Machine learning for trading- from Idea to execution

• Machine learning (ML) involves algorithms that learn rules or patterns from data to achieve a goal such as minimizing a prediction error.
• The fundamental law of active management postulates that the key to generating alpha is having accurate return forecasts combined with the ability to act on these forecasts。
• Information ratio(IR) : ratio of the return difference between portfolio and a benchmark return to the volatility of those return. (The definition is more close to sharpe ratio)
• Information coefficient (IC) which measuers the quality of those forecast as the rank coefficient with outcomes. (This is rank IC, conventional IC is just the correlation of the signal and outcome)
• The square root of the breadth of a strategy expressed as the number of independent bets on these forecasts

The rise of ML in the industry

• From electronic to high frequency trading’
  • In 1997, SEC introduced competition to exchnages through electronic communication networks. ECNs are automated alternative trading systems (ATS).
  • Dark Pool are another type of private ATS. Dark pools do not publish pre-trade bids and offers, and trade prices only become public some time after execution.
  • With the rise of electronic trading, algorithms for cost-effective execution developed rapidly and adoption spread quickly from the sell-side to the buy-side and across asset classes.
  • Direct market access (DMA) gives a trader greater control over execution by allowing them to send orders directly to the exchange using the infrastructure and market participant identification of a broker who is a member of an exchange.
  • High-frequency trading (HFT) refers to automated trades in financial instruments that are executed with extremely low latency in the microsecond range and where participants hold positions for very short periods.
• Factor investing and smart beta fund
  • To the extent that specific risk characteristics predict returns, identifying and forecasting the behavior of these risk factors becomes a primary focus when designing an investment strategy.
  • Modern portfolio theory (MPT) introduced the distinction between idiosyncratic and systematic sources of risk for a given asset. Idiosyncratic risk can be eliminated through diversification, but systematic risk cannot.
  • In the early 1960s, the capital asset pricing model (CAPM) identified a single factor driving all asset returns: the return on the market portfolio in excess of T-bills.
  • In other words, assets earn a risk premium based on their exposure to underlying, common risks experienced by all assets, not due to their specific, idiosyncratic characteristics.
  • A factor is a quantifiable signal, attribute, or any variable that has historically correlated with future stock returns and is expected to remain correlated in the future.
  • These risk factors were labeled anomalies since they contradicted the efficient market hypothesis (EMH). The EMH maintains that market equilibrium would always price securities according to the CAPM so that no other factors should have predictive power.
  • The EMH maintains that market equilibrium would always price seWell-known anomalies include the value, size, and momentum effects that help predict returns while controlling for the CAPM market factor.
    • The size effect rests on small firms systematically outperforming large firms.
    • The value effect states that firms with low valuation metrics outperform their counterparts with the opposite characteristics.
    • The momentum effect states that stocks with good momentum, in terms of recent 6-12 month returns, have higher returns going forward than poor momentum stocks with similar market risk.
    • In fixed income, the value strategy is called riding the yield curve and is a form of the duration premium.In commodities, it is called the roll return, with a positive return for an upward-sloping futures curve and a negative return otherwise. In foreign exchange, the value strategy is called carry.
    • There is also an illiquidity premium.Securities that are more illiquid trade at low prices and have high average excess returns, relative to their more liquid counterparts.
  • Multifactor models define risks in broader and more diverse terms than just the market portfolio.
  • A particularly attractive aspect of risk factors is their low or negative correlation.
  • Furthermore, using leverage and long-short strategies, factor strategies can be combined into market-neutral approaches.
  • Smart beta funds take a passive strategy but modify it according to one or more factors, such as cheaper stocks or screening them according to dividend payouts, to generate better returns.