Integrating Factor Investing into Active Management Risk Management Assignment

Chapter 1: Introduction of Integrating Factor Investing into Active Management Risk Management Assignment

1.1 Introduction

In the dynamic environment of these financial transformations, the problem of balancing risks and return still presents itself as a critical issue for investment managers. That is why this project is going to address the issue of how factor investing can be incorporated in the active management framework particular to the United Kingdom’s investment climate. That is known as factor investing where one seeks to identify areas that would deliver particular things such as momentum or low risk in order to rebalance the risk-reward prospect. This approach will in turn try to enhance risk management and to also allow for effective factor tilting in order to generate better diversified returns. From the historical records it is evident that in the US momentum stocks have performed better than the overall market by an average of 2%. The low volatility has averaged returns growth of 5% per annum in the period from 2010 to 2020, however, the drawdown of low volatility stocks has been cut by 20% during market decline (Koedijk et al., 2016). The project will also research the performance of the key factors over time, design a diversified portfolio model, compare factor investing to the risk-reducing approaches, as well as estimate the factor approach’s influence on portfolio outcomes (Bergeron et al., 2018). Finally, it would like to offer a reference manual filled with best practices and recommendations to invest in factor strategies and integrate them with clients’ portfolios for professional investment managers.

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1.2 Background of study

 As the subject, factor investing has attracted much interest within the recent past as the investors strive to achieve higher returns through investing based on well-defined drivers of returns. The major ingredients defining this strategy are momentum and low volatility (Kolle et al., 2022). Momentum is an element that takes into consideration assets with regard to the continuation of their performance pattern for the better or the worse, while low volatility comprises of identifying assets which do not fluctuate much in price. The selected FTSE 100 records show that momentum stocks delivered an annualized return of 10 percent. to 3% from 2010 to 2020 , while the factor Lifetime orientation increased by / 8. 2 percent for the wider index. Therefore, it emerged that while the overall index had a beta coefficient of 1, low volatility stock in the same index had a beta of 0. 8 which is compared to the market average of 1. The company’s accounts receivable has been recorded at 8 which is far below the market average of 1. Hence, the exposure to risk decreases to 0.

The method of factor tilting is the shading of the weights assigned to these factors to align with the mentioned risks and returns expected on the overall diversified portfolio. This approach is different with normally used investment style that tend to have oriented on market capitalization or sector. Integrating the figures of the actual factor returns and market data the study is focused on optimizing the approaches for multiple factors (Flint et al., 2016). Besides, investigating on the risk characteristics of factor based portfolios will enable one determine the ability of such portfolios in managing risks compared to traditional ones. Risk adjusted returns of these factor based strategies will be evaluated by backtesting and performance analysis. Given the emphasis of the research on the practical application of the concept, the study will also provide recommendations regarding integration of factor investing into portfolios with the application of ETFs and mutual funds that collectively manage more than £1 trillion of assets in the United Kingdom market.

1.3 Research Aim

The aim of this research is to develop a comprehensive framework for integration factors investment into the active management for enhancement of the risk management and optimize return for diversified investment portfolios.

1.4 Research Objective

The particular project is based on to find some certain objectives which is as follows,

• To analyze the key factors and their historical performance.
• To develop a diversified portfolio framework incorporating multiple factors.
• To evaluate how factors investing reduces risks compared to traditional strategies.
• To assess the impact of factor integration on portfolio performance.
• To create a practical guide for integration factor investment into existing portfolio.

1.5 Research Questions

Q1. What are the key factors and their historical performance?

Q2. How to develop a diversified portfolio framework incorporating multiple factors?

Q3. How factors investing reduces risks compared to traditional strategies?

Q4. What are the impact of factor integration on portfolio performance?

Q5. What should be a practical guide for integration factor investment into existing portfolio?

1.6 Research Hypothesis

Research Hypothesis 1

“H0: Changes in interest rates do not significantly affect the performance of factor based investment portfolios”.

“H1: Changes in interest rates significantly affect the performance of factor based investment portfolios”.

Research Hypothesis 2

“H0: Factor investing strategies do not provide better risk adjusted returns compared to traditional investment strategies”.

“H1: Factor investing strategies provide better risk adjusted returns compared to traditional investment strategies”.

1.7 Research Rationale

The basis for this research lies in the observed increase in discussions around factor investing as the way to improve the portfolios’ performance and the ways to better manage risks. The old-fashioned uses of investment fail during most of the financial crisis and the recent global pandemic in early 2020. Momentum as well as low volatility factor investing provide a relatively more viable approach of investing because these factors have been known to exhibit stability of performance in down turns as compared to cap weighted investing (Nazaire et al., 2021). Taking these variables into consideration as well as outlining the method of incorporating them into active management as the major objectives of this research, it is expected to contribute to the stream of knowledge and offer investment managers some useful hints and working tools. This in turn would mean in the longer-run, healthy portfolio leverage levels could create more quality portfolios that are equally resilient to economic shocks hence positively enhancing the quality, stability and performance of investment portfolios in the US market.

1.8 Research significance

 The research is important for the following reasons as it aims to fill the existing gaps observed in investment literature in the modern world. More specifically, by concentrating on factor investing, and, particularly, momentum and low volatility factors, the study contributes to the existing literature by offering a new perspective on risk and return management (Kolm and Ritter 2021). The results can improve the position of investment managers since the evaluation of these factors provides insights on their performance under various conditions and thus can improve evaluations. Also, the intended practice that has been brought out through this study will be useful in enabling users to incorporate the factor investing strategies in the current portfolios hence making it realistic to the investors. In the same way, the promotion of more flexible and sustainable systems of investment can benefit the policymakers and the financial institutions, and will consequently be substantiated on the overall stability of the financial system in US.

1.9 Research framework

Figure 1: Research framework

1.10 Conclusion

The aim of this research is the improvement of risk management and the increase of the returns achieved by applying the factor investing approach as part of the active management in the context of the US Investment Climate. The research thus underscores the superiority of the momentum and low volatility factors by depicting historical performance data, elaborating the concept of the diversified portfolio, and comparing the measure of risk with traditional approaches. Thus, the findings that have been presented contribute to the development of better investment strategies for managers, making portfolios more dynamically stable. Lastly, this research aims at providing feasible strategies for the application of factor investing to enhance the marketplace stability and achieve the optimum results in the United States.

Chapter 2: Literature Review

2.1 Introduction

Form factor investment has emerged as one of the most popular concepts aimed at increasing the yield and diversifying risks by using factors. Thus, this literature review will consider the approaches of integrating factor investing into the active management with reference to the US context. The Momentum and low volatility were chosen for their unmistakable risk-return profiles. Their connection comprehends how they complement one another in upgrading portfolio solidness and returns. Therefore, while conducting the review of empirical studies and theoretical models, it will be possible to provide a closer look at the results in the factor strategies like momentum or low volatility in comparison with traditional methods of investments. It will also discuss how active funds data of the US can be utilized for understanding the factor sensitivity as well as the risk management strategies. Knowledge of these features and their influence in relation to the results of a portfolio will provide a clear vision of the integration of factor investing into active management and the possibilities of making a positive change in the quality of investment and its reliability.

2.2 Empirical Study

Factor Investing: A Review of Recent Empirical Studies

According to, Berk, et al. 2020, in their article "Mutual Funds: ‘‘Skill and Performance,” hence, the performances of mutual funds can be highly affected by the skillfulness of the fund managers. Their published research is concerned with understanding whether managers of mutual funds can produce alpha, better known as excess returns above the benchmark, through skill or sheer random chance. In the context of the particular study, it can be concluded that while there are some funds that show skill, the instances of sustaining superior returns are few. This analysis is imperative to factor investing as it offers a starting point to view the active management skills as a determinant of outperformance. Thus, the results indicate that despite the fact factor strategies may deliver a systematic outperformance, the ability of the manager to improve the results should not be ruled out. The evaluation of performance of mutual funds presented in this paper consequently means that while sorting out the various investment solutions it is crucial to incorporate factor investment strategies and manager effectiveness.

According to, Pástor, et al. 2020, their study talk about the effects of fund attributes like; the size, expenses, and the frequency of fund exchanges in performance results. Thereby, the research reveals that the turnover of active funds is positively associated with fees but negatively with performance, whereas lower expense ratios, a preferred factor, has correlated effects on the risk and return ratios of functioning structures. Regarding factor investing, this paper is useful because it delivers an understanding of the impact of various fund characteristics on factor-related approaches’ performance. The study postulates that identifying such trade-offs is critical when applying factor investing since it can impact how some factors such as momentum or low volatility are applied in the actual sense. Analysis of these trade-offs assists in fine-tuning factors with a view of optimizing factor solutions with regard to investor goals as well as prevailing market conditions.

Figure 2: Time Series of Average Portfolio Liquidity and Its Components

Overall, these papers help expand the literature on performance anomalies in mutual funds as well shed some light on the implementation of factor investing strategies. They provide insights on how managerial skill and characteristic of the fund will affect returns when it comes to factor based investments. Such integration proves useful in determining the offsets of factor investing to conventional fund management strategies in order to get an all-encompassing perspective on the investment process.

The Performance of Momentum Strategies: Evidence from Global Stock Markets

According to, Wermers, 2020, the article “Active Investing and the Efficiency of Security Markets” the dependence of the effectiveness of active investing strategies, thus, momentum, on the level of market efficiency which were revealed by the author. Hence, Wermers assesses the effectiveness and specificity of using different market inefficiencies by active managers to earn returns and examines the role of the momentum strategies in this process. The paper established that despite momentum strategies outperforming other strategies, many of them relies of trends that persist in the market and the ability of managers to exploit such trends. It should be noted that the study shows that the presence of momentum as a significant factor in attaining much superior returns can be influenced by the market conditions and efficiency achieved. Thus, this analysis takes a deeper look at the efficiencies in the market that exists or is docketed to exist when using momentum strategies, a view that is lacking when offering a broad perspective about their performance.

Figure 3: AUM for U.S-domiciled domestic equity mutual funds ETFs

According to, Chui, et al. 2022, the article “Momentum, Reversals, and Investor Clientele,” momentum strategies are affected by former and market conditions so as investor clientele effects. Propositions of the study focus on the ways in which retail and institutional investors as well as other investors’ characteristics affect the performance and robustness of the momentum strategies. Using momentum trade, the authors discover that they are very efficient in practice especially in the markets where the investor pattern results in long trends, but their performance may experience turnarounds at instances of trend reversion or shift of investor sentiment. The study supports data as confirmation that investor clientele influences the distinguished performance of the momentum strategies across more than fifteen stock markets around the world. Thus, this study suggests that in analyzing momentum strategies, there are crucial factors relating to investors’ behavior and market sentiment that need to be taken into consideration as well as providing valuable information as to the applicability of momentum strategies in various markets.

These studies offer important information of how momentum strategies perform by researching the interaction of the efficiency of the markets, the behavior of investors and the reversals of trends. This research by Wermers offers further understanding on when momentum styles can produce the best results as Chui, Subrahmanyam, and Titman’s study complements by pointing to the issue of investor clientele affecting the efficiency of momentum. Altogether, these works provide a broad picture of the behaviour of momentum strategies in various world markets, underlining the need to take into consideration various features of the market and investors for increasing effectiveness of the strategies.

Low Volatility Investing: An Analysis of Risk and Return Profiles

According to, Antonakakis et al. 2020, the study look at how low volatility has been utilized in relation to different kinds of assets, and their respective implied volatilities. It is focused on the understanding of low-volatility strategies with reference to oil and other forms of investments. Antonakakis and colleagues successfully determine that the low-volatility strategies are especially efficient in minimizing the risk during the period of high market volatility since the mentioned strategies imply that an investor should invest in assets that have stable price fluctuations. As a result of conducting the research, it has been noted that low-volatility investing is a useful tool for risk management because such strategies can offer significant protection against adverse market events. The study finds out that low volatility investment is less risky and offer better Sharpe ratios than high volatility assets through the analysis of implied volatility. This paper offers helpful information of how the low-volatility strategies can be applied in different investment environments with focus done on the importance of low-volatility strategies in dealing with market risk.

Figure 4: Volatility Indices

According to, Pavolova, et al. 2021, the emphasis is made not on how portfolio managers choose industries for low volatility investing, and what it implies to risks and returns. The study assesses investment strategies based on the industry and especially focuses on sectors whose performance indicators are associated with low volatility. Pavolova and colleagues noted that the more appropriate business had higher stability which is better for those investors who preferred industries that have lower volatility to manage their risks. The study shows that portfolio managers tend to focus on industries with lower imprecise levels of return by construction such as utilities and consumer staples to boost the stability. The analysis also reveals that low-volatility industries usually result in greater consistency so this group can be characterized by rather low growth rates as compared to the volatile industries group. Thus, this research focuses on the issues of industry selection when employing low volatility strategies and presents a comprehensive analysis of low volatility investment risk/return characteristics by different industries.

Altogether, these works can be credited with enhancing the overall comprehension of low volatility investing as they investigate the applicability of the strategy. Although evaluating hedging ability of strategies is the objective of this paper, it will be useful to see how different authors support hedging role of low volatility strategies across various asset types as was done by Antonakakis et al. (2020). Pavolova et al., (2021) provide information about low volatility investments by industry which gives a glimpse on how portfolio managers apply the strategies to obtain low volatility returns. Altogether these papers stress the effectiveness of low volatility strategies in strengthening the portfolios’ stability and controlling risks, and at the same note reveal possibility of the trade off between stability and profitability.

Factor Investing and Portfolio Diversification

According to, Paliienko, et al. 2020, the research carried in “An Empirical Investigation of the Fama-French Five-Factor Model,” established by Paliienko, Naumenkova and Mishchenko, factor investing can be evaluated by the Fama French factor model, consisting of market factor, size factor, value factor, profitability factor, and investment factor. The paper conducts an empirical analysis of this model as applied to cross section of stock returns and its use for diversification of portfolios. Paliienko and colleagues discover that, consequently, the five-factor model offers a significantly superior quality to the three-factor model that is traditionally used in explaining stock returns by including profitability and investment factors. These studies demonstrate that factor investing, rooted in this model, becomes profitable for the expansion of diversification in portfolios – to include multiple factors that can identify different types of risk and return. Thus, the given study gives empirical support to the Fama-French model when it comes to creating diversified portfolios across these factors would result in higher risk-adjusted returns and manifest a decrease in the risk.

According to, Elsayed, et al. 2020, in "Time-Varying Co-Movements Between Energy Market and Global Financial Markets: In the paper titled: “Implication for Portfolio Diversification and Hedging Strategies”, the concern is on how factor investing can be used to diversify portfolios based on an analysis of the energy market’s relationship to world equity markets. This paper evaluates pattern transitions and their relevance to diversification and hedging approaches. Incorporating factors related to the energy market including the oil prices as pointed by Elsayed et al can be used in the global financial portfolios in order to enhance the diversification and develop efficient hedge against the movement in the market. The research again becomes important when one has to establish diversified risks portfolios and risk management techniques for hedges. In the case of the study used there is an establishment of the pattern of the co movements between the various markets that shows that factor investing will enable efficient diversification of the overall portfolio risk which is efficient especially at a time of high fluctuations in the markets.

Figure 5: Time Series Plot of Level Data

These papers provide important knowledge to the society on how factor investing can help build more diversified portfolios. Paliienko et al. (2020) also try to give an empirical evidence to the Fama-French five-factor model principally, it means that a multi-factor approach enhances portfolio’s profitability and can capture different factors of risk. As stated by Elsayed et al. (2020), it is important to include sector-specific factors that belong to the company’s area of operation such as the energy market to properly address diversification risks. Commonly, these papers point to the factor investing strategies as the effective methods of creating diversified portfolios and managing risk, thereby giving the holistic view of the factor strategy techniques in portfolio management and hedging.

Comparing Factor Investing and Traditional Investment Strategies

According to, Sattar et al. 2020, in ‘Behavioral Finance Biases in Investment Decision Making’ argue that while behavioral biases affects the factors of investment and its comparison with the conventional factors of investment. The behavior of the investors is discussed in the context of several biases which include overconfidence and loss aversion. Sattar and colleagues share the opinion of these writers in claiming that factor investing could provide more benefits than regular strategies because it manages these biases systematically. For instance, factor investing works on the premise of analytically determinable characteristics of the assets to be invested in, meaning it may reduce biases that influence other investment strategies. According to the findings of the research, the present study is likely to shed light on different behavioral finance biases incorporating the best performing factor investing models, with a view to discovering how one can outcompete the other, since the biases often distort value hunt and improve the efficiency of factor- based methodologies.

According to, Ikwue et al. 2023, emphasis is given to the action of the afore mentioned ESG principles and their implications for the optimal investment approach in the management of pension funds. The paper seeks to compare sustainable investment practices, specifically factor investing with conventional approaches to investment in the United States of America and Nigeria. According to Ikwue and colleagues, the main goal of ESG is to assess investments that are based on factor investing strategies by using environmental, social, and governance principles. This integration is done in relation to conventional investment strategies that might focus on classic financial performance indicators disregarding the ESG aspects. It also shows how factor investing approaches incorporating ESG factors can provide better and more sustainable long-term perfomance as opposed to traditional means. Thus, using the example of adopting ESG criteria, the paper offers a comparative analysis of how factor investing can solve both the financial and non-financial tasks, which indicates the possibilities of overcoming the disadvantages of traditional investments.

These papers provide useful knowledge to the research on the difference between factor investing and conventional methods of investment. Sattar et al. (2020) offer a behavioural finance view, stressing on the possibility of overcoming or lessening the impacts of biases on conventional methods through factor investing. The paper of Ikwue et al. (2023) provides a comprehensive comparison of the ESG principles in which they explain how the factor investment can be used to add sustainability factors into it to pursue long term objectives. Altogether, these papers enrich the discussion on how factor investing enriches portfolio management and responds to the modern characteristics of the investment process compared to traditional strategies, providing a more balanced evaluation.

According to, Fan, et al. 2022, the view of the study is concerned with momentum – cross section of stock volatility nexus. Contained in the Journal of Economic Dynamics and Control, the paper examines the consequences of momentum trading on stock oscillations and for asset pricing. The authors establish that there is a strong and positive relationship between momentum and volatility, where high-momentum stocks are characterised by differing volatility profiles to their low-momentum counterparts. From their study they recommend that it is possible to enhance the estimation of volatility and offer useful information towards risk management when including momentum effects information into the models. This works strengthens the argument for models that take into consideration momentum, along with volatility of stocks as studied in this research.

According to, Grobys, et al. 2024, the paper specifics of the interaction of low volatility and momentum in the context of the Nordic equities market are reviewed. This paper by Hexists in Applied Economics and provides knowledge on how combining low-volatility and momentum strategies affects the investment results of the Nordic countries. The authors also provide the evidence to show that the portfolios formed by using these strategies dominate the conventional methods of asset allocation since it benefits from both the stationary and trending parts of the data. From their discussion, they conclude that the low-volatility and momentum act as an effective way of explaining risk and return. There is clearly a potential roles for utilising dual-factor strategies, and more broadly the presented findings of this study could be applied to clarify their effectiveness in improving the performance and development of investment in the context of emerging markets.

In the Nguyen’s paper, the assessment of such aspects as momentum and low volatility during various time periods allows understanding how these factors influence each other. The facts such as a title and a journal or magazine of the publication of Nguyen are unknown, however, a focus on the historical behavior of the momentum and low volatility strategies and their characteristics during various periods are analyzed. The results indicate that both factors have displayed high though fluctuating performance during historical periods, with maximum benefit during trending periods in case of momentum strategies and during volatile periods in case of low volatility strategies. Such analysis does not hinder and allows understanding the sustainability of these strategies and their position in the analysis of assets.

According to, Chabot, et al. 2014, the study responds to these issues. The glimpse of working paper from National Bureau of Economic Research provides the evidence that momentum strategies result in return chasing behaviour and hence predictable crashes. The authors claimed that he trading strategy of momentum trading help generate high profits but at the same time, helps build up systematic risk that cause market correction. Using their research results, they caution on using momentum strategies and recommend integrating risk management to reduce the impacts of a potential crash. This study gives basic knowledge of the reciprocation existing between trading rate progress strategies as well as market stability.

Finally, in year 2023, Momentum and Minimum Volatility factors have an extreme-high level of average correlation of 0. 71 as against the historical average correlation of -0. 09. 6%. This combination, recently supported by market declines and informational role of the stock inclusion in momentum factors, distorted factor investing narratives. In the past they happen after market declines, and they reverse as the markets improve. Still, the latest data shows that this high correlation has started departing from the norm inversely to the previous patterns of mean reversion that have often manifested themselves after major changes in the market. Investors may wish to look at multi-factor strategies where such cyclicality may affect returns on investment in the long-term and the factor of diversification.

2.3 Theories and Models

To comprehend factor investing as well as its impacts on the portfolio concepts, it is necessary to utilize a strong theoretical background and some models. This section describes three theories and three models that are used in the investigation of factor investing and in explaining the effects on the risk and return.

Theories:

Efficient Market Hypothesis (EMH)

The Efficient Market Hypothesis which has been formulated by Eugene Fama posits that an asset’s price incorporates all information which is available in the market at any one time (Ehiedu, and Obi, 2022). Following EMH, it becomes very difficult if not virtually impossible to outperform the market average by choosing particular securities, or prognosticating market changes as any such information alters within the shortest time and is reflected in the prices of the securities. More so, in the context of factor investing, the efficiency of the market negates the possibility of systematic factor strategies beating the market rates. However, factor investing is based on the assumption that there are inefficiencies, while markets are efficient – therefore, there could be market defects that factor strategies use (Nyakurukwa, and Seetharam, 2023). This theory aids in interpreting the strengths and weaknesses of factor investing because even factors contain information, the efficiency of the process depends on the efficiency of the market [Referred to Appendix 1].

Arbitrage Pricing Theory (APT)

Arbitrage Pricing Theory by Stephen Ross is a multi-factor model that seeks to explain the returns on the assets in relation to many factors that are adverse in the market instead of one single market factor. While that of CAPM in specifying which factors are relevant and which ones are not included, APT only provides a framework for selecting the factors, which have an influence on the asset prices but does not restrict the potentiality of its user from considering certain factors as irrelevant (Yadav, and Hegde, 2021). This is important for factor investing because this model offers a way of explaining how and to what extent different economic, financial and firm level factors impact on returns. APT agrees with the view that there are different types of risk through which factor investing can achieve better diversification and return hence the factor approach flexibility in deployment [Referred to Appendix 2].

Behavioral Finance Theory

Behavioral Finance Theory is concerned with investigating the various psychological aspects that affect the behaviour of investors in the market. This theory presents this notion, thus contrasting with rationality of EMH and several other theories in the finance field. To a great extent it exemplifies how different mental patterns like over confidence and herd mentality could result to market imperfections (Takemura, 2021). Essentially, factor investing can be concluded as a reaction to behavioural biases as they target factors that indicate returns irrespective of people’s conduct. This theory is imperative to grasp in order to understand how factor investing might profit from the market anomalies due to the cognitive biases of investors; the information below can help one to realise how psychological factors influence the effectiveness and integration of factor-based approaches [Referred to Appendix 3].

Models:

Fama-French Three-Factor Model

Built by Eugene Fama and Kenneth French, it is an improvement of CAPM which considers size (small and large) and value (high and low book-to-market ratio). It has also helped in explaining that size and value factors can help to explain improvements in stock return volatility other than captured by market risk (Li, and Duan, 2021). In factor investing, the Fama-French three-factor model acts as the foundation for the other factors that needs to be added to this model. It assists to explain how these factors are associated with portfolio returns and the construction of diversified portfolios which in their turn try to capture these extra sources of return [Referred to Appendix 4].

Fama-French Five-Factor Model

An extension of the Three-Factor Model, the Five-Factor Model includes two additional factors: and investment profitability (Dirkx, and Peter, 2020). The conceptual development from this replaces the Three-Factor Model and introduces the idea of return disparities with regards to profitability and investment. The Five-Factor Model is especially suitable for factor investing because it offers a richer perspective into the factors of return on equity (Paliienko, et al. 2020). It assists the investors and researchers in comparing the results of the factor-based strategies by including more factors that influence the returns and further improving the construction of multifactor portfolios for better risk diversification among the various components of risk and return [Referred to Appendix 5].

The Capital Asset Pricing Model (CAPM)

The Capital Asset Pricing Model came from William Sharpe and it measures the acceptance of systematic risk for one to expect increased returns (Pramono, et al. 2022). According to CAPM, the expected return of an asset depends with the beta which is the measure of an asset’s sensitivity to the movement of the market index. While factor investing goes a step further by including other factors of risk besides the market beta. For instance, the Fama-French three factors modify market beta by adding size and value factors, whereas the five factors also include profitability and investment (Agouram, et al. 2020). The CAPM is used as a benchmark to define how the traditional risk and return dynamics move to a new level through factor investing, in order to propose a reference framework to measure the factor-based investment strategies based on CAPM.

All these theories and models present a clear way of analysing factor investing and its impacts of the factor investing on portfolios. The concepts help in providing knowledge concerning efficiency of the market, link between risk and return, attitude of investors, and multiple factor implementation, which improve the comprehension and practice of factor investing [Referred to Appendix 6].

2.4 Conceptual Framework

Figure 6: Conceptual Framework

The variable used in this study is factor investing is used as the independent variable The conceptual framework in this research study is embedded on Factor Investing. The dependent variables are portfolio performance, risk management performance, and diversification advantages. It is expected that these dependent variables will be changed in the direction of the better, depending on the nature of factor investing as improving portfolio returns, risk management and diversification than the traditional investment practices (Sciarelli, et al. 2021). The existence of the framework is to explain how the factor investing affects these outcomes and this paper sought to establish the relationship between factor investing and the dependent variables and a guide on how the independent variables affects these dependent variables was established.

2.5 Literature Gap

However, as for now, there are quite a few long-standing gaps in the pieces of literature on factor investing. Relevant prior research tends to employ rather limited or reasonably well-defined markets or factors with relatively few cross-sectional comparisons. To date, relatively few papers focus on the integration of factor investing with newer sustainable, and ESG trends. Additionally, there is limited research documenting the factors’ use and effectiveness in managing factor portfolios in various macroeconomic conditions (Sangiorgi, and Schopohl, 2021). This methodology can help to fill the identified gaps, and, therefore, produce a more comprehensive insight into factor investing’s effectiveness and relevance.

2.6 Conclusion

This chapter has focused on reviewing the relevant pieces of literature and discussing the main theories and models that may help explain factor investing within the context of examining return and risk patterns. Thus, arguing for additional research on factor investing’s real-world applications and performance relative to benchmarks, it looks at similar empirical works and discusses the literature’s limitations. By establishing structuring ideas the subsequent analysis and discussion of the effects of FI on portfolio return, risk, and diversification is introduced here.

Chapter 3: Methodology

3.1 Introduction

Factor investing has gained major traction in present years in the form of a method for performance of portfolio enhancement, and risk mitigation. This research focuses on two particular factors integration, momentum, and low volatility, into active management strategies through the use of the Factor Tilting method majorly.

3.2 Method Outline

Different types of integrating factors are used for investment and active management risk measurement. Therefore, the study of integrating factors for active management used the secondary data collection process (Battiston et al. 2021). The “positivism philosophy” adapted for the integrating factors for risk management problems. The present research study of the identification of different types of integrating factors followed the quantitative design for the data collection process. Investing in active management risk management also follows “Factor Tilting” by which the strategic factors for risk management are easily identified. [Referred to Appendix: 7]

3.3 Research onion

Figure 7: Research Onion

3.4 Research Philosophy

The positivism research philosophy is adopted in this research, acknowledging that while existence is noticed in the objective reality, at that the understanding of it is present in the form of a probabilistic, and imperfect way (Saługa et al. 2020). The markets of finance are recognized in this regard, which is present in the form of complex systems influenced by many variables, regarding them the knowledge is subject to continuous revision. Regarding the historical data, the empirical analysis is combined in this approach with the theoretical understanding of the dynamics of the market. The relationships and patterns are aimed to be identified that can inform decisions regarding investment while staying aware regarding the limitations of the methods, and potential for unforeseen factors to influence outcomes. [Referred to Appendix:8]

3.5 Research Approach

The integrating factors for risk managements followed the inductive approach for the identification of risk management and active management factors in integrating factor investment analysis (Zheng et al. 2024). The “inductive approach” also helped to observe the strategic risk from the overall risk management process.

3.6 Research design

The risk management from active management research analysis followed the “quantitative design” for gathering secondary datasets for risk management of identification of different factors like low volatility, momentum, etc. (Chițimiea et al. 2021). The “quantitative design” provides benefits for collecting the risk management factors from the integrating factors. 

Figure 8: Research design

(Source: Self-created in MS Word)

3.7 Research Strategy

This research employs a secondary data collection strategy, which is combined with the quantitative data analysis strategy. The approach is primarily quantitative through the use of historical data on finance for the performance analysis regarding momentum, and low volatility factors performance, and their impact when integrated into active management strategies through Factors Tilting. Statistical analysis is going to be conducted for the risk adjustment returns evaluation, reduction in volatility, and overall performance of portfolio (Sun et al. 2020). In addition to that, the incorporation is going to be done regarding the qualitative elements by reviewing existing literature. 

3.8 Research Method

The present research study followed the “factor tilting method” that helps to identify risk management from the factor investigating approaches. Application of the “factor tilting method” helped to classify the specific classes of fund data sets into similar categories (Sun et al. 2020). The “quantitative design” is used for collecting the “secondary” 1500-1600 fund data for which method of “mono research method” is followed for the above research study.

3.9 Data collection process

The “data collection process” helps to collect the relevant data set for the identification of integrating factors for risk management and active management for factor investigation. The present research work used the secondary data collection of US active funds data (Campiglio et al. 2023). Total of 1500-1600 funds data are collected from the active funds records of the US based on which the entire risk management of some factors of low volatility, and momentum are easily identified. The main analysis of the risk management and factor investment is done based on data collection of the fund data set. The historical performance data are collected from the active fund records of US funds by which the entire risk management process of integrating factor investment are easily identified.

3.10 Data analysis process

The “data analysis” part is another important section that helps to conduct the entire research analysis for investigating the key factors for managing active management and risk. The secondary data collections are taken from 1500-1600 US funds data based on which the statistical analysis is performed. The SPSS software is used to identify the active management process for risk management (Chen et al. 2021). The correlation is performed to identify the associated factors for active management of risk management factors of US funds data. Excel is used for data filtering data cleaning and graphical analysis parts. Different types of statistical analysis of “correlation, descriptive statistics, regression analysis, t test, ANOVA trend analysis” are performed in the analysis process.

3.11 Research Ethics

This research adheres to strict standards of ethics in research of finance. All information used is available publicly and anonymized for individual privacy protection. No insider information or privileged information was used in the analysis of this research. The methodology of this research includes definitions of factors and tilting techniques which are documented transparently to ensure reproductively (Tan and Salo, 2023). Major conflicts of interest were disclosed, and efforts were made for objectivity maintenance throughout the research. The factor investing strategies' implications on the efficiency of the market and major risks of the system are considered in this regard. In addition to that, the limitations are acknowledged in this research regarding historical information in predicting future performance, and the importance is emphasized in this regard, of ongoing monitoring, and management of risk in factor-based strategies' implementation.

The present research analysis also avoids any type of biased analysis from the entire research study. This above research study of the identification of active management for identification of risk management also avoids any type of environmental degradation. The honesty principle, avoidance of carefulness, and data integrity ethics are followed for the identification of active management process for risk management from collected US fund data sets (Tan and Salo, 2023). The confidentiality principle is also followed during US fund data collection process to avoid any kind of confidential issues. Following the above ethical principles the entire research analysis of active management for risk management research analysis is conducted.

3.12 Research limitation

The above research analysis of the identification of the active management process for risk management followed the secondary quantitative data collection process. Therefore, there exists a lack of primary data collection due to lag of time constraints and the associated cost of conducting of entire research study (Guo et al. 2024). The problem of “out-of-data” is another big limitation during conduction of the entire research analysis of the investigation of active management for risk management process.

3.13 Time Plan

Figure 9: Time Plan

(Source: Self-created in Project Libra)

3.14 Conclusion

Risk management is a big factor in the entire active management process for identification of the integrating factor identified from the US funds data. The above section describes the entire methodology section that is adopted for conducting the entire research analysis. The next section describes the finding analysis from identified collected data sources for active management of the risk managing process.

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Author Bio

Jack Roberts   9 years | PhD

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