Amzn stock price的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列股價、配息、目標價等股票新聞資訊

應用多類別支持向量機做限價委託簿之價格趨勢預測

為了解決Amzn stock price的問題，作者宋晉德 這樣論述：

我們的論文主要有兩個主題：機器學習和限價單（LOB）。在第一個主題中，我們專注於機器學習，尤其是支持向量機（SVM）。在第二個主題中，LOB 數據集來自 Lobster學術研究數據集。該數據集在 2012 年 6 月 21 日包含納斯達克股票市場數據，包括AAPL、GOOG、AMZN、INTC和MSFT。順便說一下，我們的研究使用AAPL、GOOG和AMZN股票。LOB數據集是免費和公開。 我們改善了A.N. Kercheval和Y. Zhang的方法，其用於在LOB數據中捕獲特徵。我們在bid-ask spread crossing趨勢預測方面的表現，優於A.N. Kerch

eval和Y.Zhang的方法。此外，我們的研究，還與J.Han等人的方法進行比較，我們建立了C和Sigma的準確度地圖，並提出一個開放的想法，即AAPL和GOOG的特徵，具有相同的複雜性。

政治人物，推特與金融市場： 來自川普推特的證據

為了解決Amzn stock price的問題，作者孔美玲 這樣論述：

The use of Twitter as a key political communication tool has become synonymous with U.S. President Donald Trump’s regime. However, Trump’s tweets can also tend to be unabashedly critical of companies, states, and other political figures. Whether or not these negative tweets have an impact on financ

ial markets is debatable. This paper uses the synthetic control method (SCM) to examine the effects of Trump’s negative trade- and business-related tweets on financial markets, particularly stock prices and exchange rates. Three publicly traded U.S. companies (Boeing, Amazon, Harley-Davidson) and th

ree currencies (Euro, Canadian dollar, Mexican peso) were chosen, while 1-2 tweets were collected for each treatment unit. To create the synthetic control for each treatment unit, extensive control unit data was also collected. Then, for each treatment unit, two synthetic control models were created

, with one model containing all outcome lags and all covariates whilst the other contained all outcome lags and some covariates. We found that for each treatment unit, the two models were similar, indicating that the results were robust. Overall, we found that results varied depending on the “target

” of Trump’s tweets, with the causal effect being most significant for Amazon and Mexico, likely due to the fact that traders or investors may react differently to Trump’s tweets and may base their decisions on certain company- or country-specific characteristics or features, such as type of industr

y, trade ties, and geographical proximity, among others.