Paper: arXiv 2409.03762

Abstract

The decisions traders make to buy or sell an asset depend on various analyses, with expertise required to identify patterns that can be exploited for profit. In this paper we identify novel features extracted from emergent and well-established financial markets using linear models and Gaussian Mixture Models (GMM) with the aim of finding profitable opportunities. We used approximately six months of data consisting of minute candles from the Bitcoin, Pepecoin, and Nasdaq markets to derive and compare the proposed novel features with commonly used ones. These features were extracted based on the previous 59 minutes for each market and used to identify predictions for the hour ahead. We explored the performance of various machine learning strategies, such as Random Forests (RF) and K-Nearest Neighbours (KNN) to classify market movements. A naive random approach to selecting trading decisions was used as a benchmark, with outcomes assumed to be equally likely. We used a temporal cross-validation approach using test sets of 40%, 30% and 20% of total hours to evaluate the learning algorithms’ performances. Our results showed that filtering the time series facilitates algorithms’ generalisation. The GMM filtering approach revealed that the KNN and RF algorithms produced higher average returns than the random algorithm.

Complexity vs Empirical Score

  • Math Complexity: 6.0/10
  • Empirical Rigor: 5.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced machine learning methods (GMM, RF, KNN) and complex feature engineering, constituting moderate mathematical density. It also includes significant empirical components such as real market data, temporal cross-validation, and backtesting-like performance metrics, though the data span is relatively short.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Predict Financial Market<br>movements for profit"] --> B{"Data Acquisition"}
  
  B --> C["Inputs: 6 months of minute candles<br>Bitcoin, Pepecoin, Nasdaq<br>59-minute windows for hour-ahead prediction"]
  
  C --> D["Feature Extraction & Filtering"]
  D --> E["Method A: Linear Models<br>Method B: GMM"]
  
  E --> F["Classification Algorithms"]
  F --> G["Random Forest (RF)"]
  F --> H["K-Nearest Neighbours (KNN)"]
  
  G --> I["Evaluation: Temporal Cross-Validation"]
  H --> I
  
  I --> J["Benchmark: Random Selection<br>(Naive trading)"]
  
  J --> K["Key Findings/Outcomes"]
  
  K --> L["GMM Filtering + KNN/RF<br>produced higher average returns<br>than random benchmark<br>Improved algorithm generalization"]

Cited References (39)

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  8. A review of machine learning experiments in equity investment decision-making: why most published research findings do not live up to their promise in real life (2021)
and 31 more
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  3. Preprocessing large datasets using Gaussian mixture modelling to improve prediction accuracy of truck productivity at mine sites (2022)
  4. Using deep neural networks coupled with principal component analysis for ore production forecasting at open-pit mines (2024)
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2 of 39 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.