投資一直是現代社會關注的議題，常見投資市場包含了銀行定存、外幣買賣、儲蓄險、基金、債劵、虛擬貨幣及股票，因為投資理財資訊越來越容易取得，使得更多人們透過理財提早規劃自己退休生活，讓自己達到財富自由，其中股票更是投資者投入最普遍的標的之一，大眾常在財經節目中聽取建議，找尋投資目標，但在下單的時間點，往往已經錯失投資的黃金機會，而導致血本無歸或是套牢在股海裡。
近年來隨著科技的進步，市面上開發出人工智慧新穎的投資工具，提供投資人使用，但往往建議投資人投資的股票，有時會因為證劵公司的私利，而提供投資者不是那麼公正的投資標的；故本研究利用公司股價(開盤價、最高價、最低價及收盤價)、基本面、技術面及籌碼面作為輸入變數，並以機器學習方法建立股市漲跌預測模型，如K-近鄰演算法 (k-nearest neighbors, kNN)、決策樹 (decision tree, DT)、支援向量機 (support vector machine, SVM)、隨機梯度下降法 (Stochastic gradient descent, SGD)、隨機森林 (Random Forest, RF)、人工神經網路 (Artificial Neural Network, ANN)、單純貝氏分類器 (Navie Bayes, NB)、邏輯式迴歸 (Logistic Regression, LGR)及AdaBoost (Adaptive Boosting)等工具，實驗結果整體表現以單純貝氏分類器最佳。

Investment has always been a topic of concern in modern society. Common investment markets include bank deposits, foreign currency trading, savings insurance, funds, bonds, virtual currencies, and stocks. Because investment and financial information becomes more and more accessible, many financial management tools have been developed. With the use of these tools, people can plan their retirement life and realize the freedom of wealth eariler. Investing in stocks is the most common for many investors. People often listen to financial suggestions and tips via audiovisual programs. However, at the time of placing an order, they often miss the critical time for investment, which leads to loss of money or being stuck in the stock market.
In recent years, with the advancement of science and technology, new investment tools with artificial intelligence techniques have been developed for investors to use, but stocks that investors are often recommended to invest in are sometimes not so fair to investors because of the private interests of securities companies. Therefore, this research uses the company’s stock price (e.g., opening price, highest price, lowest price and closing price), fundamental analysis, technical analysis, and chip analysis as input variables. The sample of this study uses the Taiwan Economic Journal Database. A number of machine learning methods have been used to build stock price prediction models, including K-nearest neighbor (kNN), decision tree (DT), support vector machine (SVM), stochastic gradient descent (SGD), random forest (RF), artificial neural network (ANN), navie Bayes (NB), logistic regression (LGR), adaptive boosting (AdaBoost). The best results of the experimental is navie Bayes have the great prediction.

Barak, S., Arjmand, A., & Ortobelli, S. (2017). Fusion of multiple diverse predictors in stock market. Information Fusion, 36, 90-102.
Basak, S., Kar, S., Saha, S., Khaidem, L., & Dey, S. R. (2019). Predicting the direction of stock market prices using tree-based classifiers. The North American Journal of Economics and Finance, 47, 552-567.
Beyaz, E., Tekiner, F., Zeng, X.-j., & Keane, J. (2018). Comparing technical and fundamental indicators in stock price forecasting. Paper presented at the 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), Exeter, United Kingdom.
Chen, M.-Y. (2012). Visualization and dynamic evaluation model of corporate financial structure with self-organizing map and support vector regression. Applied Soft Computing, 12(8), 2274-2288.
Chen, Y.-J., & Chen, Y.-M. (2013). A fundamental analysis-based method for stock market forecasting. Paper presented at the 2013 Fourth International Conference on Intelligent Control and Information Processing (ICICIP), Beijing, China.
Chen, Y., & Hao, Y. (2017). A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction. Expert Systems with Applications, 80, 340-355.
Chen, Y., & Hao, Y. (2018). Integrating principle component analysis and weighted support vector machine for stock trading signals prediction. Neurocomputing, 321, 381-402.
Fu, X., Du, J., Guo, Y., Liu, M., Dong, T., & Duan, X. (2018). A Machine Learning Framework for Stock Selection. arXiv preprint arXiv.
Galindo, G., & Rengifo, E. (2016). Is Quality Investing Feasible in Frontier Markets Based on Publicly Available Financial Information? Handbook of Frontier Markets (pp. 245-267). London, UK： Elsevier.
Gordon, M. J. (1959). Dividends, Earnings, and Stock Prices. The Review of Economics and Statistics, 41(2), 99–105. https：//doi.org/10.2307/1927792.
Henrique, B. M., Sobreiro, V. A., & Kimura, H. (2018). Stock price prediction using support vector regression on daily and up to the minute prices. The Journal of finance and data science, 4(3), 183-201.
Khalid Alkhatib, Hassan Najadat, Ismail Hmeidi, & Mohammed K. Ali Shatnawi, (2013). Stock Price Prediction Using K-Nearest Neighbor (kNN) Algorithm. International Journal of Business, Humanities and Technology, Vol. 3, No. 3, (March, 2013), pp. 32-44.
Koesrindartoto, D. P., Aaron, A., Yusgiantoro, I., Dharma, W. A., & Arroisi, A. (2020). Who moves the stock market in an emerging country–Institutional or retail investors? Research in International Business and Finance, 51, 101061.
Kumar, I., Dogra, K., Utreja, C., & Yadav, P. (2018). A comparative study of supervised machine learning algorithms for stock market trend prediction. Paper presented at the 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, India.
Malkiel, B. G., & Fama, E. F. (1970). Efficient capital markets： A review of theory and empirical work. The journal of Finance, 25(2), 383-417.
Namdari, A., & Li, Z. S. (2018). Integrating Fundamental and Technical Analysis of Stock Market through Multi-layer Perceptron. Paper presented at the 2018 IEEE Technology and Engineering Management Conference (TEMSCON), Evanston, IL, USA.
Park, K., Fang, Z., & Ha, Y. H. (2019). Stock and bond returns correlation in Korea： Local versus global risk during crisis periods. Journal of Asian Economics, 65, 101136.
Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Systems with Applications, 42(1), 259-268.
Shen, K.-Y., & Tzeng, G.-H. (2015). Combined soft computing model for value stock selection based on fundamental analysis. Applied Soft Computing, 37, 142-155.
Tan, Z., Yan, Z., & Zhu, G. (2019). Stock selection with random forest： An exploitation of excess return in the Chinese stock market. Heliyon, 5(8), e02310.
Tsai, L.-J., Shu, P.-G., & Chiang, S.-J. (2019). Foreign investors’ trading behavior and market conditions： Evidence from Taiwan. Journal of Multinational Financial Management, 100591.
Wafi, A. S., Hassan, H., & Mabrouk, A. (2015). Fundamental analysis vs technical analysis in the Egyptian stock exchange–Empirical study. International Journal of Business and Management Study–IJBMS, 2(2).
Weng, B., Ahmed, M. A., & Megahed, F. M. (2017). Stock market one-day ahead movement prediction using disparate data sources. Expert Systems with Applications, 79, 153-163.
Zhang, R., Lin, Z.-a., Chen, S., Lin, Z., & Liang, X. (2018). Multi-factor Stock Selection Model Based on Kernel Support Vector Machine. Journal of Mathematics Research, 10(5), 9-18.
Zhang, Y., Zeng, Q., Ma, F., & Shi, B. (2019). Forecasting stock returns： Do less powerful predictors help? Economic Modelling, 78, 32-39.
Zhou, F., Zhang, Q., Sornette, D., & Jiang, L. (2019). Cascading logistic regression onto gradient boosted decision trees for forecasting and trading stock indices. Applied Soft Computing, 84, 105747.
Zhong, X., & Enke, D. (2017). Forecasting daily stock market return using dimensionality reduction. Expert Systems with Applications, 67, 126-139.