- Jul 07, 2025
- Strategy
Macro trends in financial markets are often hidden within the vast flood of information. HSAM's Macro Strategy (MS) leverages natural language processing (NLP) technology as its core driver to build a comprehensive analytical framework spanning information extraction to trend forecasting. By deeply analyzing unstructured data such as macro policy texts, industry dynamics, corporate financial narratives, and changes in equity structures, the strategy identifies potential trends before market consensus forms, providing professional support for investors to capture sustained major market trends.
Strategy Core: NLP Technology Redefines Macro Analysis Paradigms
The underlying logic of the MS strategy is that major market trends often originate from policy directions, industry transformations, or structural changes in corporate fundamentals, which initially exist primarily as textual information. HSAM's NLP technology framework comprises three core capabilities:
Multi-source information integration: Through a customized text extraction system, it collects real-time text data from multiple dimensions, including central bank policy statements, government work reports, industry white papers, corporate financial statement notes, and shareholder change announcements. For example, when a country's Ministry of Finance releases a fiscal stimulus plan document, the system automatically parses keywords in the document (such as “infrastructure investment” and “tax cut magnitude”) and associates them with the affected industry sectors.
Semantic logic analysis: Utilizes deep learning models to perform semantic disambiguation and logical inference on text. For example, when industry news mentions “overcapacity warnings in a certain industry,” the model combines upstream and downstream supply chain information to assess the direction and extent of the impact on the profitability expectations of relevant companies, rather than merely interpreting the text at face value.
Sentiment Mapping: By constructing industry-specific sentiment dictionaries, the model converts emotional tendencies in text (e.g., “positive,” “cautious,” ‘pessimistic’) into signals of market expectations. For example, when analyzing the “Management Discussion” section of a company's annual report, if the density of optimistic statements significantly exceeds historical levels, the strategy maps this as a potential signal of an upward revision in profit expectations.
Strategy Execution: From Information Conclusion to Market Capture
The execution chain of the MS strategy follows a scientific process of “information decoding - logical verification - trend following”:
Information Decoding Stage: The NLP system performs word segmentation, part-of-speech tagging, and entity recognition on the original text, extracting key events (such as policy issuance dates, industry policy objectives), affected entities (such as beneficiary industries, affected companies), and transmission pathways (such as “monetary policy easing → reduced corporate financing costs → improved profitability”).
Logical Validation Stage: The strategy team combines historical data and market behavior to validate the rationality of information transmission. For example, when the NLP system identifies information such as “increased subsidies for a certain industry,” it retrospectively analyzes the duration and magnitude of the impact of such policies on the stock prices of relevant companies in historical scenarios to form an expected return model.
Trend Following Stage: Once the market logic corresponding to the textual information is validated by transaction data such as trading volume and capital flows (e.g., sustained capital inflows into policy-benefiting sectors), the strategy initiates cross-market allocation, constructing a trend-following portfolio using tools like stocks, futures, and options, and dynamically adjusting positions based on updates to the textual information stream.
Strategy Value: Profit Capture Capability in Major Market Trends
The core value of the MS strategy lies in its ability to proactively capture sustained major market trends. When events such as shifts in macroeconomic policy (e.g., the Federal Reserve's monetary policy cycle transition), disruptive technological breakthroughs in industries (e.g., battery technology innovations in the new energy sector), or major strategic adjustments by companies (e.g., global mergers and acquisitions by leading firms) occur, related information is first reflected in text data. The MS strategy leverages NLP technology to establish positions before market consensus forms. Historical experience shows that such macro-driven trends typically persist for extended periods and have broad market implications. By continuously monitoring text information flows, the strategy can optimize portfolio structures across different phases of the trend (e.g., policy expectation phase, implementation phase, and effectiveness validation phase) to accumulate returns.
HSAM's macro strategy MS uses NLP technology as a bridge to convert text information into actionable investment decisions, providing investors with a professional tool to navigate market noise and capture macro trends.