This study aims to find an effective linear combination of factors in different economic cycle periods and then construct two factor timing multi-factor alpha models, one each for the expansion and contraction periods. Then, we wish to examine a further two effects, namely calendar effect and cross effect. The calendar periods are divided into the first half year and the second half year. The cross effect is the combination of the economic cycle and the calendar effect. In addition, this study puts different loadings in core and satellite descriptors, which means we wish to examine which descriptors are more important when we rebalance our portfolio weekly. The empirical results show that the Value factor is effective in expansion and the first half year, and the Size and Earnings Quality factors are effective in contraction and the second half year. Moreover, the Price Momentum and Trading Activity factors are effective most of the time. We find that the optimal weight for core descriptors is 0.3 and for satellite descriptors is 0.7. Finally, the information ratios of our models are superior to the Standard alpha model by Hsu et al. (2011) and the Market Trend-based alpha model by Wang (2011). Taking the AMCross as an example, when the tracking error is below 3%, the IR is 1.40, the active return is 3.09%, the tracking error is 2.20%, the turnover rate is 207% and the transaction costs are 1.2%.

The objective of this study is to build a complete process of quantitative stockselection model construction that combines a Multi-Factor Model and informationanalysis. Based on the quantitative stock selection model, we construct anenhanced index fund that uses the Taiwan 50 index as its benchmark. Stock prices change for a multitude of reasons, and these reasons may changeover time. In this study, we use a Multi-Factor Model and information analysis to find the relationship between stock price behavior and a factor‟s condition. Wecan use this relationship as a basis for stock selection. Moreover, the purpose of this study is to construct an enhanced index fund,hence we need to control the tracking error. We use an intuitive portfolio construction method, the original weight retention rate of the benchmark, to control tracking error. In addition, the turnover rate of a portfolio is also a significant problem as it may cause the profit of a portfolio to decreasesignificantly. In this study, we use the smoothing alpha score method to control the turnover rate of our portfolio.

本研究的目的為建立一套適用於台灣股市的主動式量化投資策略。本研究利用多因子 Alpha 模型為分析架構，試圖掌握多維度的股價影響因子，以資訊係數（Information Coefficient）、T-test of ICs、成功率（Success rate）以及 Quintile 累積報酬做因子有效性的檢定，篩選出穩定且有效解釋股價報酬的月頻率因子，再組合因子形成Alpha 股票評分，Alpha 可拆解成三部分，包括市場波動度、因子預測下一期報酬的能力以及因子的獲利能力。本論文以此評分做為股票投資權重的依據，建構一個以台灣中型 100 指數為標竿指數的投資組合。實證結果發現，此主動式量化投資策略能夠有效擊敗標竿指數，獲得平均每個月 3.7%的超額報酬。 　　本研究並嘗試以設定原始權重保留率的方法，控制追蹤誤差以降低週轉率與交易成本，實證結果發現，此方法可有效降低追蹤誤差，但隨著保留率提升，資訊比率（Information Ratio）與投資組合的超額報酬將降低。 / The objective of this study is to build an investment process of active quantitative stock selection model. In this study, we use the Alpha Multi-factor model to find a multitude of factors which are significantly relative to the stock return. The tests we conduct to select the factors that end up in the final multi-factor model are monthly Information Coefficient, T-test of ICs, success rate and quintile cumulative return. Then we examine how to optimally combine correlated factors and calculate the Alpha score for each stock for each period. Alpha is Volatility times IC times Score. Volatility is the cross-sectional volatility of the residual return. IC is the predictive power of the model. And Score are the cross-sectional scores for each stock. We utilize a simple method to construct the portfolio that uses the Alpha score to adjust the weight of component stocks in the benchmark. The empirical result reveals that this investment process successfully outperform the Taiwan Mid-Cap 100 Index benchmark. Moreover, this study tries to decrease the turnover rate and transaction costs by controlling the tracking error. We set the original weight retention rate of the benchmark to control the tracking error. The empirical result reveals that the method works. But as the retention rate rises, the Information ratio and the excess return drops.

This thesis explores the implementation of three quantitative investment strategies – the dividend yield strategy, the EV/EBITDA strategy, and the momentum strategy – within the Swedish stock market using Equal-Weighted Portfolios (EWP) and Value-Weighted Portfolios(VWP). The analysis is based on backtesting during the periods 2009 − 2022, 2001 − 2022, and 1992 − 2022, for each strategy respectively. The research aims to assess the risk-adjusted returns of these strategies and compare the performance of the EWP and the VWP. The results indicate that all the tested quantitative investment strategies beat the market. Moreover, the VWP achieve higher annual returns compared to the EWP. However, when considering risk-adjusted returns, the EWP generally demonstrate superior performance. Specifically, the EWP incorporating momentum monthly rebalancing exhibit the largest risk-adjusted returns.