金融環境瞬息萬變，謹慎投資可以讓我們在未來換取獲取利潤。本研究分別利用財務模型、統計模型及神經網路模型以不同基準挑選資產建構投資組合，比較不同投資組合間的績效，以及投資期的長短對於績效是否有影響、動態投資策略是否有較好的績效表現。實證研究顯示三因子模型投資組合績效表現有時會超過主成分迴歸模型及神經網路模型，套索迴歸模型投資組合較不穩定，三階段模型投資組合績效表現較好。在投資期六個月時各投資組合的風險較大，在投資期二十四個月時所有投資組合幾乎都沒有下方風險，投資時間越長就幾乎能保證獲利。而靜態投資策略不考慮投資期間市場變化，動態投資策略更能掌握市場狀態為投資組合帶來更棒的效益。

The financial environment is changing rapidly, cautiously investing can allow us to earn profits in the future. This research uses financial models, statistical models and neural network models to select assets based on different benchmarks to construct the portfolios, and compare the performance of different portfolios. Also, discuss whether the investment period has any impact on performance or whether dynamic investment strategies performs better. Empirical research shows that the performance of the three-factor model portfolio sometimes outperforms the principal component regression model and neural network model. The portfolio of Lasso is unstable, and the portfolio of Ohit usually has superior performance. When the investment period is six months, each portfolio is volatile, and all the portfolios have small downside risks in twenty-four months. The longer the investment period, the more profitable it is almost guaranteed. While static investment strategies do not pay attention on the market during the investment period, dynamic investment strategies can better catch the opportunity on the market and bring more benefits to the portfolio.

Andrecut, M. (2013). Portfolio optimization in r. Papers, arXiv.org.
Asness, C., Frazzini, A., and Pedersen, L. (2012). Leverage aversion and risk
parity. Financial Analysts Journal, 68.
Barber, B. M. and Odean, T. (2000). Trading is hazardous to your wealth: The
common stock investment performance of individual investors. The Journal of
Finance, 55(2):773–806.
Brandt, M. W. and Santa-Clara, P. (2006). Dynamic portfolio selection by
augmenting the asset space. The Journal of Finance, 61(5):2187–2217.
Brinson, G. P., Hood, L. R., and Beebower, G. L. (1986). Determinants of portfolio
performance. Financial Analysts Journal, 42(4):39–44.
Carhart, M. M. (1997). On persistence in mutual fund performance. The Journal
of Finance, 52(1):57–82.
Fama, E. F. and French, K. R. (1993). Common risk factors in the returns on
stocks and bonds. Journal of Financial Economics, 33(1):3–56.
Grossman, S. J. (1995). Dynamic asset allocation and the informational efficiency
of markets. The Journal of Finance, 50(3):773–787.
Hotelling, H. (1933). Analysis of a complex of statistical variables into principal
components. Journal of Educational Psychology, 24:498–520.
Ing, C.-K. and Lai, T. L. (2011). A stepwise regression method and consistent
model selection for high-dimensional sparse linear models. Statistica Sinica,
21(4):1473–1513.
James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to
Statistical Learning: with Applications in R. Springer.
Kolari, J., Moorman, T., and Sorescu, S. (2007). Foreign exchange risk and the
cross-section of stock returns. SSRN Electronic Journal.
Ledoit, O. and Wolf, M. (2004). Honey, i shrunk the sample covariance matrix.
The Journal of Portfolio Management, 30(4):110–119.
Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1):77–91.
Partovi, M. H. and Caputo, M. (2004). Principal Portfolios: Recasting the Efficient
Frontier. Economics Bulletin, 7(3):1–10.
Pearson, K. (1901). LIII. On lines and planes of closest fit to systems of points in
space.
Plaxco, L. M. and Arnott, R. D. (2002). Rebalancing a global policy benchmark.
The Journal of Portfolio Management, 28(2):9–22.
Ross, S. A. (1976). The arbitrage theory of capital asset pricing. Journal of
Economic Theory, 13(3):341–360.
Shumway, R. and Stoffer, D. (2011). Time Series Analysis and Its Applications
With R Examples, volume 9.
Tay, F. E. and Cao, L. (2001). Application of support vector machines in financial
time series forecasting. Omega, 29(4):309–317.
Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal
of the Royal Statistical Society (Series B), 58:267–288.
Welch, I. and Goyal, A. (2008). A comprehensive look at the empirical performance
of equity premium prediction. The Review of Financial Studies, 21(4):1455–
1508.
Yen, Y.-M. (2016). Sparse Weighted-Norm Minimum Variance Portfolios. Review
of Finance, 20(3):1259–1287.
Yuan, M. and Lin, Y. (2006). Model selection and estimation in regression with
grouped variables. Journal of the Royal Statistical Society Series B, 68:49–67.
Zou, H. (2006). The adaptive lasso and its oracle properties. Journal of the
American Statistical Association, 101(476):1418–1429.