Sustainability has become a pressing global issue due to environmental and social challenges caused by human activity which has led to a rise in sustainable investing, including ESG investing. Research on financial performance and sustainable investing have not only showed mixed results, but they are also generally conducted in greater markets such as the US, Europe, and Asia-pacific markets. Currently, there is a lack of research on performance of sustainable investment strategies in the Nordic Region. The purpose of this paper is to examine the performance of portfolios constructed with an ESG investment strategy, which involves creating two portfolios consisting of top and bottom ESG scored companies. The portfolios are measured against each other and a market index benchmark, in the context of various theories, including the efficient market hypothesis, adaptive market hypothesis, shareholder theory, and stakeholder theory. The theoretical framework includes asset-pricing models and portfolio theory. A quantitative study with a deductive approach is utilized to construct the portfolios, focusing on mid-cap companies in the Nordics with data collected from Refinitiv Eikon’s database. The portfolio construction process yields financial metrics such as returns, volatility, and risk-adjusted returns. To test for outperformance in returns, the unpaired t-test is utilized. The Carhart four-factor model is also used to explain variations in returns related to risk factors and investigate the presence of positive and significant abnormal returns. The results demonstrate that the bottom ESG portfolio exhibits superior portfolio characteristics compared to the top ESG portfolio and the index benchmark, including annual returns and risk-adjusted returns. Furthermore, this study identifies significant positive abnormal returns when using the Carhart four-factor model, and evidence of outperformance in mean cumulative returns for the bottom ESG portfolio relative to the top ESG portfolio and index benchmark. On the other hand, the performance of the top ESG portfolio and index benchmark is inconclusive, with mixed results across different performance metrics and years. Although the top ESG portfolio outperforms in two out of three years in terms of annual returns, volatility, and risk-adjusted returns, no evidence of positive abnormal returns is found. Meanwhile, the index benchmark demonstrates evidence of outperformance in terms of cumulative returns. Overall, the findings suggest that the bottom ESG investment strategy is more effective in generating superior performance, while the mixed results of the top ESG portfolio make it difficult to draw definitive conclusions about its performance characteristics.

Investing has become increasingly popular among individuals in recent years,this has led to multiple investing strategies formalizing. One of them being factorinvesting, a strategy where investors search for companies with certain strongfirm specific financial metrics through screening. Many researchers try to findwhich these metrics are, and which of them has an effect on the cross-sections ofstock returns. This study examines the relationship between the three metrics,earnings-to-price, dividend yield, debt over equity and the European stock marketbetween January 2010 to December 2022. This is done by using the two-stageregression model suggested by Fama and Macbeth (1973). Our results show thatthere is an anomaly in the European stock market and that there is a firmcharacteristic risk associated with these metrics. This suggests that when lookingat individual firms, investors are willing to pay a premium for the metrics studiedin this paper and it is therefore important to take them into account whenscreening for individual companies. As the previous research is mainly focusedon the American stock market and emerging markets in Europe, our thesis fills agap by providing a view on factor premiums in the European market as a whole.

近年來(2005-2014)，因子投資在市值、帳面市值比和動能在美國股市無法得到比大盤更好的表現，所以本研究以五因子亦即市值因子、帳面市值比因子、動能因子、低市場因子和品質因子，進行特定產業美國科技股研究，不同於大部分因子投資研究都以地區作為區分標準，期望可以得到不同的因子投資策略。研究結果發現無論是個別因子投資或是因子投資組合確實可以在美國科技股中提高夏普指標並擊敗那斯達克指數，此外檢視因子投資組合在不同時間的投資效益，發現因子投資組合在金融海嘯的壞時機期間，相對於投資那斯達克，更具有抗跌作用、投資的效益，在經濟穩定的好時機時，也能具備足夠的收益率並能夠提供投資者更好夏普指標，顯示因子投資在美國科技股中是一個可以成功的投資策略。 / Recently, factor investing in market value, book/market ratio and momentum had no outperformance comparing to the market index in terms of return and sharpe ratio. The research focus on technology stocks in U.S to construct 5 factors which are market value, book/market ratio, momentum, betting against beta, quality. Unlike other regional-based researches, this paper is industrial-based and aims to create a new factor investing strategy. The result shows that some factors and factory portfolios do outperform NASDAQ index with higher sharpe ratio. Moreover, analyses in timing for factor portfolios show that factor portfolios provide better return and sharpe ratio during financial crisis in U.S. On the other hand, under stable economy circumstance, factor portfolios still beat NASDAQ in terms of higher sharpe ratio.

Investors and fund managers have, since the start of financial markets, always been on the lookout for new ways of beating the market. However, researchers of the Efficient Market Hypothesis have shown that markets are usually highly efficient, implying that there are few possibilities of earning returns that are higher than the market returns, on a risk adjusted basis. Prevailing theories, such as the Capital Asset Pricing Model, has shown that increased return must stem from taking on higher risk. Though, this model’s explanatory power has been challenged by numerous researchers who propose different factors, other than market risk, which could hold explanatory power when it comes to returns in the stock market. This area of research is called factor investing, and has shown that factors such as momentum, size, and value, all can lead to outperforming the market.This study examines how a model based on two common factors, quality and value, would have performed on the Swedish stock market. The study is based on five portfolios chosen by the quality and value factors, each one held for 5 years, examined over a 25-year time span and uses the capital asset pricing model as a tool to measure whether or not the selected factors outperform the market. The study has taken a quantitative approach to examining the research question, using a positivistic and objectivistic view.The results of the study show evidence that the quality and value factors can lead to significant outperformance relative to the market index. Both total returns and risk adjusted returns were higher than the market index for some of the portfolios created using the quality and value factors. Furthermore, statistical evidence was found of that CAPM not fully explains all returns, and thus, that the returns are in part explained by the quality and value factors. The findings led to the conclusion that the quality and value factors does, in fact, hold explanatory power beyond that of CAPM. Purchasing quality companies at a reasonable price is shown to be a sound investment strategy, and that a portfolio created using the quality and value factors has good chances of outperforming the market index.

本文以全球第一個演算法交易雲端平台－Quantopian進行研究，藉由平台社群討論區內公開之演算法交易策略，透過交易策略篩選和初步優化，以演算法交易策略為投資標的，搭配不同權重策略建構投資組合。權重策略部分，本文提出適用於組合式交易策略的績效指標加權 (Performance Index Weighted) 法，應用因子投資的觀念，融合排序相關性較低、不同面向之績效指標作為報酬率驅動因子，並參考Asness et al. (2013) 以因子排序作為權重計算依據，提供了簡單直覺、非最適化求解而且穩健的加權方式，更直接地將交易策略各面向績效的優劣反應在權重上。 根據數值分析，發現組合式交易策略長期而言，整體績效表現平均優於個別演算法交易策略，最小變異、績效指標加權和均等權重投資組合的風險亦明顯低於個別交易策略，且最小變異、績效指標加權和均等權重投資組合在降低投資組合風險的同時，並未犧牲過多報酬，風險調整後績效表現優於個別交易策略。而績效指標加權投資組合之年化報酬率、風險衡量和風險調整後績效表現皆優於最小變異、平均數－變異數、均等權重的加權投資組合，此種權重策略可使投資組合之夏普比率 (Sharpe ratio) 顯著提升，且投資組合的風險大幅降低，最大跌幅 (Max drawdown) 在四年半的實驗區間內降至10%以下的水準，風險調整後績效優異。 透過Quantopian社群演算法交易平台，個人投資者也能站在巨人的肩膀上學習，集合眾人的力量，憑藉量化交易創造出和機構法人一樣具有競爭力的投資組合。如Chan (2009) 所言，個人投資者也能憑藉量化交易，設計一套演算法交易策略。 / Quantopian is a crowd-sourced hedge fund which allows members on the platform to develop their own algorithmic strategies and even get capital allocations from Quantopian. In this paper, we constructed portfolios by Quantopian trading platform and proposed Performance Index Weighted method which generate consistently profit in our study. First, we filtered algorithmic trading strategies shared on the Quantopian community and improved the performance slightly. Second, we combined multiple algorithmic strategies with varied portfolio weight method, such as minimize-variance, performance index weighted, mean-variance, and equal weighed method to construct a portfolio. To elaborate, Performance Index Weighted portfolio is actually an application of factor investing, in which the portfolio weight depends on the ranking of performance index (factors), and these index measure returns, risk, and also risk-adjusted returns, which truly reflects how well the algorithmic strategy is. As a result, we used the performance index as a return driver and invested more in well-ranked strategies directly. Performance index weighted is a simple, robust, and fully intuitively way to construct a portfolio. In numerical analysis, we found that using multiple strategies to construct a portfolio could generate better performance than a single algorithm strategy on average. Moreover, the annual returns, risk measure, and risk-adjusted returns of Performance Index Weighted portfolio turn out to be better than minimize-variance portfolio, mean-variance portfolio, and equal weighted portfolio. As a result, Performance Index Weighted portfolio has significantly higher Sharpe ratio and lower Max Drawdown (lower than 10% in our out-of-sample test) than other portfolios, which shows excellent risk-adjusted performance. Most important of all, retail traders could learn more precisely by standing on the shoulders of giants through Quantopian trading platform. Also, by collecting wisdom from the crowd, we create an opportunity for retail traders to construct competitive portfolios just as institutional investors do.