A Stock Trading System for a Medium Volatile Asset using Multi Layer Perceptron

• URL: http://arxiv.org/abs/2201.12286v1
• Date: Mon, 17 Jan 2022 16:08:40 GMT
• Title: A Stock Trading System for a Medium Volatile Asset using Multi Layer Perceptron
• Authors: Ivan Letteri, Giuseppe Della Penna, Giovanni De Gasperis, Abeer Dyoub
• Abstract summary: We propose a stock trading system having as main core the feed-forward deep neural networks (DNN) to predict the price for the next 30 days of open market. The results were promising bringing a total profit factor of 3.2% in just one month from a very modest budget of $100.
• Score: 0.6882042556551609
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Stock market forecasting is a lucrative field of interest with promising profits but not without its difficulties and for some people could be even causes of failure. Financial markets by their nature are complex, non-linear and chaotic, which implies that accurately predicting the prices of assets that are part of it becomes very complicated. In this paper we propose a stock trading system having as main core the feed-forward deep neural networks (DNN) to predict the price for the next 30 days of open market, of the shares issued by Abercrombie & Fitch Co. (ANF) in the stock market of the New York Stock Exchange (NYSE). The system we have elaborated calculates the most effective technical indicator, applying it to the predictions computed by the DNNs, for generating trades. The results showed an increase in values such as Expectancy Ratio of 2.112% of profitable trades with Sharpe, Sortino, and Calmar Ratios of 2.194, 3.340, and 12.403 respectively. As a verification, we adopted a backtracking simulation module in our system, which maps trades to actual test data consisting of the last 30 days of open market on the ANF asset. Overall, the results were promising bringing a total profit factor of 3.2% in just one month from a very modest budget of $100. This was possible because the system reduced the number of trades by choosing the most effective and efficient trades, saving on commissions and slippage costs.

Related papers

• 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)
• NoxTrader: LSTM-Based Stock Return Momentum Prediction for Quantitative Trading [0.0]
  NoxTrader is a sophisticated system designed for portfolio construction and trading execution. The underlying learning process of NoxTrader is rooted in the assimilation of valuable insights derived from historical trading data. Our rigorous feature engineering and careful selection of prediction targets enable us to generate prediction data with an impressive correlation range between 0.65 and 0.75.
  arXiv  Detail & Related papers  (2023-10-01T17:53:23Z)
• 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)
• Using a Deep Learning Model to Simulate Human Stock Trader's Methods of Chart Analysis [0.276240219662896]
  The proposed scheme looks at stock prices of the previous 600 days and predicts whether the stock price will rise or fall 10% or 20% within the next D days. Using the proposed method for the Korea market it gave return of 75.36% having Sharpe ratio of 1.57, which far exceeds the market return by 36% and 0.61, respectively. On the US market it gives total return of 27.17% with Sharpe ratio of 0.61, which outperforms other benchmarks such as NASDAQ, S&P500, DOW JONES index by 17.69% and 0.27, respectively.
  arXiv  Detail & Related papers  (2023-04-28T14:27:18Z)
• 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)
• A Game of NFTs: Characterizing NFT Wash Trading in the Ethereum Blockchain [53.8917088220974]
  The Non-Fungible Token (NFT) market experienced explosive growth in 2021, with a monthly trade volume reaching $6 billion in January 2022. Concerns have emerged about possible wash trading, a form of market manipulation in which one party repeatedly trades an NFT to inflate its volume artificially. We find that wash trading affects 5.66% of all NFT collections, with a total artificial volume of $3,406,110,774.
  arXiv  Detail & Related papers  (2022-12-02T15:03:35Z)
• Quantum computational finance: martingale asset pricing for incomplete markets [69.73491758935712]
  We show that a variety of quantum techniques can be applied to the pricing problem in finance. We discuss three different methods that are distinct from previous works.
  arXiv  Detail & Related papers  (2022-09-19T09:22:01Z)
• Implementation of a Type-2 Fuzzy Logic Based Prediction System for the Nigerian Stock Exchange [0.0]
  This research is to develop a prediction system for stock market using Fuzzy Logic Type2. A total of four different technical indicators were selected for this research. The Fuzzy System is fuzzified to Low, Medium, and High using the Triangular and Gaussian Membership rules.
  arXiv  Detail & Related papers  (2022-02-04T12:41:04Z)
• Stock Price Prediction Under Anomalous Circumstances [81.37657557441649]
  This paper aims to capture the movement pattern of stock prices under anomalous circumstances. We train ARIMA and LSTM models at the single-stock level, industry level, and general market level. Based on 100 companies' stock prices in the period of 2016 to 2020, the models achieve an average prediction accuracy of 98%.
  arXiv  Detail & Related papers  (2021-09-14T18:50:38Z)
• Trader-Company Method: A Metaheuristic for Interpretable Stock Price Prediction [3.9189409002585562]
  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.
  arXiv  Detail & Related papers  (2020-12-18T13:19:27Z)
• GA-MSSR: Genetic Algorithm Maximizing Sharpe and Sterling Ratio Method for RoboTrading [0.4568777157687961]
  Foreign exchange is the largest financial market in the world. Most literature used historical price information and technical indicators for training. To address this problem, we designed trading rule features that are derived from technical indicators and trading rules.
  arXiv  Detail & Related papers  (2020-08-16T05:33:35Z)

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.