Alphas in Quantitative Finance

Alphas in Quantitative Finance

• Alphas are mathematical models designed to forecast the future price movements of financial instruments.
• Holding frequency refers to the anticipated duration an investor plans to hold a portfolio or security, influencing risk, returns, and transaction costs.
• Data delay is the period before an alpha can utilize data in its backtesting process, which is crucial for mitigating look-ahead biases.
• Delay 1 Alpha employs data from the day preceding the backtest date to prevent look-ahead bias.
• Delay 0 Alpha uses data from the same day, up to a specific time chosen for the simulation.

Alpha Classifications Based on Holding Period

• Medium to low frequency Alphas involve holding periods that extend from days to months, or even longer.
• Intraday trading Alphas entail frequent position updates, ranging from every minute to a few hours, to leverage short-term price patterns.
• These alphas utilize data such as price, volume signals, news, and earnings announcements, in addition to faster intraday data like sentiment or options.
• High frequency trading (HFT) Alphas involve positions held for extremely short durations, from nanoseconds to a few seconds.
• These strategies encompass market making, price arbitrage, and short-term directional trades.

Preventing Biases in Backtesting

• Look-ahead bias occurs when future information, unavailable during the analysis period, is used, leading to overly optimistic performance predictions.
• Delayed data is employed to counter look-ahead bias and ensure the integrity of backtesting results.
• Delay 1 Alphas rely on the previous day's price data to avoid using future information.
• Delay 0 Alphas use same-day price data, limited to a specific time, for backtesting.
• Delay 0 Alphas, although expected to perform better, typically have lower trading capacity due to the constraints of using same-day data.

Delay Zero Alpha Example: Volatility Data

• Focus is placed on at-the-money implied volatility of call options expiring within 4 months, along with Parkinson's volatility.
• Parkinson’s volatility is a measure of realized volatility, calculated using the high and low prices within a single day.
• Parkinson's volatility measures realized volatility through intraday high and low prices.
• It effectively captures large price movements occurring within a day.
• Strike price indicates the price point at which a call option begins to generate profit.
• Implied volatility reflects the expected future stock movement, significantly influenced by option demand.
• At-the-money implied volatility aggregates the implied volatilities of call options with strike prices matching the stock's current price, expiring within 4 months.

Alpha Idea and Implementation

• The ratio of implied volatility to Parkinson's volatility is utilized as a key factor.
• A high ratio suggests expectations of high future stock returns. Conversely, a low ratio indicates lower return expectations.
• Proprietary Expressions language on WorldQuant Brain is used for implementation.
• Results demonstrated a consistent Sharpe ratio of approximately two across several years, with 14% gross returns, a 1.2 returns to drawdown ratio, 29% turnover, and reasonable coverage across the selected universe.