Stock Trading Strategy Research Articles

Leveraging LSTM Neural Networks for Stock Price Prediction and Trading Strategy Optimization in Financial Markets

In the Artificial Intelligence development market in recent years, the atmosphere felt by the financial field will be relatively strong, and some, especially in the deep learning model, widely exist in the performance of complex financial data, which has certain advantages. Therefore, as one of the most prominent models of deep learning, LSTM neural network models are just good at processing some complex financial data of the rest of the time series, such as stock price prediction and trading strategy, optimization, and so on. However, in the actual application process, the stock price prediction of such models still has certain data quality, historical data market fluctuations, complex, non-linear data and other related factors, so there are certain challenges and development space in the process of processing.Nevertheless, by properly addressing these issues and combining them with best practices, LSTM algorithms are a powerful tool to help uncover underlying patterns in financial markets and optimize trading decisions.

A study of hybrid deep learning model for stock asset management.

Crafting a lucrative stock trading strategy is pivotal in the realm of investments. However, the task of devising such a strategy becomes challenging task the intricate and ever-changing situation of the stock market. In recent years, with the development of artificial intelligence (AI), some AI technologies have been proven to be successfully applied in stock price and asset management. For example, long short-term memory networks (LSTM) can be used for predicting stock price variation, reinforcement learning (RL) can be used for control stock trading, however, they are generally used separately and cannot achieve simultaneous prediction and trading. In this study, we propose a hybrid deep learning model to predict stock prices and control stock trading to manage assets. LSTM is responsible for predicting stock prices, while RL is responsible for stock trading based on the predicted price trends. Meanwhile, to reduce uncertainty in the stock market and maximize stock assets, the proposed LSTM model can predict the average directional index (ADX) to comprehend the stock trends in advance and we also propose several constraints to assist assets management, thereby reducing the risk and maximizing the stock assets. In our results, the hybrid model yields an average R 2 value of 0.94 when predicting price variations. Moreover, employing the proposed approach, which integrates ADX and constraints, the hybrid model augments stock assets to 1.05 times than initial assets.

AI-Driven Optimization of Financial Quantitative Trading Algorithms and Enhancement of Market Forecasting Capabilities

Abstract: The application of artificial intelligence (AI) into financial markets has revolutionised quantitative trading and market forecasting by increasing the efficiency of algorithmic trading, improving the accuracy of market predictions and facilitating real-time market decisions. This paper will provide an overview of the application of Al in the financial markets focusing on the use of machine learning (ML), deep learning (DL) and reinforcement learning (RL) in optimizing the trading algorithms, specifically the capability of Al to process very high data points and complex relationships that other quantitative models are unable to capture. We will discuss trading algorithms such as XGBoost, deep neural networks such as long short-term memory (LSTM) networks and convolutional neural networks (CNNs), how they can outperform traditional quantitative trading models and real-time decision making in stock price prediction, pattern recognition and trading strategy optimisation. We will also look at Al-enhanced predictive models that utilise deep learning and layered models, such as Natural language processing (NLP) sentiment analysis to capture the public sentiment in the market to forecast employing diverse datasets such as historical prices, market volatility, macroeconomic factors and social media sentiment to improve the forecasting accuracy. By going through several experiments and case studies, this paper will shed light on the impact of entrusting quantitative trading and market forecasting decisions to AI for improved performance and reduced errors. There are many challenges ahead but AI plays a constructive role in improving the trading strategies and forecasting market outcomes accurately.

AI-Driven Optimization of Financial Quantitative Trading Algorithms and Enhancement of Market Forecasting Capabilities

The application of artificial intelligence (AI) into financial markets has revolutionised quantitative trading and market forecasting by increasing the efficiency of algorithmic trading, improving the accuracy of market predictions and facilitating real-time market decisions. This paper will provide an overview of the application of Al in the financial markets focusing on the use of machine learning (ML), deep learning (DL) and reinforcement learning (RL) in optimizing the trading algorithms, specifically the capability of Al to process very high data points and complex relationships that other quantitative models are unable to capture. We will discuss trading algorithms such as XGBoost, deep neural networks such as long short-term memory (LSTM) networks and convolutional neural networks (CNNs), how they can outperform traditional quantitative trading models and real-time decision making in stock price prediction, pattern recognition and trading strategy optimisation. We will also look at Al-enhanced predictive models that utilise deep learning and layered models, such as Natural language processing (NLP) sentiment analysis to capture the public sentiment in the market to forecast employing diverse datasets such as historical prices, market volatility, macroeconomic factors and social media sentiment to improve the forecasting accuracy. By going through several experiments and case studies, this paper will shed light on the impact of entrusting quantitative trading and market forecasting decisions to AI for improved performance and reduced errors. There are many challenges ahead but AI plays a constructive role in improving the trading strategies and forecasting market outcomes accurately.

CNN-BiLSTM-Attention Based Stock Price Prediction and Quantitative Investment Strategy

With the rapid development of technology and the increasing maturity of financial markets, stock price prediction has become a hot topic and an important trend in the financial field. However, the stock market has complexity and volatility, in order to reduce the investment risk and ensure the maximization of benefits, selecting the optimal stock to predict the stock price and formulating the quantitative trading strategy are extremely important issues for financial academics and investors. For the research of stock price prediction and quantitative trading, firstly, the quantitative stock selection model combining XGBoost and multi-factor stock selection model is constructed, and four stocks are screened out, and then the CNN-BiLSTM-Attention model is proposed to predict the stock price trend of the selected stocks, and it is found from the prediction results of the four stocks that the prediction accuracies all reach more than 95%, which is higher than that of the single model prediction. accuracy and passed the validity test of the fitted model. Secondly, based on the quantitative investment portfolio given in the above analysis and the quantitative stock trading strategy of MACD, the prediction results are verified, and the total return of most stocks based on the strategy is higher than 40% at the highest, and the loss is controlled within 10%, which indicates that the trading strategy in this paper is effective and feasible. The study concludes that greater returns can be obtained by calculating the total returns of stock portfolios based on different portfolio approaches.

A study on the efficacy of an attention mechanism-based strategy planning method for stock trading portfolios

In order to take full advantage of the fact that there is a large amount of information and data available in the field of stock trading, this paper develops a new type of stock trading strategy, which reduces investment risk. A stock trading portfolio strategy planning method based on an attention mechanism, as disclosed in the present invention, relates to the field of stock trading technology. The introduction of an attention mechanism allows the strategy to focus on those stocks with higher evaluation coefficients, thereby improving the selectivity and accuracy of the strategy and thus the effectiveness of the portfolio strategy. Furthermore, based on the prediction results of the deep spatio-temporal neural network model, it is possible to determine the stock's short-term trends and identify the optimal trading points. This information can be utilized as a reference for investors, assisting them in making more informed decisions regarding the timing of buying and selling, improving the success rate and yield of trading, enabling them to swiftly and accurately assess the performance of each stock, facilitating timely adjustments to their portfolios, and enabling them to devise the optimal trading strategy and timing for each stock, enhancing the accuracy and efficiency of their trading decisions.

Investment management approaches: A comprehensive review of equity trading simulators, methodological challenges, and future directions

This paper provides a comprehensive review of equity trading simulators, focusing on their performance in assuring pre-trade compliance and portfolio investment management. A systematic search was conducted that covered the period of January 2000 to May 2023 and used keywords related to equity trade simulators, portfolio management, pre-trade compliance, online trading, and artificial intelligence. Studies demonstrating the use of simulators and online platforms specific to portfolio investment management, written in English, and matching the specified query were included. Abstracts, commentaries, editorials, and studies unrelated to finance and investments were excluded. The data extraction process included data related to challenges in modern portfolio trading, online stock trading strategies, the utilization of deep learning, the features of equity trade simulators, and examples of equity trade simulators. A total of 32 studies were included in the systematic review and were approved for qualitative analysis. The challenges identified for portfolio trading included the subjective nature of the inputs, variations in the return distributions, the complexity of blending different investments, considerations of liquidity, trading illiquid securities, optimal portfolio execution, clustering and classification, the handling of special trading days, the real-time pricing of derivatives, and transaction cost models (TCMs). Portfolio optimization techniques have evolved to maximize portfolio returns and minimize risk through optimal asset allocation. Equity trade simulators have become vital tools for portfolio managers, enabling them to assess investment strategies, ensure pre-trade compliance, and mitigate risks. Through simulations, portfolio managers can test investment scenarios, identify potential hazards, and improve their decision-making process.

International crash risk premium

This paper investigates the international crash risk and the cross-section of stock index returns. We use the ex-ante model-free negative skewness measured by country-specific index options, proposed in Bakshi et al. (2003), as a proxy of the crash risk. We find that a country’s stock index with a high crash risk relates to a higher stock return as a risk premium across countries. The international crash risk premium exists robustly after controlling for volatility risk, macroeconomic variables, sensitivities to the international risk factors, and realized return moments. In contrast, other international risk premiums do not exist based on the exposure of such control variables. Based on the crash risk premium, we construct international stock trading strategies by sorting option-implied skewness across countries that outperform benchmark strategies by sorting the above control variables.

Improving algorithmic trading consistency via human alignment and imitation learning

Research on algorithmic trading using reinforcement learning has become increasingly popular in recent years. Although most of the current reinforcement learning methods are employed to train the agent for some kind of modeling or data problem, it is worthwhile to explore in aligning agents with human behavior in applications as crucial as financial trading. Achieving such consistency by incorporating human expert experience into agent behavior is a key for potential improvements in this field. Imitation learning learns directly from examples of humans or other agents performing tasks. However, using imitation learning alone suffers from the problem of transitionally fitting expert example data. By combining the advantages of imitation learning and the Advantage Actor–Critic method, the Human Alignment Advantage Actor–Critic (HA3C) algorithm is proposed, to enhance single-asset trading strategy. First, by adding daily and weekly frequency trading data as input features to TimesNet, which is specifically designed to extract correlated temporal patterns from time-series data, it can capture both short-term and long-term features, thus capturing time-series features more comprehensively. Second, an expert action labeling method is proposed to train a strategy prediction network through supervised learning of behavior imitation. Third, a pre-trained strategy network is transferred to balance the exploration and exploitation of the agent’s behavior. Imitation learning techniques leverage finance-specific knowledge to enhance algorithmic trading consistency. This approach enables algorithms to mimic and adapt human decision-making patterns in finance, ultimately improving overall performance. This paper introduces a novel return-based function that efficiently balances short-term and long-term returns over flexible time horizons. It considers the maximum return from different positions and uses flexible time windows to capture trends while maximizing returns. Finally, evaluation on six commonly used datasets, such as DJI and SP500, demonstrates the advantages of the proposed HA3C algorithm compared with other classical and reinforcement learning-based strategies. Notably, on the HSI dataset, the HA3C strategy significantly outperforms other methods, achieving an impressive cumulative return of 681.55% and a Sharpe ratio of 5.07. These results show the superior performance of the HA3C algorithm in enhancing stock trading strategies and its potential to impact algorithmic trading consistency through aligning agent behavior with human expertise.

Multi-factor stock trading strategy based on DQN with multi-BiGRU and multi-head ProbSparse self-attention

Reinforcement learning is widely used in financial markets to assist investors in developing trading strategies. However, most existing models primarily focus on simple volume-price factors, and there is a need for further improvement in the returns of stock trading. To address these challenges, a multi-factor stock trading strategy based on Deep Q-Network (DQN) with Multi-layer Bidirectional Gated Recurrent Unit (Multi-BiGRU) and multi-head ProbSparse self-attention is proposed. Our strategy comprehensively characterizes the determinants of stock prices by considering various factors such as financial quality, valuation, and sentiment factors. We first use Light Gradient Boosting Machine (LightGBM) to classify turning points for stock data. Then, in the reinforcement learning strategy, Multi-BiGRU, which holds the bidirectional learning of historical data, is integrated into DQN, aiming to enhance the model’s ability to understand the dynamics of the stock market. Moreover, the multi-head ProbSparse self-attention mechanism effectively captures interactions between different factors, providing the model with deeper market insights. We validate our strategy’s effectiveness through extensive experimental research on stocks from Chinese and US markets. The results show that our method outperforms both temporal and non-temporal models in terms of stock trading returns. Ablation studies confirm the critical role of LightGBM and multi-head ProbSparse self-attention mechanism. The experiment section also demonstrates the significant advantages of our model through the presentation of box plots and statistical tests. Overall, by fully considering the multi-factor data and the model’s feature extraction capabilities, our work is expected to provide investors with more precise trading decision support.Graphical abstract

Research on finance Credit Risk Quantification Model Based on Machine Learning Algorithm

Machine learning is a branch of artificial intelligence (AI) technology that enables systems to learn and make predictions and decisions without the need for explicit programming. Machine learning algorithms learn patterns and relationships from data and are able to gradually improve their accuracy.This paper mainly introduces the application of deep learning in financial academia and the financial industry, with a particular focus on the application of machine learning and deep learning in financial quantitative trading. The paper mentions the complexity and challenges of financial markets and the difficulties machine learning faces in processing financial time series data, such as overfitting, non-stationarity, heteroscedasticity and autocorrelation. To overcome these challenges, the paper explores machine learning model design in financial quantitative trading and risk prediction, including data transformation and model construction. In the related work part, it introduces the history and development of quantitative investment, as well as the application of quantitative investment, such as high-frequency trading, arbitrage strategy and commodity trading advisory strategy. It then focuses on the application of machine learning in financial quantification, including the use of supervised and unsupervised learning in stock price forecasting and trading strategy generation. The paper also compares traditional quantitative strategies with machine learning strategies and discusses the advantages of machine learning in solving high-dimensional and non-linear problems. The methodology section introduces the application of the random forest model in financial quantification, including the model principle, feature importance calculation and experimental design. Through the pre-processing, feature selection and model construction of the credit risk prediction dataset, the construction and evaluation process of the random forest model is demonstrated. Finally, the performance of the random forest model is evaluated and compared with other models to demonstrate its advantages and applicability in financial quantification.

Deep Reinforcement Learning for Adaptive Stock Trading

In this study, the authors explore how financial institutions make decisions about stock trading strategies in a rapidly changing and complex environment. These decisions are made with limited, often inconsistent information and depend on the current and future strategies of both the institution itself and its competitors. They develop a dynamic game model that factors in this imperfect information and the evolving nature of decision-making. To model reward transitions, they utilize a combination of t-Copula simulation of a non-stationary Markov chain, probabilistic fuzzy regression, and chaos optimization algorithms. They then apply deep q-network, a method from deep reinforcement learning, to ensure the effectiveness of the chosen strategy during ongoing decision-making. The approach is significant for both researchers across fields and practical professionals in the finance industry.

Optimal profit-making strategies in stock market with algorithmic trading

<p>Machine learning (ML) techniques are being increasingly applied to financial markets for analyzing trends and predicting stock prices. In this study, we compared the price prediction and profit-making performance of various ML algorithms embedded into stock trading strategies. The dataset comprised daily data from the CSI 300 Index of the China stock market spanning approximately 17 years (2006–2023). We incorporated investor sentiment indicators and relevant financial elements as features. Our trained models included support vector machines (SVMs), logistic regression, and random forest. The results show that the SVM model outperforms the others, achieving an impressive 60.52% excess return in backtesting. Furthermore, our research compared standard prediction models (such as LASSO and LSTM) with the proposed approach, providing valuable insights for users selecting ML algorithms in quantitative trading strategies. Ultimately, this work serves as a foundation for informed algorithm choice in future financial applications.</p>

Моделювання системи інтелектуального прогнозування торгів на фондових біржах

The article highlights the reasons for changes in the price quotations of financial assets on stock exchanges. The article models the process of a situation when a trader fixes the period of holding his trading position. It defines periods of buying and selling and, taking into account that high-frequency stock trading on ultra-short intervals shows low profitability, introduces an important condition that allows a stock trader to freely open and close trading positions during the entire period of buying and selling with consideration of the proposed restrictions. The article offers modelling of the trader's strategy of carrying out trading actions aimed at maximisation of profit. Taking into account the liquidity constraints and quantitative limitations for trading orders, the article proposes to determine the optimal high-frequency trading strategy for buying and selling by a trader, which can be formulated as the task of minimising the cost of trading orders. Based on the number of available exchange trade orders and the values relative to the respective trade order at specific moments, determining the optimal high-frequency trading strategy for buying and selling a trader can be reduced to solving a simple cost minimisation problem under the given conditions of liquidity constraints for each trade order, completion of the trading portfolio without active positions before the end of the period and the total number of exchange buy and sell transactions. The key phases in building the structure on which the stock trading strategy itself is based are described. The need to determine what data will be entered into the algorithm of the artificial neural network based on the input data and to determine which algorithm will be used for a particular task is established. The structure of the software model of the system for intelligent forecasting of trading on stock exchanges is designed. The complex of the automated trading system includes the development of a graphical display of quotes and a tool for visual analysis. At the same time, information about proven trading strategies can be stored in a database that can be added and deleted by traders in the developed intelligent system for forecasting trading on stock exchanges.

An Empirical Bayes Approach to Controlling the False Discovery Exceedance

In large-scale multiple hypothesis testing problems, the false discovery exceedance (FDX) provides a desirable alternative to the widely used false discovery rate (FDR) when the false discovery proportion (FDP) is highly variable. We develop an empirical Bayes approach to control the FDX. We show that, for independent hypotheses from a two-group model and dependent hypotheses from a Gaussian model fulfilling the exchangeability condition, an oracle decision rule based on ranking and thresholding the local false discovery rate (lfdr) is optimal in the sense that the power is maximized subject to the FDX constraint. We propose a data-driven FDX procedure that uses carefully designed computational shortcuts to emulate the oracle rule. We investigate the empirical performance of the proposed method using both simulated and real data and study the merits of FDX control through an application for identifying abnormal stock trading strategies.

A novel Deep Reinforcement Learning based automated stock trading system using cascaded LSTM networks

Deep Reinforcement Learning (DRL) algorithms have been increasingly used to construct stock trading strategies, but they often face performance challenges when applied to financial data with low signal-to-noise ratios and unevenness, as these methods were originally designed for the gaming community. To address this issue, we propose a DRL-based stock trading system that leverages Cascaded Long Short-Term Memory (CLSTM-PPO Model) to capture the hidden information in the daily stock data. Our model adopts a cascaded structure with two stages of carefully designed deep LSTM networks: it uses one LSTM to extract the time-series features from a sequence of daily stock data in the first stage, and then the features extracted are fed to the agent in the reinforcement learning algorithm for training, while the actor and the critic in the agent also use a LSTM network. We conduct experiments on stock market datasets from four major indices: the Dow Jones Industrial index (DJI) in the US, the Shanghai Stock Exchange 50 (SSE50) in China, S&P BSE Sensex Index (SENSEX) in India, and the Financial Times Stock Exchange 100 (FTSE100) in the UK. We compare our model with several benchmark models, including: (i) a model based on a buy-and-hold strategy; (ii) a Proximal Policy Optimization (PPO) model with Multilayer Perceptron (MLP) policy; (iii) some up-to-date models like the MLP model, LSTM model, Light Gradient Boosting Machine (LGBM) model, and histogram-based gradient boosting model; and (iv) an ensemble strategy model. The experimental results show that our model outperforms the baseline models in several key metrics, such as cumulative returns, maximum earning rate, and average profitability per trade. The improvements range from 5% to 52%, depending on the metric and the stock index. This indicates that our proposed method is a promising way to build an automated stock trading system.

A novel deep reinforcement learning framework with BiLSTM-Attention networks for algorithmic trading

The financial market, as a complex nonlinear dynamic system frequently influenced by various factors, such as international investment capital, is very challenging to build trading strategies from the obtained market information. Deep Reinforcement Learning (DRL) combines the perceptual capability of deep learning and the control decision making capability of reinforcement learning to learn the mapping between financial market states and trading decisions by interacting with the environment. In this paper, an enhanced stock trading strategy, denominated efficient deep State-Action-Reward-State-Action (SARSA), is presented to tackle the algorithmic trading problem of determining optimal trading positions in the daily trading activities of the stock market. This algorithm is recognized for its properties of stable learning and convergence, attributes of critical significance within the financial domain, where stability assumes paramount importance, and excessive risk-taking should be averted. Furthermore, a novel deep network architecture called Bidirectional Long Short-Term Memory (BiLSTM)-Attention is introduced to address the challenge of accurately presenting the complex and volatile stock market. The BiLSTM-Attention greatly enhances the network’s capacity to recognize key features and patterns in stock market data, allowing agents to focus on the most relevant aspects of the data. Evaluations on DJI, SP500, GE, IXIC datasets from January 1, 2008 to December 31, 2022 show that our efficient deep SARSA algorithm outperforms a wide range of traditional strategies (B&H, S&H, MR, TF) and DRL-based strategies (TDQN, DQN-Vanilla). For example, on the IXIC dataset, the efficient deep SARSA strategy achieves an attractive Cumulative Return (CR) of 582.17% and a Sharpe Ratio (ShR) of 1.86, outperforming all other methods. These experimental results prove the performance of our method in enhancing stock trading strategies.

A hybrid decision support system for adaptive trading strategies: Combining a rule-based expert system with a deep reinforcement learning strategy

Stock trading strategies pose challenging applications of machine learning for significant commercial yields in the finance industry, drawing the attention of both economists and computer scientists. Until now, many researchers have proposed various methods to implement intelligent trading strategy systems that can support decisions regarding stock trading. Some studies have shown that the problem of trading strategies can be successfully addressed by applying hybrid approaches. Motivated by this, we propose a hybrid decision support system for adaptive trading strategies that combines a rule-based system with deep reinforcement learning to self-improve by learning with human expertise. This study overcomes the limitations of previous hybrid models that mainly have focused on optimizing trading decisions and improving forecasting accuracy. The proposed hybrid model combines decision-making information from a rule-based model to enable the agent of reinforcement learning to capture more trading opportunities. In addition, the investor's available balance states facilitate adaptive learning by interacting with the environment. Moreover, the proposed trading mechanism adjusts the volume size using the policy gradient algorithm's action probabilities, resulting in improved risk-adjusted returns. The proposed hybrid model has the potential to be a reliable trading system in real-world applications through its ability to adapt to different market scenarios, withstand stressful market conditions, reduce transaction costs, scale to various index funds, and extend the proposed hybrid structure. This study highlights the applicability of more advanced machine learning in financial areas, and we also suggest expanding this approach to adaptive decision-making systems in other fields.

DADE-DQN: Dual Action and Dual Environment Deep Q-Network for Enhancing Stock Trading Strategy

Deep reinforcement learning (DRL) has attracted strong interest since AlphaGo beat human professionals, and its applications in stock trading are widespread. In this paper, an enhanced stock trading strategy called Dual Action and Dual Environment Deep Q-Network (DADE-DQN) for profit and risk reduction is proposed. Our approach incorporates several key highlights. First, to achieve a better balance between exploration and exploitation, a dual-action selection and dual-environment mechanism are incorporated into our DQN framework. Second, our approach optimizes the utilization of storage transitions by utilizing independent replay memories and performing dual mini-batch updates, leading to faster convergence and more efficient learning. Third, a novel deep network structure that incorporates Long Short-Term Memory (LSTM) and attention mechanisms is introduced, thereby improving the network’s ability to capture essential features and patterns. In addition, an innovative feature selection method is presented to efficiently enhance the input data by utilizing mutual information to identify and eliminate irrelevant features. Evaluation on six datasets shows that our DADE-DQN algorithm outperforms multiple DRL-based strategies (TDQN, DQN-Pattern, DQN-Vanilla) and traditional strategies (B&H, S&H, MR, TF). For example, on the KS11 dataset, the DADE-DQN strategy has achieved an impressive cumulative return of 79.43% and a Sharpe ratio of 2.21, outperforming all other methods. These experimental results demonstrate the performance of our approach in enhancing stock trading strategies.

Forecasting Stock Market Indices Using the Recurrent Neural Network Based Hybrid Models: CNN-LSTM, GRU-CNN, and Ensemble Models

Various deep learning techniques have recently been developed in many fields due to the rapid advancement of technology and computing power. These techniques have been widely applied in finance for stock market prediction, portfolio optimization, risk management, and trading strategies. Forecasting stock indices with noisy data is a complex and challenging task, but it plays an important role in the appropriate timing of buying or selling stocks, which is one of the most popular and valuable areas in finance. In this work, we propose novel hybrid models for forecasting the one-time-step and multi-time-step close prices of DAX, DOW, and S&P500 indices by utilizing recurrent neural network (RNN)–based models; convolutional neural network-long short-term memory (CNN-LSTM), gated recurrent unit (GRU)-CNN, and ensemble models. We propose the averaging of the high and low prices of stock market indices as a novel feature. The experimental results confirmed that our models outperformed the traditional machine-learning models in 48.1% and 40.7% of the cases in terms of the mean squared error (MSE) and mean absolute error (MAE), respectively, in the case of one-time-step forecasting and 81.5% of the cases in terms of the MSE and MAE in the case of multi-time-step forecasting.