國立虎尾科技大學 |

The Impact of Macroeconomic Factors on Stock Returns : = Using the S&P 500 Index as an Example = Hong guan jing ji yin zi dui gu piao shou yi lu de ying xiang yan jiu : yi biao zhun Puer 500 zhi shu wei li.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	The Impact of Macroeconomic Factors on Stock Returns :/
其他題名:	Using the S&P 500 Index as an Example = Hong guan jing ji yin zi dui gu piao shou yi lu de ying xiang yan jiu : yi biao zhun Puer 500 zhi shu wei li.
其他題名:	Hong guan jing ji yin zi dui gu piao shou yi lu de ying xiang yan jiu :
作者:	Guo, Yangui.
面頁冊數:	1 online resource (111 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
標題:	Finance. -
電子資源:	click for full text (PQDT)
ISBN:	9798382328195

The Impact of Macroeconomic Factors on Stock Returns : = Using the S&P 500 Index as an Example = Hong guan jing ji yin zi dui gu piao shou yi lu de ying xiang yan jiu : yi biao zhun Puer 500 zhi shu wei li.
Guo, Yangui.

The Impact of Macroeconomic Factors on Stock Returns :Using the S&P 500 Index as an Example = Hong guan jing ji yin zi dui gu piao shou yi lu de ying xiang yan jiu : yi biao zhun Puer 500 zhi shu wei li.Hong guan jing ji yin zi dui gu piao shou yi lu de ying xiang yan jiu :yi biao zhun Puer 500 zhi shu wei li - 1 online resource (111 pages)

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

Thesis (D.B.A.)--Arizona State University, 2024.

Includes bibliographical references

This article uses the S&P 500 index as an example to analyze the impact of macroeconomic factors on stock returns. By using the S&P 500 index data from 1968 to 2020 as the dependent variable, and the monthly data of 221 macroeconomic variables such as the consumer price index and the US mid-term election as the independent variable, this paper finds that: (1) a wavelet denoising method helps to capture the low-frequency and long-term fluctuations in monthly returns of the index, which can effectively remove the short-term fluctuations in returns, better reflect the macroeconomic trend, and improve the power of out-of-sample forecasting. (2) the Granger causality test may be used to pick the top 30 most significant variables, which can be incorporated into several prediction models. Among all the prediction models, the combined prediction algorithm has the best out-of-sample prediction effect. (3) investors need to consider investment practices under timing strategies. Elastic network, scaling principal component analysis, combination prediction, and other algorithms are used to select the time, and the best results are obtained based on the scaling principal component analysis algorithm and the combination prediction algorithm when the transaction fee is set to 5‰. The returns based on these two algorithms have reached 14.00% and 12.59%, their Sharpe ratios are the highest among all algorithms, reaching 0.69 and 0.62, respectively, and this result is significantly better than the historical mean model used as a measurement benchmark (with the average return of 8.14%, and a Sharpe ratio of 0.34). (4) explore investment practice under a stock-picking strategy. We use methods such as sector rotation strategy and mean-variance of sectors for stock selection, and find that the strategy returns achieved by investing in stocks using the sector rotation strategy are the best, reaching 14.30%, and the Sharpe ratio is the highest at 0.79, significantly better than the benchmark S&P 500 index (with the average return of 8.8% and a Sharpe ratio of 0.57).

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382328195Subjects--Topical Terms:

559073
Finance.
Subjects--Index Terms:

Stock returnsIndex Terms--Genre/Form:

554714
Electronic books.

The Impact of Macroeconomic Factors on Stock Returns : = Using the S&P 500 Index as an Example = Hong guan jing ji yin zi dui gu piao shou yi lu de ying xiang yan jiu : yi biao zhun Puer 500 zhi shu wei li.
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500 $a Advisor: Wu, Xu;Yan, Hong.
502 $a Thesis (D.B.A.)--Arizona State University, 2024.
504 $a Includes bibliographical references
520 $a This article uses the S&P 500 index as an example to analyze the impact of macroeconomic factors on stock returns. By using the S&P 500 index data from 1968 to 2020 as the dependent variable, and the monthly data of 221 macroeconomic variables such as the consumer price index and the US mid-term election as the independent variable, this paper finds that: (1) a wavelet denoising method helps to capture the low-frequency and long-term fluctuations in monthly returns of the index, which can effectively remove the short-term fluctuations in returns, better reflect the macroeconomic trend, and improve the power of out-of-sample forecasting. (2) the Granger causality test may be used to pick the top 30 most significant variables, which can be incorporated into several prediction models. Among all the prediction models, the combined prediction algorithm has the best out-of-sample prediction effect. (3) investors need to consider investment practices under timing strategies. Elastic network, scaling principal component analysis, combination prediction, and other algorithms are used to select the time, and the best results are obtained based on the scaling principal component analysis algorithm and the combination prediction algorithm when the transaction fee is set to 5‰. The returns based on these two algorithms have reached 14.00% and 12.59%, their Sharpe ratios are the highest among all algorithms, reaching 0.69 and 0.62, respectively, and this result is significantly better than the historical mean model used as a measurement benchmark (with the average return of 8.14%, and a Sharpe ratio of 0.34). (4) explore investment practice under a stock-picking strategy. We use methods such as sector rotation strategy and mean-variance of sectors for stock selection, and find that the strategy returns achieved by investing in stocks using the sector rotation strategy are the best, reaching 14.30%, and the Sharpe ratio is the highest at 0.79, significantly better than the benchmark S&P 500 index (with the average return of 8.8% and a Sharpe ratio of 0.57).
520 $a 本文以标普500指数为例，分析宏观经济因子对股票收益率的影响。具体而言，本文使用 1968-2020年标普 500指数数据作为因变量，使用居民消费价格指数、美国中期选举等 221个宏观经济变量的月度数据作为自变量，发现：（ 1）通过使用小波去噪方法捕捉月度收益率曲线中的低频、长周期波动，能够有效去除收益率曲线中的噪音，使其反映平缓变化的宏观经济趋势，提高样本外预测效果。（ 2）使用格兰杰因果检验筛选出每期所有自变量中最为显著的 30个变量投入不同预测模型中进行训练， 6种预测模型中，组合预测的算法样本外预测效果最好。（ 3）考虑择时策略下的投资实践。使用弹性网络、缩放主成分分析、组合预测等算法进行择时选择，在交易手续费设置为 5‰的情况下，基于缩放主成分分析算法和组合预测算法得到的结果最佳。各项指标中，这两种算法的策略收益率分别达到 14.00%和 12.59%，夏普比率在所有算法中最高，分别达到 0.69和 0.62 效果均显著优于作为衡量基准的历史均值模型（策略收益率 8.14%, 夏普比率 0.34 。（ 4）考虑选股策略下的投资实践。使用行业轮动策略、行业均值方差等方法进行股票选择，发现使用行业轮动策略进行选股投资得到的策略效果最佳。各项指标中，策略收益率达到14.30%，夏普比率在所有算法中最高，达到 0.79，效果显著优于作为衡量基准的标普500指数（策略收益率 8.8%, 夏普比率 0.57）。
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650 4 $a Statistics. $3 556824
653 $a Stock returns
653 $a Macroeconomic factors
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653 $a Granger causality test
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880 1 4 $6 245-01/$1 $a The Impact of Macroeconomic Factors on Stock Returns : $b Using the S&P 500 Index as an Example = 宏观经济因子对股票收益率的影响研究 : 以标准普尔 500指数为例.
880 3 1 $6 246-02/$1 $a 宏观经济因子对股票收益率的影响研究 : 以标准普尔 500指数为例.