1 – M4 L3a 01 Intro Efficient Market Hypothesis And Arbitrage Opportunities V3

Welcome to the overview of alpha factors. This is the extra exciting part where you finally get to start turning hypotheses into code and testing that code against data. Alpha factors are tools operating on data that give us signals about how stocks may perform relative to one another. You’ve heard a little about alpha factors already and how they differ from risk factors. Remember that alpha factors are hopefully predictive of future mean returns, while risk factors impart information about commonality of return variance. The search for alpha factors is essentially the search for deviations from the efficient market hypothesis. The efficient market hypothesis has different forms, but generally it means that all available information is reflected in the current price of an asset, which suggests that assets are priced fairly. When searching for exceptions to the efficient market hypothesis, we’re looking for mispricing in the market. We learned about arbitrage before, which is the simultaneous buying and selling of a perfect substitute to make a profit. In the context of this lesson, we’ll also use the term arbitrage. But here, it refers to buying an asset that may be underpriced or shorting one that may be overpriced, where the expected return is in excess of what it should be for the risk we bear. In this lesson, we’ll start by discussing academic research as a source for hypotheses and for alpha factor generation. We’ll cover techniques for processing a raw alpha factor including methods, such as sector neutralization, ranking, Z-scoring, smoothing, and conditioning factors on other factors. Each of these techniques is important in the process of turning the numbers in our alpha vectors into signals that represent whether to buy or short each stock in a portfolio and by how much. That is to say, these techniques are relevant to the goals that we have for real-world portfolios. We’ll also cover techniques for evaluating factors, such as the sharpe ratio, information coefficient, information ratio, and turnover analysis. These will help you decide whether your alphas provide enough return relative to risk, and whether they will cause too much trading to likely be profitable in the real world. The topics we discuss here will prepare you for the following lesson, in which we extract alpha factor ideas from academic research papers, and then implement those ideas in code. This is exactly the type of work that quants do in the real world. In this lesson, we step into the typical life of a quant researcher. You’ll see how quants generate alpha factor ideas that may make their way into real hedge fund portfolios.

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