A Case Study of Next Portfolio Prediction for Mutual Funds

• URL: http://arxiv.org/abs/2410.18098v1
• Date: Tue, 08 Oct 2024 12:49:00 GMT
• Title: A Case Study of Next Portfolio Prediction for Mutual Funds
• Authors: Guilherme Thomaz, Denis Maua,
• Abstract summary: This work frames mutual fund portfolio prediction as a Next Novel Basket Recommendation (NNBR) task. We create a benchmark dataset using publicly available data and evaluate the performance of various recommender system models on the NNBR task.
• Score: 0.0
• License:
• Abstract: Mutual funds aim to generate returns above market averages. While predicting their future portfolio allocations can bring economic advantages, the task remains challenging and largely unexplored. To fill that gap, this work frames mutual fund portfolio prediction as a Next Novel Basket Recommendation (NNBR) task, focusing on predicting novel items in a fund's next portfolio. We create a comprehensive benchmark dataset using publicly available data and evaluate the performance of various recommender system models on the NNBR task. Our findings reveal that predicting novel items in mutual fund portfolios is inherently more challenging than predicting the entire portfolio or only repeated items. While state-of-the-art NBR models are outperformed by simple heuristics when considering both novel and repeated items together, autoencoder-based approaches demonstrate superior performance in predicting only new items. The insights gained from this study highlight the importance of considering domain-specific characteristics when applying recommender systems to mutual fund portfolio prediction. The performance gap between predicting the entire portfolio or repeated items and predicting novel items underscores the complexity of the NNBR task in this domain and the need for continued research to develop more robust and adaptable models for this critical financial application.

Related papers

• Conformal Predictive Portfolio Selection [10.470114319701576]
  We propose a framework for predictive portfolio selection using conformal inference, called Conformal Predictive Portfolio Selection ( CPPS) Our approach predicts future portfolio returns, computes corresponding prediction intervals, and selects the desirable portfolio based on these intervals. We demonstrate the effectiveness of our CPPS framework using an AR model and validate its performance through empirical studies.
  arXiv  Detail & Related papers  (2024-10-19T15:42:49Z)
• Harnessing Earnings Reports for Stock Predictions: A QLoRA-Enhanced LLM Approach [6.112119533910774]
  This paper introduces an advanced approach by employing Large Language Models (LLMs) instruction fine-tuned with a novel combination of instruction-based techniques and quantized low-rank adaptation (QLoRA) compression. Our methodology integrates 'base factors', such as financial metric growth and earnings transcripts, with 'external factors', including recent market indices performances and analyst grades, to create a rich, supervised dataset. This study not only demonstrates the power of integrating cutting-edge AI with fine-tuned financial data but also paves the way for future research in enhancing AI-driven financial analysis tools.
  arXiv  Detail & Related papers  (2024-08-13T04:53:31Z)
• RelBench: A Benchmark for Deep Learning on Relational Databases [78.52438155603781]
  We present RelBench, a public benchmark for solving tasks over databases with graph neural networks. We use RelBench to conduct the first comprehensive study of Deep Learning infrastructure. RDL learns better whilst reducing human work needed by more than an order of magnitude.
  arXiv  Detail & Related papers  (2024-07-29T14:46:13Z)
• GenBench: A Benchmarking Suite for Systematic Evaluation of Genomic Foundation Models [56.63218531256961]
  We introduce GenBench, a benchmarking suite specifically tailored for evaluating the efficacy of Genomic Foundation Models. GenBench offers a modular and expandable framework that encapsulates a variety of state-of-the-art methodologies. We provide a nuanced analysis of the interplay between model architecture and dataset characteristics on task-specific performance.
  arXiv  Detail & Related papers  (2024-06-01T08:01:05Z)
• Developing An Attention-Based Ensemble Learning Framework for Financial Portfolio Optimisation [0.0]
  We propose a multi-agent and self-adaptive portfolio optimisation framework integrated with attention mechanisms and time series, namely the MASAAT. By reconstructing the tokens of financial data in a sequence, the attention-based cross-sectional analysis module and temporal analysis module of each agent can effectively capture the correlations between assets and the dependencies between time points. The experimental results clearly demonstrate that the MASAAT framework achieves impressive enhancement when compared with many well-known portfolio optimsation approaches.
  arXiv  Detail & Related papers  (2024-04-13T09:10:05Z)
• Cryptocurrency Portfolio Optimization by Neural Networks [81.20955733184398]
  This paper proposes an effective algorithm based on neural networks to take advantage of these investment products. A deep neural network, which outputs the allocation weight of each asset at a time interval, is trained to maximize the Sharpe ratio. A novel loss term is proposed to regulate the network's bias towards a specific asset, thus enforcing the network to learn an allocation strategy that is close to a minimum variance strategy.
  arXiv  Detail & Related papers  (2023-10-02T12:33:28Z)
• Fuzzy Expert System for Stock Portfolio Selection: An Application to Bombay Stock Exchange [0.0]
  Fuzzy expert system model is proposed to evaluate and rank the stocks under Bombay Stock Exchange (BSE) The performance of the model proved to be satisfactory for short-term investment period when compared with the recent performance of the stocks.
  arXiv  Detail & Related papers  (2022-04-28T10:01:15Z)
• HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information [73.40830291141035]
  Several methods were recently proposed to mine the shared information through stock concepts extracted from the Web to improve the forecasting results. Previous work assumes the connections between stocks and concepts are stationary, and neglects the dynamic relevance between stocks and concepts. We propose a novel stock trend forecasting framework that can adequately mine the concept-oriented shared information from predefined concepts and hidden concepts.
  arXiv  Detail & Related papers  (2021-10-26T14:04:04Z)
• Price graphs: Utilizing the structural information of financial time series for stock prediction [4.4707451544733905]
  We propose a novel framework to address both issues regarding stock prediction. In terms of transforming time series into complex networks, we convert market price series into graphs. We take graph embeddings to represent the associations among temporal points as the prediction model inputs.
  arXiv  Detail & Related papers  (2021-06-04T14:46:08Z)
• Combining Task Predictors via Enhancing Joint Predictability [53.46348489300652]
  We present a new predictor combination algorithm that improves the target by i) measuring the relevance of references based on their capabilities in predicting the target, and ii) strengthening such estimated relevance. Our algorithm jointly assesses the relevance of all references by adopting a Bayesian framework. Based on experiments on seven real-world datasets from visual attribute ranking and multi-class classification scenarios, we demonstrate that our algorithm offers a significant performance gain and broadens the application range of existing predictor combination approaches.
  arXiv  Detail & Related papers  (2020-07-15T21:58:39Z)
• Meta-Learned Confidence for Few-shot Learning [60.6086305523402]
  A popular transductive inference technique for few-shot metric-based approaches, is to update the prototype of each class with the mean of the most confident query examples. We propose to meta-learn the confidence for each query sample, to assign optimal weights to unlabeled queries. We validate our few-shot learning model with meta-learned confidence on four benchmark datasets.
  arXiv  Detail & Related papers  (2020-02-27T10:22:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.