Trader-Company Method: A Metaheuristic for Interpretable Stock Price Prediction

• URL: http://arxiv.org/abs/2012.10215v1
• Date: Fri, 18 Dec 2020 13:19:27 GMT
• Title: Trader-Company Method: A Metaheuristic for Interpretable Stock Price Prediction
• Authors: Katsuya Ito and Kentaro Minami and Kentaro Imajo and Kei Nakagawa
• Abstract summary: There are several challenges in financial markets hindering practical applications of machine learning-based models. We propose the Trader-Company method, a novel evolutionary model that mimics the roles of a financial institute and traders. Our method predicts future stock returns by aggregating suggestions from multiple weak learners called Traders.
• Score: 3.9189409002585562
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Investors try to predict returns of financial assets to make successful investment. Many quantitative analysts have used machine learning-based methods to find unknown profitable market rules from large amounts of market data. However, there are several challenges in financial markets hindering practical applications of machine learning-based models. First, in financial markets, there is no single model that can consistently make accurate prediction because traders in markets quickly adapt to newly available information. Instead, there are a number of ephemeral and partially correct models called "alpha factors". Second, since financial markets are highly uncertain, ensuring interpretability of prediction models is quite important to make reliable trading strategies. To overcome these challenges, we propose the Trader-Company method, a novel evolutionary model that mimics the roles of a financial institute and traders belonging to it. Our method predicts future stock returns by aggregating suggestions from multiple weak learners called Traders. A Trader holds a collection of simple mathematical formulae, each of which represents a candidate of an alpha factor and would be interpretable for real-world investors. The aggregation algorithm, called a Company, maintains multiple Traders. By randomly generating new Traders and retraining them, Companies can efficiently find financially meaningful formulae whilst avoiding overfitting to a transient state of the market. We show the effectiveness of our method by conducting experiments on real market data.

Related papers

• Predicting Bitcoin Market Trends with Enhanced Technical Indicator Integration and Classification Models [6.39158540499473]
  This study presents a machine learning model based on classification to forecast the direction of the cryptocurrency market. It is trained using historical data and important technical indicators such as the Moving Average Convergence Divergence, the Relative Strength Index, and Bollinger Bands. The results show a buy/sell signal accuracy of over 92%.
  arXiv  Detail & Related papers  (2024-10-09T14:29:50Z)
• When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments [55.19252983108372]
  We have developed a multi-agent AI system called StockAgent, driven by LLMs. The StockAgent allows users to evaluate the impact of different external factors on investor trading. It avoids the test set leakage issue present in existing trading simulation systems based on AI Agents.
  arXiv  Detail & Related papers  (2024-07-15T06:49:30Z)
• Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction [54.21695754082441]
  Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility. Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions. We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction. Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
  arXiv  Detail & Related papers  (2023-08-18T16:21:15Z)
• Data Cross-Segmentation for Improved Generalization in Reinforcement Learning Based Algorithmic Trading [5.75899596101548]
  We propose a Reinforcement Learning (RL) algorithm that trades based on signals from a learned predictive model. We test our algorithm on 20+ years of equity data from Bursa Malaysia.
  arXiv  Detail & Related papers  (2023-07-18T16:00:02Z)
• Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [51.3422222472898]
  We document the capability of large language models (LLMs) like ChatGPT to predict stock price movements using news headlines. We develop a theoretical model incorporating information capacity constraints, underreaction, limits-to-arbitrage, and LLMs.
  arXiv  Detail & Related papers  (2023-04-15T19:22:37Z)
• Identifying Trades Using Technical Analysis and ML/DL Models [1.181206257787103]
  The importance of predicting stock market prices cannot be overstated. It enables investors to make informed investment decisions, manage risks, and ensure the stability of the financial system. Deep learning has shown promise in accurately predicting stock prices, but there is still much research to be done.
  arXiv  Detail & Related papers  (2023-04-12T18:46:35Z)
• Factor Investing with a Deep Multi-Factor Model [123.52358449455231]
  We develop a novel deep multi-factor model that adopts industry neutralization and market neutralization modules with clear financial insights. Tests on real-world stock market data demonstrate the effectiveness of our deep multi-factor model.
  arXiv  Detail & Related papers  (2022-10-22T14:47:11Z)
• Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets [84.90242084523565]
  Traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics. By adopting a state-of-the-art second-order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention. By addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts, we thoroughly compare our Bayesian model with traditional ML alternatives.
  arXiv  Detail & Related papers  (2022-03-07T18:59:54Z)
• Deep Q-Learning Market Makers in a Multi-Agent Simulated Stock Market [58.720142291102135]
  This paper focuses precisely on the study of these markets makers strategies from an agent-based perspective. We propose the application of Reinforcement Learning (RL) for the creation of intelligent market markers in simulated stock markets.
  arXiv  Detail & Related papers  (2021-12-08T14:55:21Z)
• Financial Markets Prediction with Deep Learning [11.26482563151052]
  We propose a novel one-dimensional convolutional neural networks (CNN) model to predict financial market movement. The customized one-dimensional convolutional layers scan financial trading data through time, while different types of data, such as prices and volume, share parameters ( Kernels) with each other. Our model automatically extracts features instead of using traditional technical indicators.
  arXiv  Detail & Related papers  (2021-04-05T19:36:48Z)
• Deep Portfolio Optimization via Distributional Prediction of Residual Factors [3.9189409002585562]
  We propose a novel method of constructing a portfolio based on predicting the distribution of a financial quantity called residual factors. We demonstrate the efficacy of our method on U.S. and Japanese stock market data.
  arXiv  Detail & Related papers  (2020-12-14T04:09:52Z)

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.