مقاله انگلیسی رایگان در مورد احساسات شادی روزانه توییتر و پیش بینی بازدهی سهام (الزویر)
مشخصات مقاله | |
انتشار | مقاله سال ۲۰۱۷ |
تعداد صفحات مقاله انگلیسی | ۱۷ صفحه |
هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
منتشر شده در | نشریه الزویر |
نوع مقاله | ISI |
عنوان انگلیسی مقاله | Twitter’s daily happiness sentiment and the predictability of stock returns |
ترجمه عنوان مقاله | احساسات شادی روزانه توییتر و پیش بینی بازدهی سهام |
فرمت مقاله انگلیسی | |
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 |
کلمات کلیدی | احساس سرمایه گذار، خوشحالی روزانه، بازده سهام، گرنجر غیر علیت، رگرسيون Quantile |
کد محصول | E5298 |
وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
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۱٫ 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. |