| dc.contributor.advisor | 黃泓智 | zh_TW |
| dc.contributor.advisor | Huang, Hong-Zhi | en_US |
| dc.contributor.author (Authors) | 楊尚儒 | zh_TW |
| dc.contributor.author (Authors) | Yang, Shang-Ju | en_US |
| dc.creator (作者) | 楊尚儒 | zh_TW |
| dc.creator (作者) | Yang, Shang-Ju | en_US |
| dc.date (日期) | 2023 | en_US |
| dc.date.accessioned | 9-Mar-2023 18:49:08 (UTC+8) | - |
| dc.date.available | 9-Mar-2023 18:49:08 (UTC+8) | - |
| dc.date.issued (上傳時間) | 9-Mar-2023 18:49:08 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0109358026 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/143881 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 風險管理與保險學系 | zh_TW |
| dc.description (描述) | 109358026 | zh_TW |
| dc.description.abstract (摘要) | 本研究欲將常見的技術指標，如：移動平均線（MA）、隨機指標（KD）、相對強弱指標（RSI）、平滑異同移動平均線指標（MACD）等等，兩兩結合使用，並針對表現相對優異的MACD以及RSI技術指標做調整，以期較技術指標單獨使用時更佳的投資報酬，藉由個股股價回測以及績效分析，尋找相對適合台灣股市投資的技術指標方法。實證結果顯示，部分技術指標兩兩結合使用就可以帶來較單一技術指標更高的報酬，而針對個股RSI指數做簡單平均平滑、指數平均平滑或是進出場門檻的調整，再與其他技術指標結合使用，可以進一步提升投資績效，唯需注意部分技術指標方法存在投資標的之流動性風險，以及在空頭市場中表現欠佳的問題。 | zh_TW |
| dc.description.abstract (摘要) | The purpose of this study is trying to combine common technical indicators, such as: Moving Average (MA), Stochastic Oscillator (KD), Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), etc. Indicators are combined or adjusted in order to achieve better investment returns than they were used alone. Stock price backtesting and performance analysis of individual stocks are conducting to find technical indicators that are relatively suitable for Taiwan stock market investment. The empirical results show that the combination of two technical indicators can bring higher returns than a single technical indicator, and smoothing RSI or adjusting the entry and exit thresholds can further improve performance. However, it is necessary to pay attention to the liquidity risk of investment targets in some technical indicator methods and the problem of poor performance in the bear market. | en_US |
| dc.description.tableofcontents | 第壹章 緒論 7第貳章 文獻回顧 9第一節 傳統型技術指標文獻探討 9第二節 綜合型技術指標文獻探討 10第參章 研究方法 13第一節 研究架構 13第二節 技術指標 14第三節 技術指標之結合及調整 22第四節 績效分析指標 24第肆章 實證分析 26第一節 單一技術指標實證結果 26第二節 結合後技術指標實證結果 28第三節 調整後技術指標實證結果 34第四節 基本面篩選及空頭市場下之實證結果 47第伍章 結論與建議 54參考文獻 56附錄一：其他結合後技術指標實證結果 58附錄二：其他技術指標結合不同週期SMA_RSI績效 60附錄三：其他技術指標結合不同平滑方式RSI之績效 62附錄四：其他技術指標搭配門檻調整之平滑RSI 64 | zh_TW |
| dc.format.extent | 1883537 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0109358026 | en_US |
| dc.subject (關鍵詞) | 技術指標結合 | zh_TW |
| dc.subject (關鍵詞) | 個股股價回測 | zh_TW |
| dc.subject (關鍵詞) | 績效分析 | zh_TW |
| dc.subject (關鍵詞) | Combination of technical indicators | en_US |
| dc.subject (關鍵詞) | Backtesting of Stock price | en_US |
| dc.subject (關鍵詞) | Performance analysis | en_US |
| dc.subject (關鍵詞) | MA | en_US |
| dc.subject (關鍵詞) | KD | en_US |
| dc.subject (關鍵詞) | RSI | en_US |
| dc.subject (關鍵詞) | MACD | en_US |
| dc.title (題名) | 以技術指標建構投資策略之實證研究—以台灣個股為例 | zh_TW |
| dc.title (題名) | Empirical Study on Constructing Investment Strategies based on Technical Indicators – Taking Stocks in Taiwan for Example | en_US |
| dc.type (資料類型) | thesis | en_US |
| dc.relation.reference (參考文獻) | 1. 福永博之。最強技術指標組合：日本人氣分析師親授1+1>2的賺錢術。2019年7月。2. Appel, G.(1979). The Moving Average Convergence Divergence Method, Signalert.3. Kang, B.K. (2021). Improving MACD Technical Analysis by Optimizing Parameters and Modifying Trading Rules: Evidence from the Japanese Nikkei 225 Futures Market. Journal of Risk and Financial Management 14, 37.4. Fama, E.F. & Blume, M. (1966). Filter Rules and Stock Market Trading Profits. Journal of Business 39, Special Supplement, 226-241.5. Froot, K.A., Scharfstein, D.S. & Stein, J.C. (1992). Heard on the street : information inefficiencies in a market with short-term speculators. Journal of Finance 47, 1461-1484.6. Chan, P.M. (2018). Dynamically Adjustable Moving Average (AMA’) technical analysis indicator to forecast Asian Tigers’ futures markets. Physica A 509, 336-345.7. Jensen, M.C. & Benington, G.A. (1970). Random Walks and Technical Theories: Some Additional Evidence. Journal of Finance, 469-482.8. Murphy, J.J. (1999). Technical Analysis of the Financial Markets. New York Institute of Finance, 1-5, 24-31.9. Borowski, K. & Izabela, P.G. (2019). Optimal lengths of moving averages for the MACD oscillator for companies listed on the Warsaw Stock Exchange. Bank i Kredyt 50(5), 457-478.10. Menkhoff, L. (2010). The use of technical analysis by fund managers: International evidence. Journal of Banking & Finance 34, 2573–2586.11. Wong, W. K., Manzur, M. & Chew, B.K. (2003). How rewarding is technical analysis? Evidence from Singapore stock market. Applied Financial Economics, 13(7), 543–551.12. Nison, S. (1994). Beyond Candlesticks: New Japanese Charting Techniques Revealed, John Wiley and Sons.13. Gold, S. (2015). The Viability of Six Popular Technical Analysis Trading Rules in Determining Effective Buy and Sell Signals: MACD, AROON, RSI, SO, OBV, and ADL. Journal of Applied Financial Research, Gulfport Vol.2:8-29. | zh_TW |
