Data for: Impact of consumer confidence on the expected returns of the Tokyo Stock Exchange: A comparative analysis of consumption and production-based asset pricing models
收藏资源简介：
Using all stocks listed in the Tokyo Stock Exchange and macroeconomic data for Japan, the dataset comprises the following series: 1. Monthly returns for 25 size-book-to-market equity portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database) 2. Monthly returns for 20 momentum portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database) 3. Monthly returns for 25 price-to-cash flow-dividend yield portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database) 4. Fama and French three-factors (RM, SMB and HML), following the Fama and French (1993) methodology. (Raw data source: Datastream database) 5. Fama and French five-factors (RM, SMB, HML, RMW, and CMA), following the Fama and French (2015) methodology for all factors, except for RMW, which is determined using the return on assets as sorting variable, as in Hou, Xue and Zhang (2014). (Raw data source: Datastream database) 6. Private final consumption expenditure, in national currency and constant prices, non-seasonally adjusted, for Japan. (Raw data source: OECD) 7. Consumer Confidence Index (CCI) for Japan. (Raw data source: OECD) 8. Three-month interest rate of the Treasury Bill for Japan. (Raw data source: OECD) 9. Gross Domestic Product (GDP) for Japan. (Raw data source: OECD) 10. Consumer Price Index (CPI) growth rate for Japan. (Raw data source: OECD) We have produced all return series using the following data from Datastream: (i) total return index (RI series), (ii) market value (MV series), (iii) market-to-book equity (PTBV series), (iv) total assets (WC02999 series), (v) return on equity (WC08301 series), (vi) price-to-cash flow ratio (PC series), and (vii) dividend yield (DY series). We have used the generic rules suggested by Griffin, Kelly, & Nardari (2010) for excluding non-common equity securities from Datastream data. We also exclude stocks with less than twelve observations in the period from July 1992 to June 2018. Accordingly, our sample comprises a total number of 5,312 stocks. REFERENCES: Fama, E. F. and French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3–56. Fama, E. F. and French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116, 1–22. Griffin, J. M., Kelly, P., and Nardari, F. (2010). Do market efficiency measures yield correct inferences? A comparison of developed and emerging markets. Review of Financial Studies, 23, 3225–3277. Hou K, Xue C, Zhang L. (2014). Digesting anomalies: An investment approach. Review of Financial Studies, 28, 650-705.
本数据集涵盖东京证券交易所上市的全部股票及日本宏观经济数据，包含以下序列： 1. 遵循法玛-弗伦奇（Fama and French）1993年研究方法构建的25组市值-账面市值比股票投资组合的月度收益率。（原始数据来源：Datastream数据库） 2. 遵循法玛-弗伦奇1993年研究方法构建的20组动量股票投资组合的月度收益率。（原始数据来源：Datastream数据库） 3. 遵循法玛-弗伦奇1993年研究方法构建的25组市现率-股息率股票投资组合的月度收益率。（原始数据来源：Datastream数据库） 4. 法玛-弗伦奇三因子（RM、SMB与HML），采用法玛-弗伦奇1993年研究方法构建。（原始数据来源：Datastream数据库） 5. 法玛-弗伦奇五因子（RM、SMB、HML、RMW与CMA），除RMW因子外均采用法玛-弗伦奇2015年研究方法构建；其中RMW因子以资产收益率作为分组变量，沿用侯、薛与张（Hou, Xue and Zhang）2014年的研究设定。（原始数据来源：Datastream数据库） 6. 日本以本币计价、经不变价格调整且未经季节性调整的私人最终消费支出。（原始数据来源：OECD） 7. 日本消费者信心指数（Consumer Confidence Index, CCI）。（原始数据来源：OECD） 8. 日本3个月期国库券利率。（原始数据来源：OECD） 9. 日本国内生产总值（Gross Domestic Product, GDP）。（原始数据来源：OECD） 10. 日本消费者价格指数（Consumer Price Index, CPI）增长率。（原始数据来源：OECD） 本数据集所有收益率序列均基于Datastream提供的以下数据计算得到：(i) 总收益指数（RI系列）、(ii) 市值（MV系列）、(iii) 市值账面比（PTBV系列）、(iv) 总资产（WC02999系列）、(v) 净资产收益率（WC08301系列）、(vi) 市现率（PC系列）以及(vii) 股息率（DY系列）。本研究采用格里芬、凯利与纳尔达里（Griffin, Kelly, & Nardari）2010年提出的通用规则，从Datastream数据中剔除非普通股证券。同时，我们剔除了1992年7月至2018年6月期间观测值少于12个的股票。最终样本共包含5312只股票。 参考文献： 1. 法玛, E. F. 与弗伦奇, K. R. (1993). 股票与债券收益中的共同风险因子. 《金融经济学杂志》, 33, 3–56. 2. 法玛, E. F. 与弗伦奇, K. R. (2015). 五因子资产定价模型. 《金融经济学杂志》, 116, 1–22. 3. 格里芬, J. M., 凯利, P., 与纳尔达里, F. (2010). 市场效率测度能否得出正确推论？发达市场与新兴市场的比较. 《金融研究评论》, 23, 3225–3277. 4. 侯, K., 薛, C., 与张, L. (2014). 解读异象：基于投资视角. 《金融研究评论》, 28, 650–705.
