Bibliographic record
Abstract
Citation (2020), "Index", Lee, C.F. and Yu, M.-T. (Ed.) Advances in Pacific Basin Business, Economics and Finance (Advances in Pacific Basin Business, Economics and Finance, Vol. 8), Emerald Publishing Limited, Bingley, pp. 237-242. https://doi.org/10.1108/S2514-465020200000008012 Publisher: Emerald Publishing Limited Copyright © 2020 Emerald Publishing Limited INDEX Abnormal return, 143 Accounting performance, 123–124 Adjusted return (AR), 135 Artificial intelligence (AI) method, 24–25 Artificial neural network (ANN) model, 28–30 artificial intelligence (AI) method, 24–25 in-sample performance, 39–44 model specification, 38 out-of-sample risk forecast, 44–49 ASEAN Economic Community (AEC), 150–151 Asset pricing, 67 Autoregressive conditional heteroscedastic (ARCH), 27–28 Autoregressive moving average, generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model artificial neural network (ANN) model, 25, 27–28, 46–49 in-sample performance, 39 root-mean-squared-error (RMSE), 39 samples, 32–33 The Basel Committee on Banking Supervision, 2019, 26 Bid–ask spreads, 3–4 Black box hypothesis, 202 Black–Scholes option pricing formula, 53 Bloomberg Barclays US Aggregate Bond Index, 31 Body mass index (BMI), 182 Bond analysis, 67 Bond momentum, 71 excess returns, 90–91 liquidity, 66, 87–88 longer holding periods, 76 rating, 73–74 subperiods, 84–86 Trading Reporting and Compliance Engine (TRACE), 75 Buy-and-hold returns (BHR), 134–135 Capital structure theories pecking order theory (POT), 151–152 trade-off theory (TOT), 151 Cash holdings, 104 Category-based thinking, 3 China stock market and Accounting Research (CSMAR), 8, 11–12 Chinese economy, 201 Christoffersen’s test, 35 Closed-form option pricing formula model setting, 55–56 moment generating function, 57–58 option pricing formula, 56–60 Component-driven regime switching (CDRS) models, 54–55, 61–62 Computable general equilibrium (CGE), 215 Constant Difference of Elasticities (CDE), 216 Constant elasticity of substitution (CES), 216 Controlling shareholders corporate governance, 107–109 firm attributes, 105–107 market condition, 107 Control system, 202 Control variables, 182, 183 Corporate bond market asset pricing, 67 bond analysis, 67 bond momentum, 71, 73–74 cross-sectional regression cests, 94–95 different return horizons, 72–75 empirical results, 69–91 excess returns, 90–91 factors loading, 92–93 Fama-French five factors, 89 Fixed Investment Securities Database (FISD), 67, 68 health maintenance organizations (HMOs), 68 The Lehman Brothers Fixed Income Database (LBFI), 67, 69 liquidity, 83, 87–88 longer holding periods, 76–81 momentum effects, 72–75, 89–91 National Association of Insurance Commissioners Database (NAIC), 67, 69 risk-adjusted momentum returns, 66 short-term momentum returns, 66 statistics, 70 subperiods, 75–86 Trading Reporting and Compliance Engine (TRACE), 67–69, 75–83 Corporate governance, 107–109 board seats, 108 cash flow right, 108 cooperative, 109 family-controlled firms, 109 professional managers, 108–109 state-controlled firms, 109 types, 108–109 Correlation matrix, 10 Cross-sectional regression cests, 94–95 Cumulative abnormal returns, 143 Cumulative average return (CAR), 135 Data snooping, 44 Discrete stochastic autoregressive volatility (DSARV) models, 54–55, 59–60 Dynamic panel data (DPD), 157–158 Economic globalization, 205–206 Equal-weighted portfolios adjusted return (AR), 135 book-to-market, 138 cumulative average return (CAR), 135 distribution, 135 holding-period returns, 137–138 long-run stock returns, 137–138, 139–141 Excess returns, 90–91 Expected shortfall (ES), 26–27 Expected tail loss (ETL), 23–24 Fama-French factor model, 6–7, 89, 143–147 Financial Services Authority (FSA), 102 Firm attributes, 105–107 capital, 105 credit rating, 106–107 leverage, 106 Firm characteristics/capital structure, 150–151 data sample, 154 descriptive statistics, 156–157 dynamic model specification, 154–155 empirical model, 157–158 empirical results and discussion, 158–159 expected impacts, 153 hypotheses, 152–153 nondebt tax shields, 153 postestimation efficiency tests, 155–156 profitability, 152 size, 152 tangibility, 153 theories, 151–152 variables, 154 Fixed Investment Securities Database (FISD), 67–68 Force field hypothesis, 203 Forecasting accuracy, 33–34 Generalized method of moments (GMM), 150 Global Trade Analysis Project (GTAP), 217 Global village model, 205 Governance variables, 11–12 Health maintenance organizations (HMOs), 68 Holding-period returns (HPRs), 134 Human capital investment, 177–178 Information components, 11 Information efficiency, 4 Information system, 202 Initial Public Offering (IPO), 163–164 initial return, 164 Malaysia, 164 offer price, 164 trading volume, 164–165 Initial returns, 164 descriptive statistics, 170 D1IRi , 168 D2PSi , 168 D3PSi , 168 hierarchical regression, 172 regression, 171 Investor opinion divergence, 6–8 Japanese seasoned equity offerings, 131–133 data and methodologies, 133–135 equal-weighted portfolios, 135–142 Fama-French three-factor, 143–147 value-weighted portfolios, 142–143 Kupiec’s test, 35, 39–41 Large closed economy model, 204 Leapfrogging strategy, 229–231 The Lehman Brothers Fixed Income Database (LBFI), 67, 69 Levenberg–Marquardt backpropogation training function, 38 Liquidity, 83, 87–88 Logit models, 119–121 Long-run performance, 132 Malaysian IPO, 164 Market black box hypothesis, 202 control system, 202 exchange activity, 202–203 force field hypothesis, 203 information system, 202 institution, 202 place and integral sum, 202–203 production coordinating mechanism, 202 transactions, 202–203 Market conditions, controlling shareholders, 107 beta, 107 share price, 107 Market ownership, 206 Market property right, 203–204 closed economy, 204 cost components, 206–209 economic significance, 210–211 execution and operation costs, 207–209 extremely simple market transaction model, 204 global village model, 205 market establishment cost, 207–209 monitoring cost, 207–209 multicountry open economy model, 205 two-country open economy model, 205 Markov switching multifractal (MSM), 55, 59, 61 Markov switching stochastic volatility (MSSV) models Black–Scholes option pricing formula, 53 closed-form option pricing formula, 55–60 component-driven regime switching (CDRS) models, 54–55, 61–62 discrete stochastic autoregressive volatility (DSARV) models, 54–55, 59–60 empirical example, 60–62 generalized autoregressive conditional heteroscedasticity (GARCH) models, 53 Markov switching multifractal (MSM), 55, 59, 61 mean-squared-error (MSE), 62 root-mean-squared-error (RMSE), 61–62 Mean Absolute Error, 33 Mean-squared-error (MSE), 62 Microstructure theory, 5 Momentum effects, 72–75, 89–91 Monash-type investment function, 216 Multicountry open economy model, 205 Multivariate regressions, 10–11 National Association of Insurance Commissioners Database (NAIC), 67, 69 Neural Network Toolbox of MathWorks, 38 Nontariff trade barriers (NTBs), 215, 217 Opinion divergence, 4–6 Option pricing formula, 56–60 Ordinary least squares (OLS), 150, 182 Oversubscription ratio/trading volume data and methodology, 166–169 definition, 164 descriptive statistics, 169–170 initial return, 169–170 Pacific-Basin Capital Market (PACAP), 134 Pecking order theory (POT), 151–152 Physical activities Asian and Western societies, 178 body mass index (BMI), 182 children and adolescents, 178 control variables, 182–183 data source, 184 data statistics, 187–189 dummy variables, 186 frequency grid, 189 gross domestic product (GDP), 181 gross national product (GNP), 181 instrumental variable (IV) approach, 179 intensity of participation, 185 Mincer’s earning function, 182 ordinary least squares (OLS), 182 participation and intensity, 179, 185, 190–192 persistent participation, 185–187, 192–195 starting salary, 190 variable and indicators, 184–187 Pooling regressions, 14 Production coordinating mechanism, 202 Rate of return (ROR), 216 Return on assets (ROA), 103–104, 111 Return on equity (ROE), 103–104, 110 Risk-adjusted momentum returns, 66 Robustness checks, 15–17 Root-mean-squared-error (RMSE), 61 Seasoned equity offerings (SEOs), 132 abnormal return, 135–137 distribution, 135, 142 holding-period returns, 135, 138 long-run stock returns, 135, 139, 141 underperformance, 133 Self-sufficiency, 229–231 SEOs. See Seasoned equity offerings (SEOs) Short-term momentum returns, 66 Small closed economy model, 204 Stock pledging accounting performance, 123–124 cash holdings, 104 controlling shareholders, 105–109 correlation matrix, 118 data availability, 102–103 determinants, 114–119 financial performance, 124–127 Financial Services Authority (FSA), 102 firm performances, 123–127 logit models, 119–121 methodology, 111–114 return on assets (ROA), 103–104 return on equity (ROE), 103–104 sample selection, 109–111 share financing, 100 tobit models, 122–123 working capital, 104 Stock price movements bear and bull markets, 12–15 bid–ask spreads, 3–4 China stock market and Accounting Research (CSMAR) database, 8 correlation matrix, 10 data, 8 governance variables, 11–12 information components, 11 information efficiency, 4 investor opinion divergence, 3, 6–8 market environments, 5 market microstructure theory, 1 measuring, 6 microstructure theory, 5 multivariate regressions, 10–11 opinion divergence, 4–6 pooling regressions, 14 proxies, 9 robustness checks, 15–17 statistics, 9 traditional asset pricing theory, 2 Stock return volatility, 3–4 Subperiods analysis, 75–83 ratings, 84–86 Taiwanese business enterprises, 215–216 Taiwan Higher Education Dataset (THED), 178–179 Taiwan’s macro economy, 219 Tobit models, 122–123 Tokyo Stock Exchange (TSE), 132, 133 Trade diversion, 215, 220 Trade-off theory (TOT), 151 Trade shocks, 217–218 Trading Reporting and Compliance Engine (TRACE), 67–68, 69, 75–83, 95 Traditional asset pricing theory, 2 Two-country open economy model, 205 US–China trade war, spillover effect closure, 217 computable general equilibrium (CGE), 215 foreign direct investment (FDI), 215–216, 224–225 market share, 226–228 nontariff trade barriers (NTBs), 215 policy implications, 229–231 recursive dynamic framework, 216–217 sectoral shifts, 220–223 Taiwan’s macro economy, 219 Taiwan total exports, 221–222 Taiwan total imports, 221, 223 trade shocks and empirical works, 217–218 trade similarity index, 226–228 US, Mexico, and Canada (USMCA) Trade Agreement, 223–224 Value at risk (VaR) artificial intelligence (AI) method, 24–25 artificial neural network (ANN), 24–25 assessments, 25–26 autoregressive moving average, generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model, 24–25 backtesting, 34–36 expected shortfall, 26–27 expected tail loss (ETL), 23–24 forecasting accuracy, 33–34 measurement approaches, 27–28 model assessment, 33–38 risk properties, 25–26 Value-weighted portfolios abnormal return (AR), 143 cumulative abnormal returns (CAR), 143 distribution, 142–143 three-factor regressions, 142–143, 145–146 and value weighted buy-and-hold, 142–144 Variance infiation factor (VIF), 157 Violation ratio, 35 Book Chapters Prelims Divergent Opinion, Trading Information, and Stock Price Co-movements Assessing Asset Tail Risk with Artificial Intelligence: The Application of Artificial Neural Network Option Pricing with Markov Switching Stochastic Volatility Models Further Evidence of Momentum in Corporate Bond Returns The Causes and Consequences of Stock Pledging by Controlling Shareholders: The Case of Taiwan Japanese SEOs Before the Lost Decade: A Revisit of the Long-Run Stock Performance Firm Characteristics and Capital Structure: Evidence from ASEAN-4 Economies Impact of Oversubscription Ratio and Trading Volume on IPO First THREE-day Initial Return Does Participating in Physical Activities Raise Starting Salaries of Female College Graduates? Evidence from an Asian Society On Market Property Right, Its Cost Components and Economic Significance The Spillover Effect of the US–China Trade War on Taiwan's Economy Index
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".