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 A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from China

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243080
Author(s):  
Yanhui Chen ◽  
Hanhui Zhao ◽  
Ziyu Li ◽  
Jinrong Lu
Keyword(s):  
Stock Market ◽  
Optimal Path ◽  
Investor Sentiment ◽  
Principal Component ◽  
Realized Volatility ◽  
Investor Attention ◽  
Proxy Variables ◽  
Dynamic Relationship ◽  
Behavior Finance ◽  
The Relationship

Investor sentiment is a research focus in behavior finance. This paper chooses five proxy variables according to China’s reality and uses a two-step principal component analysis to construct an investor sentiment index. The five proxy variables are the number of new stock accounts, turnover ratio, margin balance, net active purchasing amount, and investor attention. In the final part of this study, using the price data from the Shanghai and Shenzhen Security Exchange, this paper investigates the dynamic relationship between investor sentiment and stock market realized volatility based on the thermal optimal path. The empirical results show that when the market fluctuates severely, investor sentiment leads stock market realized volatility over one or two steps. The prediction power is also checked. The results indicate that investor sentiment indeed forecasts the realized volatility. This research supports regulators and financial institutions in taking advanced measures.

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.

scholarly journals Empirics of Korean Shipping Companies’ Default Predictions

Risks ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 159
Author(s):  
Sunghwa Park ◽  
Hyunsok Kim ◽  
Janghan Kwon ◽  
Taeil Kim
Keyword(s):  
Logit Model ◽  
Prediction Models ◽  
Predictive Ability ◽  
Financial Ratios ◽  
Probability Of Default ◽  
Shipping Industry ◽  
Market Participants ◽  
Korean Economy ◽  
Default Prediction ◽  
Financial Characteristics

In this paper, we use a logit model to predict the probability of default for Korean shipping companies. We explore numerous financial ratios to find predictors of a shipping firm’s failure and construct four default prediction models. The results suggest that a model with industry specific indicators outperforms other models in predictive ability. This finding indicates that utilizing information about unique financial characteristics of the shipping industry may enhance the performance of default prediction models. Given the importance of the shipping industry in the Korean economy, this study can benefit both policymakers and market participants.

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 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 Forecasting Volatility with Time-Varying Coefficient Regressions

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Qifeng Zhu ◽  
Miman You ◽  
Shan Wu
Keyword(s):  
Constant Coefficient ◽  
Stock Exchange ◽  
Composite Index ◽  
Predictive Ability ◽  
Varying Coefficient Models ◽  
Time Varying ◽  
Varying Coefficient ◽  
Varying Coefficients ◽  
Out Of Sample ◽  
Time Varying Coefficients

We extend the heterogeneous autoregressive- (HAR-) type models by explicitly considering the time variation of coefficients in a Bayesian framework and comprehensively comparing the performances of these time-varying coefficient models and constant coefficient models in forecasting the volatility of the Shanghai Stock Exchange Composite Index (SSEC). The empirical results suggest that time-varying coefficient models do generate more accurate out-of-sample forecasts than the corresponding constant coefficient models. By capturing and studying the time series of time-varying coefficients of the predictors, we find that the coefficients (predictive ability) of heterogeneous volatilities are negatively correlated and the leverage effect is not significant or inverse during certain periods. Portfolio exercises also demonstrate the superiority of time-varying coefficient models.

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.

Markov Breaks in Regression Models

2012 ◽  
Vol 4 (1) ◽  
Author(s):  
Aaron Smith
Keyword(s):  
Regression Models ◽  
Yield Curve ◽  
Likelihood Function ◽  
Weighted Average ◽  
Predictive Ability ◽  
Error Variance ◽  
Monte Carlo Integration ◽  
Linear Regression Models ◽  
Out Of Sample ◽  
Low Dimensional

This article develops a new Markov breaks (MB) model for forecasting and making inference in linear regression models with breaks that are stochastic in both timing and magnitude. The MB model permits an arbitrarily large number of abrupt breaks in the regression coefficients and error variance, but it maintains a low-dimensional state space, and therefore it is computationally straightforward. In particular, the likelihood function can be computed analytically using a single iterative pass through the data and thereby avoids Monte Carlo integration. The model generates forecasts and conditional coefficient predictions using a probability weighted average over regressions that include progressively more historical data. I employ the MB model to study the predictive ability of the yield curve for quarterly GDP growth. I show evidence of breaks in the predictive relationship, and the MB model outperforms competing breaks models in an out-of-sample forecasting experiment.

Home is Where You Know Your Volatility – Local Investor Sentiment and Stock Market Volatility

2018 ◽  
Vol 19 (2) ◽  
pp. 209-236 ◽  
Author(s):  
D. Schneller ◽  
S. Heiden ◽  
A. Hamid ◽  
M. Heiden
Keyword(s):  
Investor Sentiment ◽  
Realized Volatility ◽  
International Markets ◽  
Return Volatility ◽  
Time Varying ◽  
Stock Market Volatility ◽  
Time Varying Parameter ◽  
Significant Difference ◽  
Out Of Sample ◽  
Local Stock

Abstract Using a new variable to measure investor sentiment we show that the sentiment of German and European investors matters for return volatility in local stock markets. A flexible empirical similarity (ES) approach is used to emulate the dynamics of the volatility process by a time-varying parameter that is created via the similarity of realized volatility and investor sentiment. Out-of-sample results show that the ES model produces significantly better volatility forecasts than various benchmark models for DAX and EUROSTOXX. Regarding other international markets no significant difference between the forecasts can be observed.

scholarly journals Using Text Mining to Predicate Exchange Rates with Sentiment Indicators

2019 ◽  
Vol 7 (2) ◽  
pp. p60 ◽  
Author(s):  
Hassabelrasul Yusuuf ALtom Shihabeldeen
Keyword(s):  
Social Media ◽  
Exchange Rate ◽  
Text Mining ◽  
Linear Regression ◽  
Behavioral Finance ◽  
Regression Models ◽  
Model Building ◽  
User Participation ◽  
Linear Regression Models ◽  
Out Of Sample

Recent innovations in text mining facilitate the use of novel data for sentiment analysis related to financial markets, and promise new approaches to the field of behavioral finance. Traditionally, text mining has allowed a near-real time analysis of available news feeds. The recent dissemination of web 2.0 has seen a drastic increase of user participation, providing comments on websites, social networks and blogs, creating a novel source of rich and personal sentiment data potentially of value to behavioral finance. This study explores the efficacy of using novel sentiment indicators from Market Psych, which analyses social media in addition to newsfeeds to quantify various levels of individual’s emotions, as a predictor for financial time series returns of the Australian Dollar (AUD)-US Dollar (USD) exchange rate. As one of the first studies evaluating both news and social media sentiment indicators as explanatory variables for linear and nonlinear regression algorithms, our study aims to make an original contribution to behavioral finance, combining technical and behavioral aspects of model building. An empirical out-of-sample evaluation with multiple input structures compares Multivariate Linear Regression models (MLR) with multilayer perceptron (MLP) neural networks for descriptive modelling. The results indicate that sentiment indicators are explanatory for market movements of exchange rate returns, with nonlinear MLPs showing superior accuracy over linear regression models with a directional out-of-sample accuracy of 60.26% using cross validation.

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