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All.literature review.reference

Literature Review#

Meta#

  • Year - Title
  • Paper authors:
  • Link to the paper (ideally on gdrive)
  • Review author / date
  • Score in [0, 5], where:
    • 5/5: Must-read
    • 4/5: Some interesting ideas we can reuse
    • 3/5: Pretty much what one would have done as first experiment
    • 2/5: ...
    • 1/5: Horrible: same bullet-proof logic as in a politician speech
  • Summary:
    • At most 5-10 bullet points explaining what the paper tries to accomplish
    • Describe the data used, setup, model formulation, ...
    • Good references
  • Praises:
    • At most 5 bullet points
    • Focus on what is different, interesting, and not on the obvious
  • Critiques:
    • At most 5 bullet points
    • Explain what is not solid in the analysis, suggestions on how to improve
  • Next steps:
    • What next steps should we take, if any, e.g.,
      • Read the bibliography
      • Try experiments

To cut and paste#

### Year - Title
- Paper authors:
- [Link]()
- Review author / date:
- Score:
- Summary:
- Praises:
- Critiques:
- Next steps:

News for commodity prediction#

2015 - The role of news in commodity markets#

  • Paper authors: Borovkova
  • Link
  • Review author / date: GP, 2019-11-22
  • Score: 4/5
  • Summary:
    • Dataset: prepackaged Thomson-Reuters sentiment (TRNA)
    • Studies the effect of sentiment on commodities through event studies
    • Forecast prices and volatility
  • Praises:
    • Decent statistics about the data set
    • States that one needs to understand if the sentiment is attached to demand and supply
      • Not sure if TR actually does that
    • Confirms our point about "momentum-related news" (i.e., news about the fact that the price is going up)
    • Confirms periodicity we are aware of
    • Interesting local level model to extract the hidden sentiment
      • Very similar to what we thought to do (including the idea of using Kalman smoother)
  • Critiques:
    • Nothing really
  • Next steps:
    • Understand if TR considers sentiment distinguishing supply or demand
      • We should do this (not sure how PR does that)
    • Remove carefully momentum-related news
    • Remove or count carefully repeated news (maybe use a measure of similarity between articles)
    • How to deliver "event study" models to customers? Should we "unroll the model" for them providing a stream of predictions?

Social sentiment#

2015, Predicting global economic activity with media analytics#

  • Paper authors: Peterson et al.
  • Link: In Tech/papers
  • Review author / date: GP, 2019/12/08
  • Score: 2/5
  • Summary:
    • Predict PMI indices (which are related to the
  • Praises:
    • Interesting approach for going beyond polarity in sentiment considering
  • Critiques:
    • No seasonal component
    • Usual problems with methodology OOS
  • Next steps:
    • Consider the TRMI "indices" (optimism, fear, joy, trust, violence)
    • Consider the difference in professional news vs social news sentiment
      • What does it mean if there are large statistically significant difference?

2018 - Twitter, Investor Sentiment and Capital Markets, what do we know?#

  • Paper authors:
  • Review author: GP, 2019-08-21
  • Link:
  • Score: 3 / 5
  • Summary:
    • Good survey of the literature about social sentiment used for finance
    • Most authors report predictivity of social sentiment for:
    • Different metrics (returns, risk, trading volume)
    • Assets (US stocks, exchange rates, commodities)
    • Events (IPO, earnings)
  • Next steps:
    • Read all the bibliography and reproduce some of the results
  • TODO: Update this to new template

Time series#

On-Line Learning of Linear Dynamical Systems: Exponential Forgetting in Kalman Filters#

  • Paper authors: Mark Kozdoba, Jakub Marecek, Tigran Tchrakian, and Shie Mannor
  • Review author: Paul, 2019-12-02
  • arXiv, AAAI
  • Score: 4/5
  • Summary:
    • Interesting insight into how to approximate a non-convex optimization problem with an approximate convex one
    • Shows that for observable Linear Dynamical Systems with non-degenerate noise, the dependence of the Kalman filter on the past decays exponentially
    • For this class of systems, predictions may be modeled as autoregressions. In practice, not many terms are needed for a "good" approximation.
    • The algorithm is on-line
    • Comparison to the Kalman filter is formalized with regret bounds
    • IBM / Technion research
    • The setting is one where we are learning the best fixed but unknown autoregression coefficients (rather than one where we are interested in truly dynamic updates)
      • The learning rate decays like \(1 / \sqrt{t}\), and so under some mild constraints on the time series being modeled, the autoregression coefficients converge
      • The linear dynamical system setup considered is one where the state transition matrix and the observation direction are time-independent
  • Praises:
    • References standard big works in the time series literature, like West and Harrison (1997) and Hamilton (1994)
    • Introduces a relatively simple online technique that competes well with the more complex Kalman filter
  • Critiques:
    • Bounds / constants aren't quantitative
  • Next steps:
    • Look at the code accompanying the paper: https://github.com/jmarecek/OnlineLDS
    • Implement and compare to, e.g., z-scoring (a particularly simple case of Kalman filtering)
    • If we have a long history, it may be better to perform a single autoregression over the whole history
      • This suggests
    • What if we keep the learning rate fixed over time?
      • This would effectively allow for "drifting" dynamics
      • The proofs of the results of the paper would no longer apply
      • It isn't obvious how the learning rate ought to be chosen

Predictive State Smoothing (PRESS): Scalable non-parametric regression for high-dimensional data with variable selection#

  • Paper author: Georg M. Goerg
  • Review author: Paul, 2019-12-03
  • Link
  • Score: 4/5
  • Summary:
    • A kernel smoother, but unlike traditional ones, it
      • Allows non-local (with respect to the x-var space) pooling
      • Is scalable (e.g., computationally efficient)
    • PRESS is a generative, probabilistic model
    • States are interpretable
    • Compatible with deep neural networks (though experiments referenced in the paper suggest depth doesn't help, e.g., a wide net with one softmax is enough)
    • Competitive with SVMs, Random Forests, and DNN

    • Predictive state representations are statistically and computationally > efficient for obtaining optimal forecasts of non-linear dynamical systems > (Shalizi and Crutchfield, 2001). Examples include time series forecasting > via epsilon-machines (Shalizi and Shalizi, 2004)...

  • Praises:
    • Combines some clever insights
    • References a TensorFlow implementation and suggests that implementing in various frameworks is straightforward
  • Critiques:
    • No pointers to actual implementations
    • Time series applications are referenced in Section 2, but many relevant (to our work) practical ts-specific points are not developed in the paper
  • Next steps:
    • See if someone has already implemented PRESS publicly
    • If no implementation is available, scope out how much work a minimal pandas-compatible implementation would require

2019, High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes#

  • Paper authors: David Salinas, Michael Bohlke-Schneider, Laurent Callot, Roberto Medico, Jan Gasthaus
  • Review author: Paul, 2019-12-28
  • arXiv
  • Score: 4/5
  • Summary:
    • Learns covariance structure and model together
    • Handles series with time-varying, high-dimensional covariance structure
    • Simultaneously handles series at different scales (in terms of the range)
    • Uses a non-linear, deterministic state space model with transition dynamics parametrized using an LSTM-RNN
  • Praises:
    • Implemented in GluonTS (https://github.com/awslabs/gluon-ts/pull/497) by one of the coauthors who works on time series forecasting at AWS
    • Code for the paper at https://github.com/mbohlkeschneider/gluon-ts/tree/mv_release
    • Good choice of baselines comparisons
    • Demonstrates the importance of data transformations
  • Next steps:
    • Use in cases where we have a large number of time series known to have meaningful correlations

2014, The topology of macro financial flow using stochastic flow diagrams#

  • Paper authors: Calkin, De Prado
  • Link
  • Review author / date: GP, 2020-01-17
  • Score: 1 / 5
  • Summary:
  • Praises:
    • PCA on futures sectors
    • Interesting graphical representation
      • Width of the arc represents strength of relationship (in terms of \(R^2\))
      • Color (green / red) and intensity represent sign and magnitude
      • Lags are delays
      • Geometric topology represents relationships better than tables
      • Connectivity of a vertex represents importance
    • Agreed that econometrics as it is, is close to a pseudo-science that more complex techniques are needed than inverting a matrix
  • Critiques:
    • Various inflammatory remarks and very little content
  • Next steps:
    • None

Computer engineering#

2015, Hidden technical debt in machine learning systems#

  • Paper authors: Sculler et al.
  • Link
  • Review author / date: GP, 2020-01-07
  • Score: 4/5
  • Summary:
    • Many interesting little observations about ML practices and engineering
  • Praises:
    • Validates how approach of minimizing technical debt and paying it off the interest, e.g.,
      • Treat configuration as code, as we do
      • Design abstraction carefully
      • Routinely clean up the code
      • No distinction in quality between research and production
      • Use a single language for everything
      • Need for committing to the healthy engineering practices
  • Critiques:
    • None
  • Next steps:
    • None