scholarly journals An approach to predict Spanish mortgage market activity using Google data

2019 ◽  
Vol 8 (4) ◽  
pp. 209
Author(s):  
Marcos González-Fernández ◽  
Carmen González-Velasco
Keyword(s):  
Prediction Accuracy ◽  
Mortgage Market ◽  
Sample Analysis ◽  
Market Activity ◽  
Out Of Sample

The aim of this paper is to use Google data to predict Spanish mortgage market activity during the period from January 2004 to January 2019. Thus, we collect monthly Google data for the keyword hipoteca, the Spanish expression for mortgage, and then, we perform a regression and an out-of-sample analysis. We find evidence that the use of Google data significantly improves prediction accuracy.

scholarly journals Modeling and Predictability of Exchange Rate Changes by the Extended Relative Nelson–Siegel Class of Models

2018 ◽  
Vol 6 (3) ◽  
pp. 68
Author(s):  
Hokuto Ishii
Keyword(s):  
Exchange Rate ◽  
Prediction Accuracy ◽  
Factor Model ◽  
Five Factor Model ◽  
Model Fitting ◽  
Extended Model ◽  
Out Of Sample ◽  
Exchange Rate Changes ◽  
Relative Spread ◽  
Future Exchange

This paper investigates the predictability of exchange rate changes by extracting the factors from the three-, four-, and five-factor model of the relative Nelson–Siegel class. Our empirical analysis shows that the relative spread factors are important for predicting future exchange rate changes, and our extended model improves the model fitting statistically. The regression model based on the three-factor relative Nelson–Siegel model is the superior model of the extended models for three-month-ahead out-of-sample predictions, and the prediction accuracy is statistically significant from the perspective of the Clark and West statistic. For 6- and 12-month-ahead predictions, although the five-factor model is superior to the other models, the prediction accuracy is not statistically significant.

Grey Prediction of CBN Grinding Process

2011 ◽  
Vol 5 (3) ◽  
pp. 420-426
Author(s):  
Neng-Hsin Chiu ◽  
◽  
Jie-Wei Lee
Keyword(s):  
Acoustic Emission ◽  
Prediction Accuracy ◽  
Surface Grinding ◽  
Machining Process ◽  
Grey Model ◽  
Grinding Process ◽  
Grey Prediction ◽  
Ae Energy ◽  
Out Of Sample

Surface grinding is a machining process with unstable quality which is usually deteriorated as the process proceeds. If grinding can be forecast to alarm before unsatisfactory, the process could be controlled better. The purpose of this paper is to construct a grey model for CBN grinding based upon acoustic emission (AE) energy extracted from the AE grinding signal to reflect ground roughness variation. A grey model from the conducted experiment was found to be well correlated with the grinding trends. The prediction accuracy, inor out- of- sample, exceeds 90%, making grey prediction suitable for prognostic monitoring of grinding.

scholarly journals Analyzing Vaccination Priority Judgments for 132 Occupations Using Word Vector Models

2021 ◽  
Author(s):  
Atsushi Ueshima ◽  
Hiroki Takikawa
Keyword(s):  
Resource Allocation ◽  
Knowledge Representation ◽  
Vector Space ◽  
Division Of Labor ◽  
Prediction Accuracy ◽  
Scarce Resource ◽  
Psychological Dimension ◽  
Out Of Sample ◽  
Out Of Sample Prediction ◽  
High Degree

Most human societies conduct a high degree of division of labor based on occupation. However, determining the occupational field that should be allocated a scarce resource such as vaccine is a topic of debate, especially considering the COVID-19 situation. Though it is crucial that we understand and anticipate people’s judgments on resource allocation prioritization, quantifying the concept of occupation is a difficult task. In this study, we investigated how well people’s judgments on vaccination prioritization for different occupations could be modeled by quantifying their knowledge representation of occupations as word vectors in a vector space. The results showed that the model that quantified occupations as word vectors indicated high out-of-sample prediction accuracy, enabling us to explore the psychological dimension underlying the participants’ judgments. These results indicated that using word vectors for modeling human judgments about everyday concepts allowed prediction of performance and understanding of judgment mechanisms.

scholarly journals Forecasting Daily Dynamic Hedge Ratios of Indian Index Future Contracts

2019 ◽  
Vol 8 (3) ◽  
pp. 485-493
Keyword(s):  
Performance Measures ◽  
Minimum Mean Square Error ◽  
Multivariate Garch ◽  
Sample Analysis ◽  
Hedge Ratio ◽  
Hedge Ratios ◽  
Forecasting Performance ◽  
Out Of Sample ◽  
Egarch Model ◽  
T Distribution

Forecasting plays a crucial role in determining the direction of future trends and in making necessary investment decisions. This research presents the forecasting performance of three multivariate GARCH models: SGARCH, EGARCH, and GJR-GARCH based on Gaussian and Student’s t-distribution. The forecasting ability of the models is evaluated on the basis of forecasting performance measures: MAE, SSE, MSE, and RMSE. This is done by examining the hedged portfolios of three indices of NSE: NIFTY50, BANKNIFTY, and NIFTYIT. Daily data from Jan 2006 to Dec 2017 is taken and forecasts are conducted using out of sample data from Jan 2016-Dec 2017. Minimum mean square error (MMSE) forecasting method is used to generate conditional variance and covariance forecasts which in turn generate hedge ratios and corresponding hedged portfolio. Minimum variance hedge ratio framework of Ederington (1979) is used for hedging. The in-sample analysis shows that SGARCH with both the distribution performed better than the other models while out-of-sample analysis provides mixed results. EGARCH model assigns the lowest hedge ratio to NIFTY50 and BANKNIFTY while SGARCH model assigns the lowest hedge ratio to NIFTYIT. Forecasting performance measures show the least value for SGARCH and EGARCH model. In future these models are able to reduce maximum risk from the spot market. The results of this research has important implications for financial decision and policy makers.

scholarly journals Investor attention and cryptocurrency: Evidence from the Bitcoin market

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246331
Author(s):  
Panpan Zhu ◽  
Xing Zhang ◽  
You Wu ◽  
Hao Zheng ◽  
Yinpeng Zhang
Keyword(s):  
Behavioral Finance ◽  
Prediction Accuracy ◽  
Predictive Ability ◽  
Realized Volatility ◽  
The Novel ◽  
Investor Attention ◽  
Linear Connections ◽  
Out Of Sample ◽  
Behavior Finance ◽  
Financial Characteristics

This paper adds to the growing literature of cryptocurrency and behavioral finance. Specifically, we investigate the relationships between the novel investor attention and financial characteristics of Bitcoin, i.e., return and realized volatility, which are the two most important characteristics of one certain asset. Our empirical results show supports in the behavior finance area and argue that investor attention is the granger cause to changes in Bitcoin market both in return and realized volatility. Moreover, we make in-depth investigations by exploring the linear and non-linear connections of investor attention on Bitcoin. The results indeed demonstrate that investor attention shows sophisticated impacts on return and realized volatility of Bitcoin. Furthermore, we conduct one basic and several long horizons out-of-sample forecasts to explore the predictive ability of investor attention. The results show that compared with the traditional historical average benchmark model in forecasting technologies, investor attention improves prediction accuracy in Bitcoin return. Finally, we build economic portfolios based on investor attention and argue that investor attention can further generate significant economic values. To sum up, investor attention is a non-negligible pricing factor for Bitcoin asset.

scholarly journals Integrating across neuroimaging modalities boosts prediction accuracy of cognitive ability

2020 ◽  
Author(s):  
Javier Rasero ◽  
Amy Isabella Sentis ◽  
Fang-Cheng Yeh ◽  
Timothy Verstynen
Keyword(s):  
Individual Differences ◽  
Cognitive Function ◽  
Cognitive Abilities ◽  
Prediction Accuracy ◽  
Predictive Power ◽  
Learning Approach ◽  
Cognitive Domains ◽  
Out Of Sample ◽  
Multi Level ◽  
Brain Measurements

AbstractVariation in cognitive ability arises from subtle differences in underlying neural architectural properties. Understanding and predicting individual variability in cognition from the differences in brain networks requires harnessing the unique variance captured by different neuroimaging modalities. Here we adopted a multi-level machine learning approach that combines diffusion, functional, and structural MRI data from the Human Connectome Project (N=1050) to provide unitary prediction models of various cognitive abilities: global cognitive function, fluid intelligence, crystallized intelligence, impulsivity, spatial orientation, verbal episodic memory and sustained attention. Out-of-sample predictions of each cognitive score were first generated using a sparsity-constrained principal component regression on individual neuroimaging modalities. These individual predictions were then aggregated and submitted to a LASSO estimator that removed redundant variability across channels. This stacked prediction led to a significant improvement in accuracy, relative to the best single modality predictions (approximately 1% to 4% boost in variance explained), across a majority of the cognitive abilities tested. Further analysis found that diffusion and brain surface properties contribute the most to the predictive power. Our findings establish a lower bound to predict individual differences in cognition using multiple neuroimaging measures of brain architecture, both structural and functional, quantify the relative predictive power of the different imaging modalities, and reveal how each modality provides unique and complementary information about individual differences in cognitive function.Author summaryCognition is a complex and interconnected process whose underlying mechanisms are still unclear. In order to unravel this question, studies usually look at one neuroimaging modality (e.g. functional MRI) and associate the observed brain properties with individual differences in cognitive performance. However, this approach is limiting because it fails to incorporate other sources of brain information and does not generalize well to new data. Here we tackled both problems by using out-of-sample testing and a multi-level learning approach that can efficiently integrate across simultaneous brain measurements. We tested this scenario by evaluating individual differences across several cognitive domains, using five measures that represent morphological, functional and structural aspects of the brain network architecture. We predicted individual cognitive differences using each brain property group separately and then stacked these predictions, forming a new matrix with as many columns as separate brain measurements, that was then fit using a regularized regression model that isolated unique information among modalities and substantially helped enhance prediction accuracy across most of the cognitive domains. This holistic approach provides a framework for capturing non-redundant variability across different imaging modalities, opening a window to easily incorporate more sources of brain information to further understand cognitive function.

scholarly journals Improving the option pricing performance of GARCH models in inefficient market

2020 ◽  
Vol 17 (2) ◽  
pp. 14-25
Author(s):  
Noureddine Lahouel ◽  
Slaheddine Hellara
Keyword(s):  
Option Pricing ◽  
Market Efficiency ◽  
Real Data ◽  
Sample Analysis ◽  
New Approach ◽  
Out Of Sample ◽  
Garch Processes ◽  
The Difference ◽  
The Stability ◽  
Standard Risk

Understanding the relation between option pricing and market efficiency is important. Indeed, emphasizing this relation generates new insights that are appropriate in practice. These insights give a better understanding of the current limitations of the option pricing and hedging methods. This article thus aims to improve the performance of the option pricing approach. To start, the relation between the option pricing methodology and the informational market efficiency was discussed. It is, therefore, useful, before proceeding to apply the standard risk-neutral approach, to check the efficiency assumption. New modified GARCH processes were used to model the dynamics of the asset returns in the option pricing framework. The new considered approaches allow describing the dynamic of returns when the market is inefficient. Using real data on CAC 40 index, the performance of different models as a function of maturity and moneyness was studied. The in-sample analysis, interested in the stability of the pricing models across time, showed that the new approach, developed under the affine GARCH process, is the most accurate. The study of the out-of-sample performance, which aims to evaluate the forecasting ability of different approaches, confirmed the results of the in-sample analysis. For the optional portfolio hedging, always the best hedging approach is that obtained under the affine GARCH model. After a regression study, it was found that the difference between theoretical and observed option values can be explained by factors, which are not taken into account in the proposed pricing formulae.

Corporate Social Irresponsibility and Credit Risk Prediction: A Machine Learning Approach

2020 ◽  
Vol 53 (4) ◽  
pp. 513-554
Author(s):  
Daniel V. Fauser ◽  
Andreas Gruener
Keyword(s):  
Machine Learning ◽  
Credit Risk ◽  
Risk Prediction ◽  
Prediction Accuracy ◽  
Explanatory Power ◽  
Machine Learning Algorithms ◽  
Out Of Sample ◽  
Corporate Social Irresponsibility ◽  
Out Of Sample Prediction ◽  
Corporate Social

This paper examines the prediction accuracy of various machine learning (ML) algorithms for firm credit risk. It marks the first attempt to leverage data on corporate social irresponsibility (CSI) to better predict credit risk in an ML context. Even though the literature on default and credit risk is vast, the potential explanatory power of CSI for firm credit risk prediction remains unexplored. Previous research has shown that CSI may jeopardize firm survival and thus potentially comes into play in predicting credit risk. We find that prediction accuracy varies considerably between algorithms, with advanced machine learning algorithms (e. g. random forests) outperforming traditional ones (e. g. linear regression). Random forest regression achieves an out-of-sample prediction accuracy of 89.75% for adjusted R2 due to the ability of capturing non-linearity and complex interaction effects in the data. We further show that including information on CSI in firm credit risk prediction does not consistently increase prediction accuracy. One possible interpretation of this result is that CSI does not (yet) seem to be systematically reflected in credit ratings, despite prior literature indicating that CSI increases credit risk. Our study contributes to improving firm credit risk predictions using a machine learning design and to exploring how CSI is reflected in credit risk ratings.

Analyst recommendations and stock price bubbles

2020 ◽  
Author(s):  
Karthik Balakrishnan ◽  
Catherine Schrand ◽  
Rahul Vashishtha
Keyword(s):  
Stock Price ◽  
Positive Association ◽  
Causal Factor ◽  
Analyst Recommendations ◽  
Positive Relation ◽  
Sample Analysis ◽  
Timely Manner ◽  
Price Bubbles ◽  
Out Of Sample ◽  
Future Return

This paper documents how analyst recommendations are related to periods of bubbles. We find a strong positive relation between the concentration in analyst buy recommendations and bubble continuation in two settings. First, we show a positive association between the concentration in buy recommendations and the tech bubble; the crash was associated with changes in buy recommendation concentration. Second, in an out-of-sample analysis of firms in multiple industries from 1994-2009, we show that analyst buy recommendation concentration predicts future return patterns that exhibit characteristics of a rational speculative bubble. While the evidence is not sufficient to conclude that analyst buy recommendations are the causal factor that perpetuates the mispricing, our findings suggest that, at a minimum, analysts do not act proactively to correct this form of mispricing in a timely manner.

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