Today's blogpost is for the technical/quant market analysts and volatility traders. We make it easy to apply MACD analysis to the public policy process, illuminating new mechanisms for spotting arbitrage opportunities and generating alpha from government activity.
The main point is that capital market pricing processes and public policy processes share comparable reaction functions. The creation of quantitative data from the public policy process opens up new avenues for quantitative anomaly detection that drives alpha generation.
The blogpost is based on research conducted at BCMstrategy, Inc., during the summer of 2023. Those interested in downloading the full white paper can find the download form at the bottom of this post.
Why Markets Measure MACD -- Reaction Functions
Even with automated trade execution, markets are fundamentally a social construct. Individual -- and independent -- decisions to buy or sell securities are influenced in part by decisions made by other market participants. The individual decisions trigger a reaction function among other market participants, which is why traders watch every tick of a stock or a bond price.
Intense information monitoring is what makes markets efficient. Better information about a company's business prospects, management quality, and competitive landscape shift the pricing of a security's net present value and potential future risks.
A trade communicates that shift in analysis, causing other investors to reevaluate their positions. A trade triggers a micro-reaction function. Millions of trades occur every second.
In other words: markets are efficient, but they are not immediately efficient. It takes time for information to impact pricing.
Technical/quant traders do not need to know much, if anything about the underlying company, security, or economic sector. They make trading decisions, spotting alpha-generation opportunities, by identifying pattern anomalies in market activity overall.
Technical/quant traders rely on exponential moving averages to smooth out daily market reaction functions, with a formula that prioritizes more recent market data. They compare the 26-day exponential moving average against the 12-day exponential moving average to construct a baseline and a 9-day exponential moving average to construct a trading signal which identifies where traded market dynamics are converging or diverging from historical patterns.
Systematic traders deploy automated trade execution often at high frequencies to capture the temporary divergences from the normal trading pattern. Volatility traders use the same signals to capture gains from discontinuous price movements where the departure from moving averages is abrupt or large.
MACD analysis by its nature requires quantitative data (stock market prices). PolicyScope Data makes it possible for capital markets also to spot anomalies in the policy process quantitatively in order to inform their trading decisions.
How To Use MACD For Public Policy Risk Detection
It will come as a shock to many readers that markets ALREADY apply a form of MACD analysis regarding public policy risks.
Capital markets have always hired subject matter experts and former government officials to analyze the policy process and identify whether the current day's policy activity converges or diverges from the normal pattern of behavior. This is a VERBAL MACD process.
But the process is neither quantitative nor automated. Until now.
PolicyScope data enables capital markets to apply the same mechanism for detecting convergence and anomalies in the public policy process. This is why PolicyScope Data delivers a MACD Analysis Made Easy for application to capital market evaluation of the public policy process.
For the first time, markets can measure MACD quantitatively for a key policy issue and draw an apples-to-apples comparison with MACD values for every tradeable asset. We facilitate alignment with automated workflows by tickerizing PolicyScope data.
For example, see what happens when we measure MACD for Solar Power policy against the MACD for the leading ETF focused on solar power (LIT) using data from 2Q2023:
Using MACD analysis opens up entirely new opportunities to spot price arbitrage opportunities because it can take at least 24 hours if not days or weeks for policy shifts to be fully reflected in market prices.
PolicyScope data also supports additional quantitative analytics, including rate of change (delta) and Momentum direction detection. We will address those signals in subsequent blog posts.
BCMstrategy, Inc. delivers industry-defining quantitative and language data to help power advanced decision intelligence in capital markets, advocacy, and strategy consulting. The award-winning patented process provides multifactor time series data and structured language data designed from the beginning to support a wide range of AI-powered predictive analytics solutions.
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