B2 中高級 美國腔 53 分類 收藏
Can tweets about the stock market be ignored as random noise, generated by
self-proclaimed trading gurus?
A study by a team of researchers at Rotterdam School of Management, Erasmus University
now shows that tweets contain useful information that can be used
to predict stock market developments in the short and long term.
This discovery may eventually help investors make better decisions.
Back to around 2014, The Associated Press Twitter account got hacked
and a fake tweet sent out, to say, well, Barack Obama was injured.
As a result of this fake tweet the stock market went down for 1% for Dow Jones index.
How much is that? More than 100 billion US dollars got swept out.
The focal point of this study is actually to find out whether
there is additional information we can extract out of social media
in order to better predict stock market performance.
Perhaps we can extract information that goes
above and beyond what has been captured within the public news.
At the very beginning we collected about 21 weeks of tweet data and extracted all the
information in all the tweets that mentioned a top S&P 100 firm.
We had to determine the sentiment being expressed in those tweets:
positive, neutral and negative, indicating that some investor
wants to buy, keep or sell stocks that they hold for a given company.
What we demonstrated is, indeed, based on that Twitter information, with the value extracted out of
this tweet information, expressed in sentiment, you can make smarter and
better trading strategies and earn excess returns.
Even when we take into account the transaction costs, as well as the fixed costs for running this exercise,
we can see that there's still excess returns, compared to the market performance.
The implication goes way beyond just being in financial industry.
A lot of what we do and what we say, both in online and offline environment,
has been digitised. We can learn from this digitised human behavior
how individuals make their decisions, and also firms can learn from
this to make better decisions that can target and also personalise
their services and product at the individual level
in order to achieve better performance.


從推特大數據預測股票市場 (Predicting the stock market with big-data analysis of tweets)

53 分類 收藏
jenny 發佈於 2018 年 1 月 8 日    jenny 翻譯    Samuel 審核
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