Power of factor investing and how AI enhances allocation

PMS Bazaar recently organised a webinar titled "Power of Factor Investing and How AI Can Enhance Allocation," featuring Sonam Srivastava, Founder of Wright Research. This blog summarizes the key takeaways from this enlightening session.

The webinar delved into a vast array of topics, spanning from comprehensive investment strategies that emphasized data-driven methodologies to the diverse factors influencing investments and adaptive market approaches. It explored factor-based strategies and delved into performance analysis, providing a well-rounded perspective on long-term investment considerations. Additionally, it touched upon trading dynamics and discussed strategic management, showcasing how to blend data-driven decisions with risk mitigation and sector-specific evaluations for informed investment practices.

Key points of discussion during this webinar:

Data-driven investing methodology
Factors driving investment strategies
Market behaviour and adaptive investment approach
Factor investing strategy
Back-tested performance and risk mitigation
Trading, turnover, and dynamic allocation management
Data-Driven Investing Methodology

Central to Wright Research’s approach is the methodical reliance on data-driven decision-making. Srivastava elucidated the multifaceted advantages inherent in this methodology:

Bias Mitigation: Utilizing data to neutralize inherent biases often present in investors' decision-making processes ensured a more rational and objective approach to investment.

Expanded Market Coverage: Leveraging robust datasets to comprehensively cover a broad spectrum of stocks for analysis eliminating limitations posed by a confined market view.

Swift Analysis Speed: Capitalizing on advanced infrastructure to facilitate rapid analysis, enabling timely and informed decision-making processes.

Automated Risk Management: Implementing automated risk assessment mechanisms adaptable to diverse market scenarios ensures a proactive approach to risk mitigation.

Consistency in Performance: Envisioning a consistent performance trajectory based on meticulous scenario analyses and data-backed predictions.

Factors Driving Investment Strategies

Srivastava explained six fundamental factors that steered Wright Research's investment strategies:

Momentum Investing: Identifying and capitalising on ongoing trends expected to persist in future market movements.

Value Investing: Focus on undervalued stocks exhibiting strong growth potential relative to their current market valuation.

Earning Momentum: Assessing analysts' estimations and market sentiment on stock earnings, a pivotal factor shaping investment decisions.

Quality Assessment: Evaluating consistent profitability, stable fundamentals, and management metrics to identify high-quality stocks.

Growth Metrics: Analysing historical growth metrics of stocks, such as earnings, revenue, and dividend performance, to gauge growth potential.

Low Volatility Strategy: Prioritizing stocks with minimal price fluctuations, believed to outperform over the long term due to their stability.

Market Behaviour and Adaptive Investment Approach

Srivastava emphasized the importance of understanding different market stages, particularly upswings (bullish cycles) and downturns (bearish cycles), and adjusting investment strategies accordingly. She outlined how changes in investor behaviour during these stages significantly impacted the success of various investment approaches.

Adopting a forward-looking perspective, Srivastava elucidated the integration of Artificial Intelligence (AI) in predictive methods. By leveraging macroeconomic data and technical indicators, Wright Research endeavoured to forecast future market trends and risk patterns, bolstering its capacity for informed decision-making. This approach synthesized data-driven strategies, meticulous factor analysis, adaptability in strategies, and AI-empowered predictive analysis.

Furthermore, Srivastava discussed the market's influence on allocation, highlighting a shift in risk based on market performance. When the market indicated heightened risk, they reduced risk in their strategy, affecting the allocation of highly valued strategies like momentum. Conversely, favourable market conditions prompted increased risk and higher allocation to momentum strategies.

Factor Investing Strategy

The "Wright Factor Fund" focused on the top 500 stocks on BSE and NSE, ensuring a liquid stock selection that excludes micro-cap stocks. This choice relies on reliable data for strategic decisions. The fund's basket includes 20 to 25 stocks selected using specific factors to maximize returns, minimize risk, and reduce costs. They are rebalanced monthly, tactically adjusting allocations based on the model's indications, aiming to avoid excessive trading.

Back-Tested Performance and Risk Mitigation

The strategy was back-tested over ten years, exhibiting attractive CAGR numbers with lower risks and drawdowns compared to benchmarks. It incorporated a de-allocation policy in volatile scenarios, gradually moving into cash during market corrections to mitigate risks.

Risk Scenarios and Returns

During market crashes like March 2020, the strategy showed resilience, correcting less than the market and quickly recovering. In volatile times, the strategy tended to manage risks similarly or slightly lower than the benchmark. It performed exceptionally well in bullish market phases due to its momentum component but might have had muted returns in bearish phases.

Long-Term Investment Approach

While short-term predictions remained challenging, a three to five-year horizon consistently showed robust returns for the strategy. Investing with a longer-term outlook was advised, considering the strategy's performance pattern.

Trading and Turnover

The turnover in the strategy was carefully managed to minimize trading costs, ensuring a strategic balance between optimizing returns and reducing expenses.

Strategic Turnover and Dynamic Allocation Management

Turnaround and Strategy Focus

This strategy aimed to maintain turnover at less than 50%, ensuring conservative use of trading to minimize costs. Stocks are held anywhere from three months to three years, with around 70% being short-term holdings and the rest long-term, based on the research and back-tested performance.

Dynamic Allocation and Sector Diversification

The strategy dynamically allocates across various sectors such as finance, power finance, defence, railways, capital goods, banking, FMCG, cement, and small caps, adjusting allocation based on market trends. It doesn’t abruptly switch from small to large caps but gradually adjusts allocations as per market shifts.

Active Strategy vs. Passive Funds

Unlike passive funds that stick to market cap rankings, this active strategy involves identifying factors and continually adjusting between them. It is actively managed, undergoing continuous research and improvements to factors, risk assessment, and strategic adjustments, making it highly dynamic.

Market Risk Management and Allocation Shifts

During high-risk scenarios, the strategy reduces overall risk, favouring lower-risk assets like gold or bonds. Conversely, in bullish markets, the allocation moves to riskier strategies like momentum. The strategy uses a tactical approach, constantly shifting allocations based on the understanding of market conditions.

Fees, Cash Allocation, and Risk Management

The fees are structured with both fixed and variable components, and the strategy handles cash allocation based on market risk forecasts. Instead of directly moving to gold or cash, the strategy reduces risk, and as a result, assets with lower risk receive more weightage automatically.

Hedging and Predictive Analysis

Hedging is employed in the hedged strategy, using put options during market risks to mitigate downturns. Regarding performance during bearish phases, the strategy tactically adjusted to safer stocks, larger caps, and lower volatility assets, reducing overall risk.

Client-Allocation Strategies

For client allocations, the strategy may have staggered investments based on market conditions or client instructions. They don’t always allocate 100% immediately, especially if a rebalance is anticipated soon. This approach allows for a more calculated investment strategy aligned with market expectations.

Strategy Management

Factor Allocation within the Strategy:

The strategy employed a multi-factor approach that isn't fixed in allocating percentages to each factor. Instead, the allocation shifts based on market conditions. For example, in a bullish market, momentum might have been allocated a higher weight, whereas in a bearish market, factors like growth or value might have received more emphasis.

Managing Factor Interactions:

The chosen factors in the strategy represented distinct elements like momentum, value, growth, and low volatility, designed to be uncorrelated within the model. Techniques were implemented to further reduce correlations, ensuring each factor's exposure remained independent of other factors within the portfolio.

Ideal Investment Horizon and Customer Profile:

Srivastava suggested a long-term investment horizon of more than three years for this strategy. The typical investors drawn to it include those who favoured systematic approaches, such as individuals from technical backgrounds or those who appreciated data-driven methodologies. Additionally, it attracts larger investors and institutions with diverse financial goals.

Transparency in Methodology:

The strategy took pride in its transparency, aiming to be more transparent than traditional strategies. It avoided a black-box approach, instead openly explaining factor allocations and stock selections based on prevailing market phases and the factor strategies in use.

Additionally, Srivastava also offered a positive outlook for the IT sector over the next few years, expecting a surge in orders from the US due to an anticipated economic recovery. However, concrete evidence of this recovery hadn't materialized yet, leading to a delayed upturn in the IT sector. Once the recovery gains traction, the sector is expected to experience a favourable trajectory.

Sonam Srivastava comprehensively covered all the mentioned topics and addressed questions from the audience at the session's conclusion. For further insights, please watch the recording of this insightful session from the below link.