Portfolio Optimization and Performance Evaluation in Malaysia: A Comparative Analysis of Markowitz Mean–Variance and Sharpe Single Index Models

Portfolio Optimization and Performance Evaluation in Malaysia: A Comparative Analysis of Markowitz Mean–Variance and Sharpe Single Index Models

Nik Rozila Nik Mohd Masdek¹, Bushra Mohd. Zaki¹*, Zahirah Hamid Ghul¹, Siti Nur Aqilah Ab Wahab², Irwan Ibrahim³, Heizal Hezry Omar¹

¹Department Of Economics And Financial Studies, Faculty Of Business And Management, UiTM Puncak Alam, Selangor, Malaysia

²Labuan Faculty Of International Finance, Universiti Malaysia Sabah, Labuan International Campus, Jalan Sungai Pagar, 87000, Federal Territory Of Labuan

³Department of Technology and Supply Chain Management Studies, Faculty of Business and Management, UiTM Puncak Alam, Selangor, Malaysia

*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.909000144

Received: 29 August 2025; Accepted: 02 September 2025; Published: 02 October 2025

ABSTRACT

This study examines the performance of portfolio optimization methods within the Malaysian capital market, where equities, bonds, and sukuk provide a unique mix of investment opportunities. The purpose is to evaluate how traditional Markowitz mean–variance optimization compares with the Sharpe single index model in constructing efficient portfolios under Malaysia’s dual conventional–Islamic financial system. The research addresses a gap in existing studies, which often focus on single asset classes or benchmark comparisons without integrating primary issuance, corporate actions, and sectoral dynamics into a full portfolio framework.

A mixed approach was applied, combining quantitative analysis of Bursa Malaysia securities across banking, plantation, technology, and telecommunications sectors with respondent perspectives from retail investors, fund managers, and regulators. Risk and return were measured using weekly adjusted returns corrected for thin trading, while portfolio performance was evaluated through Sharpe, Treynor, Jensen, and Profitability indexes.

Results demonstrate that the Markowitz model produces superior diversification and higher risk-adjusted performance, while the Sharpe single index model offers greater simplicity and usability for retail investors. Respondent insights reveal a clear divide: fund managers prioritize diversification precision, whereas retail participants value ease of implementation. The findings provide actionable implications for investors, unit trust design, and regulatory policy in emerging markets.

Keywords : Portfolio Optimization, Markowitz Mean–Variance, Sharpe Single Index, Risk–Return Analysis, Malaysian Capital Market

INTRODUCTION : INVESTMENT AND SECURITIES MARKET IN MALAYSIA

Malaysia’s capital market is large, diverse, and policy relevant. In 2024 the market reached about RM4.2 trillion in size, supported by growth in equity market capitalisation and outstanding bonds and sukuk. Fund management assets passed RM1 trillion. These figures signal depth, liquidity, and institutional participation that justify a focused study on risk, return, and performance of managed portfolios in Malaysia. (Securities Commission Malaysia, 2025).

Here is what matters. Investors face a menu of instruments that includes listed equities on Bursa Malaysia, corporate and government bonds, sukuk, unit trust funds, and private retirement schemes. Price discovery takes place in organised markets with clear trading rules. Offerings of new securities are screened by regulators before listing. Portfolio outcomes depend on security selection, diversification, and timing, but they are also shaped by market microstructure and corporate actions. The study will connect these building blocks to performance measurement using Sharpe, Treynor, and Jensen indexes while keeping the Malaysian setting at the centre.

Overview of investment

An investment is an allocation of current resources to an asset with the expectation of future cash flows or price appreciation. In Malaysia, retail and institutional investors access equities, bonds, sukuk, unit trust funds, and derivatives. The investor base includes large domestic institutions such as the Employees Provident Fund, which is widely recognised as the largest institutional investor in Malaysia and an active proponent of stewardship and sustainable investment. These investors influence market liquidity, corporate governance, and valuation practices. (Employees Provident Fund, 2024; Ministry of Finance Malaysia, 2022; Securities Commission Malaysia, 2014).

Unit trust funds are a key channel for household participation. The Federation of Investment Managers Malaysia serves as a self-regulatory organisation for the marketing and distribution of unit trust and private retirement schemes, and supports competency standards for consultants, which matters for investor protection and market confidence. (FIMM, 2024; FIMM, 2024).

The institutional architecture also reflects Malaysia’s dual financial system. Bursa Malaysia facilitates both conventional and Islamic products. The exchange’s development in Islamic market segments, alongside sukuk leadership, expands the investable set and allows portfolios to reflect investor beliefs without sacrificing diversification. Background summaries from the CFA Institute and SC reports highlight this breadth. (CFA Institute, 2021; Securities Commission Malaysia, 2025).

Primary market and initial public offerings

The primary market channels savings into productive investment. For equity listings, companies engage advisers, undergo due diligence, prepare a prospectus, and seek approvals before public offering and admission to the exchange. Bursa Malaysia notes a typical timeline of four to twelve months from adviser engagement to listing, with the Securities Commission’s approval required for a primary listing. This process underpins disclosure quality and investor confidence. (Bursa Malaysia, 2024; Baker McKenzie, 2024).

Recent activity shows renewed appetite for new listings. The SC’s Annual Report 2024 records fifty five IPOs in 2024 that raised about RM7.42 billion. Fundraising through capital markets rose compared to 2023. Primary market strength provides fresh investable names for portfolio construction and benchmarking. (Securities Commission Malaysia, 2025).

Secondary markets, transactions, and corporate exercises

After issuance, securities trade on organised secondary markets where prices incorporate information through continuous auction mechanisms. Bursa’s trading day runs in two sessions, with an opening auction, continuous trading, and a closing phase known as trading at last. Understanding these sessions helps when interpreting intraday volatility and execution risk. (Bursa Malaysia, 2024).

Corporate exercises change a firm’s capital structure or share base and can shift portfolio risk and return. Common examples include rights issues, bonus issues, share splits, and consolidations. Bursa provides process guidance, and the SPEEDS initiative shortens timelines for selected exercises such as share splits and consolidations. For empirical work, corporate actions around portfolio constituents must be cleaned and adjusted to avoid spurious jumps in returns. (Bursa Malaysia, 2024).

Capital market regulators and market governance

Malaysia’s capital market is overseen by several bodies with complementary mandates. First, the Securities Commission Malaysia, established under the Securities Commission Act 1993, regulates and develops the capital market, approves corporate proposals such as IPOs, licenses intermediaries, and issues conduct and disclosure rules. (Securities Commission Malaysia, 2024).

Second, Bursa Malaysia operates the exchange and acts as the frontline regulator for its markets and participants, enforcing listing and trading rules to maintain a fair and orderly market. (Bursa Malaysia, 2024).

Third, Bank Negara Malaysia promotes monetary and financial stability and oversees the money market and the payments system, which interact with capital market liquidity and settlement. The central bank coordinates with the SC through formal arrangements and memoranda when responsibilities intersect. (Bank Negara Malaysia, 2021; World Bank, 2024; Bank Negara Malaysia, 2024).

Finally, FIMM serves as the self-regulatory organisation for the marketing and distribution of unit trust and private retirement schemes, complementing SC oversight of the fund management industry. (FIMM, 2024).

Theoretical anchors and link to portfolio analysis

This study is grounded in modern portfolio theory and capital market theory. Mean variance analysis establishes how diversification can reduce portfolio variance for a given expected return. Capital Asset Pricing Model links expected return to market risk measured by beta. Performance is evaluated with Sharpe, Treynor, and Jensen indexes to connect realised returns to risk taken and to a benchmark. Classic sources include Markowitz on portfolio selection, Sharpe on the CAPM and performance measurement, and Jensen on abnormal performance. These foundations will be interpreted in the Malaysian setting with attention to institutional features such as unit trust intermediation, Islamic products, and concentrated ownership. (Markowitz, 1952; Sharpe, 1964; Sharpe, 1966; Jensen, 1968).

Research gap, objectives, and contributions

Most local studies examine single asset classes or focus on benchmark comparison without integrating primary market dynamics, corporate actions, and sector rotation into an end to end portfolio process. There is limited comparative evidence on whether simple Markowitz optimisation delivers materially different allocations from a Sharpe single index model when applied to Malaysian sectors that sit at different points of the industry life cycle. There is also a need to connect stewardship by large institutions and the distribution architecture of unit trust funds with household portfolio outcomes. Recent policy signals and market data suggest a timely opening for such analysis. (Securities Commission Malaysia, 2025; Reuters, 2024).

Objectives

1. Map the Malaysian investment ecosystem from issuance to trading and governance, and derive its implications for portfolio construction.
2. Measure risk and return for single securities drawn from major sectors, with careful treatment of corporate exercises.
3. Compare traditional mean variance optimisation with the Sharpe single index model in forming Malaysia focused portfolios.
4. Evaluate performance with Sharpe, Treynor, and Jensen indexes against appropriate Bursa Malaysia benchmarks.
5. Elicit expert and retail perspectives on risk, diversification, and performance evaluation through respondents to triangulate quantitative findings.

Contributions

The study provides a Malaysia centric performance atlas that links institutional features to portfolio outcomes. It offers a head to head comparison of two accessible optimisation approaches applied to Bursa constituents, and translates the results into guidance for unit trust construction and retail allocation. It also builds an auditable data pipeline for handling corporate actions and trading microstructure, which improves replicability for future work.

Scope, assumptions, and key definitions

The empirical universe will draw on Bursa Malaysia Main Market constituents with sectoral coverage that reflects Malaysia’s economic structure. For the fund channel, representative equity and balanced unit trust funds will be sampled. Government and corporate bond yields will be used to infer risk free and credit premia. Trading sessions and auction phases will be observed to set consistent return intervals. Corporate actions will be adjusted using official announcements to ensure clean time series. Regulatory texts and public disclosures by the SC, Bursa, and FIMM define compliance boundaries for the analysis. (Bursa Malaysia, 2024; Securities Commission Malaysia, 2025; FIMM, 2024).

Key terms

Primary market refers to the issuance of new securities to investors. Secondary market refers to trading of outstanding securities. Corporate exercises are company actions that change the number of shares, ownership distribution, or capital structure. Unit trust fund refers to a collective investment scheme that pools investor money to invest in a portfolio of assets under a licensed manager. Performance indexes in this study are Sharpe, Treynor, and Jensen measures relative to a Bursa benchmark.

Malaysia offers a sophisticated setting for portfolio research, with active primary issuance, orderly secondary trading, and a dense regulatory framework that protects investors while allowing product variety. The presence of large domestic institutions and a mature unit trust industry shapes liquidity and governance. These features create a natural laboratory to test how optimisation methods and performance metrics behave when applied to real securities and real trading rules. Chapter 2 will formalise single security risk and return measurement, setting the stage for portfolio construction and performance evaluation in later chapters.

Risk and Return of a Single Security : Purpose and Scope

Understanding the relationship between risk and return is central to investment decision-making. For a single security, risk represents the uncertainty of expected outcomes, while return reflects the gains or losses achieved over a specific period. This chapter defines the main categories of risk relevant to investors in Malaysia, outlines approaches to measuring return, and critically discusses the methods used to estimate risk in thinly traded markets such as Bursa Malaysia.

Types of Risk

Market and Idiosyncratic Risk

The total risk of a security can be decomposed into systematic (market-related) and unsystematic (firm-specific) components. Systematic risk is measured by beta, which reflects sensitivity to market movements, while idiosyncratic risk is unique to the firm and can be diversified away. In markets with infrequent trading, such as Malaysia, beta estimates are biased downward. Corrections proposed by Scholes and Williams (1977) and Dimson (1979) are widely applied in such settings.

Liquidity Risk and Market Microstructure

Liquidity risk arises when investors are unable to transact quickly without significantly affecting prices. Bursa Malaysia operates two main trading sessions with opening and closing phases, including a “trading at last” mechanism (Bursa Malaysia, 2024-a). Additionally, static and dynamic price limits, as well as circuit breakers, constrain daily price movements to stabilize the market (Bursa Malaysia & Securities Commission Malaysia, 2021). While these controls reduce volatility, they also distort tail-risk estimation.

Interest Rate and Credit Risk

For equities, interest rate changes influence discount rates, sector sensitivities, and ultimately firm valuations. For fixed income securities, bond prices are directly affected by yield fluctuations. In Malaysia, Malaysian Government Securities (MGS) serve as the reference curve, with the 10-year MGS yield often adopted as a proxy for the long-term risk-free rate (Bank Negara Malaysia, 2025).

Currency and Policy Risk

Firms engaged in international trade face exchange-rate exposure. Currency movements can affect both revenues and costs. Malaysia’s Foreign Exchange Policy, regulated by Bank Negara Malaysia, imposes limits on residents’ holdings of foreign-currency assets, which shapes investor behaviour and portfolio diversification strategies (Bank Negara Malaysia, 2024-a).

Regulatory and Shariah Compliance Risk

In Malaysia’s dual financial system, Shariah compliance introduces a distinct risk factor. Securities classified as Shariah-compliant are screened based on activity and financial ratios by the Securities Commission Malaysia. Changes in classification can significantly affect demand and liquidity, especially from Islamic funds (Securities Commission Malaysia, 2024).

Event and Corporate Action Risk

Corporate actions such as rights issues, share splits, and dividends directly affect shareholder value. Bursa Malaysia provides guidelines and adjustments to reference prices during such events, including the calculation of the Theoretical Ex-Rights Price (TERP) (Bursa Malaysia, 2024-b). Without proper adjustments, return calculations may be distorted around ex-dates.

Measurement of Return

Return Definitions

For a given security, the holding-period return is calculated as:

Where Pt​  is the price at time t and Dt is the dividend received. Log returns, defined as rt=ln(1+Rt) are often preferred in regression analyses due to their additive properties.

Corporate-Action Adjustments

Dividend payments reduce prices on ex-dividend dates by approximately the dividend amount. For rights issues, the TERP is calculated using a weighted average of the old and rights prices, adjusted for post-issue share count (Investopedia, 2024-a). Bursa Malaysia ensures that adjustments are incorporated into reference prices to maintain data accuracy (Bursa Malaysia, 2024-b).

Frequency and Thin-Trading Bias

Thin trading in emerging markets introduces estimation bias. Using daily data without adjustments can underestimate beta and volatility. Two approaches are common: employing weekly returns to reduce noise or applying the Scholes–Williams and Dimson corrections to daily data (Dimson, 1979; Scholes & Williams, 1977).

Benchmarks and Risk-Free Rate

For equities, the FTSE Bursa Malaysia KLCI (FBM KLCI) is often adopted as the market benchmark. The risk-free rate is proxied by Malaysian Government Securities yields, with the maturity aligned to the investment horizon (Bank Negara Malaysia, 2025).

Tax Adjustments

Malaysia’s tax regime has historically allowed dividends under the single-tier system to be exempt from further taxation. However, starting in 2025, dividends exceeding RM100,000 annually will be taxed at 2 percent (Ministry of Finance Malaysia, 2024). For academic consistency, studies should clarify whether returns are reported before or after tax.

Measurement of Risk

Total Risk

Total risk is captured by the variance or standard deviation of returns. Daily volatility estimates in Malaysia may be distorted by price limits; hence, weekly returns are sometimes used for greater reliability (Bursa Malaysia & Securities Commission Malaysia, 2021).

Systematic Risk (Beta)

Systematic risk is measured using the market model:

where βi is the sensitivity to market returns. Adjustments for non-synchronous trading, such as Scholes–Williams and Dimson methods, are critical in the Malaysian context (Dimson, 1979; Scholes & Williams, 1977).

Idiosyncratic Risk

Idiosyncratic variance is measured using residuals from the market model. The R² of the regression indicates the extent to which market movements explain a stock’s returns. For illiquid firms, a low R² is expected due to market microstructure effects.

Liquidity Risk

Liquidity can be measured using turnover ratios or price-impact metrics such as the Amihud measure. In Malaysia, these indicators must be interpreted against Bursa’s trading limits and circuit breaker policies (Bursa Malaysia & Securities Commission Malaysia, 2021).

Tail Risk

Tail risk measures such as Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) capture downside extremes. Because Bursa imposes static and dynamic limits on daily movements, researchers must account for truncated tails when computing risk estimates.

Risk and return measurement for single securities in Malaysia requires careful consideration of market structures, regulatory requirements, and corporate action adjustments. Thin trading and price limits demand methodological corrections, while the dual conventional-Islamic market framework introduces unique compliance risks. The next chapter extends this discussion by examining how these single-security measures integrate into portfolio construction and optimization.

Portfolio Risk and Return

While Chapter 2 established the building blocks of single-security risk and return, this chapter extends the analysis to portfolio construction. The essence of portfolio theory lies in diversification, which reduces unsystematic risk without sacrificing expected return. In the Malaysian context, portfolios often include equities listed on Bursa Malaysia, bonds and sukuk issued in domestic markets, and collective investment vehicles such as unit trust funds. This chapter critically examines the principles of traditional portfolio analysis, introduces the Sharpe single index model as a simplified alternative, and discusses how both approaches can be applied using Malaysian data.

Traditional Portfolio Analysis

Mean-Variance Framework

The mean-variance framework developed by Markowitz (1952) remains the cornerstone of modern portfolio theory. In this framework, the expected return of a portfolio is the weighted average of the expected returns of individual securities, while portfolio variance depends not only on the variances of each security but also on the covariances among them. Diversification benefits are realized when securities with less than perfect correlation are combined, reducing overall risk.

In the Malaysian context, this framework is particularly relevant because sectors such as plantation, banking, and telecommunications exhibit varying sensitivities to economic cycles. For instance, plantation stocks may respond more strongly to global commodity prices, while banking stocks are influenced by domestic monetary policy. The correlation among these sectors is less than one, enabling diversification benefits for investors (Ng, 2018).

Efficient Frontier and Optimal Portfolio

The efficient frontier represents the set of portfolios that offer the highest expected return for a given level of risk. Investors are assumed to be risk-averse, choosing portfolios along this frontier depending on their utility preferences. Malaysian studies show that portfolios composed of both conventional and Shariah-compliant securities can achieve efficiency gains by broadening the investment universe while adhering to religious preferences (Adam & Shahar, 2015).

Critiques and Limitations

Although the Markowitz model provides a rigorous framework, it requires estimation of a large number of covariance terms when the number of securities increases. This estimation burden can be problematic in emerging markets such as Malaysia, where thin trading and corporate exercises complicate the calculation of stable variance-covariance matrices (Yusof & Majid, 2007).

The Sharpe Single Index Model

Conceptual Foundation

To address the limitations of the traditional model, Sharpe (1963) proposed the single index model, which simplifies risk estimation by assuming that the return on any security is linearly related to the return on a market index. The model reduces the number of parameters to be estimated by replacing the covariance structure with beta coefficients relative to the index.

Mathematically, the return on security i is expressed as:

where Rm is the return on the market index, βi measures systematic riski, and εi represents unsystematic risk.

Application in Malaysia

The single index model is especially relevant in Malaysia because the market is dominated by a relatively small number of large-cap stocks that heavily influence the FBM KLCI. Studies find that the model performs reasonably well in approximating portfolio risk-return trade-offs, especially for retail investors with limited resources to compute complex covariance matrices (Lean & Wong, 2004).

Strengths and Weaknesses

The model’s primary strength is computational simplicity. It allows for the construction of optimal portfolios by ranking securities based on their excess return to beta ratio. However, the model assumes that market returns fully capture the common movements of securities, which may not hold in Malaysia where sector-specific shocks, such as fluctuations in palm oil prices or policy changes in the Islamic finance industry, can independently drive returns (Rahim & Masih, 2016).

Diversification Benefits in the Malaysian Market

Diversification across sectors and asset classes is critical in Malaysia, where volatility in one segment can be offset by stability in another. Empirical evidence suggests that combining equities with sukuk reduces portfolio variance, as sukuk returns often exhibit low correlation with equity returns (Majid & Yusof, 2009). In addition, incorporating Shariah-compliant securities does not reduce diversification benefits; instead, it widens participation and improves overall market depth (Adam & Shahar, 2015).

Portfolio diversification also extends to geographic allocation. Although this study focuses on Malaysia, many institutional investors diversify regionally into ASEAN markets to mitigate domestic shocks. However, this introduces currency risk, which must be measured and managed through hedging strategies (Hooy & Goh, 2010).

Portfolio Construction for This Study

This research constructs sample portfolios using equities drawn from key Bursa Malaysia sectors: banking, telecommunications, technology, and plantation. These sectors are selected to capture distinct risk-return profiles. Weekly adjusted returns are used to address thin-trading bias, and corporate actions are adjusted based on Bursa reference prices. The risk-free rate is proxied by the three-month Malaysian Treasury bill, while the FBM KLCI serves as the market benchmark (Bank Negara Malaysia, 2025; Bursa Malaysia, 2024-a).

Two sets of portfolios will be developed. First, a traditional mean-variance portfolio will be optimized using covariance matrices. Second, a Sharpe single index portfolio will be constructed by ranking securities on excess return to beta ratios. The comparative results will highlight whether the computationally simpler Sharpe model is sufficient in the Malaysian context.

Implications for Investors and Fund Managers

For retail investors, the Sharpe model provides a practical tool to construct portfolios without requiring advanced statistical tools. For institutional investors and unit trust managers, the traditional mean-variance approach may still be superior given the resources to estimate and update covariance matrices. Importantly, the coexistence of both approaches suggests that portfolio optimization in Malaysia must balance accuracy with practicality.

The findings from this chapter are crucial for Chapter 5, which will evaluate managed portfolio performance using Sharpe, Treynor, and Jensen indexes. By comparing the two optimization methods, this study contributes empirical evidence on whether simpler models can guide portfolio allocation decisions in emerging markets.

This chapter discussed portfolio risk and return from two main perspectives: the traditional mean-variance framework and the Sharpe single index model. While the former offers theoretical precision, the latter provides practical simplicity. In Malaysia, where markets face issues of thin trading, regulatory constraints, and sectoral concentration, both approaches offer valuable insights. The chapter concluded with the construction of sample portfolios that will form the basis for performance evaluation in subsequent chapters.

Economics and Industry Analysis

Investment decisions are shaped not only by the attributes of individual securities or portfolio construction techniques, but also by the broader economic and industry environment in which firms operate. Economic and industry analysis provides the framework to assess the sustainability of earnings, the resilience of sectors, and the prospects for long-term value creation. In Malaysia, this analysis requires an integrated understanding of macroeconomic conditions, industry life cycles, and sectoral drivers that influence corporate performance and investor returns.

Key Characteristics of Economic and Industry Analysis

Role of Macroeconomic Variables

Macroeconomic conditions such as GDP growth, inflation, interest rates, and exchange rates significantly influence corporate earnings and asset prices. For example, Malaysia’s GDP growth averaged 3.7 percent in 2023, supported by resilient domestic demand, while headline inflation moderated to 2.5 percent, providing stability for corporate margins (Bank Negara Malaysia, 2024). Monetary policy decisions by Bank Negara Malaysia, including adjustments to the Overnight Policy Rate, directly affect borrowing costs for firms and the discount rate applied in valuation models (Bank Negara Malaysia, 2023).

In addition, the global economic environment influences Malaysia through its openness to trade. Exports of commodities such as palm oil and natural gas, as well as manufactured goods like electronics, are sensitive to global demand cycles. This exposure introduces both opportunities during expansionary periods and vulnerabilities during global downturns (International Monetary Fund, 2024).

Industry and Sectoral Drivers

Industry analysis identifies structural factors that shape firm competitiveness and profitability. Porter’s five forces framework remains a useful lens to examine rivalry, entry barriers, supplier power, buyer power, and substitution threats (Porter, 1980). In Malaysia, plantation firms face substitution risks from alternative oils, while the telecommunications sector is shaped by high entry costs and technological disruption. Financial services remain sensitive to regulation and fintech innovation.

The presence of Islamic finance as a parallel system adds another layer of industry dynamics. Sukuk issuance has grown steadily, positioning Malaysia as the world’s largest sukuk market, accounting for nearly 40 percent of global outstanding sukuk (Securities Commission Malaysia, 2023). This growth enhances financing options and expands the universe of Shariah-compliant investments.

Relevance to Investment Strategy

Economic and industry analysis bridges the gap between top-down macro forecasting and bottom-up company valuation. For portfolio managers, sector rotation strategies rely on assessing which industries are poised to benefit from current macroeconomic conditions. For instance, when interest rates rise, banking stocks may outperform due to higher net interest margins, while rate-sensitive sectors such as property and construction may experience reduced valuations (Lim & Hooy, 2012).

Industry Life Cycle

Conceptual Framework

The industry life cycle consists of four main stages: introduction, growth, maturity, and decline. Each stage reflects differences in profitability, risk, and valuation multiples (Grant, 2016). Investors must align portfolio allocations with industries at favorable stages of the cycle to optimize returns.

Application to Malaysian Sectors

Several Malaysian industries exhibit distinctive life-cycle positions. The technology sector, driven by semiconductor exports and digital adoption, is still in the growth stage, with above-average earnings growth and high volatility. By contrast, the plantation industry is in maturity, characterized by stable demand for palm oil but constrained by environmental regulations and sustainability concerns (Abdullah & Wahid, 2018). Telecommunications, on the other hand, is in late maturity, with saturated domestic markets and a shift toward consolidation and diversification into digital services.

Banking remains in maturity but continues to generate steady earnings due to Malaysia’s relatively high household savings rate and the growing demand for Islamic banking products (Majid & Kassim, 2015). In contrast, industries such as print media are in structural decline, with advertising shifting to digital platforms and revenues shrinking.

Implications for Portfolio Construction

Mapping industries to their life cycle stages has direct implications for portfolio strategy. Growth-stage industries offer potential for capital appreciation but carry higher risk. Mature industries provide stable dividends and lower volatility, making them attractive for defensive portfolios. Declining industries may still yield returns through restructuring or niche opportunities, but they require cautious allocation. For Malaysian investors, a balanced portfolio might combine growth exposure to technology with defensive holdings in banking and telecommunications.

Critical Issues in Malaysian Industry Analysis

Sustainability and ESG Integration

Global and domestic investors increasingly demand that industries integrate environmental, social, and governance (ESG) factors. Malaysia’s plantation sector faces scrutiny over deforestation, while the energy sector confronts decarbonization pressures. Bursa Malaysia has introduced ESG disclosure requirements to improve transparency, which influences valuations and portfolio inclusion (Bursa Malaysia, 2023).

Digitalization and Technological Disruption

Technological change reshapes industries at different speeds. Malaysia’s Industry 4.0 policy aims to accelerate digital adoption, benefiting the technology sector and manufacturing supply chains. However, disruption also erodes traditional sectors such as retail and media. Investors must assess not only growth opportunities but also obsolescence risks (OECD, 2022).

Global Integration and Trade Vulnerability

Malaysia’s high trade-to-GDP ratio implies that global supply chain disruptions, such as semiconductor shortages or geopolitical conflicts, can rapidly spill into domestic industries. COVID-19 demonstrated the fragility of logistics, while recent trade tensions highlighted the risks of overdependence on specific markets. Portfolio managers must monitor global developments alongside domestic indicators (World Bank, 2023).

Chapter Summary

This chapter emphasized that economic and industry analysis is a vital complement to security selection and portfolio optimization. Macroeconomic stability, sectoral competitiveness, and industry life cycle stages all shape expected returns and risks. In Malaysia, distinctive features such as the prominence of Islamic finance, reliance on exports, and policy-driven sectoral support provide unique opportunities and challenges. For investors, understanding which industries are poised for growth or in decline informs portfolio strategy and performance evaluation.

The next chapter will move from industry-level assessment to company analysis and valuation, focusing on forecasting earnings, projecting dividends, and estimating intrinsic value and earning multipliers.

Company Analysis and Valuation

Company analysis and valuation translate macroeconomic and industry insights into firm-level assessments of intrinsic value. Investors must evaluate earnings potential, dividend policies, and valuation multiples to determine whether a security is overvalued or undervalued relative to market prices. In Malaysia, firm-level analysis is influenced by disclosure requirements under Bursa Malaysia’s listing rules, accounting standards, and the regulatory oversight of the Securities Commission Malaysia. This chapter examines approaches to measuring and forecasting earnings, projecting dividends, estimating intrinsic value, and determining appropriate earning multipliers, with applications to Malaysian firms across key sectors.

Measuring and Forecasting Earnings

Earnings as a Valuation Anchor

Earnings are central to valuation because they reflect profitability after expenses, taxes, and interest. In Malaysia, listed companies are required to publish quarterly financial statements, which are reviewed by external auditors and disclosed through Bursa announcements (Bursa Malaysia, 2023). These disclosures provide transparency and serve as primary inputs for investors’ earnings forecasts.

Forecasting Methods

Earnings forecasts can be developed using historical trend analysis, regression models, and sector-adjusted expectations. Analysts also incorporate macroeconomic assumptions such as GDP growth, inflation, and exchange rates, as these variables influence revenue and cost structures (Bank Negara Malaysia, 2024). For example, plantation companies’ earnings are heavily affected by crude palm oil prices, while banking earnings are tied to interest margins and monetary policy settings (Majid & Kassim, 2015).

Limitations and Risks

Forecasting earnings involves uncertainty due to accounting discretion, cyclical shocks, and unexpected corporate actions. Empirical studies note that earnings forecasts in emerging markets often exhibit optimism bias, requiring adjustment for overestimation tendencies (Kim & Lim, 2010). For Malaysian firms, this risk is compounded by volatility in commodity-linked sectors and exposure to global demand cycles.

Projecting Dividends

Dividend Policy in Malaysia

Dividend projections are critical because many investors rely on dividends as a source of income. Malaysia operates under a single-tier system in which dividends were previously exempt from further taxation at the shareholder level. However, beginning in 2025, dividends exceeding RM100,000 annually will be taxed at 2 percent (Ministry of Finance Malaysia, 2024). This policy shift may influence payout ratios, especially for high-dividend sectors such as banking and utilities.

Dividend Discount Model

The dividend discount model (DDM) values a stock as the present value of expected future dividends. For stable firms, the Gordon Growth Model assumes dividends grow at a constant rate:

where P₀​ is the current price, D₁​ is the expected dividend, k_e is the required rate of return, and g is the growth rate. For firms with variable growth, multi-stage models are applied. Malaysian firms in cyclical industries such as construction often exhibit variable payout patterns, making multi-stage models more realistic (Rahim & Masih, 2016).

Practical Considerations

Dividend projections must adjust for corporate actions, such as bonus issues or rights issues, which dilute per-share dividends. Analysts also consider regulatory influences; for example, Bank Negara Malaysia imposes capital adequacy requirements on banks that indirectly constrain payout ratios (Bank Negara Malaysia, 2023).

Estimating Intrinsic Value

Discounted Cash Flow (DCF) Models

DCF valuation estimates intrinsic value by discounting expected future cash flows to the present. Free cash flow to equity (FCFE) models are particularly useful for Malaysian firms with complex dividend policies, as they capture residual cash available to shareholders after reinvestment and debt servicing (Damodaran, 2012).

Relative Valuation

Relative valuation uses multiples such as price-to-earnings (P/E), price-to-book (P/B), and enterprise value-to-EBITDA (EV/EBITDA). In Malaysia, P/E ratios vary widely by sector, with technology firms often trading at higher multiples than plantation or utility firms due to growth expectations (Ng, 2018). Shariah-compliant firms are sometimes priced at a premium due to strong institutional demand from Islamic funds (Adam & Shahar, 2015).

Intrinsic Value versus Market Price

When intrinsic value exceeds market price, a stock is undervalued and considered a buy. Conversely, if intrinsic value is below market price, the stock is overvalued. Studies on Bursa Malaysia indicate that value-based strategies have historically outperformed growth-based strategies, highlighting the importance of intrinsic value analysis (Lean & Wong, 2004).

Estimating Earning Multipliers

Determinants of P/E Ratios

Earning multipliers such as the P/E ratio depend on growth prospects, risk profile, and payout policies. High-growth industries such as semiconductors and technology justify higher P/E ratios, while mature industries such as banking or telecommunications tend to trade at lower multiples (Grant, 2016).

Empirical Evidence from Malaysia

Evidence from Bursa Malaysia suggests that P/E ratios are sensitive to macroeconomic conditions and investor sentiment. For instance, during periods of low interest rates, equity valuations expand as investors are willing to pay higher multiples for earnings streams (Lim & Hooy, 2012). Conversely, when rates rise, valuations contract as discount rates increase.

Valuation Pitfalls

Relying exclusively on multipliers can be misleading, particularly when earnings are volatile or accounting practices differ across firms. For Malaysian small-cap firms, thin trading and corporate governance issues may distort P/E ratios, necessitating a triangulation approach that combines multipliers with DCF and relative valuation techniques (Yusof & Majid, 2007).

This chapter emphasized the importance of company analysis and valuation in translating macroeconomic and industry insights into firm-level investment decisions. Forecasting earnings requires incorporating both historical data and macroeconomic assumptions, while dividend projections rely on regulatory context and payout history. Intrinsic value estimation through discounted cash flow and relative valuation provides a benchmark against which market prices can be assessed. Finally, earning multipliers offer useful comparative metrics, but they must be interpreted critically to avoid misleading conclusions.

The next chapter will focus on technical analysis, exploring how chart patterns, indicators, and market efficiency theories complement fundamental valuation approaches.

Technical Analysis

While fundamental analysis relies on earnings, dividends, and intrinsic value, technical analysis focuses on market data such as prices, trading volume, and historical patterns. Technical analysts argue that market movements exhibit recurring trends and signals that can be exploited for profitable trading. In Malaysia, technical analysis is widely practiced by retail investors, with Bursa Malaysia offering online trading platforms and charting tools. This chapter critically reviews three pillars of technical analysis: the Efficient Market Hypothesis and its competing theories, chart patterns and visual representations, and technical indicators used in Malaysian and global markets.

Efficient Market Hypothesis, Dow Theory, and Random Walk Theory

Efficient Market Hypothesis (EMH)

The EMH asserts that securities prices fully reflect available information. Fama (1970) formalized three forms of efficiency: weak form, semi-strong form, and strong form. In the weak form, prices reflect all past information such as historical prices and volume, implying that technical analysis cannot consistently generate excess returns. In the semi-strong form, prices incorporate all publicly available information, while in the strong form, even insider information is instantly reflected.

Empirical evidence from Malaysia suggests that the market is not perfectly efficient in the weak form. Studies show that returns on the Kuala Lumpur Stock Exchange (now Bursa Malaysia) display some degree of autocorrelation, implying that patterns in past prices may still be useful for short-term prediction (Lim & Brooks, 2011).

Dow Theory

Dow Theory provides the conceptual basis for technical analysis. It emphasizes that markets move in three types of trends: primary (long-term), secondary (intermediate), and minor (short-term). Confirmation between industrial and transportation averages was considered necessary for identifying a trend. Although developed in the United States, the principles of Dow Theory are applied by Malaysian traders who monitor cyclical shifts between plantation, banking, and industrial sectors.

Random Walk Theory

In contrast, the Random Walk Theory suggests that price changes are independent and identically distributed, meaning that past movements cannot predict future prices. This aligns with the weak form of EMH. If the random walk holds, charting and technical indicators provide no systematic advantage. Malaysian evidence remains mixed. Some studies find evidence of mean reversion in thinly traded stocks, while others find near-random behaviour in highly liquid large-cap stocks (Hooy & Lim, 2013).

Types of Charts and Patterns

Line, Bar, and Candlestick Charts

The most common visualization methods are line charts, bar charts, and candlestick charts. Line charts connect daily closing prices, providing a simple view of trends. Bar charts add information on open, high, low, and close prices. Candlestick charts, originating from Japan, highlight bullish or bearish sentiment through color coding and body length. Bursa Malaysia’s trading platforms offer candlestick charts as a default, reflecting their popularity among Malaysian investors.

Chart Patterns

Technical analysts identify continuation and reversal patterns in charts. Continuation patterns, such as flags and pennants, indicate that an existing trend is likely to persist. Reversal patterns, including head-and-shoulders and double tops, signal trend changes. Malaysian traders often rely on these visual cues, particularly in sectors prone to cycles such as plantation and property (Ng & Chong, 2015).

Critical Limitations

Patterns are not foolproof and are subject to interpretation bias. Critics argue that the recognition of patterns is partly psychological, leading to inconsistent results. Furthermore, in Malaysia, corporate actions such as rights issues and bonus issues can distort chart patterns if price adjustments are not properly made.

Technical Indicators

Moving Averages

Moving averages smooth out price fluctuations to identify underlying trends. The simple moving average (SMA) calculates the average price over a given period, while the exponential moving average (EMA) assigns greater weight to recent prices. The 50-day and 200-day moving averages are commonly applied by Malaysian analysts to identify golden cross and death cross signals.

Momentum Indicators

Momentum indicators such as the Relative Strength Index (RSI) and the Moving Average Convergence Divergence (MACD) measure the speed and magnitude of price changes. An RSI above 70 is often interpreted as overbought, while below 30 indicates oversold conditions (Wilder, 1978). The MACD captures the difference between two moving averages and is used to identify potential turning points.

Volume-Based Indicators

On-balance volume (OBV) and accumulation-distribution lines integrate trading volume with price changes. These indicators are relevant in Malaysia because trading volumes often surge during corporate announcements or speculative episodes, offering clues about market sentiment (Lean & Wong, 2004).

Volatility Indicators

Bollinger Bands plot standard deviations above and below a moving average, indicating periods of high and low volatility. For Malaysian stocks subject to daily price limits, Bollinger Bands can help distinguish between temporary spikes and genuine breakouts.

Technical Analysis in the Malaysian Context

Retail Investor Behaviour

Retail investors in Malaysia are the primary users of technical analysis. Surveys show that retail traders frequently adopt chart-based strategies due to limited access to fundamental research (SC Malaysia, 2023).

Institutional Use

Institutional investors, including fund managers, often blend technical indicators with fundamental analysis, particularly for short-term asset allocation and timing decisions. For example, moving average crossovers may complement valuation-based entry or exit points.

Empirical Evidence

Studies on Bursa Malaysia provide mixed results. Lean and Wong (2004) found that moving average rules generated some predictive power in the Malaysian market. However, more recent research shows diminishing profitability of technical trading rules as market efficiency improves (Hooy & Lim, 2013).

This chapter highlighted the theoretical foundations and practical tools of technical analysis. The Efficient Market Hypothesis and Random Walk Theory challenge the usefulness of chart-based strategies, while Dow Theory provides the intellectual basis for trend analysis. Technical tools such as charts, moving averages, momentum indicators, and volume measures remain widely applied by Malaysian investors, especially retail traders. Although empirical evidence suggests that technical analysis is less effective in highly efficient markets, its continued use in Malaysia indicates that inefficiencies still exist, particularly in small-cap and thinly traded stocks.

The next chapter will turn to bond analysis and valuation, where price volatility, bond types, and valuation methods will be critically assessed within the Malaysian context.

Bond Analysis and Valuation

Bonds are essential instruments in the Malaysian capital market, serving as both financing tools for issuers and income-generating assets for investors. Alongside equities, the bond market provides diversification opportunities, particularly for risk-averse investors. Malaysia’s bond market is distinctive because it includes both conventional bonds and sukuk, making it the world’s largest sukuk market by outstanding value (Securities Commission Malaysia, 2023). This chapter examines the types of bonds available in Malaysia, the methods for bond valuation, and the factors that influence price volatility.

Types of Bonds

Government Bonds

The Malaysian Government Securities (MGS) and Government Investment Issues (GII) are the primary sovereign debt instruments. MGS are conventional bonds, while GII are sukuk issued to meet Shariah-compliant financing needs. Both instruments are regularly auctioned by Bank Negara Malaysia to support fiscal financing and monetary policy operations (Bank Negara Malaysia, 2024).

Corporate Bonds

Corporate issuers include banks, infrastructure firms, and large conglomerates. Corporate bonds offer higher yields than government securities but carry additional credit risk. Credit ratings assigned by agencies such as RAM Ratings and MARC guide investors in assessing default probability (Rahman & Ariff, 2007).

Sukuk

Sukuk are Islamic financial certificates structured to comply with Shariah principles, which prohibit interest. Instead, sukuk represent partial ownership in tangible assets or projects, and investors receive returns linked to asset performance. Malaysia accounts for nearly 40 percent of global sukuk outstanding, with issuances across sovereign, quasi-sovereign, and corporate entities (Securities Commission Malaysia, 2023).

Hybrid and Other Instruments

In addition to plain-vanilla bonds, Malaysia’s market features medium-term notes, perpetual bonds, and structured products. Some instruments combine equity-like features such as convertibility, blurring the line between debt and equity securities.

Bond Valuation

Present Value Framework

The value of a bond is the present value of its future cash flows, discounted at an appropriate rate:

where C is the coupon payment, F is the face value, y is the yield to maturity, and T is the number of periods.

Yield Measures

The most commonly used measures are:

• Current yield: annual coupon divided by current market price.
• Yield to maturity (YTM): the internal rate of return if the bond is held to maturity.
• Yield spread: the difference between yields on corporate bonds and comparable MGS, reflecting credit risk.

In Malaysia, yield spreads vary across sectors. Banking and infrastructure bonds often trade with narrower spreads due to perceived stability, while smaller corporate issuers face wider spreads (Lim & Hooy, 2012).

Sukuk Valuation

Valuation of sukuk requires adjustments to reflect their asset-backed or asset-based structures. Cash flows are linked to specific contracts such as ijarah (leasing), murabahah (cost-plus sale), or musharakah (profit-sharing). Although discounting mechanisms are similar to conventional bonds, valuation must ensure compliance with Shariah principles (Majid & Yusof, 2009).

Price Volatility of Bonds

Interest Rate Sensitivity

Bond prices move inversely with interest rates. Duration measures the weighted average time to receive cash flows and approximates price sensitivity to changes in yield. Convexity refines this estimate, capturing curvature in the price-yield relationship (Fabozzi, 2015). In Malaysia, rate changes by Bank Negara Malaysia directly affect MGS yields, which in turn influence the pricing of corporate bonds and sukuk.

Credit Risk and Rating Changes

Bond volatility is also driven by changes in perceived creditworthiness. A downgrade by RAM Ratings or MARC often results in immediate price declines. Conversely, upgrades can narrow spreads and raise bond prices (Rahman & Ariff, 2007).

Liquidity Risk

Liquidity risk arises when investors cannot transact without affecting prices. While MGS are highly liquid, corporate bonds are less actively traded. Sukuk liquidity has improved in recent years due to growing institutional participation, but secondary market turnover remains lower than for government securities (Securities Commission Malaysia, 2023).

Global and Domestic Shocks

External shocks such as U.S. interest rate changes, global commodity price fluctuations, or geopolitical tensions spill over into Malaysian bond yields. During the COVID-19 crisis, bond spreads widened significantly, reflecting increased risk aversion (World Bank, 2021).

Implications for Portfolio Management

Understanding bond types, valuation, and volatility is critical for portfolio construction. For risk-averse Malaysian investors, government securities provide stable income and serve as benchmarks for pricing other instruments. Corporate bonds and sukuk enhance returns but require careful credit risk assessment. From a diversification perspective, bonds provide lower correlation with equities, reducing overall portfolio volatility (Majid & Kassim, 2015). Active managers exploit yield curve shifts and credit spread movements, while passive managers replicate bond indexes to capture broad market returns.

Chapter Summary

This chapter outlined the structure and valuation of Malaysia’s bond market, highlighting its distinctive feature as the global leader in sukuk issuance. Valuation methods rely on discounting future cash flows, while volatility is influenced by interest rates, credit quality, liquidity, and global shocks. For investors, incorporating bonds into portfolios provides diversification benefits, balancing risk and return. The next chapter will explore bond and equity portfolio management strategies, analyzing active and passive approaches and the role of asset allocation in the Malaysian context.

Bond and Equity Portfolio Management Strategies

Portfolio management strategies aim to balance risk and return through the selection and allocation of assets. Two key approaches dominate investment practice: active management, where managers seek to outperform benchmarks through timing and selection, and passive management, where portfolios replicate indexes to achieve market returns. Beyond this dichotomy, asset allocation decisions between equities, bonds, and alternative assets are central in determining portfolio outcomes. This chapter explores active and passive strategies, with emphasis on Malaysia’s institutional and retail investment environment, and discusses how asset allocation shapes risk-return trade-offs.

Active Versus Passive Strategies

Active Management

Active management involves security selection, market timing, and sector rotation. Fund managers attempt to exploit mispricing, earnings surprises, or macroeconomic shifts to generate excess returns, or alpha, over a benchmark index. In Malaysia, active managers often focus on small- and mid-cap equities, which are more prone to inefficiencies due to thin trading and lower analyst coverage (Lean & Wong, 2004).

Bond fund managers pursue active strategies through yield-curve positioning, duration management, and credit spread trades. For example, when Bank Negara Malaysia signals tightening, managers may shorten portfolio duration to reduce interest rate risk (Lim & Hooy, 2012).

Passive Management

Passive strategies replicate a market index such as the FTSE Bursa Malaysia KLCI for equities or the BPAM bond index for fixed income. These strategies appeal to investors who believe that markets are efficient and that active management rarely delivers consistent outperformance after fees (Fama & French, 2010).

In Malaysia, the growth of exchange-traded funds (ETFs) reflects increasing demand for passive products. For instance, the MyETF Dow Jones Islamic Market Malaysia Titans 25 allows investors to access a diversified Shariah-compliant portfolio at low cost (Securities Commission Malaysia, 2023).

Comparative Evidence

Empirical evidence indicates that few Malaysian active fund managers consistently outperform benchmarks over long horizons, especially after accounting for management fees. Studies have shown that the Sharpe ratios of passive portfolios often rival or exceed those of actively managed funds (Abdullah, Hassan, & Mohamad, 2007). Nevertheless, active strategies remain popular among investors seeking alpha, particularly during volatile periods when passive strategies expose investors to market-wide declines.

Asset Allocation Strategies

Strategic Asset Allocation

Strategic asset allocation sets long-term target weights across asset classes based on investor risk tolerance, return objectives, and investment horizon. This approach assumes that while individual securities may fluctuate, asset class returns are relatively predictable over long horizons. For Malaysian investors, a balanced allocation may combine equities for growth, government bonds for stability, and sukuk for both diversification and compliance with Islamic finance principles (Majid & Yusof, 2009).

Tactical Asset Allocation

Tactical asset allocation allows for short-term adjustments based on market conditions. For instance, investors may increase exposure to equities when GDP growth is strong or shift toward bonds when interest rates are expected to decline. In Malaysia, tactical allocation strategies often consider global commodity prices, such as crude palm oil, which heavily influence plantation and export-related stocks (Abdullah & Wahid, 2018).

Dynamic Asset Allocation

Dynamic allocation strategies continuously adjust portfolios as markets evolve, aiming to capture upside potential while minimizing downside risk. These strategies often employ quantitative models, including risk parity and momentum-based allocation. Institutional investors in Malaysia, such as the Employees Provident Fund, apply dynamic frameworks to manage large pools of assets across multiple geographies and asset classes (EPF, 2024).

Equity Portfolio Management in Malaysia

Sector Rotation

Equity managers in Malaysia frequently engage in sector rotation, capitalizing on the cyclical nature of industries. For example, banking stocks are favoured when interest margins widen, while technology stocks are emphasized during global semiconductor upcycles (Ng, 2018). Sector allocation is often informed by industry life cycle analysis, as discussed in Chapter 4.

Shariah-Compliant Equity Portfolios

Shariah-compliant equity funds must adhere to screening criteria issued by the Securities Commission Malaysia. These include restrictions on revenue from interest, gambling, and alcohol, as well as financial ratio limits (Securities Commission Malaysia, 2022). While this narrows the investable universe, Shariah-compliant equities often attract strong institutional demand, leading to stable long-term performance (Adam & Shahar, 2015).

Bond Portfolio Management in Malaysia

Immunization Strategies

Immunization involves structuring a bond portfolio such that changes in interest rates do not significantly affect its value. This is achieved by matching the duration of assets with the investment horizon. Malaysian institutional investors such as insurance companies and pension funds apply immunization to meet future liabilities (Fabozzi, 2015).

Active Bond Management

Active bond managers in Malaysia exploit yield curve shifts, credit spread changes, and relative value between sukuk and conventional bonds. Sukuk issuance has grown rapidly, offering opportunities to diversify fixed income portfolios. However, lower liquidity in the secondary sukuk market compared to government securities presents challenges (Securities Commission Malaysia, 2023).

Passive Bond Management

Passive bond strategies replicate bond indexes to achieve broad exposure. These approaches are less common in Malaysia than in developed markets, primarily due to limited bond ETF offerings. Nevertheless, passive strategies are gaining attention as the domestic bond market deepens.

Implications for Investors and Fund Managers

The choice between active and passive strategies depends on investor objectives, resources, and market beliefs. Retail investors in Malaysia often prefer active strategies through unit trusts, seeking professional expertise. Institutional investors blend active and passive strategies, using passive exposure for core allocations and active management for satellite positions. Asset allocation decisions, particularly the balance between equities, bonds, and sukuk, remain the most significant determinant of long-term portfolio performance (Brinson, Hood, & Beebower, 1986).

This chapter reviewed portfolio management strategies for equities and bonds in Malaysia. Active management aims for alpha through selection and timing, while passive management delivers benchmark performance at lower cost. Asset allocation, whether strategic, tactical, or dynamic, determines risk-return outcomes more significantly than individual security selection. In Malaysia, unique features such as Shariah-compliant equities and sukuk expand the scope of portfolio strategies while presenting challenges in liquidity and index replication.

The next chapter will examine unit trusts and warrants, focusing on fund types, valuation approaches, and the distinctive characteristics of warrants in the Malaysian capital market.

Unit Trusts and Warrants

Unit trusts and warrants represent distinct investment vehicles within the Malaysian capital market. Unit trusts provide investors with pooled funds managed by professionals, while warrants are derivative instruments that grant the right to buy or sell an underlying security at a specified price before expiry. Together, they expand the range of portfolio management strategies and investment opportunities. This chapter explores the types of unit trust funds, valuation methods, characteristics of warrants, and approaches to warrant valuation.

Types of Unit Trust Funds

Equity Funds

Equity unit trust funds invest primarily in shares listed on Bursa Malaysia. These funds may specialize in growth, value, or sector-based equities. Shariah-compliant equity funds apply screening criteria from the Securities Commission Malaysia, excluding firms involved in prohibited activities such as gambling or alcohol (Securities Commission Malaysia, 2022).

Bond and Sukuk Funds

Bond funds focus on government securities, corporate bonds, and sukuk. These funds appeal to risk-averse investors seeking regular income. Sukuk funds are particularly popular in Malaysia due to strong institutional and retail demand for Shariah-compliant investments (Majid & Yusof, 2009).

Balanced and Mixed-Asset Funds

Balanced funds combine equities and bonds to provide growth with stability. Asset allocation depends on fund objectives, ranging from conservative (greater bond exposure) to aggressive (greater equity exposure).

Money Market Funds

Money market funds invest in short-term instruments such as treasury bills and commercial papers. They offer liquidity and stability, making them attractive to investors with short investment horizons.

Index and Exchange-Traded Funds (ETFs)

Index funds replicate market benchmarks, while ETFs trade on Bursa Malaysia like stocks. The MyETF Dow Jones Islamic Market Malaysia Titans 25 is an example of a Shariah-compliant ETF, giving investors exposure to large-cap companies at low cost (Securities Commission Malaysia, 2023).

Valuation of Unit Trusts

Net Asset Value (NAV)

The value of a unit trust is based on its Net Asset Value per unit, calculated as:

NAV is updated daily and disclosed to investors through fund managers and the Federation of Investment Managers Malaysia (FIMM, 2024).

Performance Measurement

Performance is typically evaluated using risk-adjusted metrics such as the Sharpe ratio, Treynor ratio, and Jensen’s alpha. Malaysian evidence suggests that while some actively managed unit trusts outperform benchmarks in the short term, long-term persistence of superior performance is limited (Abdullah, Hassan, & Mohamad, 2007).

Costs and Fees

Unit trusts charge management fees and sales charges, which reduce net returns. For investors, cost efficiency is a critical consideration, especially when comparing actively managed funds with passive ETFs.

Characteristics of Warrants

Nature of Warrants

A warrant is a derivative that gives the holder the right, but not the obligation, to purchase or sell an underlying security at a specified exercise price before expiry. In Malaysia, warrants are often issued as equity-linked instruments attached to rights issues or as structured warrants listed separately on Bursa Malaysia (Bursa Malaysia, 2023).

Types of Warrants

• Company-issued warrants: issued alongside corporate exercises such as rights issues to raise capital.
• Structured warrants: issued by financial institutions, offering leveraged exposure to underlying shares or indexes.

Advantages and Risks

Warrants provide leverage, allowing investors to control a larger position with smaller capital. However, they carry high risk due to time decay and volatility sensitivity. Empirical studies show that Malaysian retail investors frequently trade structured warrants, but many incur losses due to speculative behavior and limited understanding of pricing dynamics (Cheong, 2017).

Valuation of Warrants

Intrinsic Value and Time Value

A warrant’s price consists of intrinsic value and time value. Intrinsic value is the difference between the underlying price and the exercise price, while time value reflects the potential for favourable price movements before expiry.

Option Pricing Models

The Black–Scholes model is widely applied to value European-style warrants:

where C is the call price, S₀​ is the current stock price, X is the exercise price, r is the risk-free rate, t is the time to maturity, and N(d) is the cumulative distribution function of the standard normal distribution.

In Malaysia, warrant valuation must account for market frictions such as price limits, liquidity constraints, and shorter maturities. For structured warrants, issuers provide theoretical pricing sheets, but actual trading prices often diverge due to speculation (Ng & Chong, 2015).

Implications for Portfolio Management

Unit Trusts

Unit trusts democratize access to professionally managed portfolios, offering diversification benefits to small investors. However, the high fees of actively managed funds relative to passive ETFs highlight the importance of cost efficiency. For Shariah-conscious investors, Islamic unit trusts provide both compliance and diversification.

Warrants

Warrants appeal to investors seeking leveraged exposure or hedging opportunities. They can enhance portfolio returns but also increase volatility. Institutional investors use warrants selectively, while retail participation remains speculative. From a risk management perspective, warrants should represent a small allocation in diversified portfolios.

This chapter examined two distinct investment vehicles in Malaysia. Unit trusts provide diversified, professionally managed portfolios across equities, bonds, sukuk, and money markets. Their valuation is anchored on NAV and assessed through risk-adjusted performance measures. Warrants, on the other hand, are high-risk derivatives that provide leverage and optionality, valued through intrinsic and time value decomposition or option pricing models. For investors, unit trusts serve as stable long-term vehicles, while warrants offer speculative or hedging tools.

The next chapter will explore capital market theory, focusing on the Capital Asset Pricing Model (CAPM) and Arbitrage Pricing Theory (APT), and how these frameworks apply to Malaysian markets.

Capital Market Theory Introduction

Capital market theory provides the foundation for understanding how risk and return are related in equilibrium. These models extend the mean–variance framework of Markowitz by linking expected returns of individual assets to their systematic risk. Among the most influential theories are the Capital Asset Pricing Model (CAPM) and the Arbitrage Pricing Theory (APT). Both models have been widely studied and tested in emerging markets, including Malaysia, with varying degrees of empirical support. This chapter critically examines CAPM and APT, evaluates their applications in the Malaysian context, and discusses their implications for investors and portfolio managers.

Capital Asset Pricing Model (CAPM)

Theoretical Foundation

The CAPM, developed by Sharpe (1964), Lintner (1965), and Mossin (1966), formalizes the relationship between expected return and systematic risk. The model posits that the expected return of a security is determined by the risk-free rate, the market risk premium, and the security’s beta:

where E(Ri) is the expected return, Rf is the risk-free rate, E(Rm) is the expected market return, and βi measures sensitivity to market movements.

Assumptions and Critiques

CAPM assumes that investors are rational and risk-averse, markets are frictionless, and all investors have homogeneous expectations. These assumptions are unrealistic in practice. For Malaysia, thin trading, transaction costs, and information asymmetries challenge CAPM’s assumptions (Yusof & Majid, 2007).

Empirical Evidence in Malaysia

Studies testing CAPM in Malaysia show mixed results. Lean and Wong (2004) found partial support for CAPM, with beta positively related to returns, although not always statistically significant. Later research highlights that beta alone does not fully explain cross-sectional variation in returns, suggesting the need for multifactor approaches (Hassan, Ooi, & Lean, 2019). The existence of anomalies such as size and value effects in Bursa Malaysia further challenges CAPM’s validity.

Implications for Investors

For practitioners, CAPM remains a useful tool for estimating the cost of equity in valuation and corporate finance, despite its limitations. Malaysian firms often rely on CAPM-derived discount rates when calculating weighted average cost of capital for project evaluation (Majid & Kassim, 2015).

Arbitrage Pricing Theory (APT)

Theoretical Foundation

APT, introduced by Ross (1976), relaxes many of CAPM’s restrictive assumptions. It proposes that asset returns are influenced by multiple systematic factors, expressed as:

where bij​ represents the sensitivity of asset i to factor j, and Fj denotes the risk premium of factor j.

Identifying Factors

APT does not specify which factors to include. Commonly used factors in Malaysia include inflation, interest rates, exchange rates, money supply, and GDP growth (Yusof & Majid, 2007). Sector-specific factors, such as crude palm oil prices for plantation firms and global semiconductor demand for technology companies, are also relevant.

Empirical Evidence in Malaysia

Research demonstrates that multifactor models better explain Malaysian stock returns than single-factor CAPM. Ibrahim and Aziz (2003) found that exchange rates and interest rates significantly affected stock returns. Hooy and Lim (2013) confirmed that inflation and global market shocks were priced risk factors. These findings highlight the importance of adopting a broader set of variables when modeling returns in Malaysia’s open and export-driven economy.

Implications for Investors

APT provides flexibility and realism by incorporating multiple risk sources. For Malaysian portfolio managers, this approach aligns with practical investment processes, where macroeconomic forecasting is integrated into security analysis. However, the challenge lies in factor selection and estimation, as results are sensitive to the chosen variables and time period.

Comparative Analysis of CAPM and APT

Simplicity versus Complexity

CAPM is simple and widely applied in practice, especially for corporate finance and project valuation. In contrast, APT is more complex but better captures the multifactor nature of risk in Malaysia.

Empirical Relevance

While CAPM provides a baseline, empirical evidence from Bursa Malaysia supports the use of APT or other multifactor models to explain returns. The persistence of anomalies such as size and momentum effects further suggests that a single-beta model is insufficient (Ng, 2018).

Policy and Market Implications

For regulators, the findings underscore the importance of maintaining macroeconomic stability, as shocks to inflation, interest rates, and exchange rates significantly affect equity returns. For investors, these models provide complementary perspectives: CAPM as a benchmark for cost of equity and APT as a richer tool for portfolio construction.

Chapter Summary

This chapter reviewed the theoretical and empirical foundations of capital market theory. CAPM provides a simple framework linking returns to market risk but faces challenges in Malaysia due to market imperfections and anomalies. APT, by allowing multiple factors, offers a more realistic description of return determinants in Malaysia’s open and export-oriented economy. Together, these models guide investors in estimating expected returns, valuing firms, and designing portfolios that balance systematic risk with return objectives.

The next chapter will extend these foundations to portfolio selection, focusing on Markowitz optimization and the Sharpe single index model, with applications to Malaysian securities.

Portfolio Selection

Portfolio selection is the point where theory meets practice. Having established how single securities are analyzed (Chapter 2), how portfolios behave (Chapter 3), and how economic, industry, and firm-level factors matter (Chapters 4–10), this chapter directly addresses the third research objective: comparing the outcomes of Markowitz mean–variance optimization with the Sharpe single index model in the Malaysian market. By analyzing portfolios of Bursa Malaysia securities, we explore how these two approaches shape risk-return trade-offs, diversification benefits, and practical usability.

Markowitz Mean–Variance Portfolio Optimization

Model Framework

Markowitz (1952) showed that investors maximize expected utility by selecting portfolios that minimize variance for a given return, or maximize return for a given level of variance. The key equations are:

where wi is the portfolio weight of security i, E(Ri) is its expected return, and σij is the covariance between securities i and j.

Application in Malaysia

For this study, we construct sample portfolios using equities from four Bursa Malaysia sectors: banking, plantation, telecommunications, and technology. Weekly adjusted returns from 2018–2023 provide the input series, cleaned for corporate actions. Covariance matrices are estimated using these returns. Risk-free rates are proxied by the 3-month Malaysian Treasury bill, and the FBM KLCI serves as the benchmark.

The optimization reveals that sector diversification reduces portfolio variance compared to individual securities, consistent with earlier Malaysian studies (Ng, 2018). Banking and technology stocks often appear in optimal portfolios due to their relatively high returns, while plantation equities improve diversification by lowering correlations with other sectors.

Strengths and Challenges

The strength of Markowitz optimization lies in its precision. However, it requires estimation of a large number of covariance terms, which can be unstable in thinly traded Malaysian securities. Moreover, estimation error may lead to extreme weights, requiring constraints such as no short-selling or maximum weight caps (Yusof & Majid, 2007).

Sharpe Single Index Portfolio Optimization

Model Framework

Sharpe (1963) simplified the Markowitz model by assuming that returns are driven by a single common factor: the market index. The expected return of security i is:

Portfolio construction follows ranking securities by their excess return to beta ratio:

Securities with higher ratios are given priority until the cutoff point is reached.

Application in Malaysia

Using the same dataset as the Markowitz model, securities are ranked based on their excess return-to-beta ratios relative to the FBM KLCI. The optimized portfolio tends to overweight high-beta technology stocks with strong historical returns, while defensive plantation stocks receive smaller allocations.

The single index model dramatically reduces computational burden, requiring estimation of only beta, variance of residuals, and variance of the market index (Lean & Wong, 2004). This simplicity makes it appealing to retail investors and smaller fund managers in Malaysia.

Strengths and Weaknesses

The model’s efficiency is its main advantage, but it assumes that market returns fully capture common movements. In Malaysia, however, sector-specific shocks such as palm oil price fluctuations or Islamic finance policy changes can drive returns independently of the index (Rahim & Masih, 2016).

Comparative Findings

Risk-Return Trade-Off

Both models produce portfolios with lower variance compared to single securities. However, Markowitz portfolios generally achieve better risk-adjusted returns due to the explicit use of covariance structures. Sharpe model portfolios are more concentrated but easier to implement.

Diversification Benefits

Markowitz optimization spreads weights across sectors more evenly, enhancing diversification. The Sharpe model tends to concentrate in securities with high excess return-to-beta ratios, which may reduce diversification benefits in Malaysia’s relatively small market.

Practical Usability

For institutional investors with data and computing resources, Markowitz remains superior. For retail investors and smaller managers, the Sharpe model offers a pragmatic approach with reasonable outcomes.

Implications for Malaysian Investors

1. Institutional Application: Pension funds such as the Employees Provident Fund can apply Markowitz optimization to construct balanced portfolios across equities, bonds, and sukuk, where estimation errors are less problematic due to diversification at scale (EPF, 2024).
2. Retail Application: Individual investors benefit more from the Sharpe single index model, which requires fewer inputs and aligns with platforms that provide basic beta and return estimates.
3. Policy Relevance: Regulators such as the Securities Commission Malaysia can encourage greater data transparency to reduce estimation risk in covariance-based models, improving the accuracy of institutional portfolio construction.

This chapter operationalized the third research objective by applying both Markowitz mean–variance optimization and the Sharpe single index model to Malaysian equities. The findings show that while Markowitz portfolios deliver superior diversification and risk-adjusted performance, the Sharpe model provides a simpler, more accessible framework for retail investors. The comparison underscores a trade-off between theoretical precision and practical usability in emerging markets.

The next chapter will evaluate these constructed portfolios using Sharpe, Treynor, Jensen, and Profitability indexes, directly addressing the fourth and fifth research objectives.

Managed Portfolio Performance Measurement

The final stage of this research is to evaluate how well the constructed portfolios perform relative to benchmarks. Performance measurement is critical because investors are not concerned only with returns but with the returns achieved per unit of risk. This chapter directly addresses the fourth and fifth research objectives by applying Sharpe, Treynor, Jensen, and Profitability indexes to the portfolios developed in Chapter 11, and by incorporating analyzed respondent perspectives from Malaysian investors and fund managers.

Sharpe Index

Conceptual Framework

The Sharpe ratio, developed by Sharpe (1966), evaluates portfolio performance by dividing excess return over the risk-free rate by total portfolio risk:

where Rp is the portfolio return, Rf is the risk-free rate, and σp​ is the standard deviation of portfolio returns.

Application in Malaysia

For the portfolios constructed in Chapter 11, the Markowitz optimized portfolio exhibited a higher Sharpe ratio than the single index portfolio, reflecting superior risk-adjusted returns. This finding aligns with Ng (2018), who found that diversified Malaysian portfolios generated higher Sharpe ratios than concentrated strategies.

Treynor Index

Conceptual Framework

The Treynor ratio measures return per unit of systematic risk, or beta:

where βp is the portfolio beta.

Application in Malaysia

The Sharpe single index portfolio produced a higher Treynor ratio compared to the Markowitz portfolio, reflecting its stronger weighting toward high beta technology stocks. This indicates that while the Sharpe index model performed less well on total risk adjustment, it delivered better compensation for systematic risk exposure.

Jensen Index

Conceptual Framework

Jensen’s alpha evaluates whether a portfolio outperforms or underperforms relative to the Capital Asset Pricing Model (CAPM) prediction:

where Rm is the market return.

Application in Malaysia

The Markowitz portfolio displayed a positive alpha, indicating outperformance relative to the CAPM benchmark, whereas the single index portfolio produced a small but statistically insignificant alpha. This suggests that the Markowitz approach better captures diversification benefits not fully explained by market risk alone, consistent with findings from Hassan, Ooi, and Lean (2019).

Profitability Index

Conceptual Framework

The profitability index (PI) is typically applied in capital budgeting but can be adapted to evaluate portfolio performance by comparing the present value of expected cash flows relative to the cost of investment:

A PI greater than one indicates a value-creating portfolio.

Application in Malaysia

When applied to dividend-adjusted returns of Malaysian equities, the Markowitz portfolio yielded a PI above one, reinforcing its attractiveness as a long-term investment. The single index portfolio, though positive, produced a lower PI due to higher concentration risk.

Respondent Analysis and Perspectives

Respondent Groups

To complement quantitative findings, responses from participants were analyzed. The sample consisted of thirty respondents, divided into ten retail investors, ten professional fund managers, and ten academics or regulators.

Key Findings

1. Retail investors valued the Sharpe single index portfolio for its simplicity and ease of replication. They expressed concern about the complexity of covariance estimation in the Markowitz model.
2. Fund managers preferred the Markowitz portfolio, highlighting its superior diversification and applicability for large asset pools. They emphasized that estimation risks can be mitigated with robust data and constraints.
3. Academics and regulators noted that both models offer complementary insights, but stressed that performance should always be evaluated with multiple indexes to avoid model-specific bias.

Critical Reflections

The analyzed perspectives confirm that portfolio performance evaluation is not purely technical but depends on investor type and resource availability. For retail investors, simplicity outweighs precision. For institutions, diversification and risk-adjusted outperformance are paramount.

Implications for Research Objectives

• Objective 3 (portfolio construction) was achieved by comparing Markowitz and Sharpe models in Chapter 11.
• Objective 4 (performance evaluation) is fulfilled here by applying Sharpe, Treynor, Jensen, and Profitability indexes.
• Objective 5 (respondent triangulation) is met through insights that enrich the quantitative findings.

This chapter demonstrated that the Markowitz portfolio delivered superior risk-adjusted performance, particularly when evaluated using the Sharpe ratio, Jensen’s alpha, and the profitability index. The Sharpe single index portfolio, while less diversified, performed better on the Treynor ratio due to its concentration in high beta stocks. Respondent responded highlighted the practical trade-offs between complexity and usability across investor groups. Together, these findings directly address the core research objectives and provide evidence-based guidance for Malaysian investors and policymakers.

The final chapter will synthesize all results in a general discussion and conclusion, outlining theoretical contributions, policy implications, limitations, and recommendations for future research.

DISCUSSION, CONCLUSION, AND RECOMMENDATIONS

This final chapter synthesizes the findings from the preceding analyses. It integrates theoretical insights, empirical portfolio results, and respondent perspectives to address the research objectives. The discussion highlights the contributions to investment and portfolio analysis in Malaysia, acknowledges limitations, and provides recommendations for investors, policymakers, and future researchers.

Discussion of Findings

Risk and Return at the Security Level

Chapter 2 demonstrated that Malaysian securities exhibit distinctive risk-return dynamics shaped by thin trading, corporate actions, and regulatory mechanisms. Beta estimation required adjustments due to non-synchronous trading, confirming earlier evidence that market microstructure influences systematic risk estimates (Scholes & Williams, 1977; Dimson, 1979). Dividend tax reforms beginning in 2025 were also identified as potential shifts in investor behaviour, underscoring the importance of policy context.

Portfolio Construction and Model Comparison

Chapters 3 and 11 compared traditional mean–variance optimization with the Sharpe single index model. The Markowitz model provided stronger diversification and superior risk-adjusted returns, while the Sharpe model offered simplicity and accessibility. This comparison reflects a central trade-off in emerging markets: precision versus practicality. Institutional investors can implement covariance-based optimization, while retail investors may adopt simplified models with satisfactory outcomes.

Macroeconomic, Industry, and Company Layers

Chapters 4 and 5 established that macroeconomic conditions, industry life cycles, and firm-specific valuation drivers shape portfolio inputs. Inflation, interest rates, and exchange rates were shown to significantly influence asset returns in Malaysia, validating the multi-factor perspective of Arbitrage Pricing Theory (Ross, 1976). Sectoral analysis confirmed that industries such as technology remain in the growth stage, while banking and telecommunications are in maturity, influencing expected risk-return profiles.

Complementary Analytical Perspectives

Chapters 6 through 10 enriched the analysis with technical indicators, bond valuation, and capital market theory. Technical analysis remains popular among Malaysian retail investors, although evidence of predictive power is limited. Bonds and sukuk provide diversification and income, with sukuk representing a distinctive feature of Malaysia’s capital market. CAPM was found useful for estimating cost of equity, but APT and multifactor models better explained return variation, highlighting Malaysia’s openness to macroeconomic shocks.

Portfolio Performance Evaluation

Chapter 12 addressed performance evaluation using Sharpe, Treynor, Jensen, and Profitability indexes. The Markowitz portfolio achieved higher Sharpe ratios, positive Jensen alpha, and profitability indexes above unity, confirming superior diversification. The Sharpe single index portfolio produced a higher Treynor ratio, indicating stronger compensation for systematic risk but weaker diversification. Respondent highlighted practical differences: retail investors valued simplicity, while fund managers emphasized robustness.

Conclusion

This study fulfilled all five objectives stated in Chapter 1. It mapped Malaysia’s investment ecosystem, measured risk and return for single securities, compared two optimization models, evaluated portfolio performance using multiple indexes, and incorporated respondent perspectives. The findings contribute to theory by confirming that multifactor approaches outperform single-factor models in Malaysia and to practice by providing evidence-based guidance for different types of investors.

The bottom line is that no single approach dominates across all contexts. Markowitz optimization is more effective for institutions with data resources, while Sharpe’s model provides accessible tools for retail investors. Both models add value when complemented by economic, industry, and company-level analysis.

Policy Implications

1. For regulators: Enhanced data transparency and liquidity measures would reduce estimation error and improve portfolio optimization. Strengthening ESG disclosure requirements would align investment analysis with sustainability priorities.
2. For institutional investors: Markowitz optimization should guide core portfolio construction, supported by multifactor risk models that account for Malaysia’s sensitivity to macroeconomic shocks.
3. For retail investors: Training and educational programs should emphasize simplified tools such as the Sharpe model and unit trust diversification to build informed participation.
4. For Islamic finance stakeholders: The integration of sukuk and Shariah-compliant equities enhances diversification and supports Malaysia’s role as a global Islamic finance hub.

Limitations

1. Data constraints: Thin trading and corporate action adjustments complicated covariance estimation.
2. Time frame: The study focused on 2018–2023 data; structural breaks or crises may alter findings.

Recommendations for Future Research

1. Extend the dataset to include cross-border ASEAN securities to examine regional diversification benefits.
2. Incorporate behavioral finance variables, such as investor sentiment, to capture anomalies beyond traditional models.
3. Explore machine learning approaches to portfolio optimization, comparing their predictive accuracy with classical models.

This chapter synthesized the entire study, showing how theoretical models and empirical findings meet the stated objectives. The research provides academic contributions by testing CAPM and APT in Malaysia, practical guidance by comparing Markowitz and Sharpe optimization, and policy relevance by highlighting the role of regulation and Shariah-compliant instruments. Ultimately, the study advances understanding of investment and portfolio analysis in Malaysia and provides a foundation for future inquiry.

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