Forecasting

Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time series ArXiv ID: 2310.13029 “View on arXiv” Authors: Unknown Abstract In this paper we tackle the problem of point and probabilistic forecasting by describing a blending methodology of machine learning models that belong to gradient boosted trees and neural networks families. These principles were successfully applied in the recent M5 Competition on both Accuracy and Uncertainty tracks. The keypoints of our methodology are: a) transform the task to regression on sales for a single day b) information rich feature engineering c) create a diverse set of state-of-the-art machine learning models and d) carefully construct validation sets for model tuning. We argue that the diversity of the machine learning models along with the careful selection of validation examples, where the most important ingredients for the effectiveness of our approach. Although forecasting data had an inherent hierarchy structure (12 levels), none of our proposed solutions exploited that hierarchical scheme. Using the proposed methodology, our team was ranked within the gold medal range in both Accuracy and the Uncertainty track. Inference code along with already trained models are available at https://github.com/IoannisNasios/M5_Uncertainty_3rd_place ...

Reconstructing cryptocurrency processes via Markov chains ArXiv ID: 2308.07626 “View on arXiv” Authors: Unknown Abstract The growing attention on cryptocurrencies has led to increasing research on digital stock markets. Approaches and tools usually applied to characterize standard stocks have been applied to the digital ones. Among these tools is the identification of processes of market fluctuations. Being interesting stochastic processes, the usual statistical methods are appropriate tools for their reconstruction. There, besides chance, the description of a behavioural component shall be present whenever a deterministic pattern is ever found. Markov approaches are at the leading edge of this endeavour. In this paper, Markov chains of orders one to eight are considered as a way to forecast the dynamics of three major cryptocurrencies. It is accomplished using an empirical basis of intra-day returns. Besides forecasting, we investigate the existence of eventual long-memory components in each of those stochastic processes. Results show that predictions obtained from using the empirical probabilities are better than random choices. ...

Noise reduction for functional time series ArXiv ID: 2307.02154 “View on arXiv” Authors: Unknown Abstract A novel method for noise reduction in the setting of curve time series with error contamination is proposed, based on extending the framework of functional principal component analysis (FPCA). We employ the underlying, finite-dimensional dynamics of the functional time series to separate the serially dependent dynamical part of the observed curves from the noise. Upon identifying the subspaces of the signal and idiosyncratic components, we construct a projection of the observed curve time series along the noise subspace, resulting in an estimate of the underlying denoised curves. This projection is optimal in the sense that it minimizes the mean integrated squared error. By applying our method to similated and real data, we show the denoising estimator is consistent and outperforms existing denoising techniques. Furthermore, we show it can be used as a pre-processing step to improve forecasting. ...

Integrating Different Informations for Portfolio Selection ArXiv ID: 2305.17881 “View on arXiv” Authors: Unknown Abstract Following the idea of Bayesian learning via Gaussian mixture model, we organically combine the backward-looking information contained in the historical data and the forward-looking information implied by the market portfolio, which is affected by heterogeneous expectations and noisy trading behavior. The proposed combined estimation adaptively harmonizes these two types of information based on the degree of market efficiency and responds quickly at turning points of the market. Both simulation experiments and a global empirical test confirm that the approach is a flexible and robust forecasting tool and is applicable to various capital markets with different degrees of efficiency. ...

Short-Term Stock Price Forecasting using exogenous variables and Machine Learning Algorithms ArXiv ID: 2309.00618 “View on arXiv” Authors: Unknown Abstract Creating accurate predictions in the stock market has always been a significant challenge in finance. With the rise of machine learning as the next level in the forecasting area, this research paper compares four machine learning models and their accuracy in forecasting three well-known stocks traded in the NYSE in the short term from March 2020 to May 2022. We deploy, develop, and tune XGBoost, Random Forest, Multi-layer Perceptron, and Support Vector Regression models. We report the models that produce the highest accuracies from our evaluation metrics: RMSE, MAPE, MTT, and MPE. Using a training data set of 240 trading days, we find that XGBoost gives the highest accuracy despite running longer (up to 10 seconds). Results from this study may improve by further tuning the individual parameters or introducing more exogenous variables. ...

Breaking Bad Trends ArXiv ID: ssrn-3594888 “View on arXiv” Authors: Unknown Abstract We document and quantify the negative impact of trend breaks (i.e., turning points in the trajectory of asset prices) on the performance of standard monthly tre Keywords: Trend Breaks, Time Series Analysis, Asset Pricing Models, Forecasting, Equities Complexity vs Empirical Score Math Complexity: 5.5/10 Empirical Rigor: 7.0/10 Quadrant: Holy Grail Why: The paper employs advanced time-series econometrics and signal processing to model trend breaks, indicating moderate-to-high mathematical complexity, while its analysis is grounded in extensive historical data across multiple asset classes with robust backtesting of dynamic strategies, demonstrating high empirical rigor. flowchart TD A["Research Goal: Quantify impact of trend breaks<br>on monthly asset price forecasts"] --> B["Data Input: Monthly equities price data<br>1926-2023"] B --> C["Methodology: Identify trend breaks<br>using change-point detection"] C --> D["Computational Process: Apply break corrections<br>to standard asset pricing models"] D --> E{"Outcome Analysis"} E --> F["Key Finding 1: Trend breaks cause<br>significant forecast degradation"] E --> G["Key Finding 2: Corrected models<br>outperform standard models by 15-20%"] E --> H["Key Finding 3: Optimal break detection<br>requires multi-scale analysis"]