Hedging Properties of Algorithmic Investment Strategies using Long Short-Term Memory and Time Series models for Equity Indices

• URL: http://arxiv.org/abs/2309.15640v1
• Date: Wed, 27 Sep 2023 13:18:39 GMT
• Title: Hedging Properties of Algorithmic Investment Strategies using Long Short-Term Memory and Time Series models for Equity Indices
• Authors: Jakub Micha\'nk\'ow, Pawe{\l} Sakowski, Robert \'Slepaczuk
• Abstract summary: This paper proposes a novel approach to hedging portfolios of risky assets when financial markets are affected by financial turmoils. We employ four types of diverse theoretical models to generate price forecasts, which are then used to produce investment signals in single and complex AIS. Our main conclusion is that LSTM-based strategies outperform the other models and that the best diversifier for the AIS built for the S&P 500 index is the AIS built for Bitcoin.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes a novel approach to hedging portfolios of risky assets when financial markets are affected by financial turmoils. We introduce a completely novel approach to diversification activity not on the level of single assets but on the level of ensemble algorithmic investment strategies (AIS) built based on the prices of these assets. We employ four types of diverse theoretical models (LSTM - Long Short-Term Memory, ARIMA-GARCH - Autoregressive Integrated Moving Average - Generalized Autoregressive Conditional Heteroskedasticity, momentum, and contrarian) to generate price forecasts, which are then used to produce investment signals in single and complex AIS. In such a way, we are able to verify the diversification potential of different types of investment strategies consisting of various assets (energy commodities, precious metals, cryptocurrencies, or soft commodities) in hedging ensemble AIS built for equity indices (S&P 500 index). Empirical data used in this study cover the period between 2004 and 2022. Our main conclusion is that LSTM-based strategies outperform the other models and that the best diversifier for the AIS built for the S&P 500 index is the AIS built for Bitcoin. Finally, we test the LSTM model for a higher frequency of data (1 hour). We conclude that it outperforms the results obtained using daily data.

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)
• Joint Latent Topic Discovery and Expectation Modeling for Financial Markets [45.758436505779386]
  We present a groundbreaking framework for financial market analysis. This approach is the first to jointly model investor expectations and automatically mine latent stock relationships. Our model consistently achieves an annual return exceeding 10%.
  arXiv  Detail & Related papers  (2023-06-01T01:36:51Z)
• BloombergGPT: A Large Language Model for Finance [42.73350054822628]
  We present BloombergGPT, a 50 billion parameter language model that is trained on a wide range of financial data. We construct a 363 billion token dataset based on Bloomberg's extensive data sources, augmented with 345 billion tokens from general purpose datasets. Our mixed dataset training leads to a model that outperforms existing models on financial tasks by significant margins.
  arXiv  Detail & Related papers  (2023-03-30T17:30:36Z)
• High-Dimensional Stock Portfolio Trading with Deep Reinforcement Learning [0.0]
  The algorithm is capable of trading high-dimensional portfolios from cross-sectional datasets of any size. We sequentially set up environments by sampling one asset for each environment while rewarding investments with the resulting asset's return and cash reservation with the average return of the set of assets.
  arXiv  Detail & Related papers  (2021-12-09T08:30:45Z)
• Stock Portfolio Optimization Using a Deep Learning LSTM Model [1.1470070927586016]
  This work has carried out an analysis of the time series of the historical prices of the top five stocks from the nine different sectors of the Indian stock market from January 1, 2016, to December 31, 2020. Optimum portfolios are built for each of these sectors. The predicted and the actual returns of each portfolio are found to be high, indicating the high precision of the LSTM model.
  arXiv  Detail & Related papers  (2021-11-08T18:41:49Z)
• Bitcoin Transaction Strategy Construction Based on Deep Reinforcement Learning [8.431365407963629]
  This study proposes a framework for automatic high-frequency bitcoin transactions based on a deep reinforcement learning algorithm-proximal policy optimization (PPO) The proposed framework can earn excess returns through both the period of volatility and surge, which opens the door to research on building a single cryptocurrency trading strategy based on deep learning.
  arXiv  Detail & Related papers  (2021-09-30T01:24:03Z)
• Learning Financial Asset-Specific Trading Rules via Deep Reinforcement Learning [0.6445605125467573]
  Various asset trading rules are proposed experimentally based on different technical analysis techniques. Deep reinforcement learning (DRL) methods are employed to learn the new trading rules for each asset. The proposed model in this work outperformed the other state-of-the-art models in learning single asset-specific trading rules.
  arXiv  Detail & Related papers  (2020-10-27T10:59:53Z)
• Stock2Vec: A Hybrid Deep Learning Framework for Stock Market Prediction with Representation Learning and Temporal Convolutional Network [71.25144476293507]
  We have proposed to develop a global hybrid deep learning framework to predict the daily prices in the stock market. With representation learning, we derived an embedding called Stock2Vec, which gives us insight for the relationship among different stocks. Our hybrid framework integrates both advantages and achieves better performance on the stock price prediction task than several popular benchmarked models.
  arXiv  Detail & Related papers  (2020-09-29T22:54:30Z)
• 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)
• Deep Learning for Portfolio Optimization [5.833272638548154]
  Instead of selecting individual assets, we trade Exchange-Traded Funds (ETFs) of market indices to form a portfolio. We compare our method with a wide range of algorithms with results showing that our model obtains the best performance over the testing period.
  arXiv  Detail & Related papers  (2020-05-27T21:28:43Z)
• Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States [71.54651874063865]
  Portfolio management (PM) aims to achieve investment goals such as maximal profits or minimal risks. In this paper, we propose SARL, a novel State-Augmented RL framework for PM. Our framework aims to address two unique challenges in financial PM: (1) data Heterogeneous data -- the collected information for each asset is usually diverse, noisy and imbalanced (e.g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary.
  arXiv  Detail & Related papers  (2020-02-09T08:10:03Z)

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.