EV - R24 Active Equity Investing: Strategies Flashcards
Active Investing - activist targets
Feature slower revenue and earnings growth than the market, suffer negative share price momentum, and have weaker than average corporate governance; By building stakes and initiating change in underperforming companies, activists hope to unlock value
Statistical arbitrage (Stat Arb)
Use statistical and technical analysis to exploit pricing anomalies. The analytical tools used include traditional technical analysis, sophisticated time series analysis and econometric models, machine learning techniques. PM take advantage of either mean reversion in share prices or opportunities created by market microstructure issues. Pair trading is an example, i.e. identify two securities that are historically highly correlated with each other, betting on a mean-reversion pattern in stock prices, the risk is the observed price divergence is not temporary (use stop-loss rule)
Pitfalls in Fundamental Investing - Behavioral bias
Cognitive errors and emotional biases. Cognitive errors are basic statistical, information-processing, or memory errors that cause a decision to deviate from the rational decisions of traditional finance. While emotional biases arise spontaneously as a result of attitudes and feelings that can cause a decision to deviate from the rational decisions of traditional finance.
Pitfalls in Fundamental Investing - cognitive error - Confirmation bias
the tendency of analysts and investors to look for information that confirms their existing beliefs about their favorite companies and to ignore or undervalue any information that contradicts their existing beliefs
Pitfalls in Fundamental Investing - cognitive error - Illusion of control
refers to the human tendency to overestimate their abilities. It is a cognitive error
Pitfalls in Fundamental Investing - cognitive error - Availability bias
it is an information processing bias whereby individuals take a mental shortcut in estimating the probability of an outcome based on the availability of the information and how easily the outcome comes to mind.
Pitfalls in Fundamental Investing - emotional bias - Loss aversion
an emotional bias whereby investors tend to prefer avoiding losses over achieving gains
Pitfalls in Fundamental Investing - emotional bias - Overconfidence bias
an emotional bias whereby investors demonstrate unwarranted faith in their own intuitive reasoning, judgement, and or cognitive abilities. Unlike the Illusion of Control, which is a cognitive error, overconfidence bias is an illusion of exaggerated knowledge and abilities
Pitfalls in Fundamental Investing - emotional bias - regret aversion bias
causes investors to avoid making decisions that they fear will turn out poorly
Value Traps
it is a stock that appears to e attractively valued - with low P/E P/B P/CF - because of a significant price fall but that may still be overpriced given its worsening future prospects. If a company doesn’t have any catalysts to trigger a reevaluation of its prospects, there is less of a chance that the stock price will adjust to reflect its fair value
Growth Traps
the company may deliver above average earnings or cash flow growth, in line with expectations, but the share price may not move any higher due to its already high starting level.
Create Fundamental active investment process
- Defined the investment universe and the market opportunities (investment thesis)
- prescreen the investment universe to obtain a manageable set of securities for further, more detailed analysis
- understand the industry and business for this screened set by performing industry and competitive analysis and analysis of financial reports
- forecast company performance, most commonly in terms of cash flows or earnings
- convert forecasts to valuation and identify ex ante profitability investments
- construct a portfolio of these investments with the desire risk profile
- rebalance the portfolio with buy and sell disciplines
Create Quantitative Investment process
- starts with a belief or hypothesis. It is based on a belief that the market is competitive but not necessary efficient.
- acquiring and processing data - company mapping, company fundamentals, survey data, unconventional data
- Back-testing the strategy
- Evaluating the strategy - out of sample back test, in which a different set of data is used to evaluate the model’s performance to confirm model robustness
- check issues - risk models and trading costs (explicit cost commissions, fees, tax, and implicit cost bid-ask spread and market impact)
Create Quantitative Investment process - 3. Back-testing the strategy
Information Coefficient (IC) Create a multifactor model
Pitfalls in Quantitative Investment Process - Survivorship bias, Look-ahead bias, data mining, and overfitting
Survivorship bias - only those companies that are currently in business today
Look ahead - results from using information that was unknown or unavailable at the time an investment decision was made
Data mining - refers to automated computational processes for discovering patterns in large datasets, often involving sophisticated statistical techniques, computation algorithms, and large scale database system
Overfitting - it can be described as excessive search analysis of past financial data to uncover patterns and to conform to a pre-determined model for potential use in investing
