Equities represent a substantial part of global investments. It offers various advantages to both organizational and individual portfolios. These advantages include capital growth, diversification with other asset types, dividend income, and potential protection against inflation. Equity portfolios tend to exist across a passive-active spectrum, ranging from those that closely follow a market benchmark to those that are not tied to any specific index. The latter are often managed by professional portfolio managers or investment firms, and their performance is often compared to a general broad market index to gauge their excess returns. Portfolios can take many forms, such as open-ended mutual funds, ETFs, smart-beta portfolios, or long-short portfolios. There are several reasons why an investor may select a portfolio along the passive-active spectrum.

Passive-Active Spectrum

Investing in equities is often guided by a client’s specific goals. Asset managers typically take into account important investment objectives and constraints, such as risk tolerance, desired returns, liquidity needs, investment time frame, tax considerations, and any unique circumstances. Equities are frequently categorized by size, style, geography, and economic sector. Consequently, there are relevant benchmarks for equities in the same category. These benchmark indices usually weight equities based on their market capitalization.

In certain groups, like large-cap companies from developed markets, firms are well-established and receive extensive coverage from financial analysts. Due to their significant market capitalization, all publicly available information is thoroughly analyzed and typically reflected in their stock prices. This reduces the chances of achieving excess alpha, diminishing the need for an active portfolio manager in these groups. Consequently, these portfolios are mostly managed passively with infrequent rebalancing throughout the year.

On the other hand, for equity groups such as mid-cap and growth stocks, or equities from emerging markets, having a professional portfolio manager is crucial. These managers actively analyze the equity mix and may rebalance the portfolio more frequently, like monthly or even weekly. This rebalancing process involves adjusting capital allocation and often includes adding new stocks or removing existing stocks to meet the risk and return objectives.

The table below illustrates various investment themes within the passive-active spectrum of equity portfolios. In certain categories, equities are entirely managed actively, whereas in others, there are mainly managed passively.

Active Investment

Several ETFs are available to track standard indices, serving as straightforward forms of passive investment. An index fund that mirrors its benchmark requires minimal rebalancing. Client preference plays a crucial role in choosing between passive and active investing. Additionally, investors’ beliefs about the potential of active strategies to generate significant excess returns are important, given that active management is generally more costly than passive management. Investors must weigh the benefits of achieving excess return against the additional costs. The table below highlights various cost components linked to active funds.

An active investment manager must be confident in their ability to outperform their benchmarks. This involves understanding their investment universe, and conducting a competitive analysis of other managers with similar investment universes. The choice of benchmark is crucial for attracting new investors. Active managers must have benchmarks consisting of sufficiently liquid securities to keep trading costs reasonable.

Quantitative Investment

Quantitative strategies refer to investment methods that leverage mathematical, statistical, and data-driven techniques. These strategies systematically exploit market inefficiencies, anomalies, and patterns. Instead of relying solely on human judgment, they follow predefined rules to generate returns. Quantitative funds enhance efficiency and risk management, relying on accurate statistical models and robust trading data. They serve as a bridge between passive and active investment, combining rule-based investing with factor-driven insights. Below are some examples of quantitative investing.

Smart-beta portfolio. Unlike traditional market-cap weighted indices, smart-beta strategies emphasize specific factors such as size, quality and momentum. Factor-based investing seeks to enhance returns or mitigate risks by quantifying investment decisions based on predefined rules. These rules, for example, select financially stable companies, identify undervalued stocks, and capitalize on price momentum. Smart-beta strategies execute these rules using computer algorithms for stock selection and capital allocation. The ultimate goal is to achieve excess returns or improve diversification while maintaining lower costs compared to active investment strategies. 

Smart-beta strategies often benefit from algorithmic trading. Algorithms employ mathematical equations to rank, select, and allocate assets. Robust historical data plays a crucial role in backtesting the chosen factors for a smart-beta strategy. During execution, algorithmic trading ensures timely implementation of smart-beta factors, optimizing efficiency and minimizing transaction costs, especially during rebalancing.

In portfolio management, algorithms monitor risk factors such as volatility and correlation. As a result, smart-beta strategies can dynamically adapt to changing market conditions. By eliminating emotion-driven investment decisions, algorithms maintain transparency in portfolio management and uphold disciplined execution aligned with smart-beta objectives.

Long-short portfolio. This is an investment strategy involving holding both long and short positions within a portfolio. Hedge funds often incorporate this strategy into their offerings. Long positions benefit from price appreciation, while short ones profit when the asset value decreases. A long-short portfolio offers the following benefits.

• Hedge risk: combining both positions, investors can reduce exposure to big market trends and effectively manage their assets.

• Profit from relative performance: once the long positions outperform the short positions or vice versa, the portfolio makes profits.

• Market-neutral: the portfolio aims to be less affected by overall market trends, even in bearish or volatile periods.

In a long-short portfolio, timely execution plays a critical role in maintaining the desired balance between long and short positions. Algorithmic trading is particularly beneficial for this strategy. It executes trades swiftly, reducing transaction costs. This efficiency is essential for maintaining the desired position ratios. Algorithms also continuously monitor risk factors such as volatility in real time. This helps maintain risk exposure within acceptable limits. Stop-loss and take-profit rules can be automated, ensuring disciplined risk management. Algorithms execute these rules without behavioral bias and algorithms incorporate factors for stock selection and timing, similar to smart-beta portfolios. They optimize for liquidity, especially in large portfolios, to ensure efficient execution.

Conclusion

Investment portfolios span a spectrum from passive to active strategies. Investors may select based on their confidence in outperforming the market, personal preferences, risk tolerance, and management costs. Passive portfolios require minimal monitoring, tracking market indices to replicate performance. Active portfolios involve more frequent reviews and active decision-making. With algorithmic trading, quantitative portfolios serve as a bridge between the two. It brings more options to investment and ensures smooth, data-driven executions. In certain market conditions, quantitative portfolios can become more effective than other portfolios thanks to algorithms targeting specific market factors. They help investors achieve precision, discipline, and responsiveness in managing portfolios.