احساسات شادی روزانه توییتر و پیش بینی بازدهی سهام / Twitter’s daily happiness sentiment and the predictability of stock returns

احساسات شادی روزانه توییتر و پیش بینی بازدهی سهام Twitter’s daily happiness sentiment and the predictability of stock returns

  • نوع فایل : کتاب
  • زبان : انگلیسی
  • ناشر : Elsevier
  • چاپ و سال / کشور: 2018

توضیحات

رشته های مرتبط اقتصاد
گرایش های مرتبط اقتصاد مالی
مجله اسناد تحقیقات مالی – Finance Research Letters
دانشگاه School of Economics and Management – Fuzhou university – P R China

منتشر شده در نشریه الزویر
کلمات کلیدی احساس سرمایه گذار، خوشحالی روزانه، بازده سهام، گرنجر غیر علیت، رگرسیون Quantile

Description

1. Introduction Stock market is one of the most important parts of financial market nowadays. Stock market prediction has attracted much attention from both of academia and business. One of an important question is whether investor sentiment predicts stock returns. Rational risk-based asset pricing models argue that prices reflect the discounted value of expected future cash flows, even though some investors are not rational, their irrationalities are offset by arbitrageurs quickly. Instead, behavioral finance theory suggests the presence of noise traders in the stock market with correlated behavior and limits on arbitrage as conditions that can lead investor sentiment to influence asset prices (Shleifer and Summers, 1990; Hughen and McDonald, 2005; Baker and Wurgler, 2006). The notable work of De Long et al., (1990) models the influence of noise trading on equilibrium prices. Baker and Wurgler (2007) indicate that investor sentiment does have predictive power with respect to equity returns. Several theoretical studies offer models establishing the nexus between investor sentiment and asset prices (Black, 1986; De Long et al., 1990). Investor sentiment predictive content in relation to the future market movements might act as an invaluable tool for the market participants in forming successful trading strategies (Baker and Wurgler, 2007). After that, a growing body of research has examined on the relationship between investor sentiment and asset prices (e.g., Schmeling, 2009; Dergiades, 2012; Kim and Kim, 2014; Huang et al., 2014; Siganos et al., 2014; Zhang et al., 2016). To empirically investigate this issue, various investor sentiment proxies, including the stock market-based proxies, the survey-based proxies, and the news and social media content-based proxies, have been employed.
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