Event driven investing has become an increasingly popular algorithmic trading strategy in recent years. By analyzing events that impact asset prices, event driven strategies aim to capitalize on the predictable market movements surrounding these events. On github, there are some useful pdf guides that provide a comprehensive introduction to this approach. In this article, we will summarize some of the key concepts and frameworks covered in these github pdf guides to event driven investing. With proper understanding of the core principles, these resources offer a great starting point for aspiring quant traders looking to implement event driven algorithms. By leveraging events like earnings announcements, macroeconomic data releases, and other catalysts, this framework has the potential to generate consistent alpha uncorrelated to traditional factor risk premia.
Common Event Types and Their Impact on Asset Prices
The github pdf guides emphasize how different event types influence asset prices in predictable ways. Earnings announcements often trigger large stock price movements as investors react to positive or negative earnings surprises. Macroeconomic data like nonfarm payrolls and GDP impact broader equity index and bond market moves. Credit rating changes lead to outsized returns in bonds issued by the affected company. M&A deals result in a convergence between the target stock price and the announced offer price. Often, the market reaction to such events can deviate from rational expectations, creating opportunities for savvy quant traders. The pdfs highlight historical data on the typical market movements surrounding these common events.
Event Signal Generation for Actionable Trading Insights
In addition to categorizing different event types, the github pdf introduction stresses the importance of generating trading signals around events. Some typical signals include earnings surprises, merger arb spreads, revisions in macroeconomic survey expectations, and changes in management guidance. By translating events into quantifiable trading signals, algos can take positions ahead of the anticipated price movements. The pdf outlines some statistical techniques like regression models to quantify signal expectations based on historical data. Accurate signal prediction forms the crux of profitable event driven strategies.
Risk Management Techniques for Event Driven Investing
While events create valuable return opportunities, the github guide notes they also expose algos to idiosyncratic risks. Earnings surprises, litigation events, and regulatory rulings often lead to extreme volatility and fat-tail outcomes. As such, robust risk management is critical for long-term success in event driven trading. The pdf suggests techniques like portfolio diversification across event types, position sizing based on signal confidence, and incorporation of market risk factors. By controlling for downside, algos can better harvest the attractive risk-adjusted returns from event strategies.
Backtesting System Development for Strategy Validation
Since event driven returns rely on historical statistical tendencies, the github pdf stresses the importance of rigorous backtesting. By replaying events and simulated trades on historical data, algos can evaluate the profitability and risks associated with a strategy before going live. The guide provides helpful insights into building a backtester suited for event driven systems, including instrumentation to model event metadata, bars aggregation for intraday strategies, and consideration of execution slippage/market impact. With effective backtesting infrastructure in place, quant traders can identify and fine-tune the most promising event driven algos.
The github pdf introductions provide a comprehensive overview of the key concepts, frameworks, and development practices fundamental to building successful event driven trading algorithms. By leveraging events to generate alpha and managing risk, event driven investing offers a profitable avenue for quants to pursue.