Asset Pricing Theory vs Machine Learning
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Questions and Answers

What are the characteristics of no-arbitrage in asset pricing theory?

  • Statistical arbitrage is common.
  • Many factors significantly influence returns.
  • Returns can be explained by a few factors. (correct)
  • High expected returns are achievable without risk.

    • Which issue presents a challenge to machine learning theory in finance?

  • Machine learning is only applicable to traditional data.
  • Complexity of reconciling many parameters with few explanatory factors. (correct)
  • Machine learning models always outperform traditional models.
  • High performance on paper leads to guaranteed profits.

    • What is the first step in building signals for a portfolio using machine learning?

  • Concatenate the results from the training samples.
  • Train multiple models with different hyperparameters.
  • Split the sample into training, validation, and test sets. (correct)
  • Validate the model's performance.

    • How can machine learning be applied to process data in finance?

    <p>By improving traditional data processing methods.</p> Signup and view all the answers

    What is the purpose of using a validation sample in machine learning?

    <p>To measure performance and identify optimal hyperparameters.</p> Signup and view all the answers

    Why is it questioned whether machine learning performance is spurious in finance?

    <p>No substantial profits have emerged from its use despite claimed performance.</p> Signup and view all the answers

    What does the process of building a portfolio entail after creating signals?

    <p>Implementing the forecast results from the best model.</p> Signup and view all the answers

    What is an important aspect often expected from students regarding code in the exam?

    <p>An ability to identify the primary function of the code.</p> Signup and view all the answers

    Which method for building portfolio weights involves buying and selling stocks proportionally to their market capitalization?

    <p>Value Weighted (VW)</p> Signup and view all the answers

    What is the primary goal when measuring the performance of a machine learning portfolio?

    <p>Achieve a high reward for low risk</p> Signup and view all the answers

    In constructing a long-short portfolio, what is the expected relationship between decile of signal and mean return?

    <p>Mean return tends to increase with increasing decile of signal</p> Signup and view all the answers

    What statistical measure is primarily used to evaluate the risk-adjusted performance of a portfolio?

    <p>Sharpe Ratio</p> Signup and view all the answers

    What is indicated by a statistically significant and positive alpha in the context of portfolio performance?

    <p>Performance cannot be trivially explained by known strategies</p> Signup and view all the answers

    Why is the Value Weighted (VW) strategy considered to have lower transaction costs compared to the Equally Weighted (EW) strategy?

    <p>It requires fewer trades and adjustments</p> Signup and view all the answers

    Which of the following factors is NOT typically included in performance benchmarking of a portfolio?

    <p>Volatility Index (VIX)</p> Signup and view all the answers

    What is a key drawback of using out-of-sample signals directly as weights in portfolio construction?

    <p>It can be risky due to reliance on model decisions</p> Signup and view all the answers

    What is a classical neural network commonly used for?

    <p>Making direct predictions from input data.</p> Signup and view all the answers

    According to the information, LLMs perform poorly in predicting outcomes for which type of firms?

    <p>Smaller firms or firms with high earnings volatility.</p> Signup and view all the answers

    What is emphasized as crucial to be fully prepared for the exam?

    <p>Completely understanding all discussed figures and tables.</p> Signup and view all the answers

    What is stated regarding the writing of code during the exam?

    <p>Students will not need to write code but should explain existing code.</p> Signup and view all the answers

    What should students focus on in addition to understanding content for the exam?

    <p>Grasping the meaning behind content instead of rote memorization.</p> Signup and view all the answers

    What effect does increasing the penalty have in the Penalized Markowitz model?

    <p>It leads to weights equaling zero with a very large penalty.</p> Signup and view all the answers

    What is a significant advantage of using Penalized Markowitz compared to traditional methods?

    <p>It is more effective with small sample sizes.</p> Signup and view all the answers

    What is the purpose of tokenizing text in the context of LLMs?

    <p>To convert text into a standardized format.</p> Signup and view all the answers

    Why does positional encoding need to use a trigonometric function in LLMs?

    <p>It encodes positions to fit within a bounded range.</p> Signup and view all the answers

    Which of the following models is considered traditional before the advent of LLMs in finance?

    <p>Word2vec</p> Signup and view all the answers

    What is the primary function of attention heads in LLMs?

    <p>To predict the next tokens based on input.</p> Signup and view all the answers

    Which technique is not associated with textual analysis in finance before LLMs?

    <p>Neural Networks</p> Signup and view all the answers

    What is a key limitation of traditional models such as Bag-of-words and TF-IDF in finance?

    <p>They fail to capture context effectively.</p> Signup and view all the answers

    What is the main function of the attention heads in a modern LLM?

    <p>To model the importance of one token for another.</p> Signup and view all the answers

    What does the latent representation produced by the attention heads represent?

    <p>The underlying view of the text by the model.</p> Signup and view all the answers

    How does the classical neural network interact with the output of the attention heads?

    <p>It predicts the probability of the next token based on the tension representation.</p> Signup and view all the answers

    What is one method used during inference to limit the randomness of token generation?

    <p>Limiting the distribution to the top-K choices or top-p nucleus.</p> Signup and view all the answers

    What proportion of the training set consists of general knowledge according to the provided information?

    <p>50%</p> Signup and view all the answers

    How is the training sample for the model created?

    <p>By compiling a large set of text and hiding the previous token during training.</p> Signup and view all the answers

    What mathematical operation is applied to calculate attention values among tokens?

    <p>Softmax function applied to the dot product of Q and K.</p> Signup and view all the answers

    What is a characteristic of the tensor produced by aggregating outputs of attention heads?

    <p>It contains more than two dimensions, encapsulating complex relationships.</p> Signup and view all the answers

    Study Notes

    Asset Pricing Theory vs Machine Learning

    • Asset pricing theory states that returns of stocks can be explained by a factor structure with only a few (5-10 max) factors explaining returns.
    • Machine learning shows high performance on paper, but its large number of parameters makes it difficult to reconcile with asset pricing theory.
    • Machine learning's high performance may be spurious and researchers may have missed something in their tests.
    • ML is used in finance for processing data that can't be processed without (text, image etc.)
    • ML is used to process traditional data better than older models.

    Using ML to make a portfolio

    • Split the sample into training, validation, and test sets.
    • Train a set of models with different hyperparameters on the training sample.
    • Use the validation sample to measure performance and find optimal hyperparameters.
    • Keep the forecast of the best model on the test sample.
    • Repeat steps 1-4 and concatenate the results to get a set of out-of-sample forecasts (signals).
    • Use out-of-sample signals to build portfolio weights using strategies like long-short decile or directly using the signals.

    Measuring Performance of ML Portfolio

    • Basic performance metric is the Sharpe ratio (annualized mean/std of return).
    • Check for high reward (mean return) and low risk (variance or std of return).
    • Analyze performance with a table split by decile or long vs short, showing mean return, std of return, and Sharpe ratio.
    • Ensure mean return tends to increase with decile, while std remains stable.
    • Look for a high Sharpe ratio in the long-short portfolio.
    • Check portfolio performance against benchmarks like the market portfolio and Fama-French factors (HmLt and SmBt).
    • Regress returns against benchmarks to check for a significant and positive alpha, indicating performance not explained by known strategies.

    Penalized Markowitz

    • Penalized Markowitz extends Markowitz portfolio optimization by incorporating a penalty term similar to Ridge regression.
    • The penalty diminishes the model's willingness to select high absolute weights, resulting in lower weights or even zero weights for large penalties.
    • It is better when dealing with small samples or lots of stocks.

    Textual Analysis—The Old Ways (Before LLMs)

    • Bag-of-words models are used to count words associated with meanings.
    • TF-IDF converts text into vectors based on term frequency and inverse document frequency.
    • Simple machine learning models like Word2Vec and Bert are used for text analysis despite being outperformed by LLMs in most financial tasks.

    LLMs Demystification

    • Model Structure:
      • LLMs process the input text by tokenizing it, adding positional encoding, and passing it through attention heads.
      • The output of the attention heads is then fed into a classical neural network to predict the next tokens.
    • Tokenization:
      • Tokens are bits of text often representing words or parts of words.
      • Tokens are represented as sets of numbers with an encoding scheme for optimal meaning representation and processing.
    • Positional Encoding:
      • LLMs use trigonometric functions to encode the position of each token. This is essential for the order dependence in understanding language.
    • Attention Heads:
      • Each attention head analyzes the importance of one token relative to another.
      • Modern LLMs have multiple attention heads to enhance analysis and modeling of token relationships.
    • Predicting Next Token:
      • The processed latent representation, a complex tensor representing the model's view of the text, is input to a classical neural network that predicts the probability of the next token.
    • Training:
      • All parameters of the LLM are trained jointly on a massive dataset to predict the next word.
      • The training process involves hiding the previous token and using the context to predict it.
    • Inference:
      • LLMs generate text by autoregressive prompting, generating one token at a time and updating the context.
      • The token prediction probability distribution is used to select the most likely tokens, often limited to the top-K or top-p nucleus for filtering rare events.

    Other Stuff to Prepare

    • Understand all figures and tables discussed in class.
    • Make sure you understand everything summarized above.
    • Understand code in tutorials and exams. You will not need to write code but should be able to explain what you've seen before.

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    Description

    Explore the intersection of asset pricing theory and machine learning in finance. This quiz discusses the challenges of reconciling traditional asset pricing factors with the complexity of machine learning models and examines how ML can enhance portfolio management. Test your knowledge on these key concepts and their applications in financial data processing.

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