1,278 research outputs found

    Regime-switching recurrent reinforcement learning for investment decision making

    Get PDF
    This paper presents the regime-switching recurrent reinforcement learning (RSRRL) model and describes its application to investment problems. The RSRRL is a regime-switching extension of the recurrent reinforcement learning (RRL) algorithm. The basic RRL model was proposed by Moody and Wu (Proceedings of the IEEE/IAFE 1997 on Computational Intelligence for Financial Engineering (CIFEr). IEEE, New York, pp 300-307 1997) and presented as a methodology to solve stochastic control problems in finance. We argue that the RRL is unable to capture all the intricacies of financial time series, and propose the RSRRL as a more suitable algorithm for such type of data. This paper gives a description of two variants of the RSRRL, namely a threshold version and a smooth transition version, and compares their performance to the basic RRL model in automated trading and portfolio management applications. We use volatility as an indicator/transition variable for switching between regimes. The out-of-sample results are generally in favour of the RSRRL models, thereby supporting the regime-switching approach, but some doubts exist regarding the robustness of the proposed models, especially in the presence of transaction cost

    Neural Network Models for Stock Selection Based on Fundamental Analysis

    Get PDF
    Application of neural network architectures for financial prediction has been actively studied in recent years. This paper presents a comparative study that investigates and compares feed-forward neural network (FNN) and adaptive neural fuzzy inference system (ANFIS) on stock prediction using fundamental financial ratios. The study is designed to evaluate the performance of each architecture based on the relative return of the selected portfolios with respect to the benchmark stock index. The results show that both architectures possess the ability to separate winners and losers from a sample universe of stocks, and the selected portfolios outperform the benchmark. Our study argues that FNN shows superior performance over ANFIS

    Machine Learning for Stock Prediction Based on Fundamental Analysis

    Get PDF
    Application of machine learning for stock prediction is attracting a lot of attention in recent years. A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods could be successfully used toward stock predicting using stocks’ historical data. Most of these existing approaches have focused on short term prediction using stocks’ historical price and technical indicators. In this thesis, we prepared 22 years’ worth of stock quarterly financial data and investigated three machine learning algorithms: Feed-forward Neural Network (FNN), Random Forest (RF) and Adaptive Neural Fuzzy Inference System (ANFIS) for stock prediction based on fundamental analysis. In addition, we applied RF based feature selection and bootstrap aggregation in order to improve model performance and aggregate predictions from different models. Our results show that RF model achieves the best prediction results, and feature selection is able to improve test performance of FNN and ANFIS. Moreover, the aggregated model outperforms all baseline models as well as the benchmark DJIA index by an acceptable margin for the test period. Our findings demonstrate that machine learning models could be used to aid fundamental analysts with decision making regarding to stock investment

    Online learning in financial time series

    Get PDF
    We wish to understand if additional learning forms can be combined with sequential optimisation to provide superior benefit over batch learning in various tasks operating in financial time series. In chapter 4, Online learning with radial basis function networks, we provide multi-horizon forecasts on the returns of financial time series. Our sequentially optimised radial basis function network (RBFNet) outperforms a random-walk baseline and several powerful supervised learners. Our RBFNets naturally measure the similarity between test samples and prototypes that capture the characteristics of the feature space. In chapter 5, Reinforcement learning for systematic FX trading, we perform feature representation transfer from an RBFNet to a direct, recurrent reinforcement learning (DRL) agent. Earlier academic work saw mixed results. We use better features, second-order optimisation methods and adapt our model parameters sequentially. As a result, our DRL agents cope better with statistical changes to the data distribution, achieving higher risk-adjusted returns than a funding and a momentum baseline. In chapter 6, The recurrent reinforcement learning crypto agent, we construct a digital assets trading agent that performs feature space representation transfer from an echo state network to a DRL agent. The agent learns to trade the XBTUSD perpetual swap contract on BitMEX. Our meta-model can process data as a stream and learn sequentially; this helps it cope with the nonstationary environment. In chapter 7, Sequential asset ranking in nonstationary time series, we create an online learning long/short portfolio selection algorithm that can detect the best and worst performing portfolio constituents that change over time; in particular, we successfully handle the higher transaction costs associated with using daily-sampled data, and achieve higher total and risk-adjusted returns than the long-only holding of the S&P 500 index with hindsight

    MetaTrader: An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimization

    Full text link
    Portfolio management is a fundamental problem in finance. It involves periodic reallocations of assets to maximize the expected returns within an appropriate level of risk exposure. Deep reinforcement learning (RL) has been considered a promising approach to solving this problem owing to its strong capability in sequential decision making. However, due to the non-stationary nature of financial markets, applying RL techniques to portfolio optimization remains a challenging problem. Extracting trading knowledge from various expert strategies could be helpful for agents to accommodate the changing markets. In this paper, we propose MetaTrader, a novel two-stage RL-based approach for portfolio management, which learns to integrate diverse trading policies to adapt to various market conditions. In the first stage, MetaTrader incorporates an imitation learning objective into the reinforcement learning framework. Through imitating different expert demonstrations, MetaTrader acquires a set of trading policies with great diversity. In the second stage, MetaTrader learns a meta-policy to recognize the market conditions and decide on the most proper learned policy to follow. We evaluate the proposed approach on three real-world index datasets and compare it to state-of-the-art baselines. The empirical results demonstrate that MetaTrader significantly outperforms those baselines in balancing profits and risks. Furthermore, thorough ablation studies validate the effectiveness of the components in the proposed approach

    Decision-Making Amplification Under Uncertainty: An Exploratory Study of Behavioral Similarity and Intelligent Decision Support Systems

    Get PDF
    Intelligent decision systems have the potential to support and greatly amplify human decision-making across a number of industries and domains. However, despite the rapid improvement in the underlying capabilities of these “intelligent” systems, increasing their acceptance as decision aids in industry has remained a formidable challenge. If intelligent systems are to be successful, and their full impact on decision-making performance realized, a greater understanding of the factors that influence recommendation acceptance from intelligent machines is needed. Through an empirical experiment in the financial services industry, this study investigated the effects of perceived behavioral similarity (similarity state) on the dependent variables of recommendation acceptance, decision performance and decision efficiency under varying conditions of uncertainty (volatility state). It is hypothesized in this study that behavioral similarity as a design element will positively influence the acceptance rate of machine recommendations by human users. The level of uncertainty in the decision context is expected to moderate this relationship. In addition, an increase in recommendation acceptance should positively influence both decision performance and decision efficiency. The quantitative exploration of behavioral similarity as a design element revealed a number of key findings. Most importantly, behavioral similarity was found to positively influence the acceptance rate of machine recommendations. However, uncertainty did not moderate the level of recommendation acceptance as expected. The experiment also revealed that behavioral similarity positively influenced decision performance during periods of elevated uncertainty. This relationship was moderated based on the level of uncertainty in the decision context. The investigation of decision efficiency also revealed a statistically significant result. However, the results for decision efficiency were in the opposite direction of the hypothesized relationship. Interestingly, decisions made with the behaviorally similar decision aid were less efficient, based on length of time to make a decision, compared to decisions made with the low-similarity decision aid. The results of decision efficiency were stable across both levels of uncertainty in the decision context

    Contribution to Financial Modeling and Financial Forecasting

    Get PDF
    This thesis consists of three chapters. Each chapter is independent research that is conducted during my study. This research is concentrated on financial time series modeling and forecasting. On first chapter, the research aims to prove that any abnormal behavior in debt level is a signal of future unexpected return for firms that is listed in indexes in this study, hence it is a signal to buy. In order to prove this theory multiple indexes from around the world were taken into consideration. This behavior is consistent in most of indexes around the word. The second chapter investigate the effect of United State president speech on value of United State Currency in Foreign Exchange Rate market. In this analysis it is shown that during the time the president is delivering a speech there is distinctive changes in USD value and volatility in global markets. This chapter implies that this effect cannot be captured by linear models, and the impact of the presidential speech is short term. Finally, the third chapter which is the major research of this thesis, suggest two new methods that potentially enhance the financial time series forecasting. Firstly, the new ARMA-RNN model is presented. The suggested model is inheriting the process of Autoregressive Moving Average model which is extensively studied, and train a recurrent neural network based on it to benefit from unique ability of ARMA model as well as strength and nonlinearity of artificial neural network. Secondly the research investigates the use of different frequency of data for input layer to predict the same data on output layer. In other words, artificial neural networks are trained on higher frequency data to predict lower frequency. Finally, both stated method is combined to achieve more superior predictive model

    Deep reinforcement learning for investing: A quantamental approach for portfolio management

    Get PDF
    The world of investments affects us all. The way surplus capital is allocated by ourselves or investment funds can determine how we eat, innovate and even educate kids. Portfolio management is an integral albeit challenging process in this task (Leković, 2021). It entails managing a basket of financial assets to maximize the returns per unit of risk, considering all the micro and macro economical, societal, political and environmental complex causal relations. This study aims to evaluate how a machine learning technique called deep reinforcement learning (DRL) can improve the activity of portfolio management. It also has a second goal of understanding if financial fundamental features (i.e., revenue, debt, assets, cash flow) improve the model performance. After conducting a literature review to establish the current state-of-the-art, the CRISP-DM method was followed: 1) Business understanding; 2) Data understanding; 3) Data preparation – two datasets were prepared, one with market only features (i.e., close price, daily volume traded) and another with market plus fundamental features; 4) Modeling – Advantage Actor-Critic (A2C), Deep Deterministic Policy Gradient (DDPG) and Twin-delayed DDPG (TD3) DRL models were optimized on both datasets; 5) Evaluation. On average, models had the same sharpe ratio performance in both datasets – average sharpe ratio of 0.35 vs 0.30 for the baseline, in the test set. DRL models outperformed traditional portfolio optimization techniques and financial fundamental features improved model robustness and consistency. Hence, supporting the use of both DRL models and quantamental investment strategies in portfolio management.Todos somos afetados pelo mundo dos investimentos. A forma como o excedente de capital é alocado tanto por nós como por fundos de investimentos determina a forma como comemos, inovamos e até mesmo como fornecemos educação às crianças. Gestão de portfólio é uma tarefa essencial e desafiadora neste processo (Leković, 2021). Envolve gerir um conjunto de ativos financeiros com o objetivo de maximizar os retornos por unidade de risco, tendo em consideração todas as relações complexas entre fatores macro e microeconómicos, sociais, políticos e ambientais. Este estudo pretende avaliar de que forma a técnica de machine learning intitulada de Aprendizagem por Reforço Profunda (ARP) consegue melhorar a tarefa de gestão de portfólios. Também tem um segundo objetivo de entender se variáveis relacionadas com a performance financeira de uma empresa (i.e., vendas, passivos, ativos, fluxos de caixa) melhoram a performance do modelo. Após o estado-de-arte ter sido definido com a revisão de literatura, utilizou-se o método CRISP-DM da seguinte forma: 1) Entendimento do negócio; 2) Entendimento dos dados; 3) Preparação dos dados – dois conjuntos de dados foram preparados, um apenas com variáveis de mercado (i.e., preço de fecho, volume transacionado) e o outro com variáveis de mercado mais variáveis de performance financeira; 4) Modelagem – usou-se os modelos Advantage Actor-Critic (A2C), Deep Deterministic Policy Gradient (DDPG) e Twin-delayed DDPG (TD3) em ambos os conjuntos de dados; 5) Avaliação. Em média, os modelos apresentaram o mesmo índice sharpe nos dois conjuntos de dados – média de 0.35 vs 0.30 para o modelo base, no conjunto de teste. Os modelos ARP apresentaram uma melhor performance do que os modelos tradicionais de otimização de portfólios e a utilização de variáveis de performance financeira melhoraram a robustez e consistência dos modelos. Tais conclusões suportam o uso de modelos ARP e de estratégias de investimentos quantamentais na gestão de portfólios
    • …
    corecore