Active equity ETFs: three ways to make more of the core

Core index-tracking exposures give investors broad and efficient access to equity markets as they stand. But they also inherit the benchmark’s composition, its largest companies, sector weights and regional tilts, whether or not those exposures reflect an investor’s own views.

Today’s markets may call for a more discerning approach. Shifting market leadership, concentrated indices and changing correlations mean investors increasingly want more selective and risk-controlled market participation.

AllianzGI’s three new SMART Equity ETFs are designed to fulfil that role across global, European and US equities.

A core equity holding provides allcourt market coverage, liquidity and diversification. Active management can add another dimension through stock selection and portfolio construction, seizing incremental opportunities while controlling active risk.

Active ETFs are increasingly being considered for this foundational portfolio role. But as active ETF launches proliferate, investors need to look beyond the wrapper and examine the investment process underneath: where alpha signals come from, how portfolios are constructed and who is responsible for the outcome.

AllianzGI’s SMART Equity UCITS ETFs are designed to combine systematic stock selection with disciplined risk controls to add substance to the portfolio core. They seek incremental alpha relative to broad passive exposures, while retaining the qualities investors value most in a core holding.

Established factors such as value, quality, momentum and growth have long helped explain differences in stock returns and form the basis of many systematic equity strategies. But these factor models do not capture every potentially useful piece of information. How professional investors are positioned, what company management teams communicate, and how different company characteristics interact as market conditions change, may also provide insights into future stock performance.

Capturing those opportunities requires the specialised toolkit that our Systematic Equity platform has been developing since the 1990s. Building on decades of systematic equity research, SMART Equity uses four AI- and alternative data-derived signals to analyse additional sources of information and capture less conventional sources of alpha.

Securities-lending data helps assess short-selling patterns and professional investor positioning. By analysing what investors do, rather than what they say, the signal identifies observable patterns in their behaviour and provides complementary insights into their true investment views.

A Random Forest machine-learning model assesses how investment styles such as valuation, profitability and earnings momentum interact with each other and with the market environment. This allows companies to be considered from several angles at once rather than relying on a single indicator, offering a differentiated and complementary view to traditional systematic multifactor investing.

Each month, natural language processing systematically analyses thousands of company earnings calls. Advanced language models convert words and sentences into numerical representations that capture meaning and context, helping to distinguish constructive communication from more cautious or defensive language. The results can be compared across companies to provide forward-looking alpha signals.

Momentum describes the tendency for stocks that have performed well to continue doing so. Momentum patterns can vary as market conditions change, so our advanced momentum signal uses deep-learning models to analyse how price trends interact with variables such as volatility, trading volume and liquidity. It then estimates the probability of a particular stock outperforming its peers.

Together, these four signals combine vast amounts of behavioural, fundamental and market data within a systematic framework for stock selection, turning complexity into investment insights that can be acted upon consistently and at scale.

The central aim of SMART Equity is to translate these four signals into selective, risk-controlled portfolios. Rather than applying small tilts across all benchmark constituents, the approach focuses on stocks where the combined signals are strongest, which may also include off-benchmark companies. This makes it more active than some enhanced-index strategies and gives the team greater scope to express views at both stock and sector level.

The challenge is to give those active views enough weight to influence returns without introducing unintended risks. Spread too thinly, signals may have too little impact; expressed too heavily, they may distort the portfolio’s core characteristics.

That’s why our portfolio construction process seeks a balance: enough selectivity for active views to matter, supported by controls designed to keep the portfolios recognisably core.

The process closely monitors diversification, tracking error, liquidity, sector and country positioning, style exposure and active stock weights, with particular attention to risks that may increase tracking error without offering a corresponding return benefit. Managing these unintended risks allows security selection to do more of the work.

Each strategy follows a systematic framework, while the signals, models and portfolio-construction process are actively researched, monitored and refined by our in-house teams.

SMART Equity casts a wide analytical net. Our models assess thousands of companies across developed equity markets.

The final portfolios remain broadly diversified across sectors, countries and companies. Large benchmark constituents may still feature prominently, and active views are expressed through relative position sizing, stock selection and choice of off-benchmark holdings.

AI can help to process information at scale and uncover insights other methods might miss. But human judgement remains essential: models need to be designed, tested and monitored, signals assessed for investment meaning, and portfolios constructed with care.

Our Systematic Equity team includes 24 experienced investment professionals, including 11 PhDs, as of June 2026. Their backgrounds span portfolio management, quantitative research, traditional finance and economics, data science, sustainable investment, risk control and the hard sciences.

Research and portfolio management work closely together, meaning the people developing the systems also help guide the decisions that turn signals into portfolios. Nothing runs on autopilot: humans shape system design, training and oversight, and remain accountable for how the final portfolios perform.

Equity markets contain more information than traditional measures can capture. Investing in them has always been about understanding companies and markets: how businesses evolve, how investors behave and how trends develop over time.

SMART Equity combines behavioural, fundamental and market signals in a systematic equity-investing process refined over decades. Whether used as more substantial active core building blocks or as diversifying components within an equity allocation, the process seeks to turn richer insights into more informed stock selection while preserving the essential characteristics of a core allocation.

The core will always be the foundation of portfolios. Our SMART Equity ETFs are designed to make it work harder.

Get active with AllianzGI.

The AllianzGI SMART Global Equity Active UCITS ETF, AllianzGI SMART Europe Equity Active UCITS ETF and AllianzGI SMART US Equity Active UCITS ETF launched in July 2026.