Measuring multidimensional investment opportunity sets with 10-K text

2021 ◽  
Author(s):  
Sudipta Basu ◽  
Xinjie Ma ◽  
Hoa Briscoe-Tran
Keyword(s):  
Information Technology ◽  
Fixed Effects ◽  
Predictive Power ◽  
Investment Opportunity ◽  
Corporate Policies ◽  
Related Variation ◽  
Opportunity Sets ◽  
Dependent Variables ◽  
Out Of Sample ◽  
Specific Factors

We show that firms' investment opportunity sets (IOS) are multidimensional. Analyzing Form 10-K texts, we identify 445 unique keywords that predict firms' future investments during 1995-2009 and combine them into 43 underlying factors. Industry-specific factors include BioPharma, Banking, Information Technology, Oil & Gas and Retail Stores, while more general factors include Equity Intensity, Debt Intensity, Lease, Going Concern and Acquisition. These factors form our multidimensional measures of IOS. They outperform Tobin's Q and/or industry fixed effects, in predicting future out-of-sample (2010-15) investments and related corporate policies, and even inform incrementally over lagged dependent variables. We trace the factors' improved predictive power to their multidimensional nature, which captures IOS-related variation within and between industries, and stability in IOS that allows 10-K texts to be more informative.

scholarly journals German forecasters’ narratives: How informative are German business cycle forecast reports?

2021 ◽  
Author(s):  
Karsten Müller
Keyword(s):  
Business Cycle ◽  
Fixed Effects ◽  
Predictive Power ◽  
Recursive Estimation ◽  
Regression Methods ◽  
Inflation Forecasts ◽  
Fixed Effects Panel Regression ◽  
Weak Evidence ◽  
Out Of Sample ◽  
Different Characteristics

AbstractBased on German business cycle forecast reports covering 10 German institutions for the period 1993–2017, the paper analyses the information content of German forecasters’ narratives for German business cycle forecasts. The paper applies textual analysis to convert qualitative text data into quantitative sentiment indices. First, a sentiment analysis utilizes dictionary methods and text regression methods, using recursive estimation. Next, the paper analyses the different characteristics of sentiments. In a third step, sentiment indices are used to test the efficiency of numerical forecasts. Using 12-month-ahead fixed horizon forecasts, fixed-effects panel regression results suggest some informational content of sentiment indices for growth and inflation forecasts. Finally, a forecasting exercise analyses the predictive power of sentiment indices for GDP growth and inflation. The results suggest weak evidence, at best, for in-sample and out-of-sample predictive power of the sentiment indices.

An Empirical Study of New Age Employment in Tour Operations and Allied Areas

2015 ◽  
Vol 8 (1and2) ◽  
Author(s):  
Shyju P. J. ◽  
Rinzing Lama
Keyword(s):  
Information Technology ◽  
Factor Analysis ◽  
Empirical Study ◽  
Business Model ◽  
Management Practices ◽  
Tourism Sector ◽  
Human Resource Management Practices ◽  
Specific Factors ◽  
New Generation ◽  
Micro Enterprises

In this study, the authors makes an attempt to understand the aspirations of the new generation employees in tour operation business and allied areas. It is being attempted with the presumption that the takeover of information technology seeded the concept of micro enterprises in tourism which functions with the business model of low investment and good turnover. The focus was in identifying employee specific factors of encouraging and discouraging in nature in the fast growing tourism sector, especially job attrition and the dynamics of human resource management practices. Factor Analysis, independent sample t-test, multiple regression have been used to establish various relationships. The findings of the study are considered to be relevant since it quantitatively establish the dynamics of employment in tourism in India.

The Utility of Family Life Cycle as a Theoretical and Empirical Tool

2003 ◽  
Vol 24 (2) ◽  
pp. 155-184 ◽  
Author(s):  
Carolyn A. Kapinus ◽  
Michael P. Johnson
Keyword(s):  
Life Cycle ◽  
Family Life ◽  
Predictive Power ◽  
National Sample ◽  
Family Life Cycle ◽  
Predictive Tool ◽  
The Family ◽  
Dependent Variables ◽  
Married Men ◽  
Using Data

Using data from a 1980 national sample of married men and women, the analysis examines the utility of the family life cycle concept, employing as dependent variables constructs from Johnson’s conceptualization of commitment. They argue, in disagreement with two classic critiques of the family life cycle concept, that the predictive power of family life cycle is, for many dependent variables, quite independent of age or length of marriage. Their analyses demonstrate that, when using dependent variables one would expect to be related to the presence and ages of children, family life cycle remains a useful predictive tool.

scholarly journals Forecasting Macedonian Business Cycle Turning Points Using Qual Var Model

2016 ◽  
Vol 5 (3) ◽  
pp. 61-78
Author(s):  
Magdalena Petrovska ◽  
Aneta Krstevska ◽  
Nikola Naumovski
Keyword(s):  
Business Cycle ◽  
Predictive Power ◽  
Turning Points ◽  
Probit Model ◽  
Economic Conditions ◽  
Var Model ◽  
Leading Indicators ◽  
Out Of Sample ◽  
The Business Cycle

Abstract This paper aims at assessing the usefulness of leading indicators in business cycle research and forecast. Initially we test the predictive power of the economic sentiment indicator (ESI) within a static probit model as a leading indicator, commonly perceived to be able to provide a reliable summary of the current economic conditions. We further proceed analyzing how well an extended set of indicators performs in forecasting turning points of the Macedonian business cycle by employing the Qual VAR approach of Dueker (2005). In continuation, we evaluate the quality of the selected indicators in pseudo-out-of-sample context. The results show that the use of survey-based indicators as a complement to macroeconomic data work satisfactory well in capturing the business cycle developments in Macedonia.

scholarly journals Effects of a dietary supplement on gastric ulcer number and severity in exercising horses1

2018 ◽  
Vol 2 (2) ◽  
pp. 119-124
Author(s):  
Alysia L Conner ◽  
Amanda J Davis ◽  
Cheryl A Porr
Keyword(s):  
Gastric Ulcer ◽  
Dietary Supplement ◽  
Fixed Effects ◽  
Gastric Lesion ◽  
Gastric Ulcers ◽  
Experimental Unit ◽  
Adaptation Period ◽  
Feeding Period ◽  
Gastric Lesions ◽  
Dependent Variables

Abstract This study evaluated the effect of a dietary supplement on the treatment of equine gastric ulcer syndrome (EGUS). Gastroscopy was performed on university riding horses of mixed breeds at two locations and only horses exhibiting gastric ulcers were selected to participate in this study (location A, n = 13; location B, n = 15). Gastric ulcer severity was assessed using two different methods depending on location before treatment (Pre). After gastroscopy, horses were fed the supplement in addition to their regular diet for 44 d (14-d adaptation period followed by 30-d feeding period). All horses were subjected to gastroscopy again at the end of the feeding period (Post) to evaluate changes in gastric lesions. Statistical analysis was performed using SAS. Individual horses were the experimental unit with dependent variables including severity and number of gastric ulcers. At location A, dependent variable included severity of gastric lesions with fixed effects of time (Pre and Post) and location (stall or pasture). For location B, dependent variables included severity and number of gastric lesions with fixed effects of time. Severity of gastric ulcers decreased at both locations in horses following the feeding period. Gastric lesion scores decreased from 2.2990 to 1.3760 (P = 0.0015) at location A and gastric lesion severity from 3.8000 to 2.5667 (P = 0.0322) at location B. No differences were found in gastric lesion scores at location A between horses housed in stalls or pastures (1.8750 and 1.8000; P = 0.7783). The number of gastric ulcers observed at location B were similar Pre and Post treatment (3.4667 and 3.5333; P = 0.8363). There were no changes in body condition score (P ≥ 0.2607), BW (P ≥ 0.4551), or behavior at either location. Results suggest that oral supplementation may decrease severity of gastric ulcers in horses participating in university riding programs.

scholarly journals Do Credit Ratings Take into Account the Sustainability Performance of Companies?

2018 ◽  
Vol 10 (11) ◽  
pp. 4272 ◽  
Author(s):  
Maite Cubas-Díaz ◽  
Miguel Martínez Sedano
Keyword(s):  
Fixed Effects ◽  
Credit Rating ◽  
Explanatory Variable ◽  
Credit Ratings ◽  
Investment Decisions ◽  
Probit Analysis ◽  
Important Indicator ◽  
Sustainability Performance ◽  
Dependent Variables ◽  
Consistent Performance

In the last few decades, sustainability performance measuring has become a widely-studied issue, and various measurement proposals have been put forward. However, it is also important to know whether those measures are actually being used in the real world. In this case, we take one very important indicator used by investors when they make investment decisions: the credit rating of the potential investment. We test whether credit ratings take into account the above-mentioned measures. Following the literature, we conduct a fixed-effects ordered probit analysis, using as controls the variables usually found in the related literature on credit rating analysis. The dependent variables are S&P ratings. We find that companies with higher sustainability performance tend to have higher credit ratings, though having a less consistent performance over time seems to have no effect. To check the robustness of our results, we also perform the analysis for different sectors and sub-periods. In addition, we conduct the analysis using sustainability scores provided by ASSET4 (Datastream) as an explanatory variable and using Fitch credit ratings as the explained variable.

The Future is a Moving Target: Predicting Political Instability

2019 ◽  
Vol 50 (4) ◽  
pp. 1405-1417 ◽  
Author(s):  
Drew Bowlsby ◽  
Erica Chenoweth ◽  
Cullen Hendrix ◽  
Jonathan D. Moyer
Keyword(s):  
Political Institutions ◽  
Armed Conflict ◽  
Predictive Power ◽  
Political Instability ◽  
Explanatory Power ◽  
State Level ◽  
Recent Period ◽  
Arab Uprisings ◽  
Out Of Sample ◽  
Over Time

AbstractPrevious research by Goldstone et al. (2010) generated a highly accurate predictive model of state-level political instability. Notably, this model identifies political institutions – and partial democracy with factionalism, specifically – as the most compelling factors explaining when and where instability events are likely to occur. This article reassesses the model’s explanatory power and makes three related points: (1) the model’s predictive power varies substantially over time; (2) its predictive power peaked in the period used for out-of-sample validation (1995–2004) in the original study and (3) the model performs relatively poorly in the more recent period. The authors find that this decline is not simply due to the Arab Uprisings, instability events that occurred in autocracies. Similar issues are found with attempts to predict nonviolent uprisings (Chenoweth and Ulfelder 2017) and armed conflict onset and continuation (Hegre et al. 2013). These results inform two conclusions: (1) the drivers of instability are not constant over time and (2) care must be exercised in interpreting prediction exercises as evidence in favor or dispositive of theoretical mechanisms.

Microstructure in the Machine Age

2020 ◽  
Author(s):  
David Easley ◽  
Marcos López de Prado ◽  
Maureen O’Hara ◽  
Zhibai Zhang
Keyword(s):  
Machine Learning ◽  
Market Efficiency ◽  
Predictive Power ◽  
Explanatory Power ◽  
Futures Contracts ◽  
Machine Age ◽  
Price Process ◽  
Out Of Sample ◽  
Asset Classes ◽  
Out Of Sample Prediction

Abstract Understanding modern market microstructure phenomena requires large amounts of data and advanced mathematical tools. We demonstrate how machine learning can be applied to microstructural research. We find that microstructure measures continue to provide insights into the price process in current complex markets. Some microstructure features with high explanatory power exhibit low predictive power, while others with less explanatory power have more predictive power. We find that some microstructure-based measures are useful for out-of-sample prediction of various market statistics, leading to questions about market efficiency. We also show how microstructure measures can have important cross-asset effects. Our results are derived using 87 liquid futures contracts across all asset classes.

Sign in / Sign up

Export Citation Format

Share Document