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# 数学代写|金融数学代写Financial Mathematics代考|MATH3090 Aligned Investor Sentiment

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## 数学代写|金融数学代写Financial Mathematics代考|Aligned Investor Sentiment

In the above set-up, the construction of the sentiment scores (Equation 7.5) or FEARS (Equation 7.2) is done first and then it is related to-for instance-the returns via (7.6) subsequently. Huang, Jiang, Tu and Zhou (2015) [207] combine the two steps, together arguing the linear combination in (7.5) is constructed better in referencing to the stock characteristics and not on its own. They demonstrate that the new combination of predictors based on the PLS method described in Section $3.2$ has much greater predictive power for the aggregate stock market and its predictability is both statistically and economically significant. Because it is a supervised learning procedure, one should expect it to fare better.

Social Media/Twitter Data: Over the past years, significant development has been made in sentiment tracking technologies that can extract indicators of public mood from social media such as blogs and Twitter feeds. Although each tweet is of limited number of characters, the aggregate of tweets may provide an accurate representation of public sentiment. This has led to the development of real-time sentiment tracking tools (Opinion Finder, GPOMS (Google Profile of Mood Status), etc.) to track the public mood and relating it to economic indicators. Crowd-sourced data generated by social network sites such as Twitter/StockTwits is being increasingly used by quantitative researchers to generate trading signals. The wisdom of the crowd surprisingly appears to outperform the wisdom of the experts in a variety of instances. For a recent application relating sentiment data to earnings announcement, see Liew, Guo and Zhang (2017) [243]. We now illustrate a simple trading algorithm using the iSentium data.
iSentium expertise lies in providing market sentiment indicators. Market-related texts from Twitter are processed through Natural Language Processing techniques and the contents are assigned sentiment scores in the range of $-30$ to 30 with positive score indicating an optimistic view and negative score, a pessimistic view. For a specific keyword the data contains the following information: Time when the tweet is recorded, sentiment Score, total number of tweets since Jan 1, 2012, whether it is a retweet, whether the author has a finance-related bio, number of followers of the author, number of tweets posted by the author, and average number of retweets for each of the author’s tweets, measuring the author’s impact.

## 数学代写|金融数学代写Financial Mathematics代考|Bloomberg News and Social Sentiment Data

Bloomberg News and Social Sentiment Data: The news agency applies statistical machine-learning techniques to process the textual information related to equities and quantifies the sentiment both at the (news) story-level and at the equity-level. The scores indicate at the story-level if the sentiment is positive, negative, or neutral with a confidence level; these are aggregated to provide the company-level sentiment. The computation is based on both news and Twitter feeds with a rolling window of halfan-hour. The predictive strength is tested out using the performance of the equity at a daily level as well as through changes in the stock price during the day. The approach is similar to cross-sectional momentum strategy that is outlined in Chapter 5. It can be shown that the stocks in the top decile of the day’s sentiment scores tend to do better during that day.

Sentiment can also be used to predict the nature of earnings reports. Event driven strategies are still a significant part of trading strategies and it can be shown that sentiment scores prior to earnings releases can augment these strategies. The intraday strategy is based on using the arrival of information during an interval of pre-defined duration and making trading decisions based on the sentiment scores and their confidence. Generally traditional news combined with a twitter feed can generate stronger signals. A methodology used to relate sentiment scores whether from traditional news or from Twitter is to first construct an index,
$$I_t=\ln \left(\frac{1+\left|B V_t\right|}{1+\left|B E_t\right|}\right)$$
that adjusts for the baseline activities, here $B V_t$ denotes the number of bullish tweets and $B E_t$ denotes the number of bearish tweets. Defining the intraday return, $r_t=$ $\ln \left(P_t^{\text {close }}\right)-\ln \left(P_t^{\text {open }}\right)$, where $P_t$ refers to the stock price on a given time unit. Then forming, $y_t=\left(I_t^N, I_t^{T W}, r_t\right)^{\prime}$, the VAR model that was discussed in Chapter 3 is used to identify the lead-lag relationships; here $I_t^N$ is the index for the news sentiment, such as Google search volume for relevant keywords and $I_t^{T W}$ refers to the index based on Twitter data. The VAR model provides a broader framework to consider other information such as market volume, market volatility, etc., that can be easily incorporated.

## 数学代写|金融数学代写Financial Mathematics代考|Bloomberg News and Social Sentiment Data

$$I_t=\ln \left(\frac{1+\left|B V_t\right|}{1+\left|B E_t\right|}\right)$$

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。