Optimizing Stock Option Forecasting with the Assembly of Machine Learning Models and Improved Trading Strategies

• URL: http://arxiv.org/abs/2211.15912v1
• Date: Tue, 29 Nov 2022 04:01:16 GMT
• Title: Optimizing Stock Option Forecasting with the Assembly of Machine Learning Models and Improved Trading Strategies
• Authors: Zheng Cao, Raymond Guo, Wenyu Du, Jiayi Gao, Kirill V. Golubnichiy
• Abstract summary: This paper introduced key aspects of applying Machine Learning (ML) models, improved trading strategies, and the Quasi-Reversibility Method (QRM) to optimize stock option forecasting and trading results.
• Score: 9.553857741758742
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduced key aspects of applying Machine Learning (ML) models, improved trading strategies, and the Quasi-Reversibility Method (QRM) to optimize stock option forecasting and trading results. It presented the findings of the follow-up project of the research "Application of Convolutional Neural Networks with Quasi-Reversibility Method Results for Option Forecasting". First, the project included an application of Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks to provide a novel way of predicting stock option trends. Additionally, it examined the dependence of the ML models by evaluating the experimental method of combining multiple ML models to improve prediction results and decision-making. Lastly, two improved trading strategies and simulated investing results were presented. The Binomial Asset Pricing Model with discrete time stochastic process analysis and portfolio hedging was applied and suggested an optimized investment expectation. These results can be utilized in real-life trading strategies to optimize stock option investment results based on historical data.

Related papers

• DeepClair: Utilizing Market Forecasts for Effective Portfolio Selection [29.43115584494825]
  We introduce DeepClair, a novel framework for portfolio selection. DeepClair leverages a transformer-based time-series forecasting model to predict market trends.
  arXiv  Detail & Related papers  (2024-07-18T11:51:03Z)
• Metric-aware LLM inference for regression and scoring [52.764328080398805]
  Large language models (LLMs) have demonstrated strong results on a range of NLP tasks. We show that this inference strategy can be suboptimal for a range of regression and scoring tasks, and associated evaluation metrics. We propose aware metric LLM inference: a decision theoretic approach optimizing for custom regression and scoring metrics at inference time.
  arXiv  Detail & Related papers  (2024-03-07T03:24:34Z)
• A quantitative fusion strategy of stock picking and timing based on Particle Swarm Optimized-Back Propagation Neural Network and Multivariate Gaussian-Hidden Markov Model [0.0]
  This research introduces a pioneering quantitative fusion model combining stock timing and picking strategy. We conduct the prediction and trading on the basis of the screening stocks and stock market state outputted by MGHMM trained. Our fusion strategy incorporating stock picking and timing presented in this article provide a innovative technique for financial analysis.
  arXiv  Detail & Related papers  (2023-12-10T04:33:59Z)
• 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)
• HireVAE: An Online and Adaptive Factor Model Based on Hierarchical and Regime-Switch VAE [113.47287249524008]
  It is still an open question to build a factor model that can conduct stock prediction in an online and adaptive setting. We propose the first deep learning based online and adaptive factor model, HireVAE, at the core of which is a hierarchical latent space that embeds the relationship between the market situation and stock-wise latent factors. Across four commonly used real stock market benchmarks, the proposed HireVAE demonstrate superior performance in terms of active returns over previous methods.
  arXiv  Detail & Related papers  (2023-06-05T12:58:13Z)
• Low-variance estimation in the Plackett-Luce model via quasi-Monte Carlo sampling [58.14878401145309]
  We develop a novel approach to producing more sample-efficient estimators of expectations in the PL model. We illustrate our findings both theoretically and empirically using real-world recommendation data from Amazon Music and the Yahoo learning-to-rank challenge.
  arXiv  Detail & Related papers  (2022-05-12T11:15:47Z)
• Machine Learning for Stock Prediction Based on Fundamental Analysis [13.920569652186714]
  We investigate three machine learning algorithms: Feed-forward Neural Network (FNN), Random Forest (RF) and Adaptive Neural Fuzzy Inference System (ANFIS) RF model achieves the best prediction results, and feature selection is able to improve test performance of FNN and ANFIS. Our findings demonstrate that machine learning models could be used to aid fundamental analysts with decision-making regarding stock investment.
  arXiv  Detail & Related papers  (2022-01-26T18:48:51Z)
• Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport [8.617532047238461]
  We propose a novel architecture, Temporal Adaptor (TRA), to empower existing stock prediction models with the ability to model multiple stock trading patterns. TRA is a lightweight module that consists of a set independent predictors for learning multiple patterns as well as a router to dispatch samples to different predictors. We show that the proposed method can improve information coefficient (IC) from 0.053 to 0.059 and 0.051 to 0.056 respectively.
  arXiv  Detail & Related papers  (2021-06-24T12:19:45Z)
• Profitability Analysis in Stock Investment Using an LSTM-Based Deep Learning Model [1.2891210250935146]
  We present a deep learning-based regression model built on a long-and-short-term memory network (LSTM) network. It extracts historical stock prices based on a stock's ticker name for a specified pair of start and end dates, and forecasts the future stock prices. We deploy the model on 75 significant stocks chosen from 15 critical sectors of the Indian stock market.
  arXiv  Detail & Related papers  (2021-04-06T11:09:51Z)
• REST: Relational Event-driven Stock Trend Forecasting [76.08435590771357]
  We propose a relational event-driven stock trend forecasting (REST) framework, which can address the shortcoming of existing methods. To remedy the first shortcoming, we propose to model the stock context and learn the effect of event information on the stocks under different contexts. To address the second shortcoming, we construct a stock graph and design a new propagation layer to propagate the effect of event information from related stocks.
  arXiv  Detail & Related papers  (2021-02-15T07:22:09Z)
• Deep Stock Predictions [58.720142291102135]
  We consider the design of a trading strategy that performs portfolio optimization using Long Short Term Memory (LSTM) neural networks. We then customize the loss function used to train the LSTM to increase the profit earned. We find the LSTM model with the customized loss function to have an improved performance in the training bot over a regressive baseline such as ARIMA.
  arXiv  Detail & Related papers  (2020-06-08T23:37:47Z)

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.