Nove and Warren Nutter. This hypothesis stresses a domestic factor as the major contributor to the disruption in industrial production: namely, the impact of Stalin's terror in the form of chaos-producing political purges.

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